Patent Description:
GLP-<NUM> drive favorable efficacy via several unique mechanisms which have benefits for diabetes management. In particular, and most importantly, GLP-<NUM> lower the glucose levels in the patient. In addition, they tend to suppress post-prandial glucagon release, delay stomach emptying, and increase insulin sensitivity. Additionally, GLP-<NUM> receptor agonists can help with weight loss and result in less hypoglycemia when used in combination with insulin. As disclosed in this application, using a fixed-ratio treatment of GLP-<NUM> and insulin via continuous subcutaneous infusion can increase patient use and further improve glycemic control.

There are several reasons why Type <NUM> patients have a low adherence to or tend to discontinue therapy altogether. The process of transitioning to an injectable therapy is challenging and a significant milestone for both patients and HCPs. Patients tend to resist the idea of moving beyond oral therapies as injections are often perceived by the patient as a signal for a worsening disease state. In addition, GLP-<NUM> are well known for their unpleasant gastrointestinal side effects, often causing the patient to experience nausea. To reduce the severity of these adverse events, medication dosing can be slowly titrated, but side effects remain. Nausea is the most common adverse effect reported within the GLP-<NUM> class, affecting up to <NUM>% of users. This negatively impacts patient adherence and creates additional labor for the HCP practice in the form of both clinical support and patient counseling. Following adverse gastrointestinal events, the method and frequency of administration precludes many patients from beginning and staying on therapy.

A number of systems provide basal delivery of drugs, such as GLP-<NUM>, insulin, chemotherapy drugs, pain management drugs and the like. Throughout a day, the basal insulin needed for a diabetic user to maintain a stable fasting glucose target setting varies <NUM>-<NUM>% or more at different times. Thus, it is important that the basal delivery of the drugs be based on a time-dependent basal profile learned by the system based on various histories regarding the delivery of the drug to the patient.

<CIT> relates to closed loop systems and methods for controlling physiological glucose concentrations in a patient using insulin and pramlintide.

As used herein, the term "GLP-<NUM> Therapeutic" is defined to include formulations of GLP-<NUM> and co-formulations of GLP-<NUM> and insulin, in any concentrations.

As used herein, the term "drug" is defined to include GLP-<NUM> Therapeutics, insulin, chemotherapy drugs, pain relief drugs (e.g., morphine), blood pressure medication, hormones, methadone, and any other single drug or combination thereof to be administered in liquid form.

The present invention relates to a device as defined in the claims.

The co-infusion of a GLP-<NUM> Therapeutic has advantages over the infusion of insulin alone. Infusing large doses of insulin has a side effect of promoting weight gain for the patient. The use of a GLP-<NUM> Therapeutic dramatically reduces the quantity of insulin required to maintain the correct elevated glucose levels in the patient, and, as a result, the susceptibility for weight gain, and/or the weight gain already experienced by the patient, may be reduced. Further, due to the manner in which GLP-<NUM> acts in the human body, for example, by suppressing post-prandial glucagon release and delaying stomach emptying, GLP-<NUM> has a tendency to promote weight loss which may tend to overcome or eclipse the potential for weight gain prompted by the use of large amounts of insulin. The combination of infusing both GLP-<NUM> and insulin together could potentiate glucose-lowering effects while minimizing the unwanted side effects of nausea and weight gain.

According to the invention, the GLP-<NUM> Therapeutic includes GLP-<NUM> and co-formulations of GLP-<NUM> and rapid-acting insulin, In an example, the GLP-<NUM> Therapeutic does not include long-acting insulin. There are particular advantages to using rapid-acting insulin instead of long-acting insulin in a co-formulation with GLP-<NUM>. GLP-<NUM> receptor agonists stimulate insulin secretion and inhibit glucagon secretion, thereby lowering blood glucose levels. Given this known mechanism, it is expected that a co-formulation with rapid-acting insulin, given in response to rising glucose levels, as disclosed herein, would be more effective and have a greater glucose lowering effect than a co-formulation with long-acting insulin could provide without glucose feedback (e.g., via a CGM). The co-formulation delivered by continuous or basal subcutaneous infusion would work synergistically to reduce fasting hyperglycemia as well as post-prandial glucose excursions, as the co-formulation is being delivered in a more physiological manner compared to delivery of a long-acting insulin or a co-formulation including long-acting insulin, which would be delivered, for example, once weekly, given the nature and intended use a long-acting insulin. Further, incretins are naturally released after eating and therefore the advantageous mode of delivery of a GLP-<NUM> Therapeutic disclosed herein more closely emulates physiological responses, i.e., larger amounts (e.g., bolus) of GLP-<NUM> Therapeutic are delivered upon food consumption and smaller amounts (e.g., basal) of GLP-<NUM> Therapeutic are delivered when fasting or not consuming food.

