Bolus calculator and method for calculating a bolus

Abstract

To provide a bolus calculator and a method for improving the accuracy, convenience and / or efficiency of a device when generating the bolus recommended to a user or the proposed sugar mass.SOLUTION: Disclosed is a bolus calculator 4.1 for determining the bolus B of insulin including an input part for storing at least one known pulse response x(τ) representing the activity profile of at least one insulin, such that a time series h(t-τ) of blood glucose levels is inputted, in which the time series of blood glucose values h (t - τ) is convoluted with the known pulse response x (τ) to obtain the bolus B.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure generally relates to a bolus calculator and method for calculating a bolus. [Background technology]

[0002] U.S. Patent No. 5,999,623 discloses that diabetes mellitus, often referred to as diabetes, is a chronic condition in which a person's blood glucose levels are elevated due to a defect in the body's ability to produce and / or use insulin. There are three major types of diabetes. Type 1 diabetes typically affects children and young adults and can be autoimmune, genetic, and / or environmental. Type 2 diabetes accounts for 90-95% of diabetes cases and is associated with obesity and physical inactivity. Gestational diabetes is a form of impaired glucose tolerance diagnosed during pregnancy and usually resolves spontaneously after birth.

[0003] Untreated, diabetes can lead to serious complications such as heart disease, stroke, blindness, kidney failure, amputation, and death associated with pneumonia and influenza.

[0004] Diabetes management is complex because the levels of blood glucose entering the bloodstream are dynamic. Fluctuations in insulin, which controls the transport of glucose from the bloodstream, also complicate diabetes management. Blood glucose levels are influenced by diet and exercise, but also by sleep, stress, smoking, travel, illness, menstruation, and other psychological and lifestyle factors unique to each individual patient. The dynamic nature of blood glucose and insulin, as well as all the other factors that affect blood glucose, often requires diabetic patients to understand ongoing patterns and predict blood glucose levels (or at least understand the body's glucose-raising and glucose-lowering actions). Therefore, therapy in the form of insulin or oral medications, or both, is timed to maintain blood glucose levels within the appropriate range.

[0005] Diabetes management is often highly invasive, requiring consistent access to reliable diagnostic information, adherence to prescribed treatments, and routine lifestyle management. Routine diagnostic information, such as blood glucose, is typically obtained from capillary blood samples sampled using a lancing device and then measured using a handheld blood glucose meter. Tissue glucose levels are obtained from a continuous blood glucose sensor worn on the body. Prescribed treatments may include insulin or oral medications, or both. Insulin can be delivered by syringe, insulin pen, portable insulin pump, or a combination of such devices. For insulin therapy, calculating the amount of insulin to be injected may require determining the dietary composition of carbohydrates, fat, and protein, along with the effects of exercise or other physiological conditions. Managing lifestyle factors, such as weight, diet, and exercise, can significantly influence the type and effectiveness of treatment.

[0006] Diabetes management requires large amounts of diagnostic and regulatory data derived from medical devices, personal health management devices, patient record information, healthcare professional biomarker data, and prescription medication and record information. Medical devices include self-monitoring BG meters, continuous glucose monitors, ambulatory insulin infusion pumps, diabetes analysis software, and diabetes device configuration software, each of which generates and / or manages large amounts of diagnostic and regulatory data. Personal health management devices include weighing scales, pedometers, and blood pressure cuffs. Patient record information includes information related to diet, exercise, and lifestyle habits, as well as prescription and non-prescription medications. Healthcare professional biomarker data includes HbA1C, fasting blood glucose, cholesterol, triglycerides, and glucose tolerance test results. Healthcare professional information includes treatments and other information related to the patient's care.

[0007] There is a need for patient devices to efficiently aggregate, manipulate, manage, present, and communicate diagnostic and regulatory data from medical devices, personal health management devices, personal record information, biomarker information, and record information to improve the care and health of patients with diabetes, thereby enabling people with diabetes to live fulfilling lives and reduce the risk of complications from diabetes.

[0008] Additionally, there is a need for a diabetes management device that can provide more accurate bolus recommendations to a user based on various user inputs that take into account recent activities and events that may affect the patient's BG levels, thereby increasing the device's accuracy, convenience, and / or efficiency in generating recommended boluses or suggested carbohydrate amounts for the user.

[0009] There remains a need for improved bolus calculators and improved methods for calculating boluses. [Prior art documents] [Patent documents]

[0010] [Patent Document 1] International Publication No. 2013 / 184896A1 Summary of the Invention [Problem to be solved by the invention]

[0011] It is an object of the present disclosure to provide an improved bolus calculator and an improved method for calculating a bolus. [Means for solving the problem]

[0012] This object is achieved by a bolus calculator according to claim 1 and a method according to claim 8.

[0013] Exemplary embodiments are provided in the dependent claims.

[0014] According to the present disclosure, a bolus calculator for determining an insulin bolus has an input configured to receive a time series of blood glucose levels and to store at least one known pulse response representing an activity profile of at least one insulin, and the bolus calculator is configured to convolve the time series of blood glucose levels with the known pulse response to obtain a bolus.

[0015] The activity profile of a fast-acting insulin shows a short but high level of insulin in the blood, while the same amount of a slow-acting insulin shows a long, flat level of activity. The bolus of drug corresponds to the area under the activity level curve, i.e., the integral of blood concentration over time.

[0016] For example, when insulin is delivered by a pen, it generates pulses similar to the Dirac pulses of insulin. In this way, the active agent, i.e., insulin, is suddenly introduced into the body's system, which responds with a step response. When a pump is used, delivery occurs very slowly or as repeated injections of small amounts (e.g., in pulses similar to the Dirac pulses).

