The present disclosure generally relates to systems and methods for assisting patients and health care practitioners in managing insulin treatment to diabetics, in which prescribed basal injections are titrated based on data sets of blood glucose values.

BACKGROUND

Diabetes mellitus (DM) is impaired insulin secretion and variable degrees of peripheral insulin resistance leading to hyperglycaemia. Type 2 diabetes mellitus is characterized by progressive disruption of normal physiologic insulin secretion. In healthy individuals, basal insulin secretion by pancreatic β cells occurs continuously to maintain steady glucose levels for extended periods between meals. Also in healthy individuals, there is prandial secretion in which insulin is rapidly released in an initial first-phase spike in response to a meal, followed by prolonged insulin secretion that returns to basal levels after 2-3 hours. Years of poorly controlled hyperglycaemia can lead to multiple health complications. Diabetes mellitus is one of the major causes of premature morbidity and mortality throughout the world.

Effective control of blood/plasma glucose can prevent or delay many of these complications but may not reverse them once established. Hence, achieving good glycaemic control in efforts to prevent diabetes complications is the primary goal in the treatment of type 1 and type 2 diabetes. Smart titrators with adjustable step size and physiological parameter estimation and pre-defined fasting blood glucose target values have been developed to administer insulin medicament treatment regimens.

Studies also support the role of glycated hemoglobin (HbA1_c) reduction in decreasing cardiovascular disease risk (Nathan et al. N Engl. J Med. 2005. 353:2643-2653; Selvin et al. Ann. Intern. Med. 2004. 141:421-431). The general goal of an HbA1c level of below 7% has been recommended by many diabetes organizations (e.g., the American Diabetes Association (ADA)). In the (UK Prospective Diabetes Study (UKPDS) Group, 50% of patients were taking insulin therapy to maintain HbA1c levels of below 7% within 6 years of the diagnosis of type 2 diabetes.

There are numerous non-insulin treatment options for diabetes, however, as the disease progresses, the most robust response will usually be with insulin. In particular, since diabetes is associated with progressive β-cell loss many patients, especially those with long-standing disease, will eventually need to be transitioned to insulin since the degree of hyperglycemia (e.g., HbA1c ≥8.5%) makes it unlikely that another drug will be of sufficient benefit.

Most patients express reluctance to beginning injectable therapy, due to discomfort and inconvenience caused by the high demands for blood glucose testing and insulin injection. Traditionally, the use of insulin to improve glycemic control was provided by medical specialists. With the increasing number of patients under primary care for whom insulin is indicated, prescribing it in the same setting appears much more convenient for the end users. Often however, insulin is not started in time, due to psychological resistance from both doctors and patients.

A consensus statement from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) was updated in 2012, and emphasized a patient-centered approach and individualized HbA1c treatment targets for the management of hyperglycemia in type 2 diabetes (T2DM). It recommended that insulin could be considered as one of the options for dual combination therapy, if an individualized HbA1c level target was not reached after metformin therapy. This choice could be based on patient and drug characteristics, with an over-riding goal of improving glycemic control while minimizing side-effects. When three-drug combinations are considered, insulin is likely to be more effective than most other agents (e.g., sulfonylurea, thiazolidinedione, dipeptidyl peptidase 4 inhibitor, glucagon-like peptide-1 receptor agonist), especially when the HbA1c level is very high (≥9.0%) (Inzucchi et al., Management of hyperglycemia in type 2 diabetes: a patient-centered approach: position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 2012. 35:1364-79).

The ideal insulin regimen aims to mimic the physiological profile of insulin secretion as closely as possible. There are two major components in the insulin profile: a continuous basal secretion and prandial surge after meals. The basal secretion controls overnight and fasting glucose while the prandial surges control postprandial hyperglycemia.

Based on the time of onset and duration of their actions, injectable formulations can be broadly divided into basal (long-acting analogues [e.g., insulin detemir and insulin glargine] and ultra-long-acting analogues [e.g., insulin degludec]) and intermediate-acting insulin [e.g., isophane insulin] and prandial (rapid-acting analogues [e.g., insulin aspart, insulin glulisine and insulin lispro]). Premixed insulin formulations incorporate both basal and prandial insulin components.

There are various recommended insulin regimes, such as (1) multiple injection regimen: rapid-acting insulin before meals with long-acting insulin once or twice daily; (2) premixed analogues or human premixed insulin once or twice daily before meals; (3) intermediate- or long-acting insulin once or twice daily. However, where possible, a long-acting insulin regimen alone or in combination with oral antidiabetic drug(s) (OADs) is usually the optimal initial regimen for subjects with T2DM as this reduces the patient burden and discomfort caused by blood glucose measurement and injection of insulin.

Recent data from the United Kingdom Prospective Diabetes Study suggest the importance of stringent glycemic control (Holman et al., 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl. J Med 2008; 359: 1577-1589) and current treatment guidelines call for early insulin treatment in type 2 diabetes patients (Nathan et al., Management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: update regarding thiazolidinediones: a consensus statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 2008; 31: 173-175). However, optimal initiation and titration methods for the long-acting basal insulins are still being determined. Evidence suggests that many patients often do not have insulin doses titrated sufficiently to achieve target levels of glucose control (remaining on suboptimal doses and failing to reach treatment targets)(UKPDS).

What has become increasingly clear is that patient empowerment is essential for motivation to reach treatment targets. Self-titration regimens facilitate empowerment of patients, allowing them to become more involved in their treatment, which can result in improved glycemic control. Until recently, titration of insulin in type 2 diabetes clinical trials was typically left up to the investigator's discretion with a simple statement of the target ranges for glucose. In type 2 diabetes trials the average glycemic control achieved was usually less than desirable. Since then a number of trials have been conducted and reported utilizing various algorithms under various conditions.

During the last decade various insulin titration algorithms have been applied in several trials initiating long or intermediate acting insulin in type 2 diabetes patients often referred to as Treat-to-target. Several of the trials were designed for other primary purposes than algorithm development and have therefore used one specific algorithm. Interpretation of algorithm merit in those cases is somewhat difficult and has to rely on cross trial comparisons. Various factors in addition to the numbers in the algorithm apparently affect the achieved results.

The algorithms for basal insulin titration and their implementation have evolved steadily further away from complete real-time health care provider control over every dose decision. Health care provides have traditionally chosen a target titration level with respect to estimated insulin sensitivity and other factors determined in the clinic. Therefore, the target is not updated between visits to the health care practitioner based upon changes in physiological parameters, unexpected responsiveness to the drug or level of adherence, even though these are factors that affect the treatment outcome. The first step towards more flexible titration approaches was the acceptance of one algorithm for all patients, which at the time was considered radical by most investigators. The second step became acceptance algorithm enforcement.

Controlled clinical trials such as the Diabetes Control and Complications Trial (The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial Research Group. N Engl J Med 1993; 329:977-86), UKPDS (Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33), UK Prospective Diabetes Study (UKPDS) Group. Lancet 1998; 352:837-53), the Veterans Affairs Diabetes Trial (Duckworth W, Abraira C, Moritz T, et al. Glucose control and vascular complications in veterans with type 2 diabetes. N Engl J Med 2009; 360:129-39), the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation trial (ADVANCE Collaborative Group, Patel A, MacMahon S, et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl. J Med 2008; 358:2560-72), and a study on Japanese patients (Ohkubo et al., Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study. Diabetes Res. Clin. Pract. 1995; 28:103-17) demonstrated that intensive glycemic control could significantly reduce the risk of microvascular complications.

Common to most basal insulin titration algorithms is that dose changes are based on averages of a varying number of days' morning fasting self-monitored blood or plasma glucose. From this general theme there are many variations.

The starting dose has been 10 U, 20 U, or based on the morning fasting plasma glucose (FPG) using the formula of Holman and Turner (Holman R R, Turner R C. A practical guide to basal and prandial insulin therapy. Diabet. Med. 1985 January; 2(1): 45-53) which is (FPG (mg/dl) −50)/10, typically yielding just short of 20 U. Within these options there does not appear to be any difference in achieved glycemic control and hypoglycemia rate.

When a clinic has to titrate the insulin dose for the individual patient, there is often a natural limitation on the possible frequency of changing the titration dose. Consequently, the clinic has to be able to make substantial dose increments at high average glucose so the patient is not left for too long a time in poor glycemic control. However, the patient alone can easily titrate often, for which there is a long tradition for those with type 1 diabetes. With more frequent titration there may be a reduced need for large dose steps at high glucose levels and the algorithms can be simplified in terms of number of steps.

Earlier titration protocols typically titrated an insulin dose based on the average of a week's worth of several fasting blood/plasma measurements. Later trials are based on dose titrations based on average fasting blood/plasma measurements based of 3 measurements per week.

At the extreme is INSIGHT (Gerstein et al., A randomized trial of adding insulin glargine vs. avoidance of insulin in people with Type 2 diabetes on either no oral glucose-lowering agents or submaximal doses of metformin and/or sulphonylureas. The Canadian INSIGHT (Implementing New Strategies with Insulin Glargine for Hyperglycaemia Treatment) Study. Diabet. Med. 2006; 23(7): 736-42), which has only one step of one unit of insulin titrated every morning by the patient.

Clinic contact initially occurred every other week, but after 4 weeks the clinic contact went to every 4 weeks and after 12 weeks to 6-week intervals. Clinic oversight was thus minimally if at all intensified compared to standard clinical practice. Patients were taught to “start with an initial dose of 10 units, and advised to increase this by 1 unit each day until achieving a FPG (FPG) mmol/liter (99 mg/dl)” The end insulin dose was 38 U and HbA1c 7.0%. From an effectiveness point of view, this is an “outstanding” result. Hence, there is a trend toward increasingly high frequency of insulin dose titration in order to improve to primary aim of insulin therapy—glycemic control.

Diabetes care guidelines and product labelling for current basal insulin analogs recommend regular blood glucose self-measurement (American Diabetes Association. Standards of Medical Care in Diabetes—2012. Diabet. Care. 2012; 35(Suppl. 1): S11-63; International Diabetes Federation Clinical Guidelines Task Force. Global Guideline for Type 2 Diabetes. 2005. Available on the Internet at www.idf.org/webdata/docs/IDF%20GGT2D.pdf (Accessed Dec. 19 2012); Canadian Diabetes Association Clinical Practice Guidelines Expert Committee. Canadian Diabetes Association. Can J Diabetes. 2008; 32 (Suppl 1): S1-201; Meneghini et al., The usage of a simplified self-titration dosing guideline (303 Algorithm) for insulin detemir in patients with type 2 diabetes-results of the randomized, controlled PREDICTIVE™ 303 study. Diabet. Obes. Metab. 2007; 9:902-13; Davies et al., Improvement of glycemic control in subjects with poorly controlled type 2 diabetes. Diabet. Care. 2005; 28:1282-8; and LANTUS® (insulin glargine [rDNA origin] injection). Sanofi-Aventis U.S. LLC, Bridgewater, N.J., USA; 2007. Health Care Professional. Dosing & Titration. Available on the Internet at www.lantus.com/hcp/titration.aspx. (Accessed Nov. 13, 2012) in order to help people with diabetes maintain appropriate glycemic control and become more actively involved in their healthcare (Benjamin E M. Self-monitoring of blood glucose: the basics. Clin Diabetes. 2002; 20(1):45-7; Schnell et al., Consensus statement on self-monitoring of blood glucose in diabetes. Diabetes, Stoffwechsel and Herz. 2009; 4:285-9; and American Diabetes Association. Insulin administration. Diabetes Care. 2012; 35:S1). Insulin dose is also typically determined and titrated up or down as needed according to algorithms based on blood glucose results (American Diabetes Association. Standards of Medical Care in Diabetes—2014. Diabetes Care. 2014; 37 Suppl. 1). Insulin dose determination is individual for each patient. The dose steps (titration model), the glycemic target as well as the absolute insulin dose are determined in an individualized and tailored manner for each individual generally by a healthcare provider (HCP).

Challenges exist that can prevent the achievement of glycemic targets with insulin, including perceptions on the part of patients and HCPs that insulin therapy can be burdensome or too complex to manage (Peyrot et al., Factors associated with injection omission/non-adherence in the Global Attitudes of Patients and Physicians in Insulin Therapy Study. Diabet. Obes. Metab. 2012; 14:1081-7; and Peyrot et al., Insulin adherence behaviors and barriers in the multinational Global Attitudes of Patients and Physicians in insulin therapy study. Diabet. Med. 2012; 29:682-90). Patients who take an active role in the management of their diabetes and titration of their insulin may feel more empowered to take charge of their self-care and have a stronger belief that their actions can influence their disease, thus leading to better treatment outcomes (Norris et al., Self-management education for adults with type 2 diabetes: a meta-analysis on the effect of glycemic control. Diabetes Care. 2002; 25:1159-71; Kulzer et al., Effects of self-management training in type 2 diabetes: a randomized, prospective trial. Diabet. Med. 2007; 24:415-23; Anderson et al. Patient empowerment: results of a randomized controlled trial. Diabetes Care. 1995; 18:943-9). In determining how self-care can best be facilitated for patients with diabetes, the cost and burden of frequent glucose testing must be considered when designing treatment plans, as these can be significant factors when added to the health, quality of life (QoL), and financial toll of poorly controlled diabetes.

Numerous studies investigating the cost of self-measured blood glucose (SMBG) testing have found that it includes a substantial portion of diabetes-related expenditures (Liebl et al., Direct costs and health-related resource utilisation in the 6 months after insulin initiation in German patients with type 2 diabetes mellitus in 2006: INSTIGATE study. Curr Med Res Opin. 2008; 24:2349-58; Yeaw et al., Self-monitoring blood glucose test strip utilization in Canada. Diabetes. 2012; 61(Suppl 1):A35; Yeaw et al., Cost of self-monitoring of blood glucose in Canada among patients on an insulin regimen for diabetes. Diabetes Ther. Epub Jun. 27 2012. doi: 10.1007/s13300-012-0007-6; and Yeaw et al., Cost of self-monitoring of blood glucose in the United States among patients on an insulin regimen for diabetes. J Manag. Care Pharm. 2012; 18:21-32). In a retrospective database analysis in the US that included more than 45,000 patients, testing accounted for 27% of diabetes care costs: total combined blood glucose testing and insulin-related costs were $2,850 USD/patient/year, with $772 USD/patient/year attributed to blood glucose testing alone (Yeaw et al., Cost of self-monitoring of blood glucose in the United States among patients on an insulin regimen for diabetes. J Manag. Care Pharm. 2012; 18:21-32). In other countries, testing includes an even higher percentage of diabetes care costs, e.g., 40% in Canada (Yeaw et al., Self-monitoring blood glucose test strip utilization in Canada. Diabetes. 2012; 61(Suppl 1):A35; Yeaw et al., Cost of self-monitoring of blood glucose in Canada among patients on an insulin regimen for diabetes. Diabetes Ther. Epub Jun. 27 2012. doi: 10.1007/s13300-012-0007-6) and 42% in Germany (Liebl et al., Direct costs and health-related resource utilization in the 6 months after insulin initiation in German patients with type 2 diabetes mellitus in 2006: INSTIGATE study. Curr Med Res Opin. 2008; 24:2349-58).

The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgment or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

Relating to the above, it is estimated that more than 60% of people with type 2 diabetes treated with insulin in the USA do not reach recommended HbA1c goals and a titration period lasting for years. These people with uncontrolled diabetes experience a significantly higher rate of diabetes complications, e.g. it is estimated that a leg is amputated every thirty seconds somewhere in the world due to diabetes. Reported reasons for failed insulin titration include lack of patient confidence in managing insulin therapy, patients' and health care professionals' (HCP) fear of hypoglycemia, and uncertainties due to vague prescribing information.

Relating to the object of providing improved medical guidance based on collected patient related treatment data, WO 2019/072818 discloses a portable medical data hub provided to allow a user better to manage their treatment. The portable medical data hub may allow a user better to treat themselves when multiple medical devices are involved in the treatment as it can allow the user to get information from multiple medical devices to an external data analysis system, e.g. a cloud-based system, in a user-friendly way.

Having regard to the above, it is an object of the present invention to provide systems and methods suitable for use in a diabetes management system that helps people with type 2 diabetes and their health care providers (HCPs) titrate basal insulin in response to measured blood glucose values and the amount of insulin recorded in previously logged doses in a safe, easy and efficient way. It is a specific object to provide a backend engine adapted to be used as part of a cloud-based large-scale diabetes management system.

DISCLOSURE OF THE INVENTION

In the disclosure of the present invention, embodiments and aspects will be described which will address one or more of the above objects or which will address objects apparent from the below disclosure as well as from the description of exemplary embodiments. Thus, in a first aspect of the invention a system for providing a long-acting or ultra-long-acting insulin adjustment day dose recommendation (ADDR) for a subject to treat diabetes mellitus is provided, wherein the system comprises one or more processors and a memory. The memory comprises instructions that, when executed by the one or more processors, perform a method responsive to receiving a dose guidance request from a client. The instructions comprise the steps of obtaining a first data structure, comprising a glucose upper target range level of the subject, and a glucose lower target range level of the subject, and obtaining a second data structure, comprising a current dose guidance baseline (DGB), and a corresponding DGB timestamp representing when the DGB was made, wherein the current DGB corresponds to a most recent adjustment day dose recommendation, or a starting basal dose. The instructions comprise the steps of obtaining a first and a second data set, the first data set comprising a plurality of glucose measurements of the subject taken over a time course and thereby establish a blood glucose history, each respective glucose measurement in the plurality of glucose measurements comprising a blood glucose level, and a corresponding blood glucose timestamp representing when in the time course the respective glucose measurement was made, the second data set comprising a basal insulin injection history of the subject, wherein the injection history comprises a plurality of injections during all or a portion of the time course and, for each respective injection in the plurality of injections comprising a corresponding injection amount, and an injection timestamp representing when in the time course the respective injection occurred. The instructions comprise the step of obtaining a third data structure comprising an injection data refresh timestamp for the subject. The instructions comprise the further step of evaluating the first data structure, the second data structure, the third data structure, the first data set, and the second data set and thereby determine whether they collectively contain a set of evaluation information, comprising the glucose upper and lower target range levels of the subject, the current dose guidance baseline and the corresponding timestamp, the injection history refresh timestamp for the subject, the plurality of glucose measurements of the subject taken over the time course, and the injection history of the subject. When a determination is made that the first data structure, the second data structure, the third data structure, the first data set, and the second data set collectively do contain the set of evaluation information, and when the dose guidance request was received within a given time limit from the injection data refresh timestamp, the method further comprises providing the long-acting or ultra-long-acting insulin adjustment day dose recommendation, the recommendation being calculated based on data from the first data structure, the second data structure, the first data set, and the second data set, or when a determination is made that the first data structure, the second data structure, the third data structure, the first data set, and the second data set fail to collectively contain the set of evaluation information, or when the dose guidance request was received after the given time limit from the injection history refresh timestamp, no long-acting or ultra-long-acting insulin adjustment day dose recommendation is provided.

In case the determination is made that the first data structure, the second data structure, the third data structure, the first data set, and the second data set fail to collectively contain the set of evaluation information (i.e. the given data types are present and within a specified range), or when the dose guidance request was received after the given time limit from the injection history refresh timestamp, one or more error messages are provided corresponding to the identified failed information.

By the above method a system is provided allowing a dose guidance request for a long-acting or ultra-long-acting insulin adjustment day dose recommendation to be processed efficiently and safely. When implemented the system can provide the back-end engine used in combination with interacting systems in the form of a client and an operating system, to help people with type 2 diabetes and their health care providers titrate basal insulin in response to blood glucose values and the amount of insulin recorded in previously logged doses.

In an exemplary embodiment, the instructions further comprise the step of, before calculating an ADDR, perform an update check to check whether the dose guidance request was received within a given time limit from the DGB timestamp, e.g. 3, 4, 5, 6 or 7 days, and if the dose guidance request was received after the given time limit from the DGB timestamp, no ADDR is provided. In this case the patient may be requested to set a new starting basal dose value and thus initiate a new titration regimen.

In this way it is assured that patients to a high degree are “actively” participating in the titration regimen and that dose guidance requests from “non-followers” are rejected to thereby encourage adherence to the regimen.

In an exemplary embodiment, the instructions further comprise the step of, before calculating an ADDR, performing one or more dose event checks to determine whether the number, timing and size of the individual injections in the injection history conform to a set of predetermined compliance requirements, wherein: if the number, timing and size of the individual injections in the injection history conform to the set of predetermined compliance requirements, then calculate the ADDR, if the number, timing and size of the individual injections in the injection history do not conform to the set of predetermined compliance requirements, but conform to a given predefined non-compliance condition, then provide a predefined ADDR corresponding to that non-compliance condition, and if the number, timing and size of the individual injections in the injection history do not conform to the set of predetermined compliance requirements, and do not conform to a given predefined non-compliance condition, then provide an error message.

In this way dose guidance rules can be designed to specifically comply with the requirements for a given titration regimen for a given type of drug.

In the same way the instructions may further comprise the step of, based on the blood glucose history, determining for each 24 hours period for a given number of 24 hours periods a titration glucose level value (TGL), and, before calculating an ADDR, performing a TGL check to determine whether the number and value of the individual TGL for the given number of periods conform to a set of predetermined compliance requirements, wherein, if the number and value of the individual TGL conform to the set of predetermined compliance requirements, then calculate the ADDR, if the number and value of the individual TGL do not conform to the set of predetermined compliance requirements, but conform to a given predefined non-compliance condition, then provide an ADDR predefined for that non-compliance condition, and if the number and value of the individual TGL do not conform to the set of predetermined compliance requirements, and do not conform to a given predefined non-compliance condition, then provide an error message.

In this way additional dose guidance rules can be designed to specifically comply with the requirements for a given titration regimen for a given type of drug.

The obtained first data set may be a CGM data set comprising for each 24 hours period a plurality of glucose measurements of the subject, the instructions further comprising:

evaluating for each 24 hours period whether the plurality of glucose measurements collectively meet a set of requirements in respect of completeness allowing a daily TGL value to be determined, for each 24 hours period having met the set of requirements in respect of completeness, calculating a daily TGL value, and calculating an overall TGL by taking the average of at least two calculated daily TGL values.

The daily TGL may be determined as the lowest BG average for a sliding window of a predetermined amount of time, e.g. 60, 120 or 180 minutes, across the BG values for the corresponding day.

The method may further comprise the step of calculating a reconstructed blood glucose history of the subject when the blood glucose history time course contains one or more gaps of a predetermined length.

Alternatively, the obtained first data set may comprise for each day in the time course a BG value representing a fasting blood glucose measurement of the subject, the instructions further comprising: evaluating for each BG value whether it meets a set of requirements to be considered a fasting BG value, e.g. being provided with a timestamp within a morning time range indicating a fasting value, for each fasting BG value having met the set of requirements in respect of being considered a fasting BG value creating a corresponding daily TGL value, and calculating an overall TGL by taking the average of at least two calculated daily TGL values.

The provision of the ADDR per se may be based on the method implementing titration rules wherein, when the overall TGL is greater than the UTR of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline plus a predetermined number of units of long-acting or ultra-long-acting insulin.

When the overall TGL is between the UTR of the subject and the LTR of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline.

When the overall TGL is less than the LTR of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline minus a predetermined number of units of long-acting or ultra-long-acting insulin.

The predetermined number of units of long-acting or ultra-long-acting insulin by which to alter the adjustment day dose recommendation is selected from the set of at least 1 IU, 2 IU, 4 IU, 6 IU and 8 IU.

To cope with the issue of e.g. split issues the method may further comprise combining any injections in the injection history that have timestamps within a pre-defined period of time in the time course into one injection. Additionally and prior to combining injections the method may apply a filtering process adapted to recognize and subsequently disregard injections which can be regarded as expelled but not injected doses, e.g. priming and air shots.