In examples, a drug delivery system that includes a memory and a controller is provided that provides the patient with basal doses of GLP-<NUM> Therapeutic. The memory may store programming code and a basal dose history and may be configured to execute the programming code. Execution of the programming code may configure the controller to titrate the GLP-<NUM> Therapeutic continuously or in small amounts over an extended period of time, to reduce the likelihood of the patient experiencing negative side effects (e.g., nausea). This is contrary to conventional methods for delivering GLP-<NUM> alone to a patient, for example all at once and via a pen.

In another example, size, and timing of each basal dose of the GLP-<NUM> Therapeutic may be varied by the programming code. In another example, the programming code may act to vary the size and timing of each basal dose based on a reading from a sensor which may include, for example, a continuous glucose monitoring (CGM) sensor providing readings of the patient's glucose level. In yet another example, the programming code may act to interrupt the basal delivery of the GLP-<NUM> Therapeutic when the user indicates onset of a negative side effect related to the basal delivery of the GLP-<NUM> Therapeutic, and the system may thereafter automatically ramp up and/or resume delivery of basal doses of the GLP-<NUM> Therapeutic. In yet another example, the programming code can provide larger doses at different times of the day, for example, after the patient has eaten a meal. Delivering the GLP-<NUM> Therapeutic in this fashion and according to exemplary examples disclosed herein has the benefit of delaying stomach emptying via the GLP-<NUM> receptor agonist and addressing an increase in blood glucose via the insulin, resulting in a combined beneficial impact that may not result from delivery of a GLP-<NUM> receptor agonist alone or insulin alone.

In an example, the drug delivery system may be pre-filled with the GLP-<NUM> Therapeutic. In other examples, the reservoir may be fillable by the patient with co-formulations of GLP-<NUM> and insulin that come pre-mixed in a single container or vial at a particular ratio of GLP-<NUM>:insulin, or in separate containers or vials, wherein the containers/vials of GLP-<NUM> or insulin can be, for example, a pen, syringe or glass vial and wherein the user fills the reservoir from each container/vial to achieve a particular ratio of GLP-<NUM> to insulin and which is tailored to the particular patient. In yet another example, the drug delivery device may be configured with two reservoirs, one containing GLP-<NUM> and the other containing insulin, such that the co-formulation of GLP-<NUM> and insulin may be varied by programming code on the drug delivery device or a separate controller on-the-fly.