[0017] The activity profile is the pharmacokinetic mode of action of different insulins that can act over different periods of time and achieve different levels of insulin in the blood. This activity profile can be determined by injecting insulin and measuring blood glucose levels over time to determine the activity of each insulin. The blood glucose level is tested by obtaining a pulse response to phosphorus. Similarly, clamp experiments are performed to demonstrate free insulin levels at constant blood glucose levels. The trends are then normalized.

[0018] Each insulin typically has a specific pharmacokinetic mode of action. The half-life is typically used to indicate when the effect of an insulin decays to half of its initial or maximum level.

[0019] In one exemplary embodiment, the bolus calculator is further configured to take into account the dead time of the time series resulting from measuring blood glucose levels in the capillary blood of the human body.

[0020] In one exemplary embodiment, the bolus calculator is further configured to calculate a bolus for one type of insulin, or to select one of multiple types of insulin and calculate a bolus for the selected type.

[0021] In one exemplary embodiment, the types of insulin include slow acting insulin, fast acting insulin, and intermediate acting insulin.

[0022] In one exemplary embodiment, the bolus calculator is further configured to store and take into account patient characteristics, particularly at least one of age, sex, weight, and body fat percentage.

[0023] In one exemplary embodiment, the bolus calculator is further configured to store a characteristic map comprising a plurality of stored pulse responses of one or more types of insulin for different sets of patient characteristics.

[0024] In one exemplary embodiment, the bolus calculator is further configured to convolve all selectable insulin types for the set patient characteristics with the time series of measured blood glucose levels and select the most appropriate insulin type.

[0025] In one exemplary embodiment, the bolus calculator is further configured to perform a teach-in run to measure the pulse response of the human body to determine the dead time.

[0026] In one exemplary embodiment, the bolus calculator is further configured to store the measured blood glucose values ​​and the calculated boluses to generate a history, and to take the history into account when calculating the bolus.

[0027] In one exemplary embodiment, an apparatus for determining an insulin bolus includes a bolus calculator, a human body model, a blood glucose sensor adapted to perform blood glucose measurements on venous or capillary blood of a human body, and a comparator for comparing the blood glucose value measured by the blood glucose sensor with the blood glucose value calculated by the human body model before passing it to the bolus calculator.

[0028] The human body model is a complex control system, subject to injection pulses and permanently influenced by metabolism, food intake, activity, circadian rhythm, age, sex, and body weight or mass, and by the type of disease. Insulin sensitivity factors are variable in type 2 diabetes. A health tracker is configured to obtain calories burned by heart rate as a function of body weight, resting heart rate, age, sex, and current heart rate (see the Harris-Benedict equation, an adapted and established standard method for determining calories burned).

[0029] In one exemplary embodiment, the blood glucose sensor is adapted to perform blood glucose measurements continuously, periodically, randomly, or pseudo-randomly.

[0030] In one exemplary embodiment, the device further includes a controller, and the bolus calculator is located within the controller.

[0031] In one exemplary embodiment, the bolus calculator is adapted to send a bolus to a drug delivery device that is adapted to automatically set the bolus.

[0032] In one exemplary embodiment, the bolus calculator is connected to a drug delivery device and adapted to control the delivery of a bolus and, optionally, the delivery of a selected type of insulin.

[0033] In one exemplary embodiment, the blood glucose sensor is attached to or implanted in the human body and adapted to determine blood glucose levels by capillary blood glucose measurement or by venous blood glucose measurement.

[0034] In one exemplary embodiment, the controller or apparatus is a mobile device.

[0035] In one exemplary embodiment, the controller is configured to communicate with the blood glucose sensor via a wired or wireless connection.

[0036] According to one aspect of the present disclosure, a method for calculating an insulin bolus includes inputting a time series of blood glucose levels into an input of a bolus calculator, storing at least one known pulse response in the bolus calculator that represents an activity profile of at least one insulin, and convolving the time series of blood glucose levels with the known pulse response to obtain a bolus.

[0037] In one exemplary embodiment, the time series dead time resulting from measuring blood glucose levels in the capillary blood of a human body is taken into account.

[0038] In one exemplary embodiment, a bolus is calculated for one type of insulin, or one of multiple types of insulin is selected and a bolus is calculated for the selected type.

[0039] In one exemplary embodiment, the types of insulin include slow acting insulin, fast acting insulin, and intermediate acting insulin.

[0040] In an exemplary embodiment, the method further includes storing and taking into account patient characteristics, in particular at least one of age, sex, weight and body fat percentage.

[0041] In one exemplary embodiment, the method further includes storing a characteristic map comprising a plurality of stored pulse responses of one or more types of insulin for separate sets of patient characteristics.

[0042] In an exemplary embodiment, the method further includes convolving all selectable insulin types for the set patient characteristics with the time series of measured blood glucose levels and selecting the most appropriate insulin type.

[0043] In an exemplary embodiment, the method further includes performing a teach-in run to measure the pulse response of the human body to determine the dead time.

[0044] In a teach-in run, parameters are adjusted for the patient and the complex model control loop is loaded with values, particularly continuous blood glucose measurements. This teach-in run works best during times when insulin is not being delivered, e.g., overnight. In subsequent stages, the insulin While insulin is delivered as before, values ​​continue to be input into the body model to continue teaching it to optimize parameters. The injections are extrapolated by continuous simulation and compared with the actual values ​​of the continuous blood glucose measurements and the algorithm used so far to obtain vernier adjustments to the parameters and indirectly determine insulin resistance. Once the control loop has found the approximate values ​​of the actual and simulated values, teach-in mode is exited and active mode is entered.

[0045] In active mode, the body model is trained and vital parameters are continuously input, such as continuous blood glucose measurements, glucose exchange (carbs) (e.g., manually entered food calories), and heart rate from a health tracker. Calorie intake also induces a kind of pulse response in the system. The control circuit can then attempt to extrapolate new values ​​through simulation (including past trends) to estimate, for example, how one insulin delivery or another at a particular time would affect the blood glucose trajectory. The goal is to minimize blood glucose fluctuations around a specified target value (e.g., 100 mg / mL). If an optimal value is identified, it is output. Because the control circuit knows the types of insulin available, the simulation can vary the order of these types. This is achieved by iterating the simulation toward the blood glucose target range. The patient typically decides when to inject (usually after a meal). Bolus optimization is then performed at that time by outputting a value to be confirmed or by controlling the pump that can next deliver a bolus.

