WO2023102114A1 - Optimisation de formulations incorporées pour l'administration de médicaments - Google Patents

Optimisation de formulations incorporées pour l'administration de médicaments Download PDF

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Publication number
WO2023102114A1
WO2023102114A1 PCT/US2022/051534 US2022051534W WO2023102114A1 WO 2023102114 A1 WO2023102114 A1 WO 2023102114A1 US 2022051534 W US2022051534 W US 2022051534W WO 2023102114 A1 WO2023102114 A1 WO 2023102114A1
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WO
WIPO (PCT)
Prior art keywords
drug
drug delivery
search space
insulin
user
Prior art date
Application number
PCT/US2022/051534
Other languages
English (en)
Inventor
Joon Bok Lee
Yibin Zheng
Jason O'connor
Ashutosh ZADE
Original Assignee
Insulet Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US17/539,270 external-priority patent/US11439754B1/en
Application filed by Insulet Corporation filed Critical Insulet Corporation
Priority to CN202280079687.5A priority Critical patent/CN118402006A/zh
Priority to KR1020247021311A priority patent/KR20240116500A/ko
Priority to AU2022401676A priority patent/AU2022401676A1/en
Priority to EP22844352.9A priority patent/EP4441750A1/fr
Priority to CA3239838A priority patent/CA3239838A1/fr
Publication of WO2023102114A1 publication Critical patent/WO2023102114A1/fr

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/13—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered from dispensers
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P3/00—Drugs for disorders of the metabolism
    • A61P3/08—Drugs for disorders of the metabolism for glucose homeostasis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P3/00—Drugs for disorders of the metabolism
    • A61P3/08—Drugs for disorders of the metabolism for glucose homeostasis
    • A61P3/10—Drugs for disorders of the metabolism for glucose homeostasis for hyperglycaemia, e.g. antidiabetics
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the drug delivery device 102 can be designed to deliver any type of liquid drug to a user.
  • the drug delivery device 102 can be, for example, an OmniPod® drug delivery device manufactured by Insulet Corporation of Acton, Massachusetts.
  • the drug delivery device 102 can be a drug delivery device such as those described in U.S. Pat. No. 7,303,549, U.S. Pat. No. 7,137,964, or U.S. Pat. No. 6,740,059, each of which is incorporated herein by reference in its entirety.
  • Wearable drug delivery devices 102 are typically configured with a processor and memory and are often powered by an internal battery or power harvesting device having limited amount of power available for powering the processor and memory. Further, because of the size of the device, the processing capability and memory for storage of software algorithms may also be limited. Due to these limitations, wearable drug delivery devices do not have an onboard medication delivery algorithm that determines, through a series of calculations based on feedback from sensors and other information, the timing and quantity of the liquid drug to be delivered to the user. Such medication delivery applications are typically found on a remote device, such as a remote personal diabetes manager (PDM) or a smartphone, for example, either of which being configured to transmit drug delivery instructions.
  • PDM personal diabetes manager
  • the medication delivery algorithm may use an optimization algorithm to periodically calculate the quantity of the liquid drug to be delivered to the user.
  • the medical delivery algorithm may operate, in one embodiment, on a 5- minute cycle.
  • the optimization algorithm may utilize a mathematical glucose model and may minimize a cost function to determine the appropriate quantities of the liquid drug to be delivered.
  • Such optimization algorithms often require a series of complex calculations with high computational and power consumption costs, making them difficult to implement in applications where only a low-power, efficient processing capability is available, such as in embedded applications. This is particularly important when it is desired to implement such optimization algorithms in disposable, small-scale electronics, such as a wearable drug delivery device 102.
  • liquid drug should be interpreted to include any drug in liquid form capable of being administered by a drug delivery device via a subcutaneous cannula, including, for example, insulin, GLP-1, pramlintide, morphine, blood pressure medicines, chemotherapy drugs, fertility drugs or the like or co- formulations of two or more of GLP-1, pramlintide, and insulin.
  • the precise solution to the optimization problem may not be required. This is largely due the fact that the difference in calculated doses for each cycle between an exact solution to the optimization problem and a close solution to the optimization problem may be below the minimum drug delivery resolution of the wearable drug delivery device 102.
  • the resolution of the delivery of the liquid drug may be limited to a fixed amount (e.g., 0.05U, or 0.0005 mL). That is, the wearable drug delivery device 102 may only be capable of delivering the liquid drug in particular, discrete amounts.
  • the medication delivery algorithm may round the delivery to the nearest discrete amount capable of being delivered and add or subtract any differences to or from the calculated dose during the next cycle.