According to the invention, the device may be configured to deliver a time-dependent basal dose of GLP-<NUM> Therapeutic based on an adaptable profile. According to the invention, the memory of the device stores software, for execution by the controller (<NUM>), the software implementing a medication delivery algorithm, , the medication delivery algorithm implementing a method that includes retrieving a basal delivery history that is a collection of basal delivery dosages of the GLP-<NUM> Therapeutic delivered according to a basal delivery schedule. The basal delivery history includes a predetermined number of basal delivery dosages of the GLP-<NUM> Therapeutic delivered over a period of time. The period of time may be further partitioned into intervals that span a time range less than the period of time. The delivery schedule may include a particular delivery time for each basal delivery dosage during each respective interval, and each basal delivery dosage is an amount of the GLP-<NUM> Therapeutic delivered at the particular delivery time, wherein each particular delivery time is one point in time during the interval. The controller may process the basal delivery dosages in the period of time to remove short term fluctuations of the basal delivery dosages. The processed basal delivery dosages within each interval are evaluated to obtain an interval profile for each of the intervals. The interval profile is a data structure including the amount of the GLP-<NUM> Therapeutic delivered in each processed basal delivery dosage and the particular delivery time associated with each processed basal delivery dosage. In the method, each processed basal delivery dosage may be analyzed at each particular delivery time in each interval profile for an interval and for every interval during the period of time. From the analysis, an average interval profile is determined. The average interval profile contains a series of average basal delivery dosages where each average basal delivery dosage in the series has a corresponding average delivery time. The controller iteratively evaluates each average basal delivery dosage in the series with respect to other average basal delivery dosages in the series to determine a similarity in the amount of the GLP-<NUM> Therapeutic delivered and a similarity in the corresponding average delivery time. Based on results of the evaluating, aggregating average basal delivery dosages meeting an amount similarity threshold is aggregated and a time range related to an aggregation of delivery times of the aggregated average basal delivery dosage is assigned based on a time similarity threshold. The basal delivery schedule is modified with an updated amount of the GLP-<NUM> Therapeutic to be delivered as an updated basal delivery dosage based on the aggregated average basal delivery dosages, and an updated delivery time based on the assigned time range. The process may cause delivery of respective updated basal delivery dosages by the controller according to the modified basal delivery schedule via a pump mechanism communicatively coupled to the controller.

In another example, a process is disclosed that includes accessing a basal delivery history of the GLP-<NUM> Therapeutic, a bolus delivery history of the GLP-<NUM> Therapeutic, a blood glucose measurement history, and a meal announcement history for a period of time from a memory coupled to the controller. The basal delivery history is a data structure containing amount data related to basal doses of the GLP-<NUM> Therapeutic delivered at respective times over the course of the basal delivery history based on an algorithm executed by the controller. The bolus drug history is a data structure containing bolus delivery time data related to delivery of bolus doses of the GLP-<NUM> Therapeutic and an amount of the GLP-<NUM> Therapeutic delivered in each bolus dose. The blood glucose measurement history is a data structure of blood glucose measurement values in which each blood glucose measurement value has a corresponding time when the blood glucose measurement value was obtained. The meal announcement history is a data structure of times when a meal announcement notification was received by the controller. Respective data in each of the basal delivery history, the bolus delivery history, the blood glucose measurement history, and the meal announcement history may be filtered by the controller according to predetermined filter settings related to a time interval of the period of time. A daily basal profile may be calculated using the filtered respective data from each of the histories. The daily basal profile may be a time schedule for delivering respective basal delivery dosages that includes times at which a basal delivery dosage is to be delivered. A control signal for delivery of a basal dosage of the calculated basal delivery dosages at a scheduled time from the daily basal profile may be generated by the controller. The control signal may be output to a pump mechanism of the drug delivery system communicatively coupled to the controller causing delivery of a respective basal dosage in the daily basal profile.

Systems, devices, computer-readable medium and methods in accordance with the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, where one or more examples are shown. The systems, devices, and methods may be embodied in many different forms and are not to be construed as being limited to the examples set forth herein. Instead, these examples are provided so the disclosure will be thorough and complete, and will fully convey the scope of methods and devices to those skilled in the art. Each of the systems, devices, and methods disclosed herein provides one or more advantages over conventional systems, components, and methods.

Various examples provide a method, a system, a device and a computer-readable medium for responding to inputs provided by sensors, such as an analyte sensor, and users of an automatic drug delivery system. The various devices and sensors that may be used to implement some specific examples may also be used to implement different therapeutic regimens using different drugs than described in the specific examples.

In one example, the disclosed methods, system, devices or computer-readable medium may perform actions related to managing a user's blood glucose in response to ingestion of a meal by the user.

The disclosed examples provide techniques that may be used with any additional algorithms or computer applications that manage blood glucose levels and GLP-<NUM> and insulin therapy. These algorithms and computer applications may be collectively referred to as "medication delivery algorithms" or "medication delivery applications" and may be operable to deliver different categories of drugs (or medications), such as diabetes treatment drugs (e.g., GLP-<NUM> Therapeutics), chemotherapy drugs, pain relief drugs, blood pressure medication, hormones, or the like.