[0046] It is known that the activity profiles of all insulins are equal or converge at the end of the active period and cannot be ignored (e.g., Apidra® remains effective even after 24 hours). Because blood glucose levels are not equal in all parts of the body and all arrive at each part of the body with some delay, dead time must be taken into account. In an exemplary embodiment, the method further includes storing the measured blood glucose levels and the calculated bolus to generate a history and taking the history into account when calculating the bolus.

[0047] In an exemplary embodiment, the method further includes comparing the blood glucose level measured by the blood glucose sensor with the blood glucose level calculated by the human body model before passing it to the bolus calculator.

[0048] In one exemplary embodiment, blood glucose measurements are taken continuously, periodically, randomly, or pseudo-randomly.

[0049] In one exemplary embodiment, the bolus is delivered to a drug delivery device where the bolus is automatically set.

[0050] In an exemplary embodiment, the method further includes controlling the delivery of the bolus and, optionally, selecting the type of insulin.

[0051] The improved bolus calculator and improved method can more accurately and reliably determine boluses of insulin, particularly to be used in intensive diabetes treatments that use several different types of insulin with different activity profiles.

[0052] The drug delivery devices described herein are configured to inject a medication into a patient. For example, delivery can be subcutaneous, intramuscular, or intravenous. Such devices are operated by the patient or a caregiver, such as a nurse or doctor, and can include various types of safety syringes, pen injectors, or auto-injectors. The devices are sealed with an adhesive tape prior to use. These devices may include cartridge-based systems that require a syringe to be punctured. The amount of medication delivered using these various devices may be from about 0.5 mL to about 2 mL. Still other devices may include large volume devices ("LVD") or patch pumps that are configured to adhere to a patient's skin for a period of time (e.g., about 5, 15, 30, 60, or 120 minutes) to deliver a "large" amount of medication (usually from about 2 mL to about 5 mL).

[0053] When paired with a particular drug, the devices described herein are also customized to operate within required specifications. For example, the device may be customized to inject the drug within a certain time period (e.g., about 3 to about 20 seconds for an auto-injector, or about 10 to about 60 minutes for an LVD). Other specifications may include low or minimal discomfort, or some other human factors-related criteria, such as shelf life, expiration date, biocompatibility, and environmental considerations. Such variations may arise due to a variety of factors, such as, for example, drugs with viscosities ranging from about 3 cP to about 50 cP. As a result, drug delivery devices often include hollow needles ranging in size from about 25 to about 31 gauge. Common sizes are 27 and 29 gauge.

[0054] The delivery devices described herein may also include one or more automated features. For example, one or more of needle insertion, medication injection, and needle retraction may be automated. Energy for one or more automated steps may be provided by one or more energy sources. The energy sources may include, for example, mechanical energy, pneumatic energy, or electrical energy. For example, a mechanical energy source may include a spring, lever, elastomer, or other mechanical mechanism for storing or releasing energy. One or more energy sources may be integrated into a single device. The device may further include gears, valves, or other mechanisms for converting energy into movement of one or more components of the device.

[0055] One or more automated functions of an auto-injector are activated via an activation mechanism. Such an activation mechanism may include one or more buttons, levers, needle sleeves, or other activation mechanisms. Activation may be a single-step or multi-step procedure; that is, a user may be required to activate one or more activation mechanisms to cause an automated function to occur. For example, a user may depress a needle sleeve against the user's body to inject medication. Other devices require a user to depress a button and retract a needle shield to inject.

[0056] Additionally, such activation can activate one or more mechanisms. For example, one activation sequence can activate at least two needle insertions, medication injections, and needle retractions. Some devices may also require a specific sequence of steps to perform one or more automated functions. Other devices may operate by sequence-independent steps.

[0057] Some delivery devices may include one or more features of a safety syringe, a pen injector, or an auto-injector. For example, a delivery device may include a mechanical energy source configured to automatically inject medication (typically found in auto-injectors) and a dose setting mechanism (typically found in pen injectors).

[0058] Further scope of applicability of the present disclosure will become apparent from the detailed description set forth below. It should be understood, however, that this detailed description and the specific examples, while indicating exemplary embodiments of the present disclosure, are given by way of illustration only, since various changes and modifications within the spirit and scope of the disclosure will become apparent to those skilled in the art from this detailed description.

[0059] The present disclosure will become more fully understood from the detailed description and the accompanying drawings, which are provided by way of illustration only and are not intended to limit the disclosure. [Brief explanation of the drawings]

[0060] [Figure 1] 1 is a schematic diagram of a drug delivery device. [Figure 2] FIG. 1 is a schematic diagram of a control loop including a controller with a bolus calculator. [Figure 3] 1 is a schematic diagram of an exemplary embodiment of a device for determining a bolus of insulin. [Figure 4] 1 is a schematic diagram of an exemplary embodiment of a device for determining a bolus of insulin. [Figure 5] 1 is a schematic diagram of an exemplary embodiment of a device for determining a bolus of insulin. [Figure 6] FIG. 1 is a schematic diagram of a simplified body model. DETAILED DESCRIPTION OF THE INVENTION

[0061] In all figures, corresponding parts are marked with the same reference symbols.

[0062] An exemplary drug delivery device 10 according to some embodiments of the present disclosure is shown in FIGS. 1A and 1B. As described above, device 10 is configured to inject a medication into a patient's body. Device 10 includes a housing 11 (e.g., a syringe) that typically contains a reservoir containing the medication to be injected, as well as components necessary to facilitate one or more stages of the delivery procedure. Device 10 may also include a cap assembly 12 that is removably attached to housing 11. Typically, a user must remove cap 12 from housing 11 before device 10 can be operated.