  • the computational cost of executing the optimization algorithm can be greatly reduced (by 99%+) with a simple implementation of the optimization algorithm that allows the system to reach a solution that need not be accurate to the smallest decimal points but is sufficiently close so as to not impact the user’s therapy.
  • FIG. 1 illustrates a functional block diagram of an exemplary system suitable for implementing the systems and methods disclosed herein.
  • FIG. 2 is a depiction of a prior art wearable drug delivery device of the type in which the invention disclosed herein would be used.
  • FIG. 3 is a flowchart showing the steps comprising the disclosed method.
  • This disclosure presents various systems, components and methods for calculating a quantity of a liquid drug to be delivered to a user during a current execution cycle of a medication delivery algorithm.
  • the embodiments described herein provide one or more advantages over conventional, prior art systems, components and methods, namely, a savings in power consumption and lower required processing power due to a simplified computational model used to solve the optimization problem.
  • Various embodiments of the present invention include systems and methods for delivering a medication to a user using a drug delivery device (sometimes referred to herein as a “pod”), either autonomously, or in accordance with a wireless signal received from an electronic device.
  • the electronic device may be a user device comprising a smartphone, a smart watch, a smart necklace, a module attached to the drug delivery device, or any other type or sort of electronic device that may be carried by the user or worn on the body of the user and that executes an algorithm that computes the times and dosages of delivery of the medication.
  • the user device may execute an “artificial-pancreas” algorithm that computes the times and dosages of delivery of insulin.
  • the user device may also be in communication with a sensor, such as a glucose sensor, that collects data on a physical attribute or condition of the user, such as a glucose level.
  • the sensor may be disposed in or on the body of the user and may be part of the drug delivery device or may be a separate device.
  • the drug delivery device may be in communication with the sensor in lieu of or in addition to the communication between the sensor and the user device.
  • the communication may be direct (if, e.g., the sensor is integrated with or otherwise a part of the drug delivery device) or remote/ wireless (if, e.g., the sensor is disposed in a different housing than the drug delivery device).
  • the drug delivery device contains computing hardware (e.g., a processor, memory, firmware, etc.) that executes some or all of the algorithm that computes the times and dosages of delivery of the medication.
  • FIG. 1 illustrates a functional block diagram of an exemplary drug delivery system 100 suitable for implementing the systems and methods described herein.
  • the drug delivery system 100 may implement (and/or provide functionality for) a medication delivery algorithm, such as an artificial pancreas (AP) application, to govern or control the automated delivery of a drug or medication, such as insulin, to a user (e.g., to maintain euglycemia - a normal level of glucose in the blood).
  • the drug delivery system 100 may be an automated drug delivery system that may include a drug delivery device 102 (which may be wearable), an analyte sensor 108 (which may also be wearable), and a user device 105.
  • Drug delivery system 100 may also include an accessory device 106, such as a smartwatch, a personal assistant device, or the like, which may communicate with the other components of system 100 via either a wired or wireless communication links 191-193.
  • an accessory device 106 such as a smartwatch, a personal assistant device, or the like, which may communicate with the other components of system 100 via either a wired or wireless communication links 191-193.
  • the user device 105 may be a computing device such as a smartphone, a tablet, a personal diabetes management (PDM) device, a dedicated diabetes therapy management device, or the like.
  • user device 105 may include a processor 151, device memory 153, a user interface 158, and a communication interface 154.
  • the user device 105 may also contain analog and/or digital circuitry that may be implemented as a processor 151 for executing processes based on programming code stored in device memory 153, such as user application 160 to manage a user’s blood glucose levels and for controlling the delivery of the drug, medication, or therapeutic agent to the user, as well for providing other functions, such as calculating carbohydrate-compensation dosage, a correction bolus dosage and the like, as discussed below.
  • the user device 105 may be used to program, adjust settings, and/or control operation of drug delivery device 102 and/or the analyte sensor 108 as well as the optional smart accessory device 106.
  • the processor 151 may also be configured to execute programming code stored in device memory 153, such as the user app 160.
  • the user app 160 may be a computer application that is operable to deliver a drug based on information received from the analyte sensor 108, the cloud-based services 111 and/or the user device 105 or optional accessory device 106.
  • the memory 153 may also store programming code to, for example, operate the user interface 158 (e.g., a touchscreen device, a camera or the like), the communication interface 154 and the like.
  • the processor 151 when executing user app 160, may be configured to implement indications and notifications related to meal ingestion, blood glucose measurements, and the like.
  • the user interface 158 may be under the control of the processor 151 and be configured to present a graphical user interface that enables the input of a meal announcement, adjust setting selections and the like as described herein.