In a second aspect of the invention a system is provided for the administration of a long-acting or ultra-long-acting insulin, e.g., thereby treating diabetes. A plurality of glucose measurements of a subject in need of treatment over a time course are recorded. For each respective glucose measurement in the plurality of glucose measurements, a corresponding timestamp—representing when in the time course the respective glucose measurement is made—is also recorded. A data structure is obtained from the subject. The device proceeds, responsive to receiving a dose guidance request, by using the glucose measurements and the data structure to provide an updated adjustment day dose recommendation of insulin to the subject.

The device, in some embodiments, may autonomously update the adjustment day dose recommendation of insulin in response to changes in the target glucose concentration and the actual glucose concentration of the subject.

Accordingly, one aspect of the present disclosure provides a device for administering a long-acting or ultra-long-acting insulin (e.g., LysB29(Nc-hexadecandioyl-y-Glu) des(B30) human insulin (insulin degludec) for use in treating type 2 diabetes mellitus in response to receiving a dose guidance request.

In one aspect of the present disclosure, the device includes collecting at least a first data structure, a second data structure, a first data set, and a second data set. In some embodiments, the first data structure includes at least a body weight of the subject, an upper limit target glucose range of the subject, a lower limit target glucose range of the subject, and an overbasalisation limit of the subject. In some embodiments, the second data structure includes at least the most recent adjustment day dose recommendation and a starting basal dose. In some embodiments, the first data set includes at least a plurality of glucose measurements of the subject taken over a time course to establish a blood glucose history and, for each respective glucose measurement in the plurality of glucose measurements, a corresponding glucose timestamp representing when in the time course the respective glucose measurement was made.

In some embodiments the first data set includes data of historic hypoglycemic events (HYPO) and for each hypoglycemic event a corresponding event timestamp representing when in the time course the respective event occurred.

In some embodiments the second data set includes at least a basal insulin injection history of the subject—which includes a plurality of injections during all or a portion of the time course and, for each respective injection in the plurality of injections, a corresponding dose event amount and a dose event timestamp representing when in the time course the respective injection event occurred—and a last injection data refresh of the subject.

In another aspect of the present disclosure, the device collects at least a first data structure of the subject to obtain a variety of data. In some embodiments, the first data structure includes one or more of: a body weight of the subject, an upper limit target glucose range of the subject, a lower limit target glucose range of the subject, an over-basalisation limit of the subject, a most recent adjustment day dose recommendation and/or a starting insulin basal dose of the subject, a basal insulin injection history of the subject, and a last injection data refresh of the subject. In some embodiments, the injection history includes a plurality of injections during all or a portion of the time course. For each respective injection in the plurality of injections, a corresponding injection dose amount and an injection timestamp representing when in the time course the respective injection event occurred are recorded.

The device continues by proceeding with providing an adjusted day dose recommendation to the subject when at least the set of evaluation information (e.g., the first data set, the second data set, the first data structure, and the second data structure) is collected. Another aspect of the present disclosure provides that updating the adjusted day dose recommendation of the long-acting or ultra-long-acting insulin is repeated.

In some embodiments, the long-acting or ultra-long-acting insulin has the following structure (I). The long-acting or ultra-long-acting insulin includes a side chain attached to the α-amino group of the N-terminal amino acid residue of the B chain or to the ε-amino group of the Lys residue present in the B chain of the parent insulin, the side chain being of the general formula:

-W-X-Y-Z

wherein W is:

(i) an α-amino acid residue having a carboxylic acid group in the side chain which residue forms, with one of its carboxylic acid groups, an amide group together with the α-amino group of the N-terminal amino acid residue of the B chain or together with the ε-amino group of a Lys residue present in the B chain of the parent insulin;

(ii) a chain composed of two, three or four α-amino acid residues linked together via amide bonds, which chain—via an amide bond—is linked to the α-amino group of the N-terminal amino acid residue of the B chain or to the ε-amino group of a Lys residue present in the B chain of the parent insulin, the amino acid residues of W being selected from the group of amino acid residues having a neutral side chain and amino acid residues having a carboxylic acid group in the side chain so that W has at least one amino acid residue which has a carboxylic acid group in the side chain; or

(iii) a covalent bond from X to the α-amino group of the N-terminal amino acid residue of the B chain or to the ε-amino group of a Lys residue present in the B chain of the parent insulin; wherein X is:

(i) —CO—;

(ii) —COCH(COOH)CO—;

(iii) —CON(CH2COOH)CH2CO—;

(iv) —CON(CH2COOH)CH2CON(CH2CO OH)CH2CO—;

(v) —CON(CH2CH2COOH)CH2CH2CO—;

(vi) —CON(CH2CH2COOH)CH2CH2CON(CH2CH2COOH)CH2CH2CO—;

(vii) —CONHCH(COOH)(CH2)₄NHCO—;

(viii) —CON(CH2CH2COOH)CH2CO—; or

(ix) —CON(CH2COOH)CH2CH2CO—

provided that:

(a) when W is an amino acid residue or a chain of amino acid residues, via a bond from the underscored carbonyl carbon forms an amide bond with an amino group in W; or

(b) when W is a covalent bond, via a bond from the underscored carbonyl carbon forms an amide bond with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin;

wherein Y is:

(i) a-(CH2)m- where m is an integer in the range of 6 to 32;

(ii) a divalent hydrocarbon chain comprising 1, 2 or 3 —CH═CH— groups and a number of —CH2— groups sufficient to give a total number of carbon atoms in the chain in the range of I0 to 32;

or

(iii) a divalent hydrocarbon chain of the formula —(CH2)vC6H (CH2)w- wherein v and w are integers or one of them is zero so that the sum of v and w is in the range of 6 to 30;

wherein Z is:

(i) —COOH;

(ii) —CO-Asp;

(iii) —CO-Glu;

(iv) —CO-Gly;

(v) —CO-Sar;

(vi) —CH(COOH)2;

(vii) —N(CH2COOH)2;

(viii) —S03H; or

(ix) —P03H;

and any Zn2+ complexes thereof, provided that when W is a covalent bond and X is —CO—, then Z is different from —COOH.

In some embodiments, the long-acting insulin or ultra-long-acting insulin is LysB29(Nc-hexadecandioyl-y-Glu) des(B30) human insulin (insulin degludec). In some embodiments, the long-acting insulin or ultra-long-acting insulin is selected from the group consisting of neutral protamine hagedorn insulin, Lente Insulin, Ultralente Insulin, Glargine Insulin, Detemir Insulin, Hypurin Bovine Lente, and Hypurin Bovine PZI.

In one aspect of the present disclosure, the long-acting insulin or ultra-long-acting for use is administered, either concurrently or consecutively, together with one or more additional drugs used in the treatment of diabetes. In some embodiments, the one or more additional drug used in the treatment of diabetes is, or includes, a drug selected from the group consisting of: insulins, sensitizers (such as biguanides and thiazolidinediones), secretagogues (such as sulfonylureas and nonsulfonylurea secretagogues), alpha-glucosidase inhibitors and peptide analogs (such as injectable incretin mimetics, gastric inhibitory peptide analogs, dipeptidyl peptidase-4 inhibitors and injectable amylin analogues). In some embodiments, the long-acting or ultra-long-acting insulin is LysB29(Nc-hexadecandioyl-y-Glu) des(B30) human insulin (insulin degludec) and is administered, concurrently or consecutively, with liraglutide.

In one aspect of the present disclosure, the device further updates the dose guidance recommendation for the subject, unless the injection history of the subject includes one or more dose events of the subject that have dose event timestamps within the current day.

In another aspect of the present disclosure, the device further calculates a reconstructed blood glucose history of the subject when the blood glucose history time course contains a gap. The reconstructed blood glucose history is calculated based on the blood glucose history of each calendar day.

In some embodiments, the device further provides a wherein the method further comprises providing a re-recommendation of the dose guidance recommendation until (i) one or more injections with an injection amount equivalent to the dose guidance baseline have occurred, (ii) one or more injections have occurred since the dose guidance baseline and when one or more injections have occurred over the current day and the past two or three or four days, and (iii) one or more does events with an injection amount greater than or equal to the dose guidance baseline have occurred.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following embodiments of the invention will be described with reference to the drawings, wherein

FIGS. 1A and 1B show a flowchart of processes and features for a first embodiment of a system providing a dose guidance recommendation,

FIGS. 2A and 2B illustrates how a TGL value is determined based on CGM data,

FIG. 3 illustrates an exemplary system topology that includes a regimen monitor device for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen for a subject, a data collection device for collecting patient data, one or more glucose sensors that measure glucose data from the subject, and one or more insulin pens that are used by the subject to inject insulin medicaments in accordance with the prescribed insulin medicament regimen, where the above-identified components are interconnected, optionally through a communications network, in accordance with an embodiment of the present disclosure,

FIG. 4 illustrates a device for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen in accordance with an embodiment of the present disclosure,

FIGS. 5A-5H collectively provide a flow chart of processes and features of a system for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen in accordance with various embodiments of the present disclosure,

FIGS. 6A-6D collectively illustrate a dose guidance request for a subject, in accordance with an embodiment of the present disclosure, and

FIG. 7 illustrates an example integrated system of connected insulin pen(s), continuous glucose monitor(s), memory and a processor for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen in accordance with an embodiment of the present disclosure.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Diabetes is a growing world health epidemic. Although diabetes can be effectively managed with established titration treatment regimens and pharmaceuticals, the access to up-to-date titration recommendations remains limited. The present disclosure provides a patient-focused guidance system for basal titration that would encourage self-titration, enhancing patient empowerment as well as substantially help reduce treatment costs by reducing the frequency of required physician consultations for dose adjustments without reducing therapeutic outcomes.

It is basically the object of a titration method for a basal insulin treatment regimen to determine the dose size that will assure that the patient blood glucose (BG) level is within a predetermined target range. Based on a relatively low starting basal dose the doses are adjusted (primarily upwards) until the treatment target of a blood glucose level within the target range.

Although the basic setup for a titration regimen for a basal insulin treatment appears simple, it has in practice proven a challenge to a large proportion of patients started on basal insulin to successfully reach the set goal.

The challenges may include difficulties in determining blood glucose values at the right time, to properly record the determined blood glucose values, to take the right amount of insulin at the right time, to properly record dose size and time taken, and to correctly calculate an adjusted basal dose. Additionally, when the patient fails to take the correct dose at the correct time, and then try to compensate for the missed injection, a further risk is introduced resulting in either too much or too little insulin being injected, both being detrimental to a successful titration process.

Correspondingly, providing a system and setup which would make it easier for the patient to record BG values, easier to record a dose log for injections made, easier to correctly calculate adjusted dose recommendations, and easier and safer to properly compensate for errors and mistakes made by the patient during the treatment regimen.

The system setup that will be described in the following is designed to be implemented as a backend engine adapted to be used as part of a cloud-based large-scale diabetes management system. Such a cloud-based system would allow the engine to always be up-to-date (in contrast to app-based systems running entirely on e.g. the patient's smartphone), would allow advanced methods such as machine learning and artificial intelligence to be implemented, and would allow data to be used in combination with other services in a greater “digital health” set-up. As such a cloud-based system ideally would handle a large amount of patient requests for dose recommendations, it is important that the system is set up to provide the intended service as quickly as possible and with an effective use of the available resources.

Overall a diabetes management system is provided that helps people with type 2 diabetes and their health care providers (HCPs) titrate basal insulin in response to blood glucose values and the amount of insulin recorded in previously logged doses. The complete system comprises the back-end engine (“the engine”) which is the main aspect of the present invention used in combination with interacting systems in the form of a client and an operating system.

The client from the engine's perspective is the software component that requests dose guidance. The client gathers the necessary data (e.g. CGM data, injection data, patient parameters) and requests dose guidance from the engine. The client then receives the response from the engine. In a typical set-up the client may be a dedicated data hub for acting as an interface between the patient's medical devices and the cloud or it may be an app running on a patient smartphone. The cloud operating system provides the engine with the minimum resources necessary to function (e.g. memory, processing). The cloud operating system acts as the environment in which the engine is installed and allows for the deployment thereof.

In the following embodiments of a system for providing a long-acting or ultra-long-acting insulin adjustment day dose recommendation (ADDR) for a subject to treat diabetes mellitus will be described, the system being suitable for implementation as a cloud-based large-scale diabetes management system.

More specifically, a system for providing a long-acting or ultra-long-acting insulin adjustment day dose recommendation (ADDR) for a subject to treat diabetes mellitus is provided. The system comprises one or more processors and a memory, the memory comprising instructions that, when executed by the one or more processors, perform a method responsive to receiving a dose guidance request (DGR).

The instructions provide that a first data structure is obtained, comprising a glucose upper target range level (UTR) of the subject, and a glucose lower target range level (LTR) of the subject, and that a second data structure is obtained, comprising a current dose guidance baseline (DGB), and a corresponding DGB timestamp representing when the DGB was made.

The current DGB may correspond to either a most recent adjusted day dose recommendation, or a starting basal dose, i.e. the basal dose typically set by the patient's doctor. As described above, these data structures represent the starting point for the titration regiment (the starting dose) respectively the target range for the patient's BG level.

In order to be able to calculate an updated ADDR the instructions provide that a first and second data set is obtained. The first data set comprises a plurality of glucose measurements of the subject taken over a time course and thereby establishing a blood glucose history (BGH), each respective glucose measurement comprising a blood glucose (BG) value, and a corresponding glucose timestamp (BGT) representing when in the time course the respective glucose measurement was made. The BG values may be in the form of stand-alone BG values, typically a daily measurement taken in the morning and representing a fasting BG. In this case each BG value represents a what can be termed a daily titration glucose level value (TGL) upon which the calculation of an updated ADDR can be based. Alternatively, the first data set may comprise a large number of BG values compiled over each day by use of a skin-mounted continuous glucose monitoring (CGM) device, e.g. providing a BG value every 5 minutes. Based on the CGM data a daily TGL can be determined using an appropriate algorithm as will be described in greater detail below.

The second data set comprises the basal insulin injection history (IH) of the subject, the history comprising a plurality of injections during all or a portion of the time course with each respective injection being characterized by an injection amount (IA), and an injection timestamp (IT) representing when in the time course the respective injection occurred. To assure that the injection history is up-to-date a third data structure comprising an injection history refresh timestamp may be obtained. The injection data may have been captured manually by the patient or they may have been captured using a drug delivery device with provided with dose logging circuitry. In the latter case it should be noted that expelled dose amounts and not necessarily injected dose amounts are logged. Indeed, this inaccuracy potentially also applies to a manually derived dose log. The issue of non-injected priming/air shots will be discussed below.

The instructions further provide that the data structures and data sets are evaluated to thereby determine whether all the data types necessary in a dose guidance request are present and within a specified range. This includes BG data, injection data, target glucose range upper and lower limits and most recent adjustment day dose recommendation or starting basal dose. This is to ensure that if something is missing, or clearly wrong or insufficient, resources are not wasted trying to process the request. In the event that a request lacks a data type or has a parameter that is an order of magnitude higher than what is considered normal, the engine will return a corresponding error message. If all data types are present and within range (quality and or quantity are not considered here), the check passes the data along for further processing.

Before calculating an ADDR a number of data checks may be performed, e.g. performing a refresh check to check whether the dose guidance request was received within a given time limit from the injection history refresh timestamp, as well as an update check to check whether the dose guidance request was received within a given time limit from the DGB timestamp.

Further, one or more dose event checks may be performed to determine whether the number, timing and size of the individual injections in the injection history (IH) conform to a set of predetermined compliance requirements.

Having described the basic components of an exemplary embodiment of the invention, a specific embodiment will be described with reference to FIG. 1 in which a flow-chart illustrates how a dose guidance request (DGR) is received and processed when the stored instructions are executed. The number of functions, the order of the functions and the specific functionality, e.g. rules, embedded in each give function are all exemplary. In the exemplary embodiment the described rules correspond to the titration label recommendations for Tresiba® from Novo Nordisk A/S.

1.1 Valid Request Check

This function is described above and checks that all the data types necessary in a dose guidance request (i.e. client input data) are present and within a specified range. In the event that a request lacks the appropriate data the function will returns an error message. Otherwise the check passes the data along.

For a given set-up additional input data may be required for a request to pass the check, e.g. a “hypo history” including any reported hypoglycemic events with a timestamp, a max basal limit for a given period of time, e.g. per day or per week, the time of request (TOR), a unique identifier of an individual dose guidance request, and a unique identifier of a client.

1.2 Last Injection Data Refresh Check

This function checks that the injection history data has been refreshed within a predetermined time limit, e.g. the last 30 seconds, this is to ensure that it has the most up to date record of the injection history data.

1.3 Injection History Check

This function checks whether there was already a dose event within the past 8 hours. If so, recommending another injection would violate the Tresiba labelling (i.e. take an injection once daily and at least 8 hours apart) and put the user at risk for a hypoglycemic event. If there are no dose events within the previous 8 hours, the check will pass the data along to the next function.

1.4 Make-Up Injection Check

The Tresiba® labelling states that the user should take an injection once daily and at least 8 hours apart. But in the event that the patient forgets to take their daily dose, they can take the forgotten dose the next day in addition to their regularly scheduled dose (spaced out by at least 8 hours). Correspondingly, this function first checks to see if there's already been a dose event within the calendar day. If so, it then checks to see if the previous calendar day is without a dose event, and if this is true, it will pass the check. If there is a dose event recorded from the previous calendar day, the check will return an error message and not give a dose recommendation.

1.5 Recorded Hypoglycemic Event Check

This function checks whether the data structure received with the request comprises an indicator for one or more recorded hypoglycemic events. If one or more hypoglycemic events have been recorded within a preset time limit no new ADDR will be calculated but the current DGB will be adjusted down with 2 UI of insulin. Hypoglycemic events may be recorded manually by the patient and/or may be registered automatically when a CGM is used for BG data capture. In the latter case the patient may be requested to confirm the event.

1.6 BG Data Quality Check

In case BG data is supplied based on individual BG measurements, e.g. fasting BG, the function will check that a valid BG value is recorded for a specified number of days out of a specified number of recent days, e.g. at least 3 valid BG values for the last 4 days. In case BG data is supplied based on CGM measurements a CGM data quality check may be performed to ensure that there are no unacceptable gaps in the data that may lead to a wrong titration glucose level determination. Exemplary criterions for what an unacceptable level of CGM data quality is are described below. In the event that the data quality is not able to meet the minimum threshold, this function will return an error message and the dose guidance request will not proceed. Alternatively, it may be possible to fill in gaps within the CGM data. This is to increase the probability that despite gaps in the data (e.g. a new sensor is warming up) a dose guidance request will pass the next CGM data quality check. Examples of suitable mathematical approaches will be described in greater detail below.

1.7 Dose Event Identification

In a pen device with integrated dose logging functionality such as NovoPen® 6, according to its instructions for use, the patient should prime the device until they see insulin squirt out. These priming “injections” (or “air shots”) are not differentiated from actual body injections by the pen and would be seen from the engine's perspective as individual injections taken into the body. As the number of actual injections taken by the patient is one of the parameters that may be checked by the dose guidance system to determine whether the patient is in compliance with the titration regimen, it is relevant to be able to filter out such priming “injections”. This filtering could take place in the dose logging circuitry (whether integrated as in NovoPen® 6 or provided as an add-on logging device to be mounted on a pen device), in the patient's smartphone app which will typically be the device adapted to receive and collect injection data and BG data before they are transmitted as part of a request for an ADDR, or in the cloud engine. The filtering will typically be based on dose size (priming doses are generally (much) smaller than injection doses, and time between injections. A number of algorithms performing this analysis are known, e.g. as disclosed in US 2019/0035500. The filtering may be implemented at more than one level, as a subsequent filtering would just filter out nothing. The same considerations apply to “split doses”, i.e. a (typically) large dose which by the patient is split into two separate injections, which should be combined to a “dose event”, see below, in order not to count as two individual doses which would jeopardize a titration regimen based on the number of dose events.

1.8 Dose Event Check

This function is checking to see if the patient has been sufficiently adherent in his/her injection routine. It looks for at least 3 dose events since the most recent ADDR, and that they have occurred within today and up to a previous 4 calendar day window. This extra calendar day that is being checked allows for a day that is without a dose (e.g. he/she forgot) and subsequently takes it the next day—in addition to the normally scheduled dose. If there are less than 3 dose events in this time frame (because the patient forgot multiple days) than this check will fail, and it will lead to a re-recommendation of the most recent ADDR made. Additionally, it will ensure only a single ADDR is dispensed and only re-recommendations follow in a scenario where repeated requests come in for an ADDR without any injections having since taken place.

1.9 DGB Check

This function checks to see when the most recent ADDR was made—or when the starting basal dose (SBD) value was set. If the most recent ADDR was made within the current and previous 7 calendar days, then the check passes. If the most recent ADDR is older than this, than a new SBD amount must be inputted, and the dose guidance request process must be re-started from this new point. The new SBD would then be considered the current DGB.

1.10 Adherence Check

This function checks the injection amounts of the three most recent dose events in the dataset to ensure that they are each at least equal to or greater than the amount of the most recent ADDR. If any of the dose events are less than this amount, the check will fail, and it will re-recommend the amount from the most recent ADDR. This ensures that the engine does not titrate up based on elevated glucose levels which are due to under-dosing.

1.11 Too Many Doses Check

This function checks whether there are more than two dose events in the last 48 hours which would be against the recommendations of the Tresiba® label. In case of more than two dose events in the last 48 hours have been recorded then . . . XXX

1.12 Daily TGL Determination

In case BG data is supplied based on individual BG measurements, e.g. fasting BG, the function will check that a valid BG value is recorded within a specified time range. In case BG data is supplied based on CGM measurements, this function looks at the CGM dataset and determines, if possible, a TGL for each day since most recent ADDR or up to a maximum of days, e.g. 4 days from the TOR, this being the dose guidance period. A “day” may be a calendar day (in which case some days would be partial), or it may be a 24 hours period calculated from e.g. the TOR, see below. It does this by applying e.g. a 3 hour “sliding window” across CGM readings one data point at the time, see FIGS. 2A and 2B, calculating the mean of each 3 hours interval from the current time up until the time of the last dose adjustment. In an exemplary embodiment a daily TGL is determined only for days having passed the BGH data quality test as described below. The lowest 3 hour mean for each day will be recorded as a TGL value and used for titration.

1.13 Daily TGL Check

This check looks for any daily TGLs that are below the lower limit of the target glucose range parameter. If so, then the next ADDR is reduced with 2 IU from the most recent ADDR given. If not, the check passes onto the next function.

1.14 Average TGL Determination

If at least a minimum number of valid daily TGL values have been determined, e.g. 2, for the dose guidance period, e.g. last 4 days, an average for the at least 2 TGL values is calculated.

1.15 Titration Determination

This function utilizes the overall TGL average, and if it is within the target range, the titration determination will be +0 IU from the most recent ADDR. If the TGL average is above the upper limit of the target range, then the next ADDR is increased by 2 IU from the most recent ADDR. If the TGL average is below the upper limit of the target range, then the next ADDR is lowered by 2 IU from the most recent ADDR.

1.16 Maximum Limit Check

To prevent overdosing this function checks a that a given dose maximum for a given period of time has not been exceeding, e.g. a maximum of 300 IU of Tresiba® for the last week. A patient specific value may also be included in the request data. Alternatively, the function may check whether the next ADDR would exceeded the patient's “overbasalisation” limit (BW (kg)*OBL (IU/kg)). This would require that a body weight (BW) of the subject, and an overbasalisation limit (OBL) of the subject have been provided as part of the data received with the request.

1.17 Output

The main output from the engine as a reply to a dose guidance request is indeed the ADDR as received by the client, however, additional information may be of use to the patient, either directly related to the patient's treatment, e.g. TGL values calculated by the engine based on CGM values as well as specific error or warning messages, or validation data to improve reliability and safety. Correspondingly, the output may comprise one or more of the following types of data: User ID, transaction ID, ADDR, day TGLs, overall TGL, dose event history, hypoglycaemia history, warning and error codes.