[0063] As shown, housing 11 is substantially cylindrical and has a substantially constant diameter along longitudinal axis X. Housing 11 has a distal region 20 and a proximal region 21. The term "distal" refers to a location relatively closer to the site of injection, and the term "proximal" refers to a location relatively further from the site of injection.

[0064] Device 10 may also include a needle sleeve 13 (see FIG. 2C ) coupled to housing 11 to allow movement of sleeve 13 relative to housing 11. For example, sleeve 13 may move in a longitudinal direction parallel to longitudinal axis X. Specifically, proximal movement of sleeve 13 may allow needle 17 to extend from distal region 20 of housing 11.

[0065] Insertion of needle 17 into the injection site can occur via several mechanisms. For example, needle 17 is fixedly positioned relative to housing 11 and is initially positioned within extended needle sleeve 13. The distal end of sleeve 13 is placed against the patient's body and housing 11 is moved distally, thereby uncovering the distal end of needle 17 as sleeve 13 moves proximally. This relative movement allows the distal end of needle 17 to extend into the patient's body. This type of insertion is referred to as "manual" insertion, because the patient manually moves housing 11 relative to sleeve 13 to manually insert needle 17.

[0066] Another form of insertion is "automated," whereby needle 17 moves relative to housing 11. Such insertion may be actuated by movement of sleeve 13 or by another form of actuation, such as button 22. As shown in FIGS. 1A and 1B, button 22 is located at the proximal end of housing 11. However, in other embodiments, button 22 is located on the side of housing 11.

[0067] Other manual or automated functions may include drug injection or needle retraction, or both. Injection refers to the process of moving the bung or piston 23 from a proximal position within the syringe (not shown) to a more distal position within the syringe to force medication from the syringe into the needle 17. In some embodiments, a drive spring (not shown) is in a compressed state before the device 10 is activated. The proximal end of the drive spring is secured within the proximal region 21 of the housing 11, and the distal end of the drive spring is configured to exert a compressive force on the proximal face of the piston 23. Following activation, at least a portion of the energy stored in the drive spring is applied to the proximal face of the piston 23. This compressive force acts on the piston 23 to move it distally. Such distal movement acts to compress the liquid medication within the syringe, forcing it out of the needle 17.

[0068] Following injection, needle 17 is retracted into sleeve 13 or housing 11. Retraction can occur when sleeve 13 moves distally as the user moves device 10 away from the patient's body. This can occur because needle 17 remains in a fixed position relative to housing 11. When the distal end of sleeve 13 passes over the distal end of needle 17, sheathing needle 17, sleeve 13 is locked. Such locking can include locking any proximal movement of sleeve 13 relative to housing 11.

[0069] Another form of needle retraction can occur when the needle 17 moves relative to the housing 11. Such movement can occur when the syringe within the housing 11 moves proximally relative to the housing 11. This proximal movement is achieved using a retraction spring (not shown) in the distal region 20. When activated, the compressed retraction spring provides sufficient force to the syringe to move it proximally. After sufficient retraction, a locking mechanism locks any relative movement between the needle 17 and the housing 11. Additionally, the button 22 or other components of the device 10 can be locked as needed.

[0070] Figure 2 is a schematic diagram of a control loop 1 including a patient body model 9, which represents the control system. This body model has a defined glucose consumption that depends on physical activity A, such as sleep, which reduces consumption, and activity events, which increase consumption. Glucose consumption also depends on the patient's age and gender, as well as external conditions E, such as the season (summer, winter, etc.) or time of day. Blood glucose consumption is also a function of time. Sleep typically leads to a decrease in blood glucose consumption. Glycoexistence C and physical activity A also affect blood glucose consumption, resulting in respective pulse responses in blood glucose levels. Furthermore, blood glucose consumption is affected by diabetes characteristics C, such as the type and severity of diabetes. Because type 1 diabetes patients do not produce any insulin, only the insulin delivered to the patient is taken into account when calculating the bolus. Type 2 diabetes patients produce different amounts of insulin depending on the patient's degree of diabetes and exhibit varying degrees of reuptake inhibition depending on their weight and metabolic state, which vary with the season of the year.

[0071] The human body responds to physical activity A and glucose exchange CE like a PI (proportional-integral) controller with an effective I (integral) component.

[0072] The external conditions E and physical activity A are determined automatically, for example, by sensors and activity trackers. The diabetic characteristics C and data related to carbohydrate exchange CE are input by the user via a user interface into the human body model 9. Similarly, the human body model 9 can estimate carbohydrate exchange based on previous inputs, especially if the patient follows their diet regularly.

[0073] The blood glucose sensor 3 is applied to perform blood glucose measurements on venous blood of a human body, for example. In an alternative embodiment, blood glucose measurements on capillary blood are performed by the blood glucose sensor 3, for example, at an earlobe, or The blood glucose measurement is performed in the eye by a blood glucose sensor 3 configured as a contact lens or a vein, whose pulse response is delayed relative to the venous blood measurement. This delay must be taken into account as a dead time represented by a dead time element 8 when calculating the insulin bolus B. The blood glucose measurements are performed continuously, periodically, randomly, or pseudo-randomly. The results of the blood glucose measurements are fed into a controller 4 which includes a bolus calculator 4.1. The controller 4 may further include a comparator 4.2 for comparing the blood glucose measurement with the blood glucose value calculated by a human body model 9 before passing it to the bolus calculator 4.1.

[0074] Bolus calculator 4.1 is configured to calculate a bolus B for one type T of insulin, or to select one of multiple types T of insulin and calculate a bolus B for this selected type T.