  • the processor 151 is also configured to execute a diabetes treatment plan (which may be stored in a memory) that is managed by user app 160.
  • a diabetes treatment plan (which may be stored in a memory) that is managed by user app 160.
  • user app 160 when user app 160 is an AP application, it may provide further functionality to determine a carbohydrate-compensation dosage, a correction bolus dosage and determine a basal dosage according to a diabetes treatment plan.
  • user app 160 provides functionality to output signals to the drug delivery device 102 via communications interface 154 to deliver the determined bolus and basal dosages.
  • the communication interface 154 may include one or more transceivers that operate according to one or more radio-frequency protocols.
  • the transceivers may comprise a cellular transceiver and a Bluetooth® transceiver.
  • the communication interface 154 may be configured to receive and transmit signals containing information usable by user app 160.
  • User device 105 may be further provided with one or more output devices 155 which may be, for example, a speaker or a vibration transducer, to provide various signals to the user.
  • output devices 155 may be, for example, a speaker or a vibration transducer, to provide various signals to the user.
  • drug delivery device 102 may include a reservoir 124 and drive mechanism 125, which are controllable by controller 121, executing a medication delivery algorithm (MDA) 129 stored in memory 123 onboard the drug delivery device (and in exemplary embodiments, a wearable drug delivery device).
  • controller 121 may act to control reservoir 124 and drive mechanism 125 based on signals received from user app 160 executing on a user device 105 and communicated to drug delivery device 102 via communication link 194.
  • Drive mechanism 125 operates to longitudinally translate a plunger through the reservoir, such as to force the liquid drug through an outlet fluid port to needle / cannula 186.
  • drug delivery device 102 may also include an optional second reservoir 124-2 and second drive mechanism 125-2 which enables the independent delivery of two different liquid drugs.
  • reservoir 124 may be filled with insulin, while reservoir 124-2 may be filled with Pramlintide or GLP-1.
  • each of reservoirs 124, 124-2 may be configured with a separate drive mechanism 125, 125-2, respectively, which may be separately controllable by controller 121 under the direction of MDA 129. Both reservoirs 124, 124-2 may be connected to a common needle / cannula 186.
  • Drug delivery device 102 may be optionally configured with a user interface 127 providing a means for receiving input from the user and a means for outputting information to the user.
  • User interface 127 may include, for example, lightemitting diodes, buttons on a housing of drug delivery device 102, a sound transducer, a micro-display, a microphone, an accelerometer for detecting motions of the device or user gestures (e.g., tapping on a housing of the device) or any other type of interface device that is configured to allow a user to enter information and/or allow drug delivery device 102 to output information for presentation to the user (e.g., alarm signals or the like).
  • Drug delivery device 102 includes a patient interface 186 for interfacing with the user to deliver the liquid drug.
  • Patient interface 186 may be, for example, a needle or cannula for delivering the drug into the body of the user (which may be done subcutaneously, intraperitoneally, or intravenously).
  • Drug delivery device 102 may further include a mechanism for inserting the needle / cannula 186 into the body of the user, which may be integral with or attachable to drug delivery device 102.
  • the insertion mechanism may comprise, in one embodiment, an actuator that inserts the needle / cannula 186 under the skin of the user and thereafter retracts the needle, leaving the cannula in place.
  • drug delivery device 102 includes a communication interface 126, which may be a transceiver that operates according to one or more radio-frequency protocols, such as Bluetooth®, Wi-Fi, near-field communication, cellular, or the like.
  • the controller 121 may, for example, communicate with user device 105 and an analyte sensor 108 via the communication interface 126.
  • drug delivery device 102 may be provided with one or more sensors 184.
  • the sensors 184 may include one or more of a pressure sensor, a power sensor, or the like that are communicatively coupled to the controller 121 and provide various signals.
  • a pressure sensor may be configured to provide an indication of the fluid pressure detected in a fluid pathway between the patient interface 186 and reservoir 124.
  • the pressure sensor may be coupled to or integral with the actuator for inserting the patient interface 186 into the user.
  • the controller 121 may be operable to determine a rate of drug infusion based on the indication of the fluid pressure.
  • the rate of drug infusion may be compared to an infusion rate threshold, and the comparison result may be usable in determining an amount of insulin onboard (IOB) or a total daily insulin (TDI) amount.
  • analyte sensor 108 may be integral with drug delivery device 102.
  • Drug delivery device 102 further includes a power source 128, such as a battery, a piezoelectric device, an energy harvesting device, or the like, for supplying electrical power to controller 121, memory 123, drive mechanisms 125 and/or other components of drug delivery device 102.