Referring to “1.6 BG data quality check” above, in the following an exemplary criterion for an unacceptable level of CGM data quality will be described.

In general, for decision support systems that wants to provide insulin dose guidance to patients with T2DM using a CGM device, the above-described system is a back-end dose guidance engine that ingests insulin, glucose, and patient data to analyze and recommend how much basal insulin to take now, so that patients can live a more normal life.

The glucose data ingested may be from a continuous glucose monitor. Current CGMs such as a Dexcom G5 reports data every 5 minutes continuously, and it is the task of the engine to determine the Titration Glucose Level (TGL) from this continuous stream of data to ultimately make a dose recommendation. Unfortunately, today's CGMs do not have the memory to store the data, and any data that isn't received by the receiving device, e.g. a mobile app running on the patient's smartphone, is lost. This creates a challenge in safely determining a TGL while dealing with these gaps, which are likely to occur in real world use. Table 1 describes a first example of a go/no-go criterion for safely handling gaps in CGM data.

TABLE 1

Overview of actions based on missing CGM data

Level of

Category
Level of data missing
uncertainty
Action to be taken by the app

1
No data gap interval larger than
Low level of
Provide next insulin dose

20 min.
uncertainty
recommendation based on

CGM data.

2
There are gaps longer than 20
Low level of
Next insulin dose

min & there is no gap longer
uncertainty
recommendation based on

than 3 hours in any of the

CGM data must take into

last three days before titration

account:

& no more than 60 of the normal

The 60 min data window that

5-min samples are missing in

contains gaps of longer than

any of the last three days.

20 min should not be used for

averaging until the window

passes the >20 min gap.

3
No more than one of the days
Moderate
Next insulin dose

(out of the last three days) has
level of
recommendation based on

CGM gaps of longer than 3
uncertainty
CGM data must take into

hours or has more than 60 of

account:

the normal 5-min samples

missing.

Exclude the day from titration,

use the other two days for

dose recommendation.

4
More than one of the days (out
High level of
No next insulin dose must be

of the last three days) has CGM
uncertainty
recommended.

gaps of longer than 3 hours or

has more than 60 of the normal

5-min samples missing.

In the following a further example of a BGH data quality test will be described.

A dose guidance period (DG_PERIOD) can be defined as the period of time starting from the time of request to the timestamp of the DGB, up to a maximum of e.g. −96:00 hr. The BGH data in the DG_PERIOD is the divided into the following possible increments: BGH_PERIOD_1: 0 to −24:00 hrs, BGH_PERIOD_2: −24:00 to −48:00 hrs, BGH_PERIOD_3: −48:00 to DGB time/−72:00 hrs, and BGH_PERIOD_4: −72:00 hrs to DGB time/−96:00 hrs.

To carry out the data quality test, each BGH_PERIOD is broken into non-overlapping 6-hour interval(s). Each interval is checked for the presence of at least 1 VALID_TGL being defined as a window of time where >50% of BGH data points are present. The window may be e.g. 3 hours. If any 6-hour interval(s) is without at least 1 VALID_TGL, the entire BGH_PERIOD has failed the data quality test, i.e. each BGH_PERIOD is tested individually and will have its own pass/fail determination. Further details in respect of CGM data quality tests are disclosed in WO 2018/228932 which are hereby incorporated by reference.

As mentioned above, it may be necessary to combine doses to create a “dose event”. In an exemplary embodiment all injections in the IH which fall within a running DE_WINDOW of, e.g. 60 minutes, are aggregated into a DOSE_EVENT amount starting from the current time. Only Injections that are within the DG_PERIOD will be aggregated into DOSE_EVENTs. The DOSE_EVENT Timestamp is, e.g. the timestamp of the most recent injection within a DE_WINDOW. As appears, in case priming and air shot expelling events are not filtered out of the IH prior to it being submitted as part of the DGR then these non-injected dose amounts will be calculated as part of the injected dose amounts. Alternatively, the engine may be provided with its owner filter functionality, e.g. rule-based flow-check detection for connected pens as described in EP application 18198410.5.

To ensure that the disclosed engine cannot only be safe, but also effective in treating patients to target, an extra data processing step may be applied to fill in certain gaps in the CGM data where previously a TGL was unable to be determined and the system unable to determine a dose guidance recommendation. By utilizing a data gap filling method, the engine may be able to calculate basal dose guidance in a more realistic “real world” environment where data can have gaps, while the system remains safe and effective for dispensing basal insulin dose guidance.

More specifically, in the following the main idea of the method is to define lowest quasi stationary states of glucose profile and fill in data gaps with respect to them. The proposed model takes into account general trends and typical behaviour of the patient.

The model was developed based on patients' CGM data from a clinical trial with 2 weeks of CGM data at the initiation of the study with basal only therapy. Analysis of the data showed that at least 30% of patients had quite big gaps in the CGM data. These gaps were at different time of the days and nights. According to the project's risk analysis, there was a risk that these gaps could severely impact algorithm dosing decisions because the gaps could occur during fasting periods, therefore preventing the titration algorithm from being able to correctly identify the TGL and giving a wrong recommendation. The task was to perform analysis and assure that proposed approach was agnostic to the CGM noise and did not lead to hypoglycaemic events.

The CGM data gap filling processing step consists of several steps. First, it checks how much data is available in the CGM stream. The requirement is to have at least 30% of data within last X amount of days needed to identify TGL and for titration (e.g. X=3 for the current algorithm implementation). Second, it needs to identify gaps in the CGM data and store them in array of gaps. It is possible to refill from 1 to 72 missing points (1 missing point=2 intervals=10 minutes gap). Even though 72 missing points gap looks big, the gap filling algorithm was able to reconstruct CGM data correctly.

Data analysis showed that the absolute value difference between all neighboring points on the 90th percentile and 75th percentile would be approximately 10 mg/dl and 6 mg/dl for users with high glycemic variability while only 5 mg/dl and 3 mg/dl for users with low glycemic variability. The next logical step is to find all gaps between 7 to 360 minutes. The reconstruction process deals with each and every gap individually. The algorithm is as the following:

- take the last point before gap and the first one after
- find what is the typical CGM rate of change (ROC) for the particular user on the 75th percentile,
- calculate maximum of difference from the last and the first points as difference between the last (first) point value minus ROC times number of missing points (denoted x3 and x4 correspondingly),
- find an intersection between two lines and store the value of time t_i,

Sometimes if difference |x1-x2| is bigger than ROC times number of missing points the intersection point will be outside of the time interval (t1, t2). It will be necessary to repeat abovementioned part of the algorithm routine with 90% percentile ROC. If the algorithm is unable to achieve intersection point within time interval (t1, t2) with 90% percentile ROC, it will connect points with a straight line. Mathematical description for that is the following, when the time of the gap is small and the person is rising from high glycaemic index meal, the worst case scenario will be a straight line (degenerate case of a V-shape line).

Further, data analysis shows that the true fasting period values form valleys with some reasonable amount of points.

The machine learning clustering method k-Means allows to find clusters of points. Standard k-Means algorithm with random initialization of cluster centroids was used. There is a need to prepare CGM data to find clusters. The simplest way of filtering CGM data is to neglect all data bigger than 30th percentile of the stream. The advantage of this approach is that the cut-off line based on the input data is shifted, while there are still enough data points to find clusters.

Defining cluster centroids is a very powerful tool and by finding the minimum cluster centroid value it can said, that points of the cluster trending lower and closer together. The last step is to define minimum of the lowest cluster and store this value x_c.

After defining cluster centroids gaps can be refilled with V-shape curves, where the head of V is defined by point [t_i,x_c]. Since the patient's body cannot be fully reproduced, all nonlinear can be approximated with lines.

When a given BG data set has passed the BG data quality check a daily TGL determination will be performed. In the following two concepts for this purpose will be described. In contrast to the above-described exemplary embodiment a sliding window of 60 minutes was utilized.

Approach 1:

Detect TGL by selecting the lowest average of 60 minutes glucose levels (i.e. the lowest of 288 13 consecutive measurements of 5 minute intervals during a 24 hour period). This approach calculates a moving average (MA) of 13 consecutive measurements of 5 minute intervals,

M
⁢

A

60
⁢
min

,
i

=

1
6

⁢

∑

k
=

i
-

1
⁢
2

i

⁢

G
k

where G is the glucose measurement and the subscript i is the current sample. The minimum MA_60min,iduring the 24 hrs between 00:00 and 23:59 are determined to be the TGL. This approach is hypothesised to mitigate b) people with irregular daily routines (shift workers etc.)

and c) people whose daily routines deviate from normal practice (e.g. not having breakfast).

Approach 2:

Detect TGL by identifying the first steep curve in the time interval 04.00 to 10.00, which is assumed to represent breakfast, and then determine TGL as the average glucose levels in the time interval −90 minutes to −60 minutes. This approach calculates a backward difference as described by Dassau et al. [XX] for meal detection using CGM data:

d
⁢

G
i

d
⁢
t

=

3
⁢

G
i

-

4
⁢

G

i
-
1

+

G

i
-
2

2
⁢
Δ
⁢
t

where G is the glucose measurement, t is time, Δt is the time difference between two sample intervals, and the subscripts i, i−1 and i−2 are the current and the two previous samples. dG/dt is calculated for all points in the predefined time period. Start of a meal is defined to be where the dG/dt is at maximum. TGL is then calculated as the average of the measurements in the time interval −90 minutes to −60 minutes. This approach is hypothesised to be more robust for people with a) regular and predictable lifestyles, but less robust when lifestyle irregularities are present.

The two approaches were challenged against each other by creating a number of scenarios, (e.g. for people with “typical” and predictable lifestyles, people with “atypical” daily routines, insulin sensitivity changes and CGM sensor deviations) and for each scenario generate CGM data sets based on virtual patient models.

Outcome measures were then calculated based on the continuous glucose output from the simulators. The outcome measures were used to evaluate the performance of the physiological models compared to clinical trial results, and to compare the safety and performance of the treatment decision algorithms.

Outcome measure 1: Frequency of severe hypoglycemic events

Outcome measure 2: Severities of too high or too low dose recommendations

Outcome measure 3: Time in range

When ranking the two approaches, approach 1 performed best in all simulation tests. Correspondingly, approach 1 is assessed to be a feasible means to detect and determine TGL and is assessed to be a safe approach to perform CGM based titration of basal insulin.

A practical application of approach 1 is to use the 3 24 hours periods prior to the request for an insulin dose request. I.e. the dose recommendation time=the dose time=t0. The 3 moving averages are found during 1) t-24 h to t0, 2) t-48 h to t-24 h and 3) t-72 h to t-48 h. There must be a time difference between TGL periods, e.g. at least 8 hours between TGL periods. This is to avoid the scenario where the same TGL period is used in two 24 hours periods.

In the following it is assessed what data quality is required from the CGM in order to safely and reliably perform the basal insulin dose recommendation. The following assumptions apply:

- Indication: Basal insulin titration for patients with T2DM
- Titration algorithm: “Simple”2-0-2 algorithm
- Approach to detection and determination of TGL: Approach 1, see above
- The basal insulin used in the trial is insulin degludec, which has a very flat action profile with t_maxof a median of 9 hours, and t_1/2of approximately 25 hours.

In principle, given these assumptions, the conservative titration algorithm combined with the conservative approach to detect and determine TGL should provide a safe automatic basal insulin titration in it-self. However, in the following the use of CGM data with missing sections is evaluated.

The proposed use of GCM data with missing sections and optimised CGM data quality is assessed using existing clinical GCM data from published clinical trial NN1218-3853 titled: “Efficacy and safety of FIAsp compared to insulin aspart in combination with insulin glargine and metformin in adults with type 2 diabetes (Onset® 2)”. The CGM data from this trial is applicable because:

- Trial includes only Type 2 diabetes patients
- The trial has an 8 week run-in period where basal insulin is titrated
- At the end of the 8 week run-in period there is CGM data for 67 subjects during 14 days
- The CGM device used is Dexcom G4

The maximum frequency (which is also called band edge frequency) of BG in diabetes patients in daily life with a typical schedule of meals, exercise, sleep, and insulin injection is 0.9 10⁻³Hz (K.-D. K. B. T. Gough D A, “Frequency characterization of blood glucose dynamics. Ann Biomed Eng. 2003; 31: 91-97”). The intrinsic BG dynamics are therefore not faster than approximately 18 min (the inverse of the band edge frequency). So, missing data gaps shorter than roughly 20 min of consecutive data do not impact the safety of dose recommendation.

Based on these considerations a rule-set for actions based on missing CGM data can be proposed as set out in Table 1 above.

Of the 917 individual CGM data sets NN 1218-3853, 134 pertain to the first 2 days where there is not yet 3 consecutive days of CGM data, and thus no actions can be recommended. 16 data sets which do not contain CGM values are not included in the table below. Of the remaining 767 data sets, the recommendations are presented in Table 2, and should represent what can be expected on a continuous basis.

TABLE 2

Categorisation of the proposed actions based on NN1218-3853 CGM data

Category (3 24 hour period measurements)
Frequency
Percentage

1. No data gap interval larger than 20 min
270
35%

2. There are gaps >20 min but <3 hours, and <60 5-minutes
317
41%

samples missing in any of the last 3 days

3. 1 day has gap of >3 hours or >60 5-min samples missing
149
19%

4. >1 day with gaps of >3 hours or >60 5-min samples missing
31
4%

Overall, 76% of the time the engine would provide dose recommendations based on 3 24 hour periods of CGM data, 19% of the time the app would provide dose recommendations based on 2 24 hour periods of CGM data, and only in 4% of the time would the app omit providing a dose recommendation based on the amount of missing CGM data.

With reference to FIG. 1 a system providing a backend engine adapted to be used as part of a cloud-based large-scale diabetes management system has been described.

In the following an exemplary embodiment for a corresponding “frontend” system set-up will be described, the frontend system providing the input data to the backend engine.

More specifically, the system comprises a skin mounted CGM unit, a drug delivery injection device with dose logging functionality and a patient unit in the form of a smartphone running a dose recommendation app adapted to wirelessly receive data from the drug delivery device and the CGM unit and function in cooperation with the cloud engine to request and receive an adjustment day dose recommendation (ADDR) for a basal insulin titration regimen.

The skin-mounted CGM unit provides a stream of BG data which is communicated wirelessly to the patient unit in which it is stored creating a BG log. The BG value may be transmitted ad hoc, e.g. every 5 minutes, as it is measured with a corresponding timestamp being added by the patient unit. The CGM unit may e.g. be a Dexcom G4 or G5 device.

The drug delivery injection device may comprise integrated dose logging circuitry, e.g. as in the NovoPen® 6 pen device provided by Novo Nordisk A/S or be in the form of an assembly comprising a drug delivery injection device in combination with an add-on dose logging device, e.g. of the type disclosed in WO 2019/0057911. The drug delivery injection device may transmit data wirelessly using e.g. NFC or Bluetooth®. Each expelled dose amount will be determined by the drug delivery injection device and a corresponding timestamp added to create a dose log. The drug delivery injection device may be set up to transmit data at the end of each drug delivery event and/or when requested to do so, e.g. initiated by actuation of the device or the patient unit. In addition to the most recent dose amount all or part of the dose log may be transmitted as well. The dose recommendation app may allow the user to enter data manually as well, e.g. to cope with missing data. When an updated adjustment day dose recommendation is needed the patient will request this using the app, this resulting in a dose guidance request being transmitted to the above-described cloud engine, e.g. by wi-fi and router or by phone-based data transmission. As described above, the request should comprise a recent injection history refresh timestamp for the subject in order to be processed. For example, just prior to transmitting the request the patient may update the dose log by a forced retransmission of the dose log from the drug delivery device, e.g. by actuation of the device.

In addition, the patient by be prompted to indicate whether non-logged dose amounts have been injected. When a request is sent the system set-up should allow an answer to be received immediate, e.g. ideally within a few seconds or less.

In the following a further exemplary embodiment of the invention will be described, the disclosure relying upon the acquisition of data regarding a data set(s) including information relating to a subject.

It should be noted that the below specifically disclosed details in respect of drugs as well as of system set-up and system components can be utilized with corresponding effect also in combination with the above-described first exemplary embodiment of the invention.

The data set(s) include a body weight of the subject, an upper limit target glucose range of the subject, a lower limit target glucose range of the subject, an over-basalisation limit of the subject, a most recent adjustment day dose recommendation and/or a starting basal dose for the subject, a basal insulin injection history of the subject—where the injection history includes a plurality of injections during all or a portion of the time course and, for each respective injection in the plurality of injections, a corresponding dose event amount and a dose event timestamp representing when in the time course the respective injection event occurred—a last injection data refresh of the subject, and a plurality of glucose measurements of the subject taken over a time course to establish a blood glucose history—and for each respective glucose measurement in the plurality of glucose measurements a corresponding glucose timestamp representing when in the time course the respective glucose measurement was made.

FIG. 3 illustrates an example of an integrated system 502 for the acquisition of such data, and FIG. 5 provides more details of such a system 502. The integrated system 502 includes one or more connected insulin pens 104, one or more glucose monitors 102, memory 506, and a processor (not shown) for performing algorithmic categorization of autonomous glucose data of a subject. In some embodiments, a glucose monitor 102 is a continuous glucose monitor.

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description of implementations, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details.

Definitions

It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first filter could be termed a second filter, and, similarly, a second filter could be termed a first filter, without departing from the scope of the present disclosure. The first filter and the second filter are both filters, but they are not the same filter. Furthermore, the terms “subject,” “individual,” and “user” are used interchangeably herein and refer to humans. Preferably, the individual is an adult individual.

By the term insulin pen is meant an injection device suitable for applying discrete doses of insulin, and wherein the injection device is adapted for logging and communicating dose related data.

The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.

The term “alkyl,” by itself or as part of another substituent, means, unless otherwise stated, a straight or branched chain, or cyclic hydrocarbon radical, or combination thereof, which may be fully saturated, mono- or polyunsaturated and can include di-, tri- and multivalent radicals, having the number of carbon atoms designated (e.g. C1-C10 means one to ten carbons).

Examples of saturated hydrocarbon radicals include, but are not limited to, groups such as methyl, ethyl, n-propyl, isopropyl, n-butyl, t-butyl, isobutyl, sec-butyl, cyclohexyl, (cyclohexyl)methyl, cyclopropylmethyl, homologs and isomers of, for example, n-pentyl, n-hexyl, n-heptyl, n-octyl, and the like. An unsaturated alkyl group is one having one or more double bonds or triple bonds. Examples of unsaturated alkyl groups include, but are not limited to, vinyl, 2-propenyl, crotyl, 2-isopentenyl, 2-(butadienyl), 2,4-pentadienyl, 3-(1,4-pentadienyl), ethynyl, 1- and 3-propynyl, 3-butynyl, and the higher homologs and isomers. The term “alkyl,” unless otherwise noted, is also meant to optionally include those derivatives of alkyl defined in more detail below, such as “heteroalkyl.” Alkyl groups that are limited to hydrocarbon groups are termed “homoalkyl”. Exemplary alkyl groups include the monounsaturated C9-10, oleoyl chain or the diunsaturated C9-10, 12-13 linoeyl chain.

The term “alkylene” by itself or as part of another substituent means a divalent radical derived from an alkane, as exemplified, but not limited, by —CH2CH2CH2CH2-, and further includes those groups described below as “heteroalkylene.” Typically, an alkyl (or alkylene) group will have from 1 to 24 carbon atoms, with those groups having 10 or fewer carbon atoms being preferred in the present invention. A “lower alkyl” or “lower alkylene” is a shorter chain alkyl or alkylene group, generally having eight or fewer carbon atoms.

The terms “alkoxy,” “alkylamino” and “alkylthio” (or thioalkoxy) are used in their conventional sense, and refer to those alkyl groups attached to the remainder of the molecule via an oxygen atom, an amino group, or a sulfur atom, respectively.

The terms “cycloalkyl” and “heterocycloalkyl,” by themselves or in combination with other terms, represent, unless otherwise stated, cyclic versions of “alkyl” and “heteroalkyl”, respectively. Additionally, for heterocycloalkyl, a heteroatom can occupy the position at which the heterocycle is attached to the remainder of the molecule. Examples of cycloalkyl include, but are not limited to, cyclopentyl, cyclohexyl, 1-cyclohexenyl, 3-cyclohexenyl, cycloheptyl, and the like. Further exemplary cycloalkyl groups include steroids, e.g., cholesterol and its derivatives. Examples of heterocycloalkyl include, but are not limited to, 1-(1,2,5,6-tetrahydropyridyl), 1-piperidinyl, 2-piperidinyl, 3-piperidinyl, 4-morpholinyl, 3-morpholinyl, tetrahydrofuran-2-yl, tetrahydrofuran-3-yl, tetrahydrothien-2-yl, tetrahydrothien-3-yl, 1-piperazinyl, 2-piperazinyl, and the like.

The terms “halo” or “halogen,” by themselves or as part of another substituent, mean, unless otherwise stated, a fluorine, chlorine, bromine, or iodine atom. Additionally, terms such as “haloalkyl,” are meant to include monohaloalkyl and polyhaloalkyl. For example, the term “halo(C1-C4)alkyl” is mean to include, but not be limited to, trifluoromethyl, 2,2,2-trifluoroethyl, 4-chlorobutyl, 3-bromopropyl, and the like.

The term “aryl” means, unless otherwise stated, a polyunsaturated, aromatic, substituent that can be a single ring or multiple rings (preferably from 1 to 3 rings), which are fused together or linked covalently. The term “heteroaryl” refers to aryl substituent groups (or rings) that contain from one to four heteroatoms selected from N, O, S, Si and B, wherein the nitrogen and sulfur atoms are optionally oxidized, and the nitrogen atom(s) are optionally quaternized. An exemplary heteroaryl group is a six-membered azine, e.g., pyridinyl, diazinyl and triazinyl. A heteroaryl group can be attached to the remainder of the molecule through a heteroatom. Non-limiting examples of aryl and heteroaryl groups include phenyl, 1-naphthyl, 2-naphthyl, 4-biphenyl, 1-pyrrolyl, 2-pyrrolyl, 3-pyrrolyl, 3-pyrazolyl, 2-imidazolyl, 4-imidazolyl, pyrazinyl, 2-oxazolyl, 4-oxazolyl, 2-phenyl-4-oxazolyl, 5-oxazolyl, 3-isoxazolyl, 4-isoxazolyl, 5-isoxazolyl, 2-thiazolyl, 4-thiazolyl, 5-thiazolyl, 2-furyl, 3-furyl, 2-thienyl, 3-thienyl, 2-pyridyl, 3-pyridyl, 4-pyridyl, 2-pyrimidyl, 4-pyrimidyl, 5-benzothiazolyl, purinyl, 2-benzimidazolyl, 5-indolyl, 1-isoquinolyl, 5-isoquinolyl, 2-quinoxalinyl, 5-quinoxalinyl, 3-quinolyl, and 6-quinolyl. Substituents for each of the above noted aryl and heteroaryl ring systems are selected from the group of acceptable substituents described below.

For brevity, the term “aryl” when used in combination with other terms (e.g., aryloxy, arylthioxy, arylalkyl) includes aryl, heteroaryl and heteroarene rings as defined above. Thus, the term “arylalkyl” is meant to include those radicals in which an aryl group is attached to an alkyl group (e.g., benzyl, phenethyl, pyridylmethyl and the like) including those alkyl groups in which a carbon atom (e.g., a methylene group) has been replaced by, for example, an oxygen atom (e.g., phenoxymethyl, 2-pyridyloxymethyl, 3-(1-naphthyloxy)propyl, and the like).

Each of the above terms (e.g., “alkyl,” “heteroalkyl,” “aryl, and “heteroaryl”) are meant to optionally include both substituted and unsubstituted forms of the indicated species. Exemplary substituents for these species are provided below.