[0075] Different Type T insulins have different activity profiles, e.g., slow-acting insulin, rapid-acting insulin, and intermediate-acting insulin. The activity profile of each Type T insulin refers to the pulse response to the administration of a bolus B of that Type T insulin. The pulse response depends on the pharmacokinetics of the selected Type T insulin, the amount of insulin in the bolus B, and patient characteristics such as age, sex, weight, and body fat percentage. Older patients may have slower metabolisms than younger patients. Heavier patients may also require more insulin.

[0076] Patient characteristics are entered into the bolus calculator 4.1 via the user interface and stored in the bolus calculator 4.1 to avoid the need for repeated entry.

[0077] Bolus Calculator 4.1 convolves a time series of blood glucose levels, representing the body's pulse response, with the known pulse response of one or more types of insulin.

[0078] The convolution integral is determined by the following formula:

number

[0079] In this embodiment, h(t-τ) is represented by the time series of measured blood glucose levels in the human body over time, and x(τ) is represented by the known pulse response (activity profile) of one or more types of insulin T.

[0080] A characteristic map is provided within Bolus Calculator 4.1 and contains stored pulse responses to one or more Type T insulins for different sets of patient characteristics such as age, sex, weight, body fat percentage, etc.

[0081] The bolus calculator 4.1 convolves all selectable insulin types T for the set patient characteristics with the time series of measured blood glucose levels and can select the most appropriate insulin type T. In this way, the most appropriate insulin type T and bolus B are selected.

[0082] The bolus B calculated by the bolus calculator 4.1 is then delivered to the body by a drug delivery device 5, for example an insulin pump or an insulin pen.

[0083] In an exemplary embodiment, the bolus calculator 4.1 may include a display device for outputting the calculated bolus B and optionally the selected type T insulin, which the user may then use to administer an injection using a drug delivery device 5, such as an insulin pen. The bolus may be set manually by the patient or automatically transmitted to the drug delivery device 5, for example via a wireless or wired connection, and set automatically by the drug delivery device 5. In another exemplary embodiment, the bolus calculator 4.1 is coupled to a drug delivery device 5, such as an insulin pump, and controls the delivery of the calculated bolus B and optionally the selected type T insulin.

[0084] When Bolus B is delivered, the body will produce a respective pulse response, thus controlling blood glucose levels to be within defined limits.

[0085] The Type T insulins that the bolus calculator 4.1 can choose from can include: slow acting insulin, fast acting insulin, intermediate acting insulin. In other embodiments, there can be only one, two, or more than three Type T insulins to choose from. For example, a slow acting insulin is used at night.

[0086] A teach-in run to measure the pulse response of the human body is performed using the controller 4 to determine the dead time to be taken into account in practical applications.

[0087] Bolus Calculator 4.1 can also store measured blood glucose values ​​and calculated Bolus B's to create a history that can be taken into account when calculating future Bolus B's.

[0088] In the described system, blood glucose measurements are obtained by a continuous blood glucose monitoring patch and transmitted to a mobile device, particularly wirelessly, for example, by NFC or Bluetooth® communication. Calorie intake is entered manually or estimated by an application running on the mobile device. A health tracker can determine heart rate and transmit this, particularly wirelessly, for example, by NFC or Bluetooth® communication. Injections of insulin are made through the abdominal wall into the adipose tissue by a pen syringe or an insulin pump.

[0089] The field model 9 can be a time-discrete system / control circuit that typically operates in the frequency domain. All values ​​exist as discrete values ​​in the time domain and are treated as such. The control circuit is described in the time domain, but must first be Z-transformed.

[0090] Comparator 4.2 is set to blood glucose target values, specifically upper and lower limits, for example, an upper limit of 150 mg / dL and a lower limit of 80 mg / dL. After running simulations with varying insulin values, Bolus Calculator 4.1 determines and outputs the optimum.

[0091] The complex control system model 9 can be a system of differential equations that is numerically solved and outputs the progression of blood glucose levels (BGM) over time, which is compared over time with the measurements of the blood glucose sensor 3 (continuous blood glucose measurements) after passing through a dead time element 8.

[0092] The progression of blood glucose BGM is calculated by BGM(t-t0)=F(t,τ,f(carb,HF,...)), where carb is carbohydrate exchange CE and HF is heart rate.

[0093] When a system is sampled, it is transformed into the frequency domain by a Z-transform and back into the time domain.

[0094] The bolus calculator 4.1 convolves all selectable insulin types T for the set patient characteristics with the time series of measured blood glucose levels, selects the most appropriate insulin type T, and determines the appropriate bolus B.

[0095] This determination is achieved by iterative simulations towards a blood glucose target range. The patient typically decides when to inject (usually after a meal). The optimization of Bolus B is then performed at that time by outputting a value to be confirmed or by controlling the pump that can then deliver the bolus.

[0096] 3 is a schematic diagram of an exemplary embodiment of an apparatus 6 for determining a bolus of insulin. A blood glucose sensor 3 is attached to or implanted on a human body 2 and is adapted to determine blood glucose levels, for example by capillary blood glucose measurement. A controller 4 (e.g., a mobile device such as a smartphone) is configured to communicate with the blood glucose sensor 3 by a wired or wireless connection 7 (e.g., Bluetooth®). The controller 4 includes a bolus calculator 4.1 (e.g., a software application). The controller 4 is also connected to one or more drug delivery devices 5 (e.g., an insulin pen 5.1 and an insulin pump 5.2) by wired or wireless connection 7 (e.g., Bluetooth®).

[0097] 4 is a schematic diagram of an exemplary embodiment of an apparatus 6 for determining a bolus of insulin. A blood glucose sensor 3 is attached to or implanted on a human body 2 and is adapted to determine blood glucose levels, for example by capillary blood glucose measurement. A controller 4 is integrated with a drug delivery device 5 (e.g., an insulin pump 5.2) and configured to communicate with the blood glucose sensor 3 by a wired or wireless connection 7 (e.g., Bluetooth®). The controller 4 includes a bolus calculator 4.1 (e.g., a software application).