  • a power source 128, such as a battery, a piezoelectric device, an energy harvesting device, or the like, for supplying electrical power to controller 121, memory 123, drive mechanisms 125 and/or other components of drug delivery device 102.
  • Drug delivery device 102 may be configured to perform and execute processes required to deliver doses of the medication to the user without input from the user device 105 or the optional accessory device 106.
  • MDA 129 may be operable, for example, to determine an amount of insulin to be delivered, IOB, insulin remaining, and the like, and to cause controller 121 to activate drive mechanism 125 to deliver the medication from reservoir 124.
  • MDA 129 may take as input data received from the analyte sensor 108 or from user app 160.
  • the reservoirs 124, 124-2 may be configured to store drugs, medications or therapeutic agents suitable for automated delivery, such as insulin, Pramlintide, GLP-1, co-formulations of insulin and GLP-1, morphine, blood pressure medicines, chemotherapy drugs, fertility drugs or the like.
  • drugs such as insulin, Pramlintide, GLP-1, co-formulations of insulin and GLP-1, morphine, blood pressure medicines, chemotherapy drugs, fertility drugs or the like.
  • Drug delivery device 102 may be a wearable device and may be attached to the body of a user at an attachment location and may deliver any therapeutic agent, including any drug or medicine, such as insulin or the like, to a user at or around the attachment location.
  • a surface of drug delivery device 102 may include an adhesive to facilitate attachment to the skin of a user.
  • drug delivery device 102 may receive signals over the wired or wireless link 194 from the user device 105 or from the analyte sensor 108.
  • the controller 121 of drug delivery device 102 may receive and process the signals from the respective external devices as well as implementing delivery of a drug to the user according to a diabetes treatment plan or other drug delivery regimen.
  • Optional accessory device 107 may be a wearable smart device, for example, a smart watch (e.g., an Apple Watch®), smart eyeglasses, smart jewelry, a global positioning system-enabled wearable, a wearable fitness device, smart clothing, or the like. Similar to user device 105, the accessory device 107 may also be configured to perform various functions including controlling drug delivery device 102.
  • the accessory device 107 may include a communication interface 174, a processor 171, a user interface 178 and a memory 173.
  • the user interface 178 may be a graphical user interface presented on a touchscreen display of the smart accessory device 107.
  • the memory 173 may store programming code to operate different functions of the smart accessory device 107 as well as an instance of the user app 160, or a pared-down version of user app 160 with reduced functionality.
  • accessory device 107 may also include sensors of various types.
  • the analyte sensor 108 may include a controller 131, a memory 132, a sensing/measuring device 133, an optional user interface 137, a power source/energy harvesting circuitry 134, and a communication interface 135.
  • the analyte sensor 108 may be communicatively coupled to the processor 151 of the management device 105 or controller 121 of drug delivery device 102.
  • the memory 132 may be configured to store information and programming code 136.
  • the analyte sensor 108 may be configured to detect multiple different analytes, such as glucose, lactate, ketones, uric acid, sodium, potassium, alcohol levels or the like, and output results of the detections, such as measurement values or the like.
  • the analyte sensor 108 may, in an exemplary embodiment, be a continuous glucose monitor (CGM) configured to measure a blood glucose value at a predetermined time interval, such as every 5 minutes, every 1 minute, or the like.
  • CGM continuous glucose monitor
  • the communication interface 135 of analyte sensor 108 may have circuitry that operates as a transceiver for communicating the measured blood glucose values to the user device 105 over a wireless link 195 or with drug delivery device 102 over the wireless communication link 108.
  • the sensing/measuring device 133 of the analyte sensor 108 may include one or more additional sensing elements, such as a glucose measurement element, a heart rate monitor, a pressure sensor, or the like.
  • the controller 131 may include discrete, specialized logic and/or components, an application-specific integrated circuit, a microcontroller or processor that executes software instructions, firmware, programming instructions stored in memory (such as memory 132), or any combination thereof.
  • the controller 131 of the analyte sensor 108 may be operable to perform many functions.
  • the controller 131 may be configured by programming code 136 to manage the collection and analysis of data detected by the sensing and measuring device 133.
  • the analyte sensor 108 is depicted in FIG. 1 as separate from drug delivery device 102, in various embodiments, the analyte sensor 108 and drug delivery device 102 may be incorporated into the same unit.
  • the analyte sensor 108 may be a part of and integral with drug delivery device 102 and contained within the same housing as drug delivery device 102 or in a housing attachable to the housing of drug delivery device 102 or otherwise adjacent thereto.