A type of medication delivery algorithm (MDA APP) may include an "artificial pancreas" algorithm-based system, or more generally, an artificial pancreas (AP) application. For ease of discussion, the software, computer programs and computer applications that implement the medication delivery algorithms or applications may be referred to herein as an "MDA application" or an "AP application. " An AP application may be configured to provide automatic delivery of a GLP-<NUM> Therapeutic based on a blood glucose sensor input, such as signals received from an analyte sensor, such as a continuous blood glucose monitor, or the like. In an example, the artificial pancreas (AP) application, when executed by a processor, may enable monitoring of a user's blood glucose measurement values, determine an appropriate level of the GLP-<NUM> Therapeutic for the user based on the monitored glucose values (e.g., blood glucose concentrations or blood glucose measurement values) and other information, such as information related to, for example, carbohydrate intake, exercise times, meal times or the like, and take actions to maintain a user's blood glucose value within an appropriate range. A target blood glucose value of the particular user may alternatively be a range blood glucose measurement values that are appropriate for the particular user. For example, a target blood glucose measurement value may be acceptable if it falls within the range of <NUM>/dL to <NUM>/dL, which is a range satisfying the clinical standard of care for treatment of diabetes. In addition, an AP application as described herein determine when a user's blood glucose wanders into the hypoglycemic range or the hyperglycemic range.

As described in more detail with reference to the examples of <FIG>, an automatic drug delivery system may be configured to implement a method to estimate and update the time-dependent basal profile for a user after a certain period of closed loop operation time (e.g. <NUM> days). The first example, illustrated in <FIG> and <FIG>, may only utilize the history of the delivery of the GLP-<NUM> Therapeutic by an AID algorithm. The described processes may be suitable for users who manage their blood glucose within acceptable range (e.g., approximately <NUM>%-<NUM>%) of their target blood glucose setting most of the time (e.g., approximately <NUM>%-<NUM>%) and bolus their meals accurately most of the time (e.g., approximately <NUM>%-<NUM>%). The second example, described with refence to <FIG>, utilizes all GLP-<NUM> Therapeutic, CGM, and meal information, and tries to compensate for non-ideal meal boluses. The second example may be implemented with fasting blood glucose measurement values that are persistently high/low, and wherein the delivered boluses over/under compensate for meals, in which case the basal delivery dosages are likely to be underestimated or overestimated.

The routine <NUM> may process the basal delivery history maintained over the certain period of closed loop operation time, which is referred to as a period of time that is maintained as the basal delivery history. This basal delivery history may be stored in a permanent, non-disposable device that may communicate with a disposable pump, such as a smartphone, a personal diabetes management (PDM) device or stored on the cloud and downloaded to be utilized in the examples within this application. The basal delivery history may, for example, include a predetermined number of basal delivery dosages of a GLP-<NUM> Therapeutic delivered over a period of time. In the examples, the basal delivery history may be a collection of basal delivery dosages delivered according to a basal delivery schedule. The schedule may, for example, include a particular delivery time for each basal delivery dosage during each respective interval (e.g., deliveries every hour, <NUM> minutes, <NUM> minutes, or the like).

In the examples, a period of time may be a month, a number of months, week, or a number of weeks less than or longer than a month, a day, a number of days, a number of hours, such as <NUM>, <NUM> or <NUM>, or the like. In routine <NUM>, the period of time may be further partitioned into a number of intervals and each interval may span a time range less than the period of time. For example, an interval may be a day, a week, a certain number of days, such as <NUM>, <NUM>, a certain number of hours, such as <NUM>, <NUM>, <NUM>, <NUM>, <NUM> or the like, or another interval of time, which is less than the period of time. A day equals <NUM> hours in these examples. For example, the period of time may be two weeks, the interval may be a day, and the day may be further segmented into respective times, such as <NUM>-hour increments. In addition, each basal delivery dosage in the collection may be an amount of the GLP-<NUM> Therapeutic delivered at the particular delivery time during each respective interval and each particular delivery time is one point in time during the interval. The particular delivery time may correspond to a respective time (t).