Substituents for the alkyl and heteroalkyl radicals (including those groups often referred to as alkylene, alkenyl, heteroalkylene, heteroalkenyl, alkynyl, cycloalkyl, heterocycloalkyl, cycloalkenyl, and heterocycloalkenyl) are generically referred to as “alkyl group substituents,” and they can be one or more of a variety of groups selected from, but not limited to: H, substituted or unsubstituted aryl, substituted or unsubstituted heteroaryl, substituted or unsubstituted heterocycloalkyl, —OR′, ═O, ═NR′, ═N—OR′, —NR′R″, —SR′, halogen, —SiR′R″R′″, OC(O)R′, —C(O)R′, —CO2R′, —CONR′R″, —OC(O)NR′R″, —NR″C(O)R′, NR′ C(O)NR″R′″, —NR″C(O)2R′, —NR—C(NR′R″R′″)═NR″″, NR C(NR′R″)═NR′″, —S(O)R′, —S(O)2R′, —S(O)₂NR′R″, NRSO2R′, —CN and —NO2 in a number ranging from zero to (2m′+1), where m′ is the total number of carbon atoms in such radical. R′, R″, R′″ and R″″ each preferably independently refer to hydrogen, substituted or unsubstituted heteroalkyl, substituted or unsubstituted aryl, e.g., aryl substituted with 1-3 halogens, substituted or unsubstituted alkyl, alkoxy or thioalkoxy groups, or arylalkyl groups. When a compound of the invention includes more than one R group, for example, each of the R groups is independently selected as are each R′, R″, R″ and R″″ groups when more than one of these groups is present. When R′ and R″ are attached to the same nitrogen atom, they can be combined with the nitrogen atom to form a 5-, 6-, or 7-membered ring. For example, —NR′R″ is meant to include, but not be limited to, 1-pyrrolidinyl and 4-morpholinyl. From the above discussion of substituents, one of skill in the art will understand that the term “alkyl” is meant to include groups including carbon atoms bound to groups other than hydrogen groups, such as haloalkyl (e.g., —CF3 and —CH2CF3) and acyl (e.g., —C(O)CH3, —C(O)CF3, —C(O)CH2OCH3, and the like). These terms encompass groups considered exemplary “alkyl group substituents,” which are components of exemplary “substituted alkyl” and “substituted heteroalkyl” moieties.

Similar to the substituents described for the alkyl radical, substituents for the aryl heteroaryl and heteroarene groups are generically referred to as “aryl group substituents.” The substituents are selected from, for example: groups attached to the heteroaryl or heteroarene nucleus through carbon or a heteroatom (e.g., P, N, O, S, Si, or B) including, without limitation, substituted or unsubstituted alkyl, substituted or unsubstituted aryl, substituted or unsubstituted heteroaryl, substituted or unsubstituted heterocycloalkyl, OR′, ═O, ═NR′, ═N—OR′, —NR′R″, —SR′, -halogen, —SiR′R″R′″, OC(O)R′, —C(O)R′, CO2R′, —CONR′R″, —OC(O)NR′R″, —NR″C(O)R′, NR′ C(O)NR″R′″, —NR″C(O)2R′, NR—C(NR′R″R′″)═NR″″, NR C(NR′R″)═NR′″, —S(O)R′, —S(O)2R′, —S(O)₂NR′R″, NRSO2R′, —CN and NO2, R′, N3, CH(Ph)2, fluoro(C1-C4)alkoxy, and fluoro(C1-C4)alkyl, in a number ranging from zero to the total number of open valences on the aromatic ring system. Each of the above-named groups is attached to the heteroarene or heteroaryl nucleus directly or through a heteroatom (e.g., P, N, O, S, Si, or B); and where R′, R″, R′″ and R″″ are preferably independently selected from hydrogen, substituted or unsubstituted alkyl, substituted or unsubstituted heteroalkyl, substituted or unsubstituted aryl and substituted or unsubstituted heteroaryl. When a compound of the invention includes more than one R group, for example, each of the R groups is independently selected as are each R′, R″, R″ and R″″ groups when more than one of these groups is present.

As used herein, the term “heteroatom” includes oxygen (O), nitrogen (N), sulfur (S) and silicon (Si), boron (B) and phosphorous (P).

The symbol “R” is a general abbreviation that represents a substituent group that is selected from H, substituted or unsubstituted alkyl, substituted or unsubstituted heteroalkyl, substituted or unsubstituted aryl, substituted or unsubstituted heteroaryl, and substituted or unsubstituted heterocycloalkyl groups.

It is understood that, in any compound described herein having one or more chiral centers, if an absolute stereochemistry is not expressly indicated, then each center may independently be of R-configuration or S-configuration or a mixture thereof. Thus, the compounds provided herein may be enantiomerically pure or be stereoisomeric mixtures. In addition, it is understood that, in any compound described herein having one or more double bond(s) generating geometrical isomers that can be defined as E or Z, each double bond may independently be E or Z a mixture thereof. Likewise, it is understood that, in any compound described, all tautomeric forms are also intended to be included.

Modifications in the insulin molecule are denoted stating the chain (A or B), the position, and the one or three letter code for the amino acid residue substituting the native amino acid residue. By “desB30” or “B(1-29)” is meant a natural insulin B chain or an analogue thereof lacking the B30 amino acid and “A(1-21)” means the natural insulin A chain. Thus, e.g., A21Gly,B28Asp,desB30 human insulin is an analogue of human insulin where the amino acid in position 21 in the A chain is substituted with glycine, the amino acid in position 28 in the B chain is substituted with aspartic acid, and the amino acid in position 30 in the B chain is deleted.

Herein, the term “amino acid residue” is an amino acid from which, formally, a hydroxy group has been removed from a carboxy group and/or from which, formally, a hydrogen atom has been removed from an amino group.

Examples of insulin analogues are such wherein Pro in position 28 of the B chain is substituted with Asp, Lys, Leu, Val, or Ala and/or Lys at position B29 is substituted with Pro, Glu or Asp. Furthermore, Asn at position B3 may be substituted with Thr, Lys, Gln, Glu or Asp. The amino acid residue in position A21 may be substituted with Gly. Also one or more amino acids may be added to the C-terminal of the A-chain and/or B-chain such as, e.g., Lys. The amino acid in position B1 may be substituted with Glu. The amino acid in position B16 may be substituted with Glu or His. Further examples of insulin analogues are the deletion analogues, e.g., analogues where the B30 amino acid in human insulin has been deleted (des(B30) human insulin), insulin analogues wherein the B1 amino acid in human insulin has been deleted (des(B1) human insulin), des(B28-B30) human insulin and des(B27) human insulin. Insulin analogues wherein the A-chain and/or the B-chain have an N-terminal extension and insulin analogues wherein the A-chain and/or the B-chain have a C-terminal extension such as with two arginine residues added to the C-terminal of the B-chain are also examples of insulin analogues. Further examples are insulin analogues comprising combinations of the mentioned mutations. Insulin analogues wherein the amino acid in position A14 is Asn, Gln, Glu, Arg, Asp, Gly or His, the amino acid in position B25 is His and which optionally further comprises one or more additional mutations are further examples of insulin analogues. Insulin analogues of human insulin wherein the amino acid residue in position A21 is Gly and wherein the insulin analogue is further extended in the C-terminal with two arginine residues are also examples of insulin analogues.

According to the present invention, the basal insulin comprises or consists of long acting insulin and ultra-long acting insulin.

A ‘long-acting insulin’ includes a derivative or analogue of a naturally occurring insulin that:

exhibits in physiological conditions, at least in part, the insulin receptor binding of the naturally occurring insulin, preferably, at least 0.01% of the insulin receptor binding of the naturally occurring insulin, for example, at least 0.1%, at least, 1%, at least 5%, at least 10%, at least 15%, at least 20%, at least 25% at least 50%, at least 65%, at least 75%, at least 85%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140% or at least 150% of the insulin receptor binding of the naturally occurring insulin, and/or, at least in part, the potency of the naturally occurring insulin, preferably, at least 25% of the potency of the naturally occurring insulin, for example, at least 50%, at least 65%, at least 75%, at least 85%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140% or at least 150% of the potency of the naturally occurring insulin; and exhibits a mean terminal half-life of at least 5 hours and less than 18 hours in physiological conditions when injected subcutaneously, for example, at least 7 hours, at least 8 hours, at least 10 hours, at least 12.5 hours, greater than 12.5 hours, at least 15 hours or at least 17.5 hours and less than 18 hours, between 5 and 17.5 hours, between 10 and 17.5 hours or between 15 and 17.5 hours.

Preferably, the ‘long-acting insulin’ also:

induces in a subject a maximum deviation from mean insulin concentration (AUCF %) over a 24 hour period of ±20, for example ≤±18, ≤±17, ≤±16, ≤±15, ≤±14, ≤±13, ≤±12, ≤±11, ≤±10, ≤±9, ≤±8, ≤±7, ≤±6, ≤±5, ≤±4, ≤±3, ≤±2, ≤±1, ≤±0.5, ≤±0.1.

An ‘ultra-long-acting’ insulin includes a derivative or analogue of a naturally occurring insulin that:

exhibits in physiological conditions, at least in part, the insulin receptor binding of the naturally occurring insulin, preferably, at least 0.01% of the insulin receptor binding of the naturally occurring insulin, for example, at least 0.1%, at least, 1%, at least 5%, at least 10%, at least 15%, at least 20%, at least 25% at least 50%, at least 65%, at least 75%, at least 85%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140% or at least 150% of the insulin receptor binding of the naturally occurring insulin, and/or, at least in part, the potency of the naturally occurring insulin, preferably, at least 25% of the potency of the naturally occurring insulin, for example, at least 50%, at least 65%, at least 75%, at least 85%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140% or at least 150% of the potency of the naturally occurring insulin;

exhibits a mean terminal half-life of at least 18 hr in physiological conditions when injected subcutaneously, for example, greater than 18 hours, at least 20 hours, greater than 20 hours, greater than 22 hours, at least 22.5 hours, or greater than 24 hours, at least 25 hours, at least 27.5 hours, at least 30 hours, at least 32.5, at least 35 hours, at least 37.5 hours, or at least 40 hours, or between 18 and 40 hours, between 20 and 40 hours, between 24 and 40 hours. Preferably, the ‘ultra-long acting insulin’ also:

induces in a subject a maximum deviation from mean insulin concentration (AUCF %) over a 24 hour period of ±20, for example, ≤±18, ≤±17, ≤±16, ≤±15, ≤±14, ≤±13, ≤±12, ±11, ≤±10, ≤±9, ≤±8, ≤±7, ≤±6, ≤±5, ≤±4, ≤±3, ≤±2, ≤±1, ≤±0.5, ≤±0.1.

In principle, the longer the half-life of the insulin, the more stable and evenly distributed the glucose-lowering effect over a dosing interval (i.e. time interval between injections).

According to the present invention, the basal insulin comprises or consists of long acting insulin and ultra-long acting insulin.

According to the present invention, the basal insulin is administered in an amount to achieve a beneficial glycemic control in said subject.

According to the present invention, the beneficial glycemic control in said subject is determined by at least the levels of HbA1c (glycosylated hemoglobin) in said subject after administration of said basal insulin.

By use of the basal insulin and its administration according to the present invention it is possible to achieve improvements in the proportion of patients in need thereof reaching HbA1c targets.

As used herein the term “U” refers to a unit of insulin (or an analogue or derivative thereof). The designation “U” with a number following indicates the concentration as measured by the number of units per ml of fluid volume (Joslin's Diabetes Deskbook, 2nd edition, Chapter 9 Using insulin to treat diabetes—general principles, page 268). Further information about the meaning of “U” can be found in a document from the EMA (reference EMEA/CHMP/BWP/124446/2005) entitled “Guideline on potency labelling for insulin analogue containing products with particular reference to the use of “International Units” or “Units”” (see http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC50 0003654.pdf). “IU” refers to an international unit of human insulin as defined according to the WHO Expert Committee on Biological Standardization. IU is a standardized parameter. For commercial insulins, the labels indicate the content of 1 U (unit) of the particular insulin analogue.

As used herein the term “administration period” means the period for which the long-acting or ultra-long-acting insulin is administered in a given dose.

The term “diabetes” or “diabetes mellitus” includes type 1 diabetes mellitus, type 2 diabetes mellitus, gestational diabetes (during pregnancy) and other states that cause hyperglycemia. The term is used for a metabolic disorder in which the pancreas produces insufficient amounts of insulin, or in which the cells of the body fail to respond appropriately to insulin thus preventing cells from absorbing glucose. As a result, glucose builds up in the blood.

DESCRIPTION

A detailed description of a system 48 for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen for a subject in accordance with the present disclosure is described in conjunction with FIGS. 3 and 4. As such, FIGS. 3 and 4 collectively illustrate the topology of the system in accordance with the present disclosure. In the topology, there is a regimen monitoring device for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen for a subject (“regimen monitor device 250”) (FIGS. 3 and 4), a device for data collection (“data collection device 200”), one or more glucose sensors 102 associated with the subject (FIGS. 3 and 7), and one or more insulin pens 104 for injecting insulin medicaments into the subject (FIGS. 3 and 7). Throughout the present disclosure, the data collection device 200 and the regimen monitor device 250 will be referenced as separate devices solely for purposes of clarity. That is, the disclosed functionality of the data collection device 200 and the disclosed functionality of the regimen monitor device 250 are contained in separate devices as illustrated in FIG. 1. However, it will be appreciated that, in fact, in some embodiments, the disclosed functionality of the data collection device 200 and the disclosed functionality of the regimen monitor device 250 are contained in a single device. In some embodiments, the disclosed functionality of the data collection device 200 and/or the disclosed functionality of the regimen monitor device 250 are contained in a single device and this single device is a glucose monitor 102 or the insulin pen 104.

Referring to FIG. 3, the regimen monitor device 250 autonomously adjusts a long acting insulin medicament dosage in a prescribed insulin regimen for a subject. To do this, the data collection device 200, which is in electrical communication with the regimen monitor device 250, receives autonomous glucose measurements originating from one or more glucose sensors 102 attached to a subject on an ongoing basis. In some embodiments, the data collection device 200 also receives insulin medicament injection data from one or more insulin pens 104 used by the subject to inject insulin medicaments. In some embodiments, the data collection device 200 receives such data directly from the glucose sensor(s) 102 and insulin pens 104 used by the subject. For instance, in some embodiments the data collection device 200 receives this data wirelessly through radio-frequency signals. In some embodiments such signals are in accordance with an 802.11 (WiFi), Bluetooth, or ZigBee standard. In some embodiments, the data collection device 200 receives such data directly, analyzes the data, and passes the analyzed data to the regimen monitor device 250. In some embodiments, a glucose sensor 102 and/or insulin pen 104 includes an RFID tag and communicates to the data collection device 200 and/or the regimen monitor device 250 using RFID communication. In some embodiments, the data collection device 200 also obtains or receives physiological measurements 210 of the subject (e.g., from wearable physiological measurement devices, from measurement devices within the data collection device 200 such as a magnetometer or thermostat, etc.). For some embodiments of the present invention, said insulin pen device is FlexPen®(s) or FlexTouch®(s). FlexPen® and or FlexTouch® are trademarks of Novo Nordisk A/S.

In some embodiments, the data collection device 200 and/or the regimen monitor device 250 is not proximate to the subject and/or does not have wireless capabilities or such wireless capabilities are not used for the purpose of acquiring glucose data, insulin medicament injection data, and/or physiological measurement data. In such embodiments, a communication network 106 may be used to communicate glucose measurements from the glucose sensor 102 to the data collection device 200 and/or the regimen monitor device 250, insulin medicament injection data from the one or more insulin pens 104 to the data collection device 200 and/or the regimen monitor device 250, and/or physiological measurement data from one or more physiological measurement devices (not shown) to the data collection device 200 and/or the regimen monitor device 250.

Examples of networks 106 include, but are not limited to, the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The wireless communication optionally uses any of a plurality of communications standards, protocols and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11ac, IEEE 802.11ax, IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for e mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of the present disclosure.

In some embodiments, there is a single glucose sensor 102 attached to the subject and the data collection device 200 and/or the regimen monitor device 250 is part of the glucose sensor 102. That is, in some embodiments, the data collection device 200 and/or the regimen monitor device 250 and the glucose sensor 102 are a single device.

In some embodiments, the data collection device 200 and/or the regimen monitor device 250 is part of an insulin pen. That is, in some embodiments, the data collection device 200 and/or the regimen monitor device 250 and an insulin pen 104 are a single device.

Of course, other topologies of the system 48 are possible. For instance, rather than relying on a communications network 106, the one or more glucose sensors 102 and the one or more insulin pens 104 may wirelessly transmit information directly to the data collection device 200 and/or regimen monitor device 250. Further, the data collection device 200 and/or the regimen monitor device 250 may constitute a portable electronic device, a server computer, or in fact constitute several computers that are linked together in a network or be a virtual machine in a cloud computing context. As such, the exemplary topology shown in FIG. 1 merely serves to describe the features of an embodiment of the present disclosure in a manner that will be readily understood to one of skill in the art.

While the system 48 disclosed in FIG. 3 can work standalone, in some embodiments it can also be linked with electronic medical records to exchange information in any way.

Referring to FIG. 4, in typical embodiments, the regimen monitor device 250 comprises one or more computers. For purposes of illustration in FIG. 4, the regimen monitor device 250 is represented as a single computer that includes all of the functionality for evaluating historical adherence to a prescribed insulin medicament dosage regimen for a subject. However, the disclosure is not so limited. In some embodiments, the functionality for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen for a subject is spread across any number of networked computers and/or resides on each of several networked computers and/or is hosted on one or more virtual machines at a remote location accessible across the communications network 106. One of skill in the art will appreciate that any of a wide array of different computer topologies are used for the application and all such topologies are within the scope of the present disclosure.

In some embodiments, an exemplary regimen monitor device 250 for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen for a subject comprises one or more processing units (CPU's) 274, a network or other communications interface 284, a memory 192 (e.g., random access memory), one or more magnetic disk storage and/or persistent devices 290 optionally accessed by one or more controllers 288, one or more communication busses 213 for interconnecting the aforementioned components, and a power supply 276 for powering the aforementioned components. In some embodiments, data in memory 192 is seamlessly shared with non-volatile memory 290 using known computing techniques such as caching. In some embodiments, memory 192 and/or memory 290 includes mass storage that is remotely located with respect to the central processing unit(s) 274. In other words, some data stored in memory 192 and/or memory 290 may in fact be hosted on computers that are external to the regimen monitor device 250 but that can be electronically accessed by the regimen monitor device 250 over an Internet, intranet, or other form of network or electronic cable (illustrated as element 106 in FIG. 2) using network interface 284.

In some embodiments, the memory 192 of the regimen monitor device 250 for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen for a subject stores:

- an operating system 202 that includes procedures for handling various basic system services;
- an insulin regimen monitoring module 204;
- a first data structure 208, the first data structure comprising physiological measurements of the subject 210 (e.g., 210-1, 210-2, and 210-X);
- a second data structure 218, the second data structure representing a Starting Basal Dose 220 of the subject, a plurality of adjustment day dose recommendations 222 for the subject and for each respective adjustment day dose recommendation 222 in the plurality of adjustment day dose recommendations (e.g., 222-1, 222-2, and 222-M) a timestamp 223 representing when the respective adjustment day dose recommendation was made;
- a first data set 224 (e.g., a blood glucose history), the first data set comprising a plurality of glucose measurements of the subject over the time course, and for each respective glucose measurement 226 in the plurality of glucose measurements (e.g., 226-1, 226-2, and 226-N), a timestamp 228 representing when the respective glucose measurement was made;
- a second data set 230 (e.g., a basal insulin injection history), the second data set comprising a plurality of insulin medicament records, where each respective insulin medicament record 232 in the plurality of insulin medicament records (e.g., 232-1, 232-2, 232-S) comprises a respective insulin medicament injection event 234 associated with the one or more insulin pens 104 in which an insulin medicament was injected into the subject and an injection event time stamp 236 that indicates when the respective medicament injection event 232 occurred.

In some embodiments, the insulin regimen monitoring module 204 is accessible within any browser (phone, tablet, laptop/desktop). In some embodiments the insulin regimen monitoring module 204 runs on native device frameworks, and is available for download onto the regimen monitor device 250 running an operating system 202 such as Android or iOS.

In some implementations, one or more of the above identified data elements or modules of the regimen monitor device 250 for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen for a subject are stored in one or more of the previously described memory devices, and correspond to a set of instructions for performing a function described above. The above-identified data, modules or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various implementations. In some implementations, the memory 192 and/or 290 optionally stores a subset of the modules and data structures identified above. Furthermore, in some embodiments the memory 192 and/or 290 stores additional modules and data structures not described above.

In some embodiments, a regimen monitor device 250 for autonomously adjusting a long acting insulin medicament dosage 216 in a prescribed insulin regimen 212 for a subject is a smart phone (e.g., an iPhone), laptop, tablet computer, desktop computer, or other form of electronic device (e.g., a gaming console). In some embodiments, the regimen monitor device 250 is not mobile. In some embodiments, the regimen monitor device 250 is mobile.

In some embodiments the regimen monitor device 250 is a smart phone. In other embodiments, the regimen monitor device 250 is not a smart phone but rather is a tablet computer, desktop computer, emergency vehicle computer, or other form or wired or wireless networked device. In some embodiments, the regimen monitor device 250 has any or all of the circuitry, hardware components, and software components found in the regimen monitor device 250 depicted in FIG. 4. In the interest of brevity and clarity, only a few of the possible components of the regimen monitor device 250 are shown in order to better emphasize the additional software modules that are installed on the regimen monitor device 250.

The regimen monitor device 250 accesses and/or stores a first data structure 210 that includes a prescribed insulin regimen 212 for the subject that is used to match as closely as possible normal physiologic insulin secretion to control fasting and postprandial plasma glucose. In the present disclosure, the prescribed insulin regimen 212 comprises a basal insulin medicament dosage regimen 214 that specifies the long acting insulin medicament dosage 216. The first data structure 210 further specifies an original target fasting blood glucose level 226 used as a basis to compute the long acting insulin medicament dosage.

In some embodiments, the glucose measurements 226 are autonomously measured. The FREESTYLE LIBRE CGM by ABBOTT (“LIBRE”) is an example of a glucose sensor that may be used as a glucose sensor 102 in order to make autonomous glucose measurements of a subject. The LIBRE allows calibration-free glucose measurements with an on-skin coin-sized sensor, which can send up to eight hours of data to a reader device (e.g., the data collection device 200 and/or the regimen monitor device 250) via near field communications, when brought close together. The LIBRE can be worn for fourteen days in all daily life activities.

In some embodiments, the long acting insulin medicament specified by the basal insulin medicament dosage regimen 214 consists of a single insulin medicament having a duration of action that is between 12 and 24 hours or a mixture of insulin medicaments that collectively have a duration of action that is between 12 and 24 hours. Examples of such long acting insulin medicaments include, but are not limited to Insulin Degludec (developed by NOVO NORDISK under the brand name Tresiba), NPH (Schmid, 2007, “New options in insulin therapy. J Pediatria (Rio J). 83(Suppl 5):5146-5155), Glargine (LANTUS, Mar. 2, 2007, insulin glargine [rDNA origin] injection, [prescribing information], Bridgewater, N.J.: Sanofi-Aventis), and Determir (Plank et al., 2005, “A double-blind, randomized, dose-response study investigating the pharmacodynamic and pharmacokinetic properties of the long-acting insulin analog detemir,” Diabetes Care 28:1107-1112).

In some embodiments, the plurality of glucose levels in the first data set 228 (e.g., the value P) is limited to glucose levels measured from the subject in the past four hours, the past twelve hours, the past 24 hours, the past two days, the past week, or the past two weeks. In other words, in some embodiments, the first data set 228 only has glucose measurements for the subject from the past four hours, the past twelve hours, the past 24 hours, the past two days, the past week, or the past two weeks.