  • the controller 121 may be able to implement the functions required for the proper delivery of the medication alone without any external inputs from user device 105, the cloud-based services 111, another sensor (not shown), the optional accessory device 106, or the like.
  • Drug delivery system 100 may communicate with or receive services from cloud-based services 111.
  • Services provided by cloud-based services 111 may include data storage that stores personal or anonymized data, such as blood glucose measurement values, historical IOB or TDI, prior carbohydrate-compensation dosage, and other forms of data.
  • the cloud-based services 111 may process anonymized data from multiple users to provide generalized information related to TDI, insulin sensitivity, IOB and the like.
  • the communication link 115 that couples the cloud-based services 111 to the respective devices 102, 105, 106, 108 of system 100 may be a cellular link, a Wi-Fi link, a Bluetooth® link, or a combination thereof.
  • the wireless communication links 115 and 191-196 may be any type of wireless link operating using known wireless communication standards or proprietary standards.
  • the wireless communication links 191-196 may provide communication links based on Bluetooth®, Zigbee®, Wi-Fi, a near-field communication standard, a cellular standard, or any other wireless protocol via the respective communication interfaces 126, 135, 154 and 174.
  • user application 160 implements a graphical user interface that is the primary interface with the user and is used to start and stop drug delivery device 102, program basal and bolus calculator settings for manual mode as well as program settings specific for automated mode (hybrid closed-loop or closed-loop).
  • User app 160 provides a graphical user interface 158 that allows for the use of large text, graphics, and on-screen instructions to prompt the user through the set-up processes and the use of system 100. It will also be used to program the user’s custom basal insulin delivery profile, check the status, of drug delivery device 102, initiate bolus doses of insulin, make changes to a patient’s insulin delivery profile, handle system alerts and alarms, and allow the user to switch between automated mode and manual mode.
  • User app 160 may be configured to operate in a manual mode in which user app 160 will deliver insulin at programmed basal rates and bolus amounts with the option to set basal profiles for different times of day or temporary basal profiles.
  • the controller 121 will also have the ability to function as a sensor-augmented pump in manual mode, using sensor glucose data provided by the analyte sensor 108 to populate the bolus calculator.
  • User app 160 may be configured to operate in an automated mode in which user app 160 supports the use of multiple target blood glucose values.
  • target blood glucose values can range from 110-150 mg/dL, in 10 mg/dL increments, in 5 mg/dL increments, or other increments, but preferably 10 mg/dL increments.
  • the experience for the user will reflect current setup flows whereby the healthcare provider assists the user to program basal rates, glucose targets and bolus calculator settings. These in turn will inform the user app 160 for insulin dosing parameters.
  • the insulin dosing parameters will be adapted over time based on the total daily insulin (TDI) delivered during each use of drug delivery device 102.
  • TDI total daily insulin
  • a temporary hypoglycemia protection mode may be implemented by the user for various time durations in automated mode.
  • hypoglycemia protection mode the algorithm reduces insulin delivery and is intended for use over temporary durations when insulin sensitivity is expected to be higher, such as during exercise.
  • the user app 160 (or MDA 129) may provide periodic insulin microboluses based upon past glucose measurements and/or a predicted glucose over a prediction horizon (e.g., 60 minutes).
  • Optimal post-prandial control may require the user to give meal boluses in the same manner as current pump therapy, but normal operation of the user app 160 will compensate for missed meal boluses and mitigate prolonged hyperglycemia.
  • the user app 160 uses a control -to-target strategy that attempts to achieve and maintain a set target glucose value, thereby reducing the duration of prolonged hyperglycemia and hypoglycemia.
  • user device 105 and the analyte sensor 108 may not communicate directly with one another. Instead, data (e.g., blood glucose readings) from analyte sensor may be communicated to drug delivery device 102 via link 196 and then relayed to user device 105 via link 194. In some embodiments, to enable communication between analyte sensor 108 and user device 105, the serial number of the analyte sensor must be entered into user app 160.
  • data e.g., blood glucose readings
  • User app 160 may provide the ability to calculate a suggested bolus dose through the use of a bolus calculator.
  • the bolus calculator is provided as a convenience to the user to aid in determining the suggested bolus dose based on ingested carbohydrates, most-recent blood glucose readings (or a blood glucose reading if using fingerstick), programmable correction factor, insulin to carbohydrate ratio, target glucose value and insulin on board (IOB).
  • IOB is estimated by user app 160 taking into account any manual bolus and insulin delivered by the algorithm, and IOB may be divided between a basal IOB and a bolus IOB, the basal IOB accounting for insulin delivered by the algorithm, and the bolus IOB accounting for any bolus deliveries.