In block <NUM>, a controller on a wearable drug delivery device, when implementing routine <NUM>, may be configured and operable to retrieve the basal delivery history from a memory coupled to the controller. The controller and memory are shown in another example.

In block <NUM>, routine <NUM> enables the controller to process the basal delivery dosages from the basal delivery history in the period of time to remove short term fluctuations of the basal delivery dosages over the period of time. For example, a low pass filter with a cutoff frequency of <NUM> (<NUM> cycle/<NUM> hours) may be applied to the data a(t) from the basal delivery history to eliminate discrepancies or outliers in the data. The respective basal delivery history may be divided into <NUM><NUM>-hour (<NUM>-day) intervals and processed by the controller using the low pass filter.

In block <NUM>, routine <NUM> evaluates the respective processed basal delivery dosages within each interval to obtain an interval profile for each of the intervals within the period of time. The interval profile may, for example, be a data structure with a number of entries that includes, for each entry, an amount of the GLP-<NUM> Therapeutic delivered in each processed basal delivery dosage and the particular delivery time associated with each processed basal delivery dosage during the interval.

In some examples, an average basal delivery dosage for each particular time within the basal delivery history may be calculated. For example, each particular basal delivery dosage delivered at the particular delivery time within each interval in the period of time may be averaged to obtain the estimate of a daily basal profile. In the example, the filtering cutoff frequency of (<NUM> cycle/<NUM> hours) reduces the <NUM> hours of the period of time into <NUM><NUM>-hour intervals, which may be referred to as basal profile intervals. Each of the <NUM> individual basal profile intervals that are identified based on the processed data from the basal delivery history may include an amount of the GLP-<NUM> Therapeutic delivered via the basal dosage during that interval, and basal profile intervals with defined amounts of the GLP-<NUM> Therapeutic within a certain threshold (such as <NUM>. 25U) may be clustered to provide a smaller number of intervals (e.g., <NUM><NUM>-hour intervals) in the daily basal profile that can be more easily utilized by the users for their personal diabetes treatment applications.

In an example, the period of time may be <NUM> week and the interval may be <NUM> day. Each <NUM>-day interval may be further segmented into times (t), which may be increments of <NUM> hour, <NUM> hours, <NUM>, <NUM>, <NUM> or <NUM> minutes or the like. In one example, the daily basal profile may initially contain the average basal delivery for each <NUM>-hour period within a given day, in which case the time (t) increments are <NUM> hours.

In block <NUM>, the controller, while executing routine <NUM>, may analyze each processed basal delivery dosage at each particular delivery time in each interval profile for an interval and for every interval during the period of time. For example, a basal dosage of the GLP-<NUM> Therapeutic may be administered every <NUM> minutes over the course of an interval (e.g., a day, <NUM> hours, <NUM> hours or the like). The amount of the basal dosage (e.g., <NUM> Units of a GLP-<NUM> Therapeutic) and the time of delivery (e.g., <NUM>) may be stored. The intervals, such as days, may be differentiated in a memory of a wearable drug delivery device based on an activation time of the wearable drug delivery device. For example, the wearable drug delivery device may have a clock or a counter that starts at the activation time, which may be at <NUM> am on the first day.

In block <NUM>, routine <NUM> determines, from the analysis, an average interval profile, which contains a series of average basal delivery dosages, wherein each average basal delivery dosage in the series has a corresponding average particular delivery time. The average basal delivery dosages may be the average amount of the GLP-<NUM> Therapeutic delivered as a basal dosage at a particular time (e.g., a time t, such as <NUM> AM during an interval (e.g., <NUM> hour period - <NUM> day)) over the entire basal delivery history. For example, due to the filtering, the <NUM> hour period of the interval may be segmented into <NUM> particular times that are separated by <NUM> hours (e.g., the particular time t=<NUM> may be <NUM> am, the particular time t=<NUM> would be <NUM> am, and the particular time t=<NUM> would be <NUM> am, and so on). The average delivery dosage for particular time between t=<NUM> and t=<NUM> in each interval may be <NUM> U, the average delivery dosage for particular time between t=<NUM> and t=<NUM> in each interval may also be <NUM> U, while average delivery dosage for the particular time between t=<NUM> to the end of the day in each interval may be <NUM> U.