[0098] 5 is a schematic diagram of an exemplary embodiment of an apparatus 6 for determining a bolus of insulin. A blood glucose sensor 3 is configured to analyze a blood glucose measuring strip 8 with a blood sample from a human body 2 and is adapted to determine a blood glucose level. A controller 4 (e.g., a mobile device such as a smartphone) is configured to communicate with the blood glucose sensor 3 by a wired or wireless connection 7 (e.g., Bluetooth®). The controller 4 includes a bolus calculator 4.1 (e.g., a software application). The controller 4 is also coupled to one or more drug delivery devices 5 (e.g., two or more different insulin pens 5.1, 5.3), one containing a slow-acting insulin, e.g., Lantus®, and the other containing a fast-acting insulin, e.g., Apidra®.

[0099] FIG. 6 is a schematic diagram of a simplified body model shown as an electronic circuit in the form of a quadrupole, including a low-pass circuit formed by a first resistor R1 and a capacitor C1, a controlled current source CS in parallel with the capacitor C1, a low-pass circuit followed by a second resistor R2, a voltage source VS, for example a battery, and a third resistor R3.

[0100] Insulin injection ΔI affects the model just as a pulse affects a quadrupole, i.e., proportional to the decrease in blood glucose level -ΔBG. The low-pass circuit represents the attenuation of insulin by the abdominal wall and its adipose tissue before it is distributed through the organism's venous system. The control current source CS represents calorie expenditure and intake due to activity, i.e., the increase or decrease in blood glucose level. Central adipose tissue and other tissues, especially the liver, are represented by the voltage source VS. Even if food intake does not occur, the voltage source VS supplies glucose from the adipose tissue into the system (controlled by insulin intake ΔI depending on the degree of diabetes).

[0101] As used herein, the term "drug" or "medicine" is used herein to describe one or more pharmaceutically active compounds. As described below, a drug or agent can include at least one small molecule or large molecule, or a combination thereof, in various types of formulations for treating one or more diseases. Exemplary pharmaceutically active compounds can include small molecules; polypeptides, peptides, and proteins (e.g., hormones, growth factors, antibodies, antibody fragments, and enzymes); carbohydrates and polysaccharides; and nucleic acids, double-stranded or single-stranded DNA (including naked and cDNA), RNA, antisense nucleic acids such as antisense DNA and RNA, small interfering RNA (siRNA), ribozymes, genes, and oligonucleotides. Nucleic acids can be incorporated into molecular delivery systems such as vectors, plasmids, or liposomes. Mixtures of one or more of these drugs are also contemplated.

[0102] The term "drug delivery device" encompasses any type of device or system configured to administer a drug into the human or animal body. Without limitation, a drug delivery device can be an injection device (e.g., a syringe, pen injector, auto-injector, large volume device, pump, perfusion system, or other device configured for intraocular, subcutaneous, intramuscular, or intravascular delivery), a skin patch (e.g., osmotic, chemical, microneedle), an inhaler (e.g., nasal or pulmonary), an implant (e.g., coated stent, capsule), or a gastrointestinal delivery system. The drugs described herein can be particularly useful in injection devices that include a needle, e.g., a small gauge needle.

[0103] The drug or agent can be contained within a primary package or "drug container" adapted for use in a drug delivery device. The drug container can be, for example, a cartridge, syringe, reservoir, or other container configured to provide a chamber suitable for storage (e.g., short-term or long-term storage) of one or more pharmaceutically active compounds. For example, in some cases, the chamber can be designed to store the drug for at least one day (e.g., from one day to at least 30 days). In some cases, the chamber can be designed to store the drug for about one month to about two years. Storage can occur at room temperature (e.g., about 20°C) or refrigerated temperatures (e.g., from about -4°C to about 4°C). In some cases, the drug container can be or include a dual-chamber cartridge configured to store two or more components of a drug formulation (e.g., a drug and a diluent, or two different types of drug) separately, one in each chamber. In such cases, the two chambers of the dual-chamber cartridge can be configured to allow mixing between the two or more components of the drug or agent before and / or during administration into the human or animal body. For example, the two chambers can be configured so that they are in fluid communication with each other (e.g., by a conduit between the two chambers) to allow the two components to be mixed by a user prior to administration, if desired. Alternatively, or in addition, the two chambers can be configured to allow the components to be mixed as they are being administered into the human or animal body.

[0104] The drug delivery devices and drugs described herein can be used to treat and / or prevent many different types of disorders. Exemplary disorders include, for example, diabetes or complications associated with diabetes, such as diabetic retinopathy, and thromboembolic disorders, such as deep vein thromboembolism or pulmonary thromboembolism. Further exemplary disorders are acute coronary syndrome (ACS), angina pectoris, myocardial infarction, cancer, macular degeneration, inflammation, hay fever, atherosclerosis, and / or rheumatoid arthritis.

[0105] Exemplary drugs for the treatment and / or prevention of diabetes or complications associated with diabetes include insulin, e.g., human insulin, or a human insulin analog or derivative, glucagon-like peptide (GLP-1), a GLP-1 analog or GLP-1 receptor agonist, or an analog or derivative thereof, a dipeptidyl peptidase-4 (DPP4) inhibitor, or a pharmaceutically acceptable salt or solvate thereof, or any mixture thereof. As used herein, the term "derivative" refers to any substance that is sufficiently structurally similar to the original substance so as to thereby have a similar function or activity (e.g., therapeutic efficacy).

[0106] Exemplary insulin analogs are Gly(A21),Arg(B31),Arg(B32) human insulin (insulin glargine); Lys(B3),Glu(B29) human insulin; Lys(B28),Pro(B29) human insulin; Asp(B28) human insulin; human insulin in which the proline at position B28 is replaced by Asp, Lys, Leu, Val, or Ala, and in which Lys at position B29 may be replaced by Pro; Ala(B26) human insulin; Des(B28-B30) human insulin; Des(B27) human insulin and Des(B30) human insulin.