  • the primary embodiment of the invention is directed to a method for simplifying an optimization algorithm used in the calculation of quantities of a liquid drug, for example, insulin, to be periodically delivered to a user by a wearable drug delivery device 102.
  • a simple stepwise exploration across all possible search spaces can be performed to bound the total computational cost and allow the calculations to be executed in embedded form.
  • a typical control algorithm can utilize a model of the system to be controlled to predict the outputs of the proposed drug dose.
  • the model can be used to determine the best dose to be given to the user.
  • the user’s glucose can be modeled as a recursive model of past glucose and insulin delivery values.
  • Eq. (1) Eq. (1) as:
  • k is the current cycle for which the glucose is being modelled
  • K is a gain factor
  • fy, b 2 , b 3 ... are weights for each cycle of MDA 129.
  • G is the estimated glucose for the current cycle, k.
  • the glucose model can be run recursively to predict glucose levels for future cycles. For each series of proposed insulin doses l(k + A) in the next N number of cycles, different glucose trajectory over those cycles G k + TV) can be calculated. Then, the total value of this projected insulin dose and glucose trajectory can be calculated by calculating a standard cost function, such as the exemplary cost expressed by Eq. (2) as: where: the left term with coefficient Q is the cost of the glucose deviations from a glucose setpoint; and the right term with coefficient R is the cost of the insulin deviations from a particular basal rate.
  • a standard cost function such as the exemplary cost expressed by Eq. (2) as: where: the left term with coefficient Q is the cost of the glucose deviations from a glucose setpoint; and the right term with coefficient R is the cost of the insulin deviations from a particular basal rate.
  • any cost function may be used.
  • An exemplary cost function is described in detail in U.S. Patent Application No. 16/789,051 (U.S. Published Patent Application No. 2021/0244881).
  • the glucose deviation and insulin delivery cost can be executed in various ways, such as by calculating the deviations against a specific target (such as a glucose control setpoint and basal insulin delivery) and the deviations can be calculated for various orders, such as quadratic or higher powers.
  • the calculation of the cost for each possible glucose and insulin trajectory, and determining the trajectories with a minimum cost can be performed by optimization algorithms with high computational cost.
  • the high computational cost optimization algorithms are not necessary.
  • step 302 of the stepwise algorithm the range of possible glucose delivery amounts for the current cycle is determined.
  • the lower end of the range will be 0.0 as MDA 129 has the option, during any cycle, to cause or recommend that no dose be delivered.
  • the upper range or limit of the possible glucose delivery amounts may be constrained by safety constraints built in the MDA 129 to prevent excess insulin from being delivered, or for any other reason.
  • the delivery range can be set between 0.0U and 0.6U per cycle. For purposes of explanation of the disclosed method, this exemplary range will be used.
  • the range is delineated into a coarse search space comprising coarse discrete quantities.
  • the search space may be delineated into 0.1U coarse discrete quantities.
  • the coarsely delineated search space would be delineated as: 0.0U, 0.1U, 0.2U, 0.3U, 0.4U, 0.5U, and 0.6U.
  • any coarse discrete quantities may be used to delineate the range, granted that they are indeed coarse relative to the minimum delivery resolution of the drug delivery device.
  • the coarse search space is optimized.
  • the search space can initially be narrowed by determining the coarse optimal insulin delivery. Specifically, all insulin delivery rates in the next N cycles can be set at a fixed value, at the coarse discrete quantities within the search space, and the corresponding glucose trajectories can be calculated.
  • the recursive model expressed by Eq. (1) can be calculated 7 times, each time assuming that I(k + 1) ... I(k + A) is calculated for each quantity in the coarse search space. Then, the corresponding costs can be calculated based on a cost function, an example of which is expressed by Eq. (2). In the explanatory example, this results in the following exemplary calculations:
  • the coarse delineation of the search space is refined.
  • the search space is narrowed to a smaller area around the coarse discrete quantity with a lowest cost (in the explanatory example, 0.3U).
  • the first insulin delivery i.e., I(k + 1)
  • I(k + 1) the first insulin delivery
  • the search space has been narrowed and centered around 0.3U. Therefore, the first projected insulin delivery can be defined to be between 0.2u and 0.4U in a finer increment (e.g., 0.025U) and the remaining projected insulin deliveries can be defined to be between 0.2U and 0.4U in a similar increment or in a coarser increment (e.g., 0.05U), but preferably in a coarser increment to reduce computational requirements.
  • the finer increment can be any desired quantity, while the range can be any range, preferably centered around the coarse solution with the minimal cost.
  • the refined search space can be optimized. In the explanatory example, the calculations could produce the following results:
  • the recommended insulin dose is finalized.