Now that details of a system 48 and device 250 for autonomously adjusting a long acting insulin medicament dosage in a prescribed insulin regimen for a subject have been disclosed, details regarding a flow chart of processes and features of the system, in accordance with an embodiment of the present disclosure, are disclosed with reference to FIGS. 5A through 5H. In some embodiments, such processes and features of the system are carried out by the insulin regimen monitoring module 204 illustrated in FIG. 4.

With reference to block 302 in FIG. 5A, a goal of the present disclosure is to provide a long-acting or ultra-long-acting insulin dose guidance recommendation for a subject to treat diabetes mellitus, using a device such as the data collection device 200 and a regimen monitoring device 250. As illustrated in FIG. 4, the device includes one or more processors 274 and a memory 192/290. The memory includes instructions that, when executed by the one or more processors, perform a method responsive to receiving a dose guidance request. For some embodiments of the present invention, the present invention is used to treat a subject that is at least 20 years. For some embodiments, the present invention is used to treat a subject whose body mass index is no greater than 35 kg/m2. For some embodiments, the present invention is used to treat a subject whose body mass index is about 25 kg/m2. For some embodiments, the present invention is used to treat a subject that has been suffering from diabetes for at least 1 year, such as at least 5 years, such as at least 10 years. For some embodiments, the present invention is capable of achieving a baseline HbA1c level for the subject being no more than 7 after 26 weeks of treatment. For some embodiments, the long-acting or ultra-long-acting insulin for use according to the present invention is delivered by injection, such as by use of an insulin pen device.

Referring to block 304, in some embodiments the long-acting or ultra-long-acting insulin has a structure of structure (I). The long-acting or ultra-long-acting insulin includes a side chain attached to the α-amino group of the N-terminal amino acid residue of the B chain or to the ε-amino group of the Lys residue present in the B chain of the parent insulin, the side chain being of the general formula:

-W-X-Y-Z

wherein W is:

(i) an α-amino acid residue having a carboxylic acid group in the side chain which residue forms, with one of its carboxylic acid groups, an amide group together with the α-amino group of the N-terminal amino acid residue of the B chain or together with the ε-amino group of a Lys residue present in the B chain of the parent insulin;

(ii) a chain composed of two, three or four α-amino acid residues linked together via amide bonds, which chain—via an amide bond—is linked to the α-amino group of the N-terminal amino acid residue of the B chain or to the ε-amino group of a Lys residue present in the B chain of the parent insulin, the amino acid residues of W being selected from the group of amino acid residues having a neutral side chain and amino acid residues having a carboxylic acid group in the side chain so that W has at least one amino acid residue which has a carboxylic acid group in the side chain; or

(iii) a covalent bond from X to the α-amino group of the N-terminal amino acid residue of the B chain or to the ε-amino group of a Lys residue present in the B chain of the parent insulin; wherein X is:

(i) —CO—;

(ii) —COCH(COOH)CO—;

(iii) —CON(CH2COOH)CH2CO—;

(iv) —CON(CH2COOH)CH2CON(CH2COOH)CH2CO—;

(v) —CON(CH2CH2COOH)CH2CH2CO—;

(vi) —CON(CH2CH2COOH)CH2CH2CON(CH2CH2COOH)CH2CH2CO—;

(vii) —CONHCH(COOH)(CH2)4NHCO—;

(viii) —CON(CH2CH2COOH)CH2CO—; or

(ix) —CON(CH2COOH)CH2CH2CO—

provided that:

(a) when W is an amino acid residue or a chain of amino acid residues, via a bond from the underscored carbonyl carbon forms an amide bond with an amino group in W; or

(b) when W is a covalent bond, via a bond from the underscored carbonyl carbon forms an amide bond with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin;

wherein Y is:

(i) a-(CH2)m- where m is an integer in the range of 6 to 32;

(ii) a divalent hydrocarbon chain comprising 1, 2 or 3 —CH═CH— groups and a number of —CH2— groups sufficient to give a total number of carbon atoms in the chain in the range of 10 to 32; or

(iii) a divalent hydrocarbon chain of the formula —(CH2)vC6H (CH2)w- wherein v and w are integers or one of them is zero so that the sum of v and w is in the range of 6 to 30;

wherein Z is:

(i) —COOH;

(ii) —CO-Asp;

(iii) —CO-Glu;

(iv) —CO-Gly;

(v) —CO-Sar;

(vi) —CH(COOH)2;

(vii) —N(CH2COOH)2;

(viii) —S03H; or

(ix) —P03H;

and any Zn2+ complexes thereof, provided that when W is a covalent bond and X is —CO—, then Z is different from —COOH.

In an alternative or additional embodiment, the side chain -W-X-Y-Z is attached to the α-amino group of the N-terminal amino acid residue of the B chain of the parent insulin.

In an alternative or additional embodiment, the side chain -W-X-Y-Z is attached to the E amino group of a Lys residue present in the B chain of the parent insulin.

In an alternative or additional embodiment, the side chain -W-X-Y-Z is attached to the E amino group of a Lys residue present in position 28 of the B chain.

In an alternative or additional embodiment, the side chain -W-X-Y-Z is attached to the ε-amino group of a Lys residue present in position 29 of the B chain.

In an alternative or additional embodiment, the side chain -W-X-Y-Z is attached to the ε-amino group of a Lys residue present in position 30 of the B chain.

The substructure W of the side chain -W-X-Y-Z can be a covalent bond. Alternatively, W can be a residue of an α-amino acid having a carboxylic acid group in the side chain and comprising a total of from 4 to 10 carbon atoms. Specifically, W can be the residue of an α-amino acid, that can be coded for by the genetic code. Thus, W can, for example, be selected from the group consisting of α-Asp, β-Asp, α Glu, and γ-Glu. Further options for W are for example α-hGlu and δ-hGlu.

In an alternative or additional embodiment, W is a chain composed of two α-amino acid residues of which one has from 4 to 10 carbon atoms and a carboxylic acid group in the side chain while the other has from 2 to 11 carbon atoms but no free carboxylic acid group. The α-amino acid residue with no free carboxylic acid group can be a neutral, codable α-amino acid residue. Examples of W according to this embodiment are: α-Asp-Gly; Gly-α-Asp; β-Asp-Gly; Gly-β-Asp; α-Glu-Gly; Gly-α-Glu; γ-Glu-Gly; Gly-γ-Glu; α-hGlu-Gly; Gly-α-hGlu; δ-hGlu-Gly; and Gly-δ-hGlu.

In an alternative or additional embodiment, W is a chain composed of two α-amino acid residues, independently having from 4 to 10 carbon atoms, and both having a carboxylic acid group in the side chain. One of these α-amino acid residues or both of them can be codable α-amino acid residues. Examples of W according to this embodiment are: α-Asp-α-Asp; α-Asp-α-Glu; α-Asp-α-hGlu; α-Asp-β-Asp; α-Asp-γ-Glu; α-Asp-δ-hGlu; β-Asp-α-Asp; β-Asp-α-Glu; β-Asp-α-hGlu; β-Asp-β-Asp; β-Asp-γ-Glu; β-Asp-δ-hGlu; α-Glu-α-Asp; α-Glu-α-Glu; α-Glu-α-hGlu; α-Glu-β-Asp; α-Glu-γ-Glu; α-Glu-δ-hGlu; γ-Glu-α-Asp; γ-Glu-α-Glu; γ-Glu-α-hGlu; γ-Glu-β-Asp; γ-Glu-γ-Glu; γ-Glu-δ-hGlu; α-hGlu-α-Asp; α-hGlu-α-Glu; α-hGlu-α-hGlu; α-hGlu-β-Asp; α-hGlu-γ-Glu; α-hGlu-δ-hGlu; δ-hGlu-α-Asp; δ-hGlu-α-Glu; δ-hGlu-α-hGlu; δ-hGlu-β-Asp; δ-hGlu-γ-Glu; and δ-hGlu-δ-hGlu.

In an alternative or additional embodiment, W is a chain composed of three α-amino acid residues, independently having from 4 to 10 carbon atoms, the amino acid residues of the chain being selected from the group of residues having a neutral side chain and residues having a carboxylic acid group in the side chain so that the chain has at least one residue which has a carboxylic acid group in the side chain. In one embodiment, the amino acid residues are codable residues.

In an alternative or additional embodiment, W is a chain composed of four α-amino acid residues, independently having from 4 to 10 carbon atoms, the amino acid residues of the chain being selected from the group having a neutral side chain and residues having a carboxylic acid group in the side chain so that the chain has at least one residue which has a carboxylic acid group in the side chain. In one embodiment, the amino acid residues are codable residues.

In an alternative or additional embodiment, W can be connected to the ε-amino group of the Lys residue in the B-chain via a urea derivative.

The substructure X of the side chain -W-X-Y-Z can be a group of the formula —CO— that, via a bond from the underscored carbonyl carbon, forms an amide bond with an amino group in W or, when W is a covalent bond, with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin.

In an alternative or additional embodiment, the substructure X of the side chain can be a group of the formula COCH(COOH)CO— that, via a bond from the underscored carbonyl carbon, forms an amide bond with an amino group in W or, when W is a covalent bond, with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin.

In an alternative or additional embodiment, the substructure X of the side chain can be a group of the formula CON(CH2COOH)CH2CO— that, via a bond from the underscored carbonyl carbon, forms an amide bond with an amino group in W or, when W is a covalent bond, with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin.

In an alternative or additional embodiment, the substructure X of the side chain can be a group of the formula CON(CH2 CH2COOH)CH2CO— that, via a bond from the underscored carbonyl carbon, forms an amide bond with an amino group in W or, when W is a covalent bond, with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin.

In an alternative or additional embodiment of the invention the long-acting insulin, the substructure X of the side chain can be a group of the formula CON(CH2COOH)CH2 CH2CO— that, via a bond from the underscored carbonyl carbon, forms an amide bond with an amino group in W or, when W is a covalent bond, with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin.

In an alternative or additional embodiment, the substructure X of the side chain can be a group of the formula CON(CH2COOH)CH2CON(CH2COOH)CH2CO— that, via a bond from the underscored carbonyl carbon, forms an amide bond with an amino group in W or, when W is a covalent bond, with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin.

In an alternative or additional embodiment, the substructure X of the side chain can be a group of the formula CON(CH2CH2COOH)CH2CH2CO— that, via a bond from the underscored carbonyl carbon, forms an amide bond with an amino group in W or, when W is a covalent bond, with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin.

In an alternative or additional embodiment, the substructure X of the side chain can be a group of the formula CON(CH2CH2COOH)CH2CH2CON(CH2CH2COOH)CH2CH2CO— that, via a bond from the underscored carbonyl carbon, forms an amide bond with an amino group in W or, when W is a covalent bond, with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin. The substructure Y of the side chain -W-X-Y-Z can be a group of the formula (CH2)m-where m is an integer in the range of from 6 to 32, from 8 to 20, from 12 to 20, or from 12-16.

In an alternative or additional embodiment, Y is a divalent hydrocarbon chain comprising 1, 2 or 3 CH═CH— groups and a number of CH2 groups sufficient to give a total number of carbon atoms in the chain in the range of from 6 to 32, from 10 to 32, from 12 to 20, or from 12-16.

In an alternative or additional embodiment, Y is a divalent hydrocarbon chain of the formula (CH2)vC6H4(CH2)W— wherein v and w are integers or one of them is zero so that the sum of v and w is in the range of from 6 to 30, from 10 to 20, or from 12-16.

In an alternative or additional embodiment, the substructure Z of the side chain -W-X-Y-Z is —COOH provided that when W is a covalent bond and X is —CO—, then Z is different from —COOH. In one embodiment Z is —COOH. In another embodiment, Z is —CO— Asp. In another embodiment, Z is —CO— Glu. In another embodiment, Z is —CO— Gly. In another embodiment, Z is —CO— Sar. In another embodiment, Z is —CH(COOH)2. In another embodiment, Z is —N(CH2COOH)2. In another embodiment, Z is —SO3H. In another embodiment, Z is —PO3H.

In an alternative or additional embodiment, W is selected from the group consisting of α-Asp, β-Asp, α-Glu, and γ-Glu; X is —CO— or —COCH(COOH)CO; Y is —(CH2)m-where m is an integer in the range of 12-18 and Z is —COOH or —CH(COOH)2.

In an alternative, the long-acting or ultra-long-acting insulin, preferably the ultra-long acting insulin, wherein said derivative has a side chain attached to the α-amino group of the N-terminal amino acid residue of the B chain or to the ε-amino group of a Lys residue present in the B chain of the parent insulin, the side chain being of the general formula:

-W-X-Y-Z

wherein W is:

an α-amino acid residue having a carboxylic acid group in the side chain which residue forms, with one of its carboxylic acid groups, an amide group together with the α-amino group of the N-terminal amino acid residue of the B chain or together with the ε-amino group of a Lys residue present in the B chain of the parent insulin wherein the α-amino acid residue is selected from the group consisting of α-Asp, β-Asp, α-Glu, γ-Glu, α-hGlu and δ-hGlu;

X is: —CO—;

Y is: —(CH2)m-where m is an integer in the range of 6 to 32;

Z is: —COOH;

and any Zn2+ complexes thereof.

The insulin moiety—in the present text also referred to as the parent insulin—of an insulin derivative can be a naturally occurring insulin such as human insulin or porcine insulin. Alternatively, the parent insulin can be an insulin analogue. In one group of parent insulin analogues, the amino acid residue at position A21 is Asn. In another group of parent insulin analogues, the amino acid residue at position A21 is Gly. Specific examples from this group of analogues are GlyA21 human insulin, GlyA21 des(B30) human insulin; and GlyA21ArgB31ArgB32 human insulin.

In another group of parent insulin analogues, the amino acid residue at position B1 has been deleted. A specific example from this group of parent insulin analogues is des(B1) human insulin.

In another group of parent insulin analogues, the amino acid residue at position B30 has been deleted. A specific example from this group of parent insulin analogues is des(B30) human insulin.

In another group of parent insulin analogues, the amino acid residue at position B28 is Asp. A specific example from this group of parent insulin analogues is AspB28 human insulin.

In another group of parent insulin analogues, the amino acid residue at position B28 is Lys and the amino acid residue at position B29 is Pro. A specific example from this group of parent insulin analogues is LysB28ProB29 human insulin.

In another group of parent insulin analogues the amino acid residue in position B30 is Lys and the amino acid residue in position B29 is any codable amino acid except Cys, Met, Arg and Lys. An example is an insulin analogue where the amino acid residue at position B29 is Thr and the amino acid residue at position B30 is Lys. A specific example from this group of parent insulin analogues is ThrB29LysB30 human insulin.

In another group of parent insulin analogues, the amino acid residue at position B3 is Lys and the amino acid residue at position B29 is Glu. A specific example from this group of parent insulin analogues is LysB3GluB29 human insulin.

In one embodiment the parent insulin is selected from the group consisting of human insulin; des(B1) human insulin; des(B30) human insulin; GlyA21 human insulin; GlyA21 des(B30)human insulin; AspB28 human insulin; porcine insulin; LysB28ProB29 human insulin; GlyA21ArgB31ArgB32 human insulin; and LysB3GluB29 human insulin.

Examples of '347 derivatives useful in the invention are the following compounds:

NεB29-(Nα-(HOOC(CH2)14CO)-γ-Glu) des(B30) human insulin;

NεB29-(Nα-(HOOC(CH2)15CO)-γ-Glu) des(B30) human insulin;

NεB29-(Nα-(HOOC(CH2)16CO)-γ-Glu) des(B30) human insulin;

NεB29-(Nα-(HOOC(CH2)17CO)-γ-Glu) des(B30) human insulin;

NεB29-(Nα-(HOOC(CH2)18CO)-γ-Glu) des(B30) human insulin;

NεB29-(Nα-(HOOC(CH2)16CO)-γ-Glu-N-(γ-Glu)) des(B30) human insulin;

NεB29-(Nα-(Asp-OC(CH2)16CO)-γ-Glu) des(B30) human insulin;

NεB29-(Nα-(Glu-OC(CH2)14CO)-γ-Glu) des(B30) human insulin;

NεB29-(Nα-(Glu-OC(CH2)14CO—) des(B30) human insulin;

NεB29-(Nα-(Asp-OC(CH2)16CO—) des(B30) human insulin;

NεB29-(Nα-(HOOC(CH2)16CO)-α-Glu-N-(β-Asp)) des(B30) human insulin;

NεB29-(Nα-(Gly-OC(CH2)13CO)-γ-Glu) des(B30) human insulin;

NεB29-(Nα-(Sar-OC(CH2)13CO)-γ-Glu) des(B30) human insulin;

NεB29-(Nα-(HOOC(CH2)13CO)-γ-Glu) des(B30) human insulin;

(NεB29-(Nα-(HOOC(CH2)13CO)-β-Asp) des(B30) human insulin;

NεB29-(Nα-(HOOC(CH2)13CO)-α-Glu) des(B30) human insulin;

NεB29-(Nα-(HOOC(CH2)16CO)-γ-D-Glu) des(B30) human insulin;

NεB29-(Nα-(HOOC(CH2)14CO)-β-D-Asp) des(B30) human insulin;

NεB29-(N—HOOC(CH2)16CO-β-D-Asp) des(B30) human insulin;

NεB29-(N—HOOC(CH2)14CO-IDA) des(B30) human insulin;

NεB29-[N—(HOOC(CH2)16CO)—N-(carboxyethyl)-Gly] des(B30) human insulin;

NεB29-[N—(HOOC(CH2)14CO)—N-(carboxyethyl)-Gly] des(B30) human insulin; and

NεB29-[N—(HOOC(CH2)14CO)—N-(carboxymethyl)-β-Ala] des(B30) human insulin.

In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(HOOC(CH2)14CO)-γ-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(HOOC(CH2)15CO)-γ-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(HOOC(CH2)16CO)-γ-Glu) des(B30) human insulin. In one embodiment the insulin is NεB29-(Nα-(HOOC(CH2)17CO)-γ-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin NεB29-(Nα-(HOOC(CH2)18CO)-γ-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(HOOC(CH2)16CO)-γ-Glu-N-(γ-Glu)) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(Asp-OC(CH2)16CO)-γ-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(Glu-OC(CH2)14CO)-γ-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(Glu-OC(CH2)14CO—) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(Asp-OC(CH2)16CO—) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(HOOC(CH2)16CO)-α-Glu-N-(β-Asp)) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(Gly-OC(CH2)13CO)-γ-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(Sar-OC(CH2)13CO)-γ-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(HOOC(CH2)13CO)-γ-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is (NεB29-(Nα-(HOOC(CH2)13CO)-β-Asp) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(HOOC(CH2)13CO)-α-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(HOOC(CH2)16CO)-γ-D-Glu) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(HOOC(CH2)14CO)-β-D-Asp) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(Nα-(HOOC(CH2)14CO)-β-D-Asp) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(N—HOOC(CH2)16CO-β-D-Asp) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-(N—HOOC(CH2)14CO-IDA) des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-[N—(HOOC(CH2)16CO)—N-(carboxyethyl)-Gly] des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-[N—(HOOC(CH2)14CO)—N-(carboxyethyl)-Gly] des(B30) human insulin. In an alternative or additional embodiment of the invention the insulin is NεB29-[N—(HOOC(CH2)14CO)—N-(carboxymethyl)-β-Ala] des(B30) human insulin.

In an alternative or additional embodiment of the invention the insulins provided in the form of essentially zinc free compounds or in the form of zinc complexes. When zinc complexes of a hexameric insulin or insulin derivative are provided, two Zn2+ ions, three Zn2+ ions or four Zn2+ ions may be bound to each insulin hexamer. In an alternative or additional embodiment of the invention the insulin is a hexameric insulin or insulin derivative in the form of a zinc complex, wherein each insulin hexamer binds two zinc ions, three zinc ions, four zinc ions, five zinc ions, six zinc ions, seven zinc ions, eight zinc ions, nine zinc ions or ten zinc ions. Solutions of zinc complexes of the insulin derivatives will contain mixtures of such species.

Details pertaining to the preparation, formulation, pharmacology and other characteristics of relevance for the '347 derivatives are set forth in WO 2005/012347, which is hereby incorporated by reference herein.

For some embodiments, the long-acting or ultra-long-acting insulin compound has an overall hydrophobicity which is essentially similar to that of human insulin.

For some embodiments, the long-acting or ultra-long-acting insulin compound has a hydrophobic index, k'rel, which is in the range from about 0.02 to about 10, from about 0.1 to about 5; from about 0.5 to about 5; or from about 0.5 to about 2.

For some embodiments, the long-acting or ultra-long-acting insulin compound is soluble at physiological pH values, such as pH values in the interval from about 6.5 to about 8.5.

When an insulin-like compound according to the invention is stated to be “soluble at physiological pH values” it means that the insulin derivative can be used for preparing injectable insulin compositions that are fully dissolved at physiological pH values. Such favorable solubility may either be due to the inherent properties of the insulin-like compound alone or a result of a favorable interaction between the insulin derivative and one or more ingredients contained in the vehicle.

Referring to block 306, in some embodiments the long-acting or ultra-long-acting insulin is LysB29(Nc-hexadecandioyl-γ-Glu) des(B30) human insulin (insulin degludec, Tresiba®).

Referring to block 308, in some embodiments the long-acting insulin or ultra-long-acting insulin is selected from the group consisting of a) neutral protamine hagedorn insulin (NHP insulin) (Humulin® N, Novolin® ge NPH), b) Lente Insulin (Humulin® L, Novolin® ge Lente), c) Ultralente Insulin (Humulin® U, Novolin® ge Ultralente), d) Glargine Insulin (Lantus®), e) Detemir Insulin (Levemir®), f) Hypurin Bovine Lente, and g) Hypurin Bovine PZI. In an alternative or additional embodiment of the invention the long-acting or ultra-long acting insulin, preferably the ultra-long acting insulin, is any one or more of the compounds disclosed in WO 2005/012347, which are incorporated herein by reference. In some instances, these compounds are referred as being “the '347 derivatives”.

Referring to block 310, in some embodiments the long-acting or ultra-long-acting insulin for use is administered, either concurrently or consecutively, together with one or more additional drugs used in the treatment of diabetes.

Referring to block 312, in some embodiments the one or more additional drugs used in the treatment of diabetes is, or includes, a drug selected from the group consisting of: insulins, sensitizers (such as biguanides and thiazolidinediones), secretagogues (such as sulfonylureas and nonsulfonylurea secretagogues), alpha-glucosidase inhibitors and peptide analogs (such as injectable incretin mimetics, gastric inhibitory peptide analogs, dipeptidyl peptidase-4 inhibitors and injectable amylin analogues). In an alternative or additional embodiment of the invention, the one or more additional drug used in the treatment of diabetes is, or includes, liraglutide.

Referring to block 313, in some embodiments the long-acting or ultra-long-acting insulin is LysB29(Nc-hexadecandioyl-γ-Glu) des(B30) human insulin (insulin degludec, Tresiba®), and the long-acting or ultra-long-acting insulin is administered, concurrently or consecutively, with liraglutide. In some embodiments, the long-acting or ultra-long-acting insulin and the one or more additional drug used in the treatment of diabetes may be administered concurrently in the same formulation.

Referring to block 314, in some embodiments the diabetes mellitus is type 2 diabetes mellitus. In an alternative or additional embodiment of the invention, a disease or condition where administration of insulin will be of benefit is selected from the group consisting of diabetes mellitus, such as type 1 diabetes mellitus or type 2 diabetes mellitus, other conditions characterized by hyperglycemia (such as pre-diabetes, impaired glucose tolerance, metabolic syndrome, obesity, cachexia, in vivo beta-cell loss/death, excessive appetite and inflammation.

Referring to block 316 in FIG. 5B, in some embodiments the long-acting or ultra-long-acting insulin dose guidance recommendation is to achieve a specific glucose target. For some embodiments, the present invention is used to treat a subject whose baseline HbA1c level before treatment is greater than 7%, such as about 8% or 9%.