[0107] Exemplary insulin derivatives include, for example, B29-N-myristoyl-des(B30) human insulin; B29-N-palmitoyl-des(B30) human insulin; B29-N-myristoyl human insulin; B29-N-palmitoyl human insulin; B28-N-myristoylLysProB29 human insulin; B28-N-palmitoyl-LysProB29 human insulin; B30-N-myristoyl-ThrB29Ly sB30 human insulin; B30-N-palmitoyl-ThrB29LysB30 human insulin; B29-N-(N-palmitoyl-γ-glutamyl)-des(B30) human insulin; B29-N-(N-lithocholyl-γ-glutamyl)-des(B30) human insulin; B29-N-(ω-carboxyheptadecanoyl)-des(B30) human insulin, and B29-N-(ω-carboxyheptadecanoyl) human insulin.Exemplary GLP-1, GLP-1 analogs and GLP-1 receptor agonists include, for example: Lixisenatide / AVE0010 / ZP10 / Lyxumia, Exenatide / Exendin-4 / Byetta / Bydureon / ITCA650 / AC-2993 (a 39 amino acid peptide produced by the salivary glands of the Gila monster), Liraglutide / Victoza, Semaglutide, Taspoglutide, Syncria / Albiglutide, Dulaglutide, glutide, rExendin-4, CJC-1134-PC, PB-1023, TTP-054, Langlenatide / HM-11260C, CM-3, GLP-1 Erigen, ORMD-0901, NN-9924, NN-9926, NN-9927, Nodexen, Viador-GLP-1, CVX -096, ZYOG-1, ZYD-1, GSK-2374697, DA-3091, MAR-701, MAR709, ZP-2929, ZP-3022, TT-401, BHM-034, MOD-6030, CAM-2036, DA-15864, ARI-2651, ARI-2255, Exenatide-XTEN and Glucagon-Xten.

[0108] An exemplary oligonucleotide is, for example: mipomersen / Kynamro, a cholesterol-lowering antisense therapeutic for the treatment of familial hypercholesterolemia.

[0109] Exemplary DPP4 inhibitors are Vildagliptin, Sitagliptin, Denagliptin, Saxagliptin, Berberine.

[0110] Exemplary hormones include pituitary or hypothalamic hormones or regulatory active peptides and their antagonists, such as gonadotropins (follitropin, lutropin, chorion gonadotropins, menotropins), somatropins (somatropins), desmopressin, terlipressin, gonadorelin, triptorelin, leuprorelin, buserelin, nafarelin, and goserelin.

[0111] Exemplary polysaccharides include glycosaminoglycans, hyaluronic acid, heparin, low molecular weight heparin, or ultra-low molecular weight heparin, or derivatives thereof, or sulfated forms of the aforementioned polysaccharides, e.g., polysulfated forms, and / or pharmaceutically acceptable salts thereof. An example of a pharmaceutically acceptable salt of polysulfated low molecular weight heparin is enoxaparin sodium. Examples of hyaluronic acid derivatives include Hylan G-F20 / Synvisc, sodium hyaluronate.

[0112] As used herein, the term "antibody" refers to an immunoglobulin molecule or an antigen-binding portion thereof. Examples of antigen-binding portions of immunoglobulin molecules include F(ab) and F(ab')2 fragments that retain the ability to bind antigen. Antibodies can be polyclonal, monoclonal, recombinant, chimeric, non-immunized, or humanized, fully human, non-human (e.g., murine), or single-chain antibodies. In some embodiments, antibodies have effector functions and can fix complement. In some embodiments, antibodies have reduced ability to bind or are unable to bind Fc receptors. For example, antibodies can be isotypes or subtypes, antibody fragments, or variants that do not support binding to Fc receptors, e.g., they have mutated or deleted Fc receptor binding regions.

[0113] The term "fragment" or "antibody fragment" refers to a polypeptide derived from an antibody polypeptide molecule (e.g., an antibody heavy and / or light chain polypeptide) that does not include the full-length antibody polypeptide but still includes at least a portion of the full-length antibody polypeptide that is capable of binding to an antigen. Antibody fragments can include truncated portions of a full-length antibody polypeptide, although the term is not limited to such truncated fragments. Antibody fragments useful in the present disclosure include, for example, Fab fragments, F(ab')2 fragments, scFv (single-chain Fv) fragments, linear antibodies, monospecific or multispecific antibody fragments such as bispecific, trispecific, and multispecific antibodies (e.g., diabodies, triabodies, tetrabodies), minibodies, chelating recombinant antibodies, tribodies or bibodies, intrabodies, nanobodies, small modular immunopharmaceuticals (SMIPs), binding domain immunoglobulin fusion proteins, camelized antibodies, and VHH-containing antibodies. Further examples of antigen-binding antibody fragments are known in the art.

[0114] The term "complementarity determining region" or "CDR" refers to short polypeptide sequences within the variable regions of both heavy and light chain polypeptides that are primarily responsible for mediating specific antigen recognition. The term "framework region" refers to amino acid sequences within the variable regions of both heavy and light chain polypeptides that are not CDR sequences but are primarily responsible for maintaining the correct positioning of the CDR sequences to enable antigen binding. Although framework regions themselves are typically not directly involved in antigen binding, as is known in the art, certain residues within the framework regions of a particular antibody may be directly involved in antigen binding, or may be involved in the binding of one or more of the CDRs. The ability of an amino acid to interact with an antigen can be affected.

[0115] Exemplary antibodies are anti-PCSK-9 mAb (e.g., Alirocumab), anti-IL-6 mAb (e.g., Sarilumab), and anti-IL-4 mAb (e.g., Dupilumab).