  • the solution that provides a minimal cost in the second, refined search space can be provided as the recommended insulin delivery trajectory.
  • the recommended insulin delivery dose for the current cycle would be 0.375U.
  • the recommended dose is delivered by drug delivery device 102 to the user, as explained above.
  • the actual dose delivered may be rounded up or down to the nearest discrete increment that the drug delivery device 102 is capable of delivering, based on the resolution. Any remainder falling in between the incremental discrete resolutions of the drug delivery device 102 may be added to or subtracted from the recommended dose for the next cycle.
  • MDA 129 may execute a cycle every 5 minutes, although other intervals may be selected.
  • the disclosed method results in significantly less iterations of the calculation of the glucose model and the cost function.
  • the 52 calculations provided in the explanatory example is significantly less calculation burdensome than a typical optimization algorithm, with a wide and multivariate search space.
  • the resolution of the drug does recommendation is still within the resolution of the drug delivery mechanism.
  • Example 1 is a method comprising the steps of defining a core search space, evaluating a model and cost function for each discrete quantity in the core search space, defining a refined search space, evaluating the model and cost function over the refined search space and selecting the refined discrete quantity having the lowest cost is the recommended dose of the drug.
  • Example 2 is an extension of Example 1, or any other example disclosed herein, base uses discrete quantities which are smaller than the discrete quantities used in the course search space.
  • Example 3 is an extension of Example 1, or any other example disclosed herein, wherein refined search space is smaller than the core search space.
  • Example 4 is an extension of Example 1, or any other example disclosed herein, wherein the range of possible doses of the drug ranges from zero to a maximum quantity determined by the medication delivery algorithm.
  • Example 5 is an extension of Example 4, or any other example disclosed herein, wherein the maximum quantity is dependent upon safety constraints built into the medication delivery algorithm.
  • Example 6 is an extension of Example 1, or any other example disclosed herein, wherein the drug is insulin delivered by a wearable drug delivery device.
  • Example 7 is an extension of Example 6, or any other example disclosed herein, wherein the model was a recursive glucose model used to predict glucose levels of the user in a predetermined number of future cycles based on the delivery of a particular quantity of insulin.
  • Example 8 is an extension of Example 7, or any other example disclosed herein, wherein the cost function calculates the cost for each possible glucose and insulin trajectory and determines a trajectory with the lowest cost.
  • Example 9 is an extension of Example 8, or any other example disclosed herein, wherein the cost function is based on glucose deviations and insulin deliveries wherein the glucose deviations are calculated against a specific target.
  • Example 10 is a system comprising a processor and software implementing a medication delivery algorithm executed by the processor, the software determining a recommended dose for drug for a current cycle of medication delivery algorithm by performing the functions of defining a core search space, evaluating a model and cost function for each discrete quantity in the core search space, defining a refined search space, evaluating the model and cost function over the refined search space and selecting the refined discrete quantity having the lowest cost is the recommended dose of the drug.
  • Example 11 is an extension of Example 10, or any other example disclosed herein, further comprising a drug delivery device for delivering the recommended dose of the drug to the user.
  • Example 12 is an extension of Example 11, or any other example disclosed herein, wherein the processor and software or integral with the drug delivery device.
  • Example 13 is an extension of Example 10, or any other example disclosed herein, wherein the refined discrete quantities are smaller than the course discrete quantities.
  • Example 14 is an extension of Example 10, or any other example disclosed herein, wherein the refined search space is smaller than the coarse search space
  • Example 15 is an extension of Example 10, or any other example disclosed herein, wherein the range of possible doses of the drug ranges from zero to a maximum quantity determined by the medication delivery algorithm.
  • Example 16 is extension of Example 15, or any other example disclosed herein, wherein the maximum quantity is dependent upon safety constraints built into the medication delivery algorithm.
  • Example 17 is an extension of Example 11, or any other example disclosed herein, wherein the drug is insulin delivered by the wearable drug delivery device.
  • Example 18 is an extension of Example 17, or any other example disclosed herein, wherein the model is a recursive glucose model used to predict glucose levels of the user in a predetermined number of future cycles based on delivery of a particular quantity of insulin.
  • Example 19 is an extension of example 18, or any other example disclosed herein, wherein the cost function calculates the cost for each possible glucose and insulin trajectory and determines a trajectory with a lowest cost.
  • Example 20 is an extension of Example 19, or any other example disclosed herein, wherein the cost function is based on glucose deviations and insulin delivery deviations, wherein the glucose deviations are calculated against the specific target.