Referring to block 318 in FIG. 5B, in some embodiments the device further comprises a wireless receiver, and wherein the first data set is obtained wirelessly from a glucose sensor affixed to the subject and/or the second data set is obtained wirelessly from the one or more insulin pens.

Referring to block 320, in some embodiments the device proceeds by obtaining a first data structure that includes at least (i) a body weight of the subject, (ii) an upper limit target glucose range of the subject, (iii) a lower limit target glucose range of the subject, and (iv) an overbasalisation limit of the subject.

Referring to block 322, in some embodiments the over-basalisation limit is at least within 1.0-0.5 units/kg, 1.5-1.0 units/kg, 0.75-0.25 units/kg, 0.5-0 units/kg, or 2.0-0.75 units/kg.

Referring to block 324, in some embodiments the device proceeds by obtaining a first data structure that includes at least (i) a body weight of the subject, (ii) an upper limit target glucose range of the subject, (iii) a lower limit target glucose range of the subject, and (iv) an overbasalisation limit of the subject.

Referring to block 326, in some embodiments the starting basal dosage is updated at least within the immediately preceding 1 day, the immediately preceding 4 days, the immediately preceding 7 days, or the immediately preceding 10 days.

Referring to block 328, in some embodiments the device proceeds by obtaining a first data set, comprising a plurality of glucose measurements of the subject taken over a time course to establish a blood glucose history and, for each respective glucose measurement in the plurality of glucose measurements, a corresponding glucose timestamp representing when in the time course the respective glucose measurement was made.

Referring to block 330, in some embodiments the time course comprises a current day and the past four days.

Referring to block 332 in FIG. 5C, in some embodiments the time course comprises the current day and between one and ten of the immediately preceding past days.

Referring to block 333, in some embodiments when the blood glucose history time course contains a gap, calculate a reconstructed blood glucose history based on the blood glucose history of each calendar day.

Referring to block 334, in some embodiments the device performs a quality check of the reconstructed blood glucose history data, and when the data quality check fails, the dose guidance recommendation is not updated.

Referring to block 335, in some embodiments the device stores the updated blood glucose history in the first data structure.

Referring to block 336, in some embodiments the device proceeds by obtaining a second data set, comprising (i) a basal insulin injection history of the subject, wherein the injection history comprises a plurality of injections during all or a portion of the time course and, for each respective injection in the plurality of injections, (ii) a corresponding dose event amount and (iii) a dose event timestamp representing when in the time course the respective injection event occurred and wherein the second data set further comprises (iv) a last injection data refresh of the subject.

Referring to block 338, in some embodiments the device updates the last injection data refresh within the immediately preceding 30 seconds or less, the immediately preceding 1 minute or less, or the immediately preceding 5 minutes or less.

Referring to block 340, in some embodiments the device updates the last injection data refresh within the immediately preceding 30 seconds or less, the immediately preceding 1 minute or less, or the immediately preceding 5 minutes or less.

Referring to block 342, in some embodiments the device combines any injections in the injection history that have timestamps within a five-minute period in the time course into one injection. However, the device may also combine any injections in the injection history that have timestamps within a within 60-minute or even 120-minute period in the time course into one injection.

Referring to block 344 in FIG. 5D, in some embodiments the device proceeds by evaluating at least the first data structure, the second data structure, the first data set, and the second data set to determine whether they collectively contain a set of evaluation information comprising at least (i) the body weight of the subject, (ii) the plurality of glucose measurements of the subject taken over the time course, (iii) the injection history of the subject, (iv) the last adjustment day dose recommendation and/or the starting long-acting or ultra-long-acting insulin dose of the subject, (v) the overbasalisation limit of the subject, (vi) the last injection data refresh for the subject, (vii) the upper limit target glucose range of the subject, and (viii) the lower limit target glucose range of the subject.

Referring to block 346, in some embodiments the device provides the long-acting or ultra-long-acting insulin dose guidance recommendation when a determination is made that the at least first data structure, second data structure, first data set, and second data set collectively do contain the set of evaluation information.

Referring to block 348, in some embodiments when a determination is made that the at least first data structure, second data structure, first data set, and second data set fail to collectively contain the set of evaluation information, no update to the long-acting or ultra-long-acting insulin dose guidance recommendation is made.

Referring to block 350, in some embodiments the device updates the dose guidance recommendation for the subject, unless the injection history of the subject comprises one or more injections of the subject that have injection timestamps within the immediately preceding 4 hours or less, the immediately preceding 8 hours or less, the immediately preceding 12 hours or less, or the immediately preceding 16 hours or less.

Referring to block 352, in some embodiments the device updates the dose guidance recommendation for the subject, unless the injection history of the subject comprises one or more injections of the subject that have injection timestamps within the current day.

Referring to block 354, in some embodiments the device updates the dose guidance recommendation for the subject when at least one injection occurred within the current day and no injections occurred in the immediately preceding one day in the injection history time course.

Referring to block 356 in FIG. 5E, in some embodiments the device provides a re-recommendation of the dose guidance recommendation.

Referring to block 358, in some embodiments the device does not provide a wherein the method further comprises providing a re-recommendation of the dose guidance recommendation until: (i) one or more injections with an injection amount equivalent to the dose guidance baseline have occurred, (ii) one or more injections have occurred since the dose guidance baseline and when one or more injections have occurred over the current day and the past two or three or four days, and/or (iii) one or more does events with an injection amount greater than or equal to the dose guidance baseline have occurred.

Referring to block 360, in some embodiments the device calculates a daily titration glucose level and an overall titration glucose level.

Referring to block 362, in some embodiments the daily titration glucose level for each calendar day is calculated by selecting the lowest average of reconstructed blood glucose history data for any predetermined first time period within the injection history time course and excluding any reconstructed blood glucose history data with a timestamp older than the timestamp of the dose guidance baseline.

Referring to block 364, in some embodiments when there are gaps larger than a second predetermined time period in the injection history time course, no daily titration glucose level is calculated.

Referring to block 366, in some embodiments the predetermined second time period is between 10 minutes and 40 minutes.

Referring to block 368, in some embodiments when there are gaps larger than a second predetermined time period in the injection history time course, no daily titration glucose level is calculated.

Referring to block 370, in some embodiments the predetermined third time period is between 1 hour and 5 hours or even between 1 hour and 15 hours.

Referring to block 372 in FIG. 5F, in some embodiments when there are gaps in the injection history time course and these gaps are at least the predetermined second time period in length and less than a predetermined third time period in length within any calendar day in the injection history time course, the daily titration glucose level averaging omits any predetermined first time period containing gaps when averaging the reconstructed blood glucose history data.

Referring to block 374, in some embodiments the predetermined number of blood glucose measurements is between 20 and 100.

Referring to block 376, in some embodiments when any calendar day in the injection history time course contains gaps longer than the predetermined third time period or when more than a predetermined number of blood glucose measurements are absent no daily titration glucose level is calculated.

Referring to block 378, in some embodiments the predetermined first time period is between 15 minutes and 120 minutes.

Referring to block 380, in some embodiments the overall titration glucose level is calculated by taking the average of the daily titration glucose levels, and wherein when no overall titration glucose level can be determined the dose guidance recommendation is not updated.

Referring to block 382 in FIG. 5G, in some embodiments, responsive to receiving a request for an updated adjustment day dose recommendation, the device performs a new recommendation procedure to determine the injection amount for the updated adjustment day dose recommendation wherein the updated adjustment day dose recommendation function is based upon at least a titration glucose level and a max basal limit.

Referring to block 384, in some embodiments the titration glucose level comprises one of (i) the overall titration glucose level is greater than the upper limit target glucose range, (ii) the overall titration glucose level is greater than or equal to the lower target glucose range and the overall titration glucose level is less than or equal to the upper limit target glucose range, and/or (iii) the daily titration glucose level is less than the lower target glucose range.

Referring to block 385, in some embodiments the upper limit target glucose range used to determine the updated adjustment day dose recommendation is within 80-180 mg/dL, 90-180 mg/dL, 100-180 mg/dL, 90-200 mg/dL, 90-250 mg/dL or 90-300 mg/dL.

Referring to block 386, in some embodiments the lower target glucose range used to determine the updated adjustment day dose recommendation is within 50-70 mg/dL, 71-90 mg/dL, 71-100 mg/dL, or 61-90 mg/dL.

Referring to block 388, in some embodiments the max basal limit consists of the overbasalisation limit multiplied by the body weight of the subject,

Referring to block 390, in some embodiments when the overall titration glucose level is greater than the upper limit target glucose range of the subject and when the most recent adjustment day dose recommendation is less than the max basal limit of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline plus a predetermined number of units of long-acting or ultra-long-acting insulin.

Referring to block 391, in some embodiments the predetermined number of units of long-acting or ultra-long-acting insulin by which to alter the adjustment day dose recommendation is selected from the set of at least 1 unit, 2 units, 4 units, 6 units and 8 units.

Referring to block 392 in FIG. 5H, in some embodiments when the overall titration glucose level is greater than the upper limit target glucose range of the subject and when the most recent adjustment day dose recommendation is greater than or equal to the max basal limit of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline.

Referring to block 394, in some embodiments when the overall titration glucose level is between the upper limit target glucose range of the subject and the lower target glucose range of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline.

Referring to block 396, in some embodiments when the daily titration glucose level is less than the lower target glucose range of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline minus a predetermined number of units of long-acting or ultra-long-acting insulin.

Referring to block 397, in some embodiments the predetermined number of units of long-acting or ultra-long-acting insulin by which to alter the adjustment day dose recommendation is selected from the set of at least 1 unit, 2 units, 4 units, 6 units and 8 units.

Referring to block 398, in some embodiments the device provides the updated adjustment day dose recommendation at least with the immediately preceding 1 day, the immediately preceding 4 days, the immediately preceding 7 days, or the immediately preceding 10 days.

FIGS. 6A-6D illustrate an example method 400 (e.g. performed at an electronic device) for providing an adjusted day dose recommendation to the subject. In some embodiments, the method of FIGS. 6A-6D is performed when the subject has not previously been provided with an adjusted day dose recommendation. In some embodiments, the method of FIG. 4 is performed to provide a re-recommendation of a previously provided adjusted day dose recommendation. In some embodiments, the method of FIGS. 6A-6D is performed to provide an updated adjusted day dose recommendation, subsequent to providing one or more previous adjusted day dose recommendations.

Referring to FIG. 6A, the device receives a dose guidance request 402. In some embodiments, the dose guidance request is automatically generated. In some embodiments, the user makes a specific request for dose guidance. The device proceeds and checks 404 that the request is valid (e.g. the device confirms that the requisite or required data is included in the request—e.g. one or more of a body weight of the subject, an upper target glucose range of the subject, a lower limit target glucose range of the subject, an over-basalisation of the subject, a previous adjustment day dose and/or a starting basal insulin dose, a blood glucose history of the subject, a basal insulin injection history of the subject and an injection data refresh of the subject). If the dose guidance request does not contain the necessary data, the device ends the process 405, provides no guidance, and optionally advises the user to return later with the proper data. In some embodiments, the device advises the user what data is missing or suggests the user seek assistance with their medical devices. If the dose guidance request contains the appropriate information, the device checks 406 the timestamp of the last injection data refresh. In some embodiments, the device provides no guidance if the last injection data refresh occurred more than 30 seconds previously, or more than 1 minute previously, or more than 5 minutes previously. If the check 406 indicates that the injection data refresh occurred within an appropriate time period, the process continues.

The device proceeds and performs a check 408 of the injection history of the subject. In some embodiments, if the injection history of the subject includes one or more injections with injection timestamps within the immediately preceding 4 hours or less, the immediately preceding 8 hours or less, the immediately preceding 12 hours or less, or the immediately preceding 16 hours or less, the device checks 410 whether the dose guidance request is for a make-up injection. If the previous calendar day in the injection history is lacking an injection timestamp, the device proceeds with the process. If the previous calendar day in the injection history contains an injection timestamp, the device ends the process 405, provides no guidance, and optionally advises the user to return later. If the most recent injection timestamp in the injection history occurred more than 4 hours ago, or more than 8 hours ago, or more than 12 hours ago, or more than 16 hours ago, the device proceeds.

The device performs 412 a transformation of blood glucose history data. In some embodiments, this reconstruction of blood glucose history data is to fill in gaps of missing data (e.g. if the blood glucose measurements have not been updated or recorded in full). The device proceeds with the reconstructed blood glucose measurements 414 (in some embodiments, the blood glucose measurements consist of continuous glucose monitoring data) and performs a quality check 416 of the data. If the quality check fails (e.g. if the blood glucose measurements and/or the reconstructed blood glucose measurements are deemed insufficient), the device end the process 405, provides no guidance, and optionally advises the user that their blood glucose monitoring equipment may be encountering difficulties or need adjustment. In some embodiments, the quality check assesses whether there is sufficient blood glucose or reconstructed blood glucose data to determine total daily glucose levels. If the quality check approves the blood glucose data and/or the reconstructed blood glucose data, the device proceeds.

The device performs an injection history reconstruction 418. In some embodiments, the reconstruction consists of combining the injection amounts of all injection events that occur within 5 minutes of each other, or that occur within 10 minutes of each other, or that occur within 15 minutes of each other into one injection event (e.g. to ensure that priming injections are not recorded as separate amounts for future calculations). The device proceeds.

The device performs an injection event check 422. In some embodiments, the injection event check consists of determining if three or more injection events have occurred since the previous dose guidance recommendation has been provided to the subject. In some embodiments, the injection event check consists of determining if three or more injection events have occurred in the current calendar day and the immediately preceding 4 calendar days (e.g. to ensure adherence of the subject to the previous dose guidance recommendation). In some embodiments, the injection event check consists of determining if three or more injection events with injection amounts greater than or equal to the dose guidance baseline have been administered. In some embodiments, the injection event check consists of any or all features of any of the other embodiments combined. If the device determines that the subject fails the injection event check, the device ends the process 423 and provides a re-recommendation of the previous dose guidance recommendation. If the device determines that the subject has complied with the injection event check, the device proceeds.

The device performs an adjusted day dose recommendation check 424. In some embodiments, the adjusted day dose recommendation check consists of determining whether the most recent adjustment day dose recommendation was made within the immediately preceding 3 calendar days, within the immediately preceding 5 calendar days, within the immediately preceding 7 calendar days, within the immediately preceding 10 calendar days, or within the immediately preceding 14 calendar days. If the device determines that the subject fails the adjustment day dose recommendation check, the device end the process 425 and determines a new starting dose for the dose guidance recommendation. If the device determines that the subject has complied with the adjustment day dose recommendation check, the device proceeds.

The device performs an adherence check 426. In some embodiments, the adherence check consists of determining whether the subject has adhered to the adjusted day dose recommendation (e.g. by administering three or more injection events that at least have the same injection amount as the most recent adjusted day dose recommendation). If the device determines that the subject fails the adherence check, the device ends the process 423 and provides a re-recommendation of the previous dose guidance recommendation. If the device determines that the subject has complied with the adjustment day dose recommendation check, the device proceeds.

The device performs a removal of excess reconstructed blood glucose history data 428. In some embodiments, the excess blood glucose history removal consists of removing data from one or more previous days from consideration (e.g. any data that is older than the most recent adjustment day dose recommendation).

The device determines 432 a titration glucose level for every calendar day included in the altered reconstructed blood glucose history data. In some embodiments the daily titration glucose level is calculated by selecting the lowest average of reconstructed blood glucose history data for any predetermined first time period within the injection history time course and excluding any reconstructed blood glucose history data with a timestamp older than the timestamp of the dose guidance baseline. In some embodiments, when there are gaps in the reconstructed blood glucose data that are larger than a second predetermined time period in the injection history time course, the device does not calculate a daily titration glucose level 433. In some embodiments, when there are gaps in the injection history time course and these gaps are at least the predetermined second time period in length and less than a predetermined third time period in length within any calendar day in the injection history time course, the daily titration glucose level averaging omits any predetermined first time period containing gaps when averaging the reconstructed blood glucose history data. In some embodiments, when any calendar day in the injection history time course contains gaps longer than the predetermined third time period or when more than a predetermined number of blood glucose measurements are absent, the device does not calculate a daily titration glucose level. In some embodiments, the predetermined first time period is between 15 minutes and 120 minutes. In some embodiments, the predetermined second time period is between 10 minutes and 40 minutes. In some embodiments, the predetermined third time period is between 1 hour and 15 hours. In some embodiments, the predetermined number of blood glucose measurements is between 20 and 100.

The device performs a daily titration glucose check 436. In some embodiments, the daily titration glucose check consists of comparing the daily titration glucose levels with the lower target glucose range of the subject. In some embodiments, if any daily titration glucose level from the injection history is less than the lower target glucose range of the subject, the device proceeds and provides an updated adjusted day dose recommendation 452 by subtracting a predetermined number of units of basal insulin 448 from the most recent adjusted day dose recommendation. In some embodiments, if the daily titration glucose levels are all greater than or equal to the lower target glucose range of the subject, the device proceeds and performs an overall titration glucose level calculation 438. In some embodiments, the overall titration glucose level calculation, consists of taking the average of all the daily titration glucose levels.

The device performs the next adjusted day dose recommendation calculation 442. In some embodiments, the next adjusted day dose recommendation consists of comparing the overall daily titration glucose level to the upper target glucose range. In some embodiments, if the overall daily titration glucose level is greater than the upper target glucose range, the device proceeds and calculates an updated adjusted day dose recommendation 449 by adding a predetermined number of units of basal insulin 444 to the most recent adjusted day dose recommendation. The device proceeds and performs an overbasalisation check 450. If the device determines that the most recent adjusted day dose recommendation is greater than or equal to the max basal limit of the subject, the device ends the process and provides a re-recommendation 423 of the most recent adjusted day dose recommendation. If the device determines that the most recent adjusted day dose recommendation is less than the max basal limit of the subject, the device provides 452 the calculated adjusted day dose recommendation 444 as the updated adjusted day dose recommendation. In some embodiments, if the overall daily titration glucose level is less than or equal to the upper target glucose range, the device proceeds provides a re-recommendation of the most recent adjusted day dose recommendation 423. In some embodiments, the predetermined number of units of insulin by which to alter the adjustment day dose recommendation is selected from the set of at least 1 unit, 2 units, 4 units, and 6 units.

Referring to fig., with the integrated system 502, autonomous timestamped insulin injection and blood glucose measurements of the subject are obtained 520. Also, in some embodiments, data from the one or more insulin pens 104 used to apply a prescribed insulin regimen to the subject is obtained 540 as a plurality of records. Each record comprises a timestamped event specifying an amount of injected long-acting or ultra-long-acting insulin medicament that the subject received as part of the prescribed insulin medicament dosage regimen. The glucose measurements (e.g., the blood glucose history) are quality assessed 504, and a reconstructed blood glucose history is calculated when necessary. The blood glucose history or the reconstructed blood glucose history is stored in non-transitory memory 506. The memory 506 includes instructions that, when executed by the one or more processors, perform a method responsive to receiving a dose guidance request. In this way, the glucose data is analyzed 508 along with further data regarding the subject (e.g., subject parameters 512), and a dose guidance recommendation (DGR) is provided 510 (e.g., to adjust the long acting insulin medicament dosage) in accordance with the methods of the present disclosure.

In some embodiments, the dose guidance request includes obtaining a first data structure (e.g., subject parameters) 512 that contains at least (i) a body weight (BW) of the subject, (ii) an upper limit target glucose range (UTR) of the subject, (iii) a lower limit target glucose range (LTR) of the subject, and (iv) an overbasalisation limit (OBL) of the subject. In some embodiments, the dose guidance request also includes obtaining a second data structure 226 (the dose guidance baseline) that contains at least (i) a most recent adjustment day dose recommendation (ADDR) and/or (ii) a starting basal dose (SBD). In some embodiments, the dose guidance request also includes obtaining a first data set 520 that includes a plurality of glucose measurements of the subject taken over a time course to establish a blood glucose history (BGH) and, for each respective glucose measurement in the plurality of glucose measurements, a corresponding glucose timestamp representing when in the time course the respective glucose measurement was made. In some embodiments, the dose guidance request also includes obtaining a second data set 540 comprising (i) a basal insulin injection history (IH) of the subject, wherein the injection history comprises a plurality of injections during all or a portion of the time course and, for each respective injection in the plurality of injections, (ii) a corresponding dose event amount (IU) and (iii) a dose event timestamp (UTC) representing when in the time course the respective injection event occurred and where the second data set further comprises (iv) a last injection data refresh (IDR) of the subject.

In some embodiments, the dose guidance request also includes evaluating at least the first data structure, the second data structure, the first data set, and the second data set to determine whether they collectively contain a set of evaluation information comprising at least (i) the body weight of the subject, (ii) the plurality of glucose measurements of the subject taken over the time course, (iii) the injection history of the subject, (iv) the last adjustment day dose recommendation and/or the starting long-acting or ultra-long-acting insulin dose of the subject, (v) the overbasalisation limit of the subject, (vi) the last injection data refresh for the subject, (vii) the upper limit target glucose range of the subject, and (viii) the lower limit target glucose range of the subject. When a determination is made that the at least first data structure, second data structure, first data set, and second data set fail to collectively contain the set of evaluation information, no update to the long-acting or ultra-long-acting insulin dose guidance recommendation is made. When a determination is made that the at least first data structure, second data structure, first data set, and second data set collectively do contain the set of evaluation information, the method further comprises providing the long-acting or ultra-long-acting insulin dose guidance recommendation 510.

In some embodiments, the administration of the long-acting or ultra-long-acting insulin includes one data structure includes (a) recording (520) a plurality of glucose measurements of a subject in need of treatment taken over a time course and, for each respective glucose measurement in the plurality of glucose measurements, a corresponding glucose timestamp representing when in the time course the respective glucose measurement was made. The administration continues with (b) obtaining (512) in a data structure the (i) a body weight of the subject, (ii) an upper limit target glucose range of the subject, (iii) a lower limit target glucose range of the subject, (iv) an overbasalisation limit of the subject, (v) a most recent adjustment day dose recommendation and/or a starting insulin basal dose of the subject, (vi) a basal insulin injection history of the subject, wherein the injection history comprises a plurality of injections during all or a portion of the time course and, for each respective injection in the plurality of injections, a corresponding injection amount and an injection timestamp representing when in the time course the respective injection occurred, and (vii) a last injection data refresh of the subject. The administration continues (c) with using (508) the blood glucose measurements (e.g., the blood glucose history or the reconstructed blood glucose history) and the above data structure to (d) provide an updated adjustment day dose recommendation (510) of the long-acting or ultra-long-acting insulin injection amount to the subject.

In some embodiments, the provision of an updated adjustment day dose recommendation of the administration of the long-acting or ultra-long-acting insulin is performed for an administration period of, or at least of, 1 day; for example, of, or at least of, 2 days; of, or at least of, 3 days; of, or at least of, 4 days; of, or at least of, 5 days; of, or at least of, 6 days; of, or at least of, 7 days; of, or at least of, 8 days; of, or at least of, 9 days; of, or at least of, 10 days; of, or at least of, 11 days; of, or at least of, 12 days; of, or at least of, 13 days; of, or at least of, 14 days; or at least of, 15 days; or for an administration period of between 1 and 15 days, for example, between 1 and 13 days; between 2 and 12 days; between 3 and 11 days; between 4 and 10 days; between 5 and 9 days; or for an administration period of between 6 and 8 days.

Insulin dose may be determined through a variety of methods, for example, may be calculated based on weight (and/or height), fasting blood glucose and gender (thereafter adjusted empirically according to fast blood glucose and/or HbA1c level outcome). It is increasingly common to use a titration method wherein, after administering an initial dose standard or empirically determined dose, further doses are adjusted by pre-determined increments (e.g. titration algorithm), as necessary, based on blood glucose/plasma measurements in order to reach and maintain a target blood glucose/plasma and/or HbA1c level. Such titration models are however always given as guidance only and individual adjustments are applicable on a case by case basis.