[0116] The compounds described herein can be used in pharmaceutical preparations comprising (a) the compound or its pharmaceutically acceptable salt, and (b) a pharmaceutically acceptable carrier.The compounds can also be used in pharmaceutical preparations that contain one or more other active pharmaceutical ingredients, or in pharmaceutical preparations in which the compound or its pharmaceutically acceptable salt is the only active ingredient present.Therefore, the pharmaceutical preparations of the present disclosure encompass any preparation that is made by mixing the compounds described herein and a pharmaceutically acceptable carrier.

[0117] Pharmaceutically acceptable salts of any of the drugs described herein are also contemplated for use in the drug delivery device. Pharmaceutically acceptable salts include, for example, acid addition salts and basic salts. Acid addition salts include, for example, HCl or HBr salts. Basic salts include, for example, salts having a cation selected from alkali or alkaline earth metals, such as Na, K, or Ca, or ammonium ions N(R)(R)(R)(R), where R to R are independently hydrogen, an optionally substituted C-C alkyl group, an optionally substituted C-C alkenyl group, an optionally substituted C-C aryl group, or an optionally substituted C-C heteroaryl group. Further examples of pharmaceutically acceptable salts are known to those skilled in the art.

[0118] Pharmaceutically acceptable solvates are, for example, hydrates or alkanolates, such as methanolates or ethanolates.

[0119] Those skilled in the art will understand that modifications (additions and / or removals) to the materials, compositions, devices, methods, systems and various components of the embodiments described herein may be made without departing from the full scope and spirit of the present disclosure, which encompasses such modifications and any and all equivalents thereof. [Explanation of symbols]

[0120] 1. Control Loop 2 human body 3 Blood Glucose Sensor 4 Controller 4.1 Bolus Calculator 4.2 Comparators 5. Drug Delivery Devices 5.1 Insulin pen 5.2 Insulin pumps 5.3 Insulin pen 6 equipment 7 Connection 8 Dead Time Element 9 Human Body Models 10 Drug delivery devices 11. Housing 12 Cap Assembly 13 Needle sleeve 17 needles 20 Distal Region 21 Proximal region 22 buttons 23 Piston A Physical activity B Bolus C. Characteristics of Diabetes CE Carbohydrate Exchange E. External Conditions T insulin type X Longitudinal Axis PS Patient characteristics

Claims

1. A bolus calculator (4.1) for determining an insulin bolus (B), comprising: The bolus calculator (4.1) has an input section configured to receive a time series of blood glucose levels (h(t-τ)) and to store at least one known pulse response (x(τ)) representing at least one insulin activity profile, where the bolus calculator (4.1) is determining a dead time of the time series (h(t-τ)) by performing a teach-in run to measure a pulse response of the human body based on a blood glucose measurement of the human body's capillary blood; Taking the dead time into account, the blood glucose time series (h(t-τ)) is convolved with the known pulse response (x(τ)) to obtain the bolus (B). wherein the dead time represents a delay in a pulse response based on a blood glucose measurement of capillary blood of the body relative to a pulse response based on a blood glucose measurement of venous blood of the body.

2. storing the measured blood glucose level and the calculated bolus (B); Generates a history based on stored measured blood glucose values ​​and calculated boluses 10. The bolus calculator of claim 1 further configured to:

3. The bolus calculator of claim 2 , further configured to calculate a further bolus (B) based on the history.

4. A bolus calculator (4.1) according to any one of claims 1 to 3, configured to calculate a bolus (B) for one type of insulin (T) or to select one of several types of insulin (T) and calculate a bolus (B) for the selected type (T).

5. The bolus calculator (4.1) according to any one of claims 1 to 4, further configured to store patient characteristics (PS), in particular at least one of age, sex, weight and body fat percentage.

6. For each distinct set of patient characteristics (PS), one or more types of indicators (T) are 6. The bolus calculator (4.1) of claim 5, further configured to store a characteristic map comprising a plurality of stored pulse responses (x(τ)) of sulin.

7. 1. A device (6) for determining a bolus (B) of insulin, said device comprising: a bolus calculator (4.1) according to any one of claims 1 to 6; a human body model (9); a blood glucose sensor (3) adapted to perform blood glucose measurements on venous or capillary blood of a human body (2); and a comparator (4.2) for comparing the blood glucose value measured by the blood glucose sensor (3) with the blood glucose value calculated by the human body model (9) before passing it to the bolus calculator (4.1).

8. A method of operation of a bolus calculator (4.1) for calculating a bolus of insulin (B), comprising: inputting a time series of blood glucose levels (h(t-τ)) into an input of a bolus calculator (4.1) and storing at least one known pulse response (x(τ)) in the bolus calculator (4.1) representing at least one insulin activity profile; determining the dead time of the time series (h(t-τ)) by performing a teach-in run to measure the pulse response of the human body based on blood glucose measurements of the human body's capillary blood; The method includes convolving a time series of blood glucose levels (h(t-τ)) with a known pulse response (x(τ)) to obtain a bolus (B), wherein the dead time represents a delay in the pulse response relative to a measurement of the pulse response based on blood glucose measurements of venous blood from the body.

9. 9. The method of claim 8, wherein a bolus (B) is calculated for one type (T) of insulin, or one of a plurality of types (T) of insulin is selected and a bolus (B) is calculated for the selected type (T).

10. 10. The method according to claim 8 or 9, comprising storing and taking into account patient characteristics (PS), in particular at least one of age, sex, weight and body fat percentage.

11. 11. The method of claim 10, further comprising storing a characteristic map comprising a plurality of stored pulse responses (x(τ)) of one or more types (T) of insulin for distinct sets of patient characteristics (PS).

12. The method of any one of claims 8 to 11, further comprising storing measured blood glucose values ​​and calculated boluses (B) to generate a history.

13. The method according to any one of claims 8 to 12, further comprising comparing the blood glucose value measured by the blood glucose sensor (3) with the blood glucose value calculated by a human body model (9) before passing it to the bolus calculator (4.1).

Images