  • Software related implementations of the techniques described herein may include, but are not limited to, firmware, application specific software, or any other type of computer readable instructions that may be executed by one or more processors. The computer readable instructions may be provided via non-transitory computer-readable media.
  • Hardware related implementations of the techniques described herein may include, but are not limited to, integrated circuits (ICs), application specific ICs (ASICs), field programmable arrays (FPGAs), and/or programmable logic devices (PLDs).
  • ICs integrated circuits
  • ASICs application specific ICs
  • FPGAs field programmable arrays
  • PLDs programmable logic devices
  • the techniques described herein, and/or any system or constituent component described herein may be implemented with a processor executing computer readable instructions stored on one or more memory components.
  • a processor executing computer readable instructions stored on one or more memory components.

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Abstract

Est divulgué ici un procédé pour exécution par un dispositif d'administration de médicaments destiné à déterminer une dose optimale d'un médicament liquide pour le cycle actuel d'un algorithme d'administration de médicaments, le procédé utilisant une évaluation pas à pas d'un modèle et une fonction de coût à travers un espace de recherche grossière constitué de quantités distinctes grossières du médicament et un espace de recherche fine constitué de quantités distinctes fines du médicament.
PCT/US2022/051534 2021-12-01 2022-12-01 Optimisation de formulations incorporées pour l'administration de médicaments WO2023102114A1 (fr)

Priority Applications (5)

Application Number Priority Date Filing Date Title
CN202280079687.5A CN118402006A (zh) 2021-12-01 2022-12-01 优化用于药物输送的嵌入式制剂
KR1020247021311A KR20240116500A (ko) 2021-12-01 2022-12-01 약물 전달을 위한 임베디드 제제 최적화
AU2022401676A AU2022401676A1 (en) 2021-12-01 2022-12-01 Optimizing embedded formulations for drug delivery
EP22844352.9A EP4441750A1 (fr) 2021-12-01 2022-12-01 Optimisation de formulations incorporées pour l'administration de médicaments
CA3239838A CA3239838A1 (fr) 2021-12-01 2022-12-01 Optimisation de formulations incorporees pour l'administration de medicaments

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US17/539,270 2021-12-01
US17/539,270 US11439754B1 (en) 2021-12-01 2021-12-01 Optimizing embedded formulations for drug delivery
US17/752,236 2022-05-24
US17/752,236 US20230166034A1 (en) 2021-12-01 2022-05-24 Optimizing embedded formulations for drug delivery

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6740059B2 (en) 2000-09-08 2004-05-25 Insulet Corporation Devices, systems and methods for patient infusion
US7303549B2 (en) 2002-04-23 2007-12-04 Insulet Corporation Transcutaneous fluid delivery system
US10332632B2 (en) * 2016-06-01 2019-06-25 Roche Diabetes Care, Inc. Control-to-range failsafes
US10881792B2 (en) * 2017-01-13 2021-01-05 Bigfoot Biomedical, Inc. System and method for adjusting insulin delivery
WO2021026004A1 (fr) * 2019-08-02 2021-02-11 Abbott Diabetes Care Inc. Systèmes, dispositifs, et procédés de recommandations sur la dose de médicaments
US20210244881A1 (en) 2020-02-12 2021-08-12 Insulet Corporation User parameter dependent cost function for personalized reduction of hypoglycemia and/or hyperglycemia in a closed loop artificial pancreas system

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6740059B2 (en) 2000-09-08 2004-05-25 Insulet Corporation Devices, systems and methods for patient infusion
US7137964B2 (en) 2000-09-08 2006-11-21 Insulet Corporation Devices, systems and methods for patient infusion
US7303549B2 (en) 2002-04-23 2007-12-04 Insulet Corporation Transcutaneous fluid delivery system
US10332632B2 (en) * 2016-06-01 2019-06-25 Roche Diabetes Care, Inc. Control-to-range failsafes
US10881792B2 (en) * 2017-01-13 2021-01-05 Bigfoot Biomedical, Inc. System and method for adjusting insulin delivery
WO2021026004A1 (fr) * 2019-08-02 2021-02-11 Abbott Diabetes Care Inc. Systèmes, dispositifs, et procédés de recommandations sur la dose de médicaments
US20210244881A1 (en) 2020-02-12 2021-08-12 Insulet Corporation User parameter dependent cost function for personalized reduction of hypoglycemia and/or hyperglycemia in a closed loop artificial pancreas system
WO2021163326A1 (fr) * 2020-02-12 2021-08-19 Insulet Corporation Réduction personnalisée de l'hypoglycémie et/ou de l'hyperglycémie dans un système en boucle fermée

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