Specific Embodiments

In one aspect, the disclosure provides a device for providing a long-acting or ultra-long-acting insulin dose guidance recommendation for a subject to treat diabetes mellitus, wherein the device comprises one or more processors and a memory, the memory comprising: instructions that, when executed by the one or more processors, perform a method responsive to receiving a dose guidance request (DGR) comprising: obtaining a first data structure that comprises at least (i) a body weight (BW) of the subject, (ii) an upper limit target glucose range (UTR) of the subject, (iii) a lower limit target glucose range (LTR) of the subject, and (iv) an overbasalisation limit (OBL) of the subject; obtaining a second data structure that comprises at least (i) a most recent adjustment day dose recommendation (ADDR) and/or (ii) a starting basal dose (SBD); obtaining a first data set, comprising a plurality of glucose measurements of the subject taken over a time course to establish a blood glucose history (BGH) and, for each respective glucose measurement in the plurality of glucose measurements, a corresponding glucose timestamp representing when in the time course the respective glucose measurement was made, obtaining a second data set, comprising (i) a basal insulin injection history (IH) of the subject, wherein the injection history comprises a plurality of injections during all or a portion of the time course and, for each respective injection in the plurality of injections, (ii) a corresponding injection amount (IU) and (iii) an injection timestamp (UTC) representing when in the time course the respective injection occurred and wherein the second data set further comprises (iv) a last injection data refresh (IDR) of the subject; and evaluating at least the first data structure, the second data structure, the first data set, and the second data set to determine whether they collectively contain a set of evaluation information comprising at least (i) the body weight of the subject, (ii) the plurality of glucose measurements of the subject taken over the time course, (iii) the injection history of the subject, (iv) the last adjustment day dose recommendation and/or the starting long-acting or ultra-long-acting insulin dose of the subject, (v) the overbasalisation limit of the subject, (vi) the last injection data refresh for the subject, (vii) the upper limit target glucose range of the subject, and (viii) the lower limit target glucose range of the subject, wherein when a determination is made that the at least first data structure, second data structure, first data set, and second data set fail to collectively contain the set of evaluation information, no update to the long-acting or ultra-long-acting insulin dose guidance recommendation is made, and when a determination is made that the at least first data structure, second data structure, first data set, and second data set collectively do contain the set of evaluation information, the method further comprises providing the long-acting or ultra-long-acting insulin dose guidance recommendation.

In one aspect, the disclosure provides a long-acting or ultra-long-acting insulin for use in treating diabetes mellitus, wherein the administration of the long-acting or ultra-long-acting insulin comprises or consists of the following steps: (a) recording a plurality of glucose measurements of a subject in need of treatment taken over a time course and, for each respective glucose measurement in the plurality of glucose measurements, a corresponding glucose timestamp representing when in the time course the respective glucose measurement was made; (b) obtaining a data structure that comprises at least: (i) a body weight (BW) of the subject, (ii) an upper limit target glucose range (UTR) of the subject, (iii) a lower limit target glucose range (LTR) of the subject, (iv) an overbasalisation limit (OBL) of the subject, (v) a most recent adjustment day dose recommendation (ADDR) and/or a starting insulin basal dose (SBD) of the subject, (vi) a basal insulin injection history (IH) of the subject, wherein the injection history comprises a plurality of injections during all or a portion of the time course and, for each respective injection in the plurality of injections, a corresponding injection amount (IU) and an injection timestamp (UTC) representing when in the time course the respective injection occurred, and (vii) a last injection data refresh (IDR) of the subject; (c) using the glucose measurements and said data structure and (d) providing an updated adjustment day dose recommendation of the long-acting or ultra-long-acting insulin injection amount to the subject.

In one aspect, the disclosure provides wherein step (d) is performed for an administration period of, or at least of, 1 day; for example, of, or at least of, 2 days; of, or at least of, 3 days; of, or at least of, 4 days; of, or at least of, 5 days; of, or at least of, 6 days; of, or at least of, 7 days; of, or at least of, 8 days; of, or at least of, 9 days; of, or at least of, 10 days; of, or at least of, 11 days; of, or at least of, 12 days; of, or at least of, 13 days; of, or at least of, 14 days; or at least of, 15 days; or for an administration period of between 1 and 15 days, for example, between 1 and 13 days; between 2 and 12 days; between 3 and 11 days; between 4 and 10 days; between 5 and 9 days; or for an administration period of between 6 and 8 days.

In some embodiments, the administration of said insulin comprises or consists of steps (a) to (d) and the following step: (e) repeating steps (a) to (d).

In some embodiments, steps (a) to (c) are performed on the same day, wherein, in step (d), said insulin is administered for an administration period of between 1 and 15 days, preferably between 5 and 9 days, between 6 and 8 days, or most preferably of 7 days, and starting on the same day as steps (a) to (c), and wherein said steps (a) to (d) are repeated continuously as long as needed by the said individual.

In some embodiments, the long-acting or ultra-long-acting insulin, preferably ultra-long-acting insulin, has a side chain attached to the α-amino group of the N-terminal amino acid residue of the B chain or to the ε-amino group of the Lys residue present in the B chain of the parent insulin, the side chain being of the general formula:

-W-X-Y-Z

wherein W is: (i) an α-amino acid residue having a carboxylic acid group in the side chain which residue forms, with one of its carboxylic acid groups, an amide group together with the α-amino group of the N-terminal amino acid residue of the B chain or together with the ε-amino group of a Lys residue present in the B chain of the parent insulin; (ii) a chain composed of two, three or four α-amino acid residues linked together via amide bonds, which chain—via an amide bond—is linked to the α-amino group of the N-terminal amino acid residue of the B chain or to the ε-amino group of a Lys residue present in the B chain of the parent insulin, the amino acid residues of W being selected from the group of amino acid residues having a neutral side chain and amino acid residues having a carboxylic acid group in the side chain so that W has at least one amino acid residue which has a carboxylic acid group in the side chain; or (iii) a covalent bond from X to the α-amino group of the N-terminal amino acid residue of the B chain or to the ε-amino group of a Lys residue present in the B chain of the parent insulin; wherein X is:

(i) —CO—;

(ii) —COCH(COOH)CO—;

(iii) —CON(CH2COOH)CH2CO—;

(iv) —CON(CH2COOH)CH2CON(CH2COOH)CH2CO—;

(v) —CON(CH2CH2COOH)CH2CH2CO—;

(vi) —CON(CH2CH2COOH)CH2CH2CON(CH2CH2COOH)CH2CH2CO—;

(vii) —CONHCH(COOH)(CH2)4NHCO—;

(viii) —CON(CH2CH2COOH)CH2CO—; or

(ix) —CON(CH2COOH)CH2CH2CO—;

provided that: (a) when W is an amino acid residue or a chain of amino acid residues, via a bond from the underscored carbonyl carbon forms an amide bond with an amino group in W;

or (b) when W is a covalent bond, via a bond from the underscored carbonyl carbon forms an amide bond with the N-terminal α-amino group in the B chain or with the ε-amino group of a Lys residue present in the B chain of the parent insulin; wherein Y is: (i) α-(CH2)m- where m is an integer in the range of 6 to 32; (ii) a divalent hydrocarbon chain comprising 1, 2 or 3 —CH═CH— groups and a number of —CH2- groups sufficient to give a total number of carbon atoms in the chain in the range of 10 to 32; or (iii) a divalent hydrocarbon chain of the formula —(CH2)vC6H (CH2)w- wherein v and w are integers or one of them is zero so that the sum of v and w is in the range of 6 to 3 0; wherein Z is: (i) —COOH; (ii) —CO-Asp; (iii) —CO-Glu; (iv) —CO-Gly; (v) —CO-Sar; (vi) —CH(COOH)2; (vii) —N(CH2COOH)2; (viii) —S03H; or (ix) —P03H; and any Zn2+ complexes thereof, provided that when W is a covalent bond and X is —CO—, then Z is different from —COOH.

In some embodiments, said insulin is LysB29(Nc-hexadecandioyl-γ-Glu) des(B30) human insulin (insulin degludec, Tresiba®).

In some embodiments, the long-acting insulin or ultra-long-acting insulin is selected from the group consisting of: a) neutral protamine hagedorn insulin (NHP insulin) (Humulin® N, Novolin® ge NPH); b) Lente Insulin (Humulin® L, Novolin® ge Lente); c) Ultralente Insulin (Humulin® U, Novolin1M ge Ultralente); d) Glargine Insulin (Lantus®); e) Detemir Insulin (Levemir®); f) Hypurin Bovine Lente; and g) Hypurin Bovine PZI.

In some embodiments, the long-acting or ultra-long-acting insulin for use is administered, either concurrently or consecutively, together with one or more additional drugs used in the treatment of diabetes.

In some embodiments, the one or more additional drug used in the treatment of diabetes is, or includes, a drug selected from the group consisting of: insulins, sensitizers (such as biguanides and thiazolidinediones), secretagogues (such as sulfonylureas and nonsulfonylurea secretagogues), alpha-glucosidase inhibitors and peptide analogs (such as injectable incretin mimetics, gastric inhibitory peptide analogs, dipeptidyl peptidase-4 inhibitors and injectable amylin analogues).

In some embodiments, the long-acting or ultra-long-acting insulin is LysB29(Nc-hexadecandioyl-γ-Glu) des(B30) human insulin (insulin degludec, Tresiba®), and the long-acting or ultra-long-acting insulin is administered, concurrently or consecutively, with liraglutide.

In some embodiments, the diabetes mellitus is type 2 diabetes mellitus.

In some embodiments, the time course comprises a current day and the past four days.

In some embodiments, the time course comprises the current day and between one and ten of the immediately preceding past days.

In some embodiments, the method further comprises updating the last injection data refresh within the immediately preceding 30 seconds or less, the immediately preceding 1 minute or less, or the immediately preceding 5 minutes or less.

In some embodiments, the method further comprises updating the dose guidance recommendation for the subject, unless the injection history of the subject comprises one or more injections of the subject that have injection timestamps within the immediately preceding 4 hours or less, the immediately preceding 8 hours or less, the immediately preceding 12 hours or less, or the immediately preceding 16 hours or less.

In some embodiments, the method further comprises updating the dose guidance recommendation for the subject, unless the injection history of the subject comprises one or more injections of the subject that have injection timestamps within the current day.

In some embodiments, the method further comprises updating the dose guidance recommendation for the subject when at least one injection occurred within the current day and no injections occurred in the immediately preceding one day in the injection history time course.

In some embodiments, the method further comprises combining any injections in the injection history that have timestamps within a five-minute period in the time course into one injection.

In some embodiments, the method further comprises calculating a reconstructed blood glucose history of the subject when the blood glucose history time course contains a gap, and wherein the reconstructed blood glucose history is calculated based on the blood glucose history of each calendar day.

In some embodiments, the method further comprises performing a quality check of the reconstructed blood glucose history data, and wherein when the data quality check fails, the dose guidance recommendation is not updated.

In some embodiments, the method further comprises providing a wherein the method further comprises providing a re-recommendation of the dose guidance recommendation until: (i) one or more injections with an injection amount equivalent to the dose guidance baseline have occurred, (ii) one or more injections have occurred since the dose guidance baseline and when one or more injections have occurred over the current day and the past two or three or four days, and (iii) one or more does events with an injection amount greater than or equal to the dose guidance baseline have occurred.

In some embodiments, the method further comprises selecting the dose guidance baseline from the set of parameters comprising at least the starting basal dose and the most recent adjustment day dose recommendation, and wherein the selected parameter has the timestamp closest to the current time.

In some embodiments, the method further comprises calculating a daily titration glucose level and an overall titration glucose level, wherein: (i) the daily titration glucose level for each calendar day is calculated by selecting the lowest average of reconstructed blood glucose history data for any predetermined first time period within the blood glucose history time course and excluding any reconstructed blood glucose history data with a timestamp older than the timestamp of the dose guidance baseline: when there are gaps larger than a second predetermined time period in the blood glucose history time course, no daily titration glucose level is calculated, when there are gaps in the blood glucose history time course and these gaps are at least the predetermined second time period in length and less than a predetermined third time period in length within any calendar day in the blood glucose history time course, the daily titration glucose level averaging omits any predetermined first time period containing gaps when averaging the reconstructed blood glucose history data, when any calendar day in the blood glucose history time course contains gaps longer than the predetermined third time period or when more than a predetermined number of blood glucose measurements are absent no daily titration glucose level is calculated; and (ii) the overall titration glucose level is calculated by taking the average of the daily titration glucose levels, and wherein when no overall titration glucose level can be determined the dose guidance recommendation is not updated.

In some embodiments, the predetermined first time period is between 15 minutes and 120 minutes.

In some embodiments, the predetermined second time period is between 10 minutes and 40 minutes.

In some embodiments, the predetermined third time period is between 1 hour and 5 hours or between 1 hour and 15 hours.

In some embodiments, the predetermined number of blood glucose measurements is between 20 and 100.

In some embodiments, the method further comprises: responsive to receiving a request for an updated adjustment day dose recommendation performing a new recommendation procedure to determine the injection amount for the updated adjustment day dose recommendation wherein the updated adjustment day dose recommendation function is based upon at least a titration glucose level and a max basal limit.

In some embodiments, the titration glucose level comprises one of (i), (ii), or (iii): (i) the overall titration glucose level is greater than the upper limit target glucose range, (ii) the overall titration glucose level is greater than or equal to the lower target glucose range and the overall titration glucose level is less than or equal to the upper limit target glucose range, (iii) the daily titration glucose level is less than the lower target glucose range.

In some embodiments, the max basal limit consists of the overbasalisation limit multiplied by the body weight of the subject.

In some embodiments, when the overall titration glucose level is greater than the upper limit target glucose range of the subject and when the most recent adjustment day dose recommendation is less than the max basal limit of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline plus a predetermined number of units of long-acting or ultra-long-acting insulin.

In some embodiments, when the overall titration glucose level is greater than the upper limit target glucose range of the subject and when the most recent adjustment day dose recommendation is greater than or equal to the max basal limit of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline.

In some embodiments, when the overall titration glucose level is between the upper limit target glucose range of the subject and the lower target glucose range of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline.

In some embodiments, wherein when the daily titration glucose level is less than the lower target glucose range of the subject, then the updated adjustment day dose recommendation is determined to be the determined dose guidance baseline minus a predetermined number of units of long-acting or ultra-long-acting insulin.

In some embodiments, the predetermined number of units of long-acting or ultra-long-acting insulin by which to alter the adjustment day dose recommendation is selected from the set of at least 1 unit, 2 units, 4 units, 6 units and 8 units.

In some embodiments, the long-acting or ultra-long-acting insulin dose guidance recommendation is to achieve a specific glucose target.

In some embodiments, the device further comprises a wireless receiver, and wherein the first data set is obtained wirelessly from a glucose sensor affixed to the subject and/or the second data set is obtained wirelessly from the one or more insulin pens.

In some embodiments, the method further comprises storing the updated blood glucose history in the first data structure, and wherein the method is repeated on a recurring basis.

In some embodiments, the upper limit target glucose range used to determine the updated adjustment day dose recommendation is within 80-180 mg/dL, 90-180 mg/dL, 100-180 mg/dL, 90-200 mg/dL, 90-250 mg/dL or 90-300 mg/dL.

In some embodiments, the lower target glucose range used to determine the updated adjustment day dose recommendation is within 50-70 mg/dL, 71-90 mg/dL, 71-100 mg/dL, or 61-90 mg/dL.

In some embodiments, the method further comprises updating the starting basal dosage at least within the immediately preceding 1 day, the immediately preceding 4 days, the immediately preceding 7 days, or the immediately preceding 10 days.

In some embodiments, the method further comprises providing the updated adjustment day dose recommendation at least within the immediately preceding 1 day, the immediately preceding 4 days, the immediately preceding 7 days, or the immediately preceding 10 days.

In some embodiments, the over-basalisation limit is at least within 1.0-0.5 units/kg, 1.5-1.0 units/kg, 0.75-0.25 units/kg, 0.5-0 units/kg, or 2.0-0.75 units/kg.

EXAMPLES
Example 1

Insulin degludec once-daily in type 2 diabetes: Simple or Step-wise titration (BEGIN™: ONCE SIMPLE USE)

Abstract Introduction: Insulin degludec (IDeg) is a new basal insulin in development with a flat, ultra-long action profile that may permit dosing using a simplified titration algorithm with less frequent self-measured blood glucose (SMBG) measurements and more simplified titration steps than currently available basal insulins.

Methods: This 26-week, multi center, open label, randomized, treat to target study compared the efficacy and safety of IDeg administered once daily in combination with metformin in insulin-naïve subjects with type 2 diabetes using two different patient-driven titration algorithms: a “Simple” algorithm (IDegSimple), with dose adjustments based on one pre-breakfast SMBG measurement (N=111) versus a “Step-wise” algorithm (IDegStep-wise), with adjustments based on three consecutive pre-breakfast SMBG values (N=111). IDeg was administered using the FlexTouch® insulin pen (Novo Nordisk A/S, Bagsvrd, Denmark), with once-weekly dose titration in both groups.

Results: Glycosylated hemoglobin decreased from baseline to Week 26 in both groups (−1.09%, IDegSimple; −0.93%, IDegStep-wise). IDegSimple was non-inferior to IDegStep-wise in lowering HbA1c (estimated treatment difference [IDegSimple-IDegStep-wise]: −0.16%-points [−0.39; 0.07]95% CI). Fasting plasma glucose was reduced (−3.27 mmol/L, IDegSimple; −2.68 mmol/L, IDegStep-wise) with no significant difference between groups. Rates of confirmed hypoglycemia (1.60, IDegSimple; 1.17, IDegStep-wise events/patient year of exposure [PYE]) and nocturnal confirmed hypoglycemia (0.21, IDegSimple; 0.10, IDegStep-wise events/PYE) were low, with no significant differences between groups. Daily insulin dose after 26 weeks was 0.61 U/kg (IDegSimple) and 0.50 U/kg (IDegStep-wise). No significant difference in weight change was seen between groups by Week 26 (+1.6 kg, IDegSimple; +1.1 kg, IDegStep-wise), and there were no clinically relevant differences in adverse event profiles.

Conclusion: IDeg was effective and well tolerated using either the Simple or Step-wise titration algorithm. While selection of an algorithm must be based on individual patient characteristics and goals, the ability to attain good glycemic control using a simplified titration algorithm may enable patient empowerment through self-titration, improved convenience, and reduced costs.

Introduction

Numerous studies investigating the cost of self-measured blood glucose (SMBG) testing have found that it comprises a substantial portion of diabetes-related expenditures [15-18]. In a retrospective database analysis in the US that included more than 45,000 patients, testing accounted for 27% of diabetes care costs: total combined blood glucose testing and insulin-related costs were $2,850 USD/patient/year, with $772 USD/patient/year attributed to blood glucose testing alone [18]. In other countries, testing comprises an even higher percentage of diabetes care costs, (e.g., 40% in Canada [16, 17] and 42% in Germany [15]).

Insulin degludec (IDeg) is a new basal insulin (currently approved in Europe, Japan, Mexico and several other countries) with a flat, ultra-long action profile that may enable subjects to achieve glycosylated hemoglobin (HbA1c) levels closer to glycemic target with fewer hypoglycemic episodes [19-21]. It was thus hypothesized that IDeg could be titrated once weekly based on a single pre-breakfast SMBG value, offering a simple, patient-focused titration algorithm that would encourage self-titration, enhancing patient empowerment as well as substantially reducing treatment costs by reducing the frequency of blood glucose measurements required for dose adjustments. In this study, after 26 weeks of treatment, we compared the efficacy and safety of two different self-titration algorithms for IDeg administered once daily (OD) plus metformin, in insulin-naïve subjects with type 2 diabetes: a “Simple” algorithm, in which 4 unit [U] dose adjustments were made based on a single pre-breakfast SMBG measurement was compared with a “Step-wise” algorithm, in which dose adjustments were made in increments of 2 U (Table 2) based on the lowest of three consecutive pre-breakfast SMBG readings. In both groups, IDeg was adjusted once weekly. The objective of this trial was to provide additional guidance on the use of IDeg in clinical practice by investigating whether good glycemic control could be attained with a more simplified titration schedule, involving fewer SMBG tests, than that previously employed during the IDeg Phase 3a development program.

Methods

The study was conducted according to the Declaration of Helsinki (2008) [22] and ICH Good Clinical Practice (1996) guidelines [23], with prior approval by appropriate ethics committees and patient consent obtained in writing prior to the start of any study-related activities. Eligible participants included insulin-naïve men or women ≥18 years of age, with type 2 diabetes, HbA1c 7.0-10.0% (inclusive), and body mass index (BMI) ≤45.0 kg/m2, who were treated with ≥1000 mg/day metformin alone or in combination with one or two other oral antidiabetic medications (OADs) (including a sulfonylurea [SU] or glinide, dipeptidyl peptidase-4 [DPP-4] inhibitors, □-glucosidase inhibitors or thiazolidinediones [TZDs]), with unchanged dosing for ≥12 weeks prior to randomization. Participants were ineligible if they had used a glucagon-like-peptide-1 (GLP-1) receptor agonist within 12 weeks prior to randomization; had initiated or significantly changed treatment that could interfere with glucose metabolism; had significant disease other than type 2 diabetes; were pregnant or breastfeeding; or had recurrent severe hypoglycemia/hypoglycemia unawareness. Subjects could be withdrawn from the trial due to withdrawal of consent, not fulfilling inclusion/exclusion criteria (randomized in error), non-compliance, or at the discretion of the investigator due to a safety concern. Subjects who were withdrawn after randomization were not to be replaced. This trial is registered at clinicaltrials.gov: NCT01326026.

Study Design and Treatment

This was a multinational (conducted in the US, Spain, Finland, and Germany), Phase 3b, multi center, two-armed, parallel group, open label, randomized, treat to target study that compared the efficacy and safety of IDeg OD [IDeg 100 U/mL, FlexTouch® pen, Novo Nordisk A/S, Bagsværd, Denmark], adjusted using two different titration algorithms in combination with metformin. The trial consisted of a 26-week period; total study duration was approximately 28 weeks (including 1 week for screening and a 7-day follow-up period). After discontinuing all OADs other than metformin, subjects were randomized 1:1 by an interactive voice/web response system (IV/WRS) to IDegSimple or IDegStep-wise insulin self-titration algorithms, as defined below. Subjects were instructed to self-titrate in accordance with their respective algorithms and continue with their pre-trial metformin dose. At randomization, Week 4 and Week 12, subjects in both treatment arms received diet and exercise counselling by an HCP. The importance of maintaining a healthy diet and exercise plan was reinforced at each visit.

A Novo Nordisk A/S safety committee blinded to treatment performed on-going safety surveillance, but could request unblinding of the data to be performed by an independent ad hoc group, if needed. Blinded insulin titration surveillance was performed by Novo Nordisk A/S.

IDeg was administered OD at a starting dose of 10 U in both groups. Variation of injection time from day to day was permitted, as long as subjects maintained a minimum of 8 and a maximum of 40 hours between injections. Self-adjustment of IDeg dose was to be performed once weekly in both groups according to the algorithms outlined in Table 2. In the IDegSimple arm, dose adjustment was based on a single pre-breakfast SMBG measurement. In the IDegStep-wise arm, dose adjustment was based on the lowest of 3 consecutive days' pre-breakfast SMBG measure.

Efficacy and Safety Assessments

HbA1c was analyzed using a Bio-Rad high-performance liquid chromatography method at Visits 1 (screening), 2 (randomization), 14 (Week 12) and 28 (Week 26). Fasting plasma glucose (FPG) blood samples were assayed using a hexokinase-UV method at Visits 2, 14 and 28. At the first visit, subjects were provided with a glucose meter for SMBG measurement and instructions for use; blood glucose was measured with test strips calibrated to plasma glucose to obtain PG-equivalent values presented in this report. Subjects performed SMBG measurements before breakfast weekly after randomization and also performed an 8-point SMBG profile prior to Visits 2, 14 and 28.