In block <NUM>, the controller, when implementing routine <NUM>, may be configured to evaluate, iteratively, each average basal delivery dosage in the series of average basal delivery dosages with respect to other average basal delivery dosages delivered at different times in the series to determine a similarity in the amount of the GLP-<NUM> Therapeutic delivered and a similarity in the corresponding average particular delivery time. Continuing with the numerical example, the controller may determine the similarity in the amount of the GLP-<NUM> Therapeutic delivered at particular times t=<NUM> and t=<NUM> is <NUM> while the similarity in the amount of drug delivered at times t=<NUM> and t=<NUM> is <NUM> or the like. The similarity of each delivery between segments can be compared by evaluating the % change in amount of GLP-<NUM> Therapeutic delivered. For instance, in the numerical example, the change in GLP-<NUM> Therapeutic delivery from <NUM> between t=<NUM> and t=<NUM>, versus <NUM> from t=<NUM> and t=<NUM>, the similarity is thus <NUM>/<NUM> = <NUM>%. means the % change, and thus the "similarity" is -<NUM>%. The routine <NUM> continues to <FIG>.

In <FIG>, as shown in block <NUM>, the controller may be further configured, when executing the routine <NUM>, and, based on results of the evaluating, to aggregate the average basal delivery dosages that meet an amount similarity threshold. The average GLP-<NUM> Therapeutic similarity threshold may be based on a percentage of the average amounts being compared, such as within <NUM>%, <NUM>% or the like. In addition, a time range related to an aggregation of particular delivery times of the aggregated average basal delivery dosage may be assigned based on the time similarity threshold. The time similarity threshold may also be a percentage of the segmented time. For example, if the time segment is <NUM> hours (<NUM> minutes), the time threshold may be <NUM>%, <NUM>% or the like, which means the time similarity threshold may be <NUM> minutes, <NUM> minutes or the like. This aggregated average basal delivery dosage, in an example, may be a sum of an amount of the GLP-<NUM> Therapeutic delivered at the first corresponding average particular delivery time and an amount of the GLP-<NUM> Therapeutic delivered at the second corresponding average particular delivery time based on the time threshold. Alternatively, the controller may use a clustering algorithm on the average basal delivery dosages that are close in range of a respective amount similarity threshold and a time delivery threshold.

In an example, the controller may be configured to combine a first average basal delivery dosage from a first corresponding average particular delivery time (e.g., between t=<NUM> and t=<NUM>) with a second average basal delivery dosage from a second corresponding average particular delivery time (e.g., between t=<NUM> and t=<NUM>). The combining of the first average basal delivery dosage and the second average basal delivery dosage provides the updated basal delivery dosage. For example, the average basal dosages of <NUM> U at particular time between t=<NUM> and t=<NUM> as well as between t=<NUM> and t=<NUM> may be aggregated as <NUM> U between t=<NUM> and t=<NUM>. In addition, the controller may be further configured to combine the first average particular delivery time with the second average particular delivery time. For example, the particular time may be aggregated to provide an aggregate time of <NUM> hours. This results in an aggregate average delivery dosage of <NUM>. 1U over <NUM> hours in this numerical example.

In block <NUM>, the controller may modify the basal delivery schedule with an updated amount of the GLP-<NUM> Therapeutic to be delivered as an updated basal delivery dosage based on the aggregated average basal delivery dosages, and an updated particular delivery time based on the assigned time range. In addition, the controller may set, for a new interval for which the modified basal delivery schedule is to be applied, a start time of the updated basal delivery dosage that corresponds to the updated particular delivery time.

The controller, at block <NUM>, may cause delivery of respective updated basal delivery dosages according to the modified basal delivery