Adverse events (AEs) and hypoglycemic episodes were documented throughout the study, with confirmed hypoglycemia defined as episodes of severe hypoglycemia (requiring assistance from another person) and episodes with PG value <3.1 mmol/L (56 mg/dL). Nocturnal confirmed hypoglycemic episodes were those occurring between 00:01 h and 05:59 h (inclusive). Laboratory safety variables, insulin dose and body weight were recorded at pre-specified intervals. Two patient-reported outcome (PRO) questionnaires (Device-Specific questionnaires I and II) were self-completed at Visits 14 and 28 to assess subject satisfaction with the FlexTouch® pen as an additional trial endpoint. The PRO questionnaire utilized here to assess patient satisfaction with FlexTouch® had previously been used in other trials to assess satisfaction with the FlexPen® device (Novo Nordisk A/S, Bagsvrd, Denmark) [24, 25].

Statistical Methods

With 218 subjects, there was 85% power to demonstrate non-inferiority at 0.4% in evaluation of the per-protocol (PP) analysis set (defined as all subjects without major protocol violations who were exposed to treatment for >12 weeks and who had a valid assessment necessary for deriving the primary endpoint), accounting for an anticipated total of 15% that would not be included in the PP analysis set. Sample size was determined using a t statistic under the assumption of a one-sided test of size 2.5% and a zero mean treatment difference. Data were reported using a 95% confidence interval (CI) and P-values for 1-sided testing for non-inferiority at alpha=0.025 for the primary analysis, and 2-sided testing with alpha=0.050 for all other analyses. Statistical analyses of all efficacy and patient-reported outcome endpoints were based on the full analysis set (FAS), defined as all randomized subjects, and followed the intention-to-treat (ITT) principle unless otherwise noted. The robustness of the results for change in HbA1c was explored by an additional analysis of the PP analysis set. Further, robustness was explored by an additional analysis of the set of all subjects who completed the trial and by using a simple model based on the FAS with only treatment and baseline HbA1c as covariates. Safety endpoints were summarized based on the safety analysis set (SAS), defined as all subjects who received at least one dose of IDeg, and analyzed based on the FAS.

Change from baseline in HbA1c after 26 weeks was analyzed using an analysis of variance (ANOVA) method with treatment, region, sex and antidiabetic therapy at screening as fixed factors, and age and baseline HbA1c as covariates. Non-inferiority was considered confirmed if the upper bound of the two-sided 95% CI for the treatment difference (IDegSimple-IDegStep-wise) for the mean change in HbA1c was 0.4%. Change in FPG and change in body weight were analyzed using an ANOVA model similar to that used for the primary analysis, but with the relevant baseline value as covariate for each measure. Responder endpoints (proportion of subjects who achieved target HbA1c and proportion who achieved target without hypoglycemia) were analyzed using a logistic regression model with the same factors and covariates as those used for the primary analysis. An 8-point SMBG profile included measurements before and 90 minutes after the start of breakfast, lunch and main evening meal, prior to bedtime, and before breakfast the following day. A mixed effect model including treatment, time, interaction between treatment and time, antidiabetic therapy at screening, sex and region as fixed factors, age as covariate and subject as random effect was fitted to the 8-point SMBG profile data. From this model, mean profile by treatment and relevant treatment differences were estimated and explored. Treatment-emergent AEs, hypoglycaemic episodes, laboratory parameters, physical examination, electrocardiogram (ECG), fundoscopy/fundusphotography, vital signs, PRO (Device-Specific questionnaires I and II) and insulin dose were summarized with descriptive statistics. The numbers of treatment-emergent confirmed and nocturnal confirmed hypoglycaemic episodes were analyzed using a negative binomial regression model with a log-link function and the logarithm of the time period for which a hypoglycaemic episode was considered treatment emergent as offset; the model included treatment, sex, region and antidiabetic treatment at screening as fixed factors and age as covariate.

Results

Demographics and Baseline

Participants were allocated 1:1 to the IDegSimple (N=111) and IDegStep-wise (N=111) arms. Of 222 randomized participants, 221 (99.5%) received trial drug. Treatment arms were well matched at baseline, with the exception of a slightly higher mean body weight and more female subjects in the IDegSimple arm. Subjects in the IDegStep-wise arm had a slightly longer mean duration of diabetes. The majority of participants in both groups were taking two OADs at baseline (61/111 subjects, 55%); ˜21% in each group were taking >2 OADs, and ˜24% in each group were taking 1 OAD. The most common pre-trial OAD other than metformin was a SU. Most (89.2% [99/111], IDegSimple; 88.3% [98/111], IDegStep-wise) subjects completed the trial. Four IDegSimple and three IDegStep-wise subjects were withdrawn due to AEs; five IDegSimple and seven IDegStep-wise subjects were withdrawn due to meeting withdrawal criteria; and three subjects in each group were withdrawn due to reasons classified as “other”.

HbA1c decreased from baseline to Week 26 in both groups; −1.09% with IDegSimple, to 7.0%, and −0.93% with IDegStep-wise, to 7.2% (FIG. 2a). IDegSimple was non-inferior to IDegStep-wise in lowering HbA1c, as the upper limit of the 95% CI for the estimated treatment difference (ETD) was <0.4%: ETD (IDegSimple-IDegStep-wise) −0.16%-points [−0.39; 0.07]95% CI. Analyses to measure robustness of results were consistent with FAS results. Significantly more IDegSimple (56.8% [63/111]) than IDegStep-wise (41.4% [46/111]) subjects achieved HbA1c <7.0% at end-of-trial; estimated odds ratio (IDegSimple/IDegStep-wise): 1.93 [1.04; 3.55]95% CI (P=0.0356). There was no significant difference in the proportion of patients achieving HbA1c<7% without confirmed hypoglycemia (40.6% [43/106] IDegSimple; 34.6% [36/104] IDegStep-wise); estimated odds ratio (IDegSimple/IDegStep-wise): 1.26 [0.69; 2.29]95% CI.

FPG decreased from baseline to Week 26 by 3.27 mmol/L with IDegSimple, to 6.1 mmol/L, and by 2.68 mmol/L with IDegStep-wise, to 6.8 mmol/L (FIG. 2b). No significant difference was seen between groups: ETD (IDegSimple-IDegStep-wise): −0.57 mmol/l [−1.30; 0.17]95% CI. The most pronounced decline in FPG occurred during the first 12 weeks. No difference between groups in 8-point SMBG profiles was seen at any of the eight measured time points at baseline or at end-of-trial (FIG. 2c).

Rates of confirmed hypoglycemia were low, at 1.60 and 1.17 events per patient year of exposure (PYE) with IDegSimple and IDegStep-wise, respectively (FIG. 3a), with no significant difference between groups (P=0.4273). One severe hypoglycemic episode occurred in the IDegSimple arm 5 days after the last treatment with IDeg. Observed rates of nocturnal confirmed hypoglycemia were very low at 0.21 (IDegSimple) and 0.10 (IDegStep-wise) events per PYE (FIG. 3b), with no significant difference between groups (P=0.2047).

The observed daily insulin dose after 26 weeks was 62 U (0.61 U/kg) in the IDegSimple arm and 48 U (0.50 U/kg) in the IDegStep-wise arm. Up to Week 4, mean doses were similar, after which the mean dose in the Simple arm was higher. The increase in IDeg dose per week began to level off in the IDegStep-wise arm at Week 14. Although subjects were permitted to adjust their dose by increments larger than 4 U in the IDegStep-wise arm, the mean weekly incremental increase was U.

Mean baseline body weight was higher in the IDegSimple arm (95.7 kg) than in the IDegStep-wise arm (91.3 kg). Modest increases in weight were observed from baseline to Week 26 in both groups: IDegSimple: (+1.6 kg, to mean weight 97.3 kg at Week 26), IDegStep-wise (+1.1 kg, to mean weight 92.4 kg at Week 26), with no statistically significant difference in weight change: ETD (IDegSimple-IDegStep-wise) 0.46 kg [−0.35; 1.26]95% CI. There were no clinically relevant differences from baseline to end-of-trial or between treatment arms for vital signs, ECG, fundoscopy, physical examination or laboratory parameters (data not shown).

No safety concerns were raised during this trial. Refer to Table 4 for an overview of the rates of AEs and serious AEs (SAEs) reported. AEs and SAEs were distributed similarly between groups. Most AEs were of mild or moderate severity and the rates of AEs classified as possibly or probably related to trial product by the investigator were low (10.0% [IDegSimple]; 7.2% [IDegStep-wise]). Injection-site reactions (ISRs) were reported by 2.7% (3 subjects with 3 events) of IDegSimple and 4.5% (5 subjects with 16 events) of IDegStep-wise subjects; one subject reported 9 of the 16 total events in the IDegStep-wise arm, and reported “pain” as the ninth ISR. No SAEs were reported in ≥5% of subjects and none were considered by the investigator to be related to trial product. One death occurred in this study 154 days after starting trial drug in an IDegStep-wise-treated participant, due to liver metastasis (the primary cancer was reported as probable small cell lung carcinoma). The event was considered by the investigator to be unlikely related to treatment. One other SAE neoplasm event (astrocytoma [IDegSimple]), and three events adjudicated as major adverse cardiovascular events (coronary artery stenosis [IDegSimple], acute myocardial infarction [IDegSimple] and coronary artery occlusion [IDegStep-wise]) occurred, all of which were considered by the investigator to be unlikely related to treatment. No IDeg-related medication errors were reported.

In the Device-Specific questionnaires, more than 90% of subjects at Week 12 and Week 26 indicated the highest levels of satisfaction (response category 1 or 2) with the FlexTouch® device. At 26 weeks, 98% of subjects reported no problems using FlexTouch® and 100% of subjects indicated that they would recommend the pen. Refer to Table 5 for additional details on the results of the questionnaires (Table 5 contains a subset of the total questions surveyed in this trial).

Discussion

Both the Simple and Step-wise titration algorithms were effective, well-tolerated methods of achieving glycemic targets with IDeg, thereby demonstrating that titration based on either a single weekly SMBG measurement with the Simple algorithm, or three measurements with the Step-wise algorithm, provide suitable options for patients with type 2 diabetes. Titration using the Simple algorithm was shown to be non-inferior to titration using the Step-wise algorithm in terms of improving HbA1c and both methods resulted in a similar FPG reduction. End-of-trial HbA1c and change from baseline in HbA1c in both treatment arms were similar to values seen with IDeg in similar previous Phase 3a (BEGIN™) trials in people with type 2 diabetes. These previous trials all demonstrated similar efficacy between IDeg and insulin glargine as demonstrated by non-inferiority in terms of change in HbA1c, were 26 or 52 weeks in duration, enrolled insulin-naïve subjects (except for the BEGIN™ Basal-Bolus T2 study in which insulin aspart was dosed with meals [26]) and employed a titration algorithm similar to the Step-wise algorithm, but with weekly titration based on the mean of 3 consecutive days' pre-breakfast SMBG measurements [26-29].

IDeg dose was increased more quickly in the IDeg Simple arm, whereas insulin dose escalation was reduced earlier in the IDegStep-wise arm, reflecting a point of differentiation between the algorithms: as pre-breakfast SMBG values approached target, the Step-wise algorithm permitted a smaller dose increase of 2 U versus the recommended 4 U increase in the IDegSimple arm. Insulin dose was higher at end-of-trial in the IDegSimple arm than in the IDegStep-wise arm, which may account for the non-significant differences seen between groups in FPG and hypoglycemia. FPG values were numerically lower over longer periods of time in the IDeg Simple arm; this may have influenced the observed rates of hypoglycemia, as these rates represented the entire treatment period. The small and non-significant difference in FPG between the IDegSimple and IDegStep-wise arms likely also contributed to the difference between groups in achieving the HbA1c target of <7%. It is important to note that there was no significant difference between groups in the achievement of the HbA1c target without confirmed hypoglycemia.

The Simple algorithm offers an easy and patient-friendly way to titrate IDeg; additionally, the capacity to adjust IDeg doses with a 4 U increase or decrease, based on a single weekly SMBG value, may substantially reduce the financial and time burden and inconvenience of titration measurements. Incidence rates of hypoglycemic episodes were very low, with no significant difference between the Simple and Step-wise arms. As shown in FIG. 3a and FIG. 3b, respectively, end-of-trial confirmed and nocturnal confirmed hypoglycemia rates seen here were comparable to or lower than rates with IDeg administered OD in the BEGIN™ trials in people with type 2 diabetes; rates that, in turn, were lower than or similar to those seen with comparator insulin glargine in other studies of the insulin degludec development program. [26-29]

Subjects in both treatment arms adhered closely to their respective algorithms. The ability and willingness of patients to adhere to a given treatment regimen is an important component in the success of insulin therapy. Surveys of physicians and patients have identified “too busy” and “complicated regimen” as prominent reasons why patients miss or omit insulin injections; 17% of patients report difficulty in adjusting insulin doses, and 60% of patients feel that their insulin regimens can be restrictive [10-11]. There is evidence to support the premise that if patients are more comfortable with, and accepting of, their dosing regimen, they may be more willing to continue treatment in the long-term [12-14]. Furthermore, patient empowerment may be enhanced by a titration algorithm that facilitates self-adjustment of basal insulin and better adherence to treatment regimens, potentially leading to improved health outcomes. In the Predictable Results and Experience in Diabetes through Intensification and Control to Target: An International Variability Evaluation (PREDICTIVE™) 303 study with insulin detemir, a simplified self-adjusted dosing algorithm in which patients tested SMBG daily and adjusted their dose every 3 days based on the mean of the previous 3 days' values was shown to significantly lower HbA1c versus standard-of-care, physician-driven adjustments over a period of 6 months [4], thus, providing further evidence that a simple self-titration method can help subjects achieve glycemic targets. Additionally, patient acceptance of the insulin delivery device used to administer doses is a factor that appears to influence adherence and persistence with a given treatment regimen [30-33]. It has been reported that positive perceptions of convenience also play an important role in the persistence of pen use [34]. In this trial, high levels of satisfaction with the FlexTouch® insulin pen device were reported in both treatment arms and all subjects indicated that they would recommend the pen to others. This reflects the experiences of patients in other IDeg trials using the same device, in which the majority of patients reported ease in using the pen and a high degree of satisfaction with FlexTouch® [35-39].

Conclusion

Achieving good glycemic control in patients with type 2 diabetes is an important way to prevent or limit diabetes complications, and control the costs of intensified healthcare utilization stemming from these complications. SMBG is an integral part of effective diabetes management; however, glucose meters, test strips, lancets, and alcohol wipes are consumable items that comprise on-going expenses, with test strips identified as a major driver of these costs [15-18]. New medications and treatment regimens that permit a reduction in the number of SMBG measurements without compromising clinical outcomes would likely benefit all basal insulin-treated patients who may find current algorithms confusing or cumbersome. These patients may be more likely to adhere to a simpler regimen that ultimately results in improved health outcomes and lower healthcare costs. This trial demonstrates that IDeg, titrated using either the Simple or Step-wise algorithm, leads to good glycemic control and is well tolerated, offering individualized titration regimens that best meet patient needs.

TABLE 3

Comparison of BEGIN ™ Once Simple titration algorithms

Dose
Dose

adjustment
adjustment

Pre-breakfast SMBG

IDeg Simple^a
IDeg Stepwise^b

mmol/L
mg/dL
U
U

<3.1
<56
−4
−4

3.1-3.9
56-70

−2

4.0-5.0
71-90
0
0

5.1-7.0
91-126
+4
+2

7.1-8.0
127-144

+4

8.1-9.0
145-162

+6

>9.0
>162

+8

^aBased on a single measurement on the day of titration;

^bBased on the lowest of 3 consecutive days' measurements.

IDeg: Insulin degludec;

SMBG: Self-measured blood glucose

TABLE 4

Demographics and baseline characteristics BEGIN ™ Once Simple

Characteristic
IDeg Simple
IDeg Step-wise

Participants in the full analysis set, n
111
111

Participants in the safety analysis set, n
110
111

Female/Male, n (%)
43 (38.7)/68 (61.3)
36 (32.4)/75 (67.6)

Ethnic Group: White/Black/Asian, American
99 (89.2)/8 (7.2)/
97 (87.4)/9 (8.1)/

Indian or Alaska Native/Other, n (%)
3 (2.7)/1 (0.9)
2 (1.8)/3 (2.7)

Age (years)
59.4
(±9.5)
58.5
(±11.1)

Body weight (kg)
95.7
(±18.9)
91.3
(±18.2)

Body mass index (kg/m²)
33.4
(±5.8)
31.5
(±5.2)

Duration of diabetes (years)
8.9
(±5.5)
9.6
(±7.2)

HbA_1c, (%)
8.1
(±0.9)
8.2
(±0.9)

FPG (mmol/L)
9.3
(±2.6)
9.4
(±2.8)

(mg/dL)
167.4
(±46.8)
169.2
(±50.4)

OAD treatment at screening, n (%)

1 OAD
27
(24.3%)
26
(23.4%)

Met
27
(24.3%)
26
(23.4%)

2 OADs
61
(55.0%)
61
(55.0%)

Met + DPP-4I
16
(14.4%)
13
(11.7%)

Met + Glinide
1
(0.9%)
2
(1.8%)

Met + SU
40
(36.0%)
42
(37.8%)

Met + TZD
4
(3.6%)
4
(3.6%)

3 OADs
23
(20.7%)
24
(21.6%)

α-glu inhib + Met + DPP-4I
1
(0.9%)
—

Met + DPP-4I + Glinide
1
(0.9%)
3
(2.7%)

Met + DPP-4I + SU
13
(11.7%)
8
(7.2%)

Met + DPP-4I + TZD
—
2
(1.8%)

Met + SU + TZD
8
(7.2%)
11
(9.9%)

Data are Presented as Number (%) or Mean (SD).

OAD oral antidiabetic drug, Met metformin, SU sulfonylurea, TZD thiazolidinedione, DPP-41 dipeptidyl peptidase 4 inhibitor, α-glu inhib, alpha-glucosidase inhibitor, FPG fasting plasma glucose, IDeg insulin degludec, SD standard deviation, HbA_1cglycosylated haemoglobin.

TABLE 5

Summary of adverse events BEGIN ™ Once Simple

IDeg Simple
IDeg Step-wise

N = 110
N = 111

N
%
E
R
N
%
E
R

AEs
66
60.0
181
346
69
62.2
197
379

AEs occurring with a
17
15.5
18
34
14
12.6
22
42

frequency ≥5%

Headache
8
7.3
8
15
8
7.2
14
27

Nasopharyngitis
10
9.1
10
19
7
6.3
8
15

SAEs
5
4.5
8
15
7
6.3
8
15

Treatment-emergent events occurring after first exposure and no later than 7 days after last exposure. Safety analysis set. n number of patients with events, % proportion of patients with events, E number of events, R number of events per 100 patient-years.

TABLE 6

Device-specific questionnaire responses BEGIN ™ Once Simple

Positive response
Neutral or Negative response

(Category 1 or 2)
(Category 3, 4 or 5)

N (%)
N (%)

1. How easy or difficult do you find it to hold the pen stable when injecting?

Wk 12
195 (94.7)
11
(5.3)

Wk 26
202 (98.5)
3
(1.5)

2. How easy or difficult is it to push down the injection button?

Wk 12
197 (95.2)
9
(4.8)

Wk 26
202 (98.0)
4
(2.0)

3. How easy or difficult is it to turn the dose selector when choosing the right dose?

Wk 12
199 (97.5)
5
(2.5)

Wk 26
196 (96.1)
8
(3.9)

4. How easy or difficult is it to know if the push button has been pushed down completely?

Wk 12
192 (93.2)
14
(6.8)

Wk 26
195 (95.2)
9
(4.8)

5. How easy or difficult is it to see the dose scale when injecting?

Wk 12
176 (85.5)
30
(14.5)

Wk 26
174 (85.2)
30
(14.8)

6. How easy or difficult was it to learn how to use this pen?

Wk 12
200 (98.0)
4
(2.0)

Wk 26
199 (98.5)
3
(1.5)

7. How easy or difficult is it to inject your usual insulin dose?

Wk 12
193 (94.6)
11
(5.4)

Wk 26
196 (97.0)
6
(3.0)

8. How easy or difficult is it to reach the dose button when injecting your insulin dose?

Wk 12
193 (94.6)
11
(5.4)

Wk 26
195 (96.5)
7
(3.5)

9. Overall, how confident are you in your management of daily insulin injection using this pen?

Wk 12
191 (93.1)
14
(6.9)

Wk 26
196 (96.1)
8
(3.9)

10. Overall, how confident are you in controlling your blood sugar level using this pen?

Wk 12
167 (81.8)
37
(18.2)

Wk 26
178 (88.6)
23
(11.4)

Data is based on FAS and summarized independent of treatment arm. % Percentage based on ITT population who answered the questionnaire. Categories for questions 1-8: 1=Very easy, 2=Somewhat easy, 3=Neither easy nor difficult, 4=Somewhat difficult, 5=Very difficult. Categories for questions 9-10: 1=Very, 2=Quite, 3=Somewhat, 4=Not very, 5=Not at all (confident). N Number, Wk Week, ITT intention to treat.

Positive response
Negative response

N (%)
N (%)

1. Did you have any problems using the pen?

Wk 12
205 (100.0)
N/A

Wk 26
201 (100.0)
N/A

2. Would you recommend the pen?

Wk 12
202 (100.0)
N/A

Wk 26
200 (100.0)
N/A

Data is based on FAS and summarized independent of treatment arm. Categories for questions 1-2: 1=No, 2=Yes. N Number, Wk Week, NA not applicable, ITT intention to treat.

Example 2

Use of glucose measurements to determine whether a fasting event is insulin regimen adherent. In some embodiments, the first data set 224 comprising a plurality of glucose measurements is obtained. In some embodiments the glucose measurements are obtain autonomously, for instance by a continuous glucose monitor 102. In this example, in addition to the autonomous glucose measurements, insulin administration events are obtained in the form of insulin medicament records 232 from one or more insulin pens 104 used by the subject to apply the prescribed insulin regimen 212. These insulin medicament records 232 may be in any format, and in fact may be spread across multiple files or data structures. As such, in some embodiments, the instant disclosure leverages the recent advances of insulin administration pens, which have become “smart” in the sense that they can remember the timing and the amount of insulin medicament administered in the past. One example of such an insulin pen 104 is the NovoPen® 5. Such pens assist patients in logging doses and prevent double dosing. It is contemplated that insulin pens will be able to send and receive insulin medicament dose volume and timing, thus allowing the integration of continuous glucose monitors 102, insulin pens 104 and the algorithms of the present disclosure. As such, insulin medicament records 232 from one or more insulin pens 104 is contemplated, including the wireless acquisition of such data from the one or more insulin pens 104.

In some embodiments, each insulin medicament record 232 comprises: (i) a respective insulin medicament injection event 234 including an amount of insulin medicament injected into the subject using a respective insulin pen in the one or more insulin pens and (ii) a corresponding electronic timestamp 236 that is automatically generated by the respective insulin pen 104 upon occurrence of the respective insulin medicament injection event.

REFERENCES CITED AND ALTERNATIVE EMBODIMENTS

All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.

All headings and sub-headings are used herein for convenience only and should not be construed as limiting the invention in any way.

The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

The citation and incorporation of patent documents herein is done for convenience only and does not reflect any view of the validity, patentability, and/or enforceability of such patent documents.

The present invention can be implemented as a computer program product that comprises a computer program mechanism embedded in a non-transitory computer readable storage medium. For instance, the computer program product could contain the program modules shown in any combination of FIGS. 3 and 4 and/or described in FIG. 7. These program modules can be stored on a CD-ROM, DVD, magnetic disk storage product, USB key, or any other non-transitory computer readable data or program storage product.

Many modifications and variations of this invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. The specific embodiments described herein are offered by way of example only. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated. The invention is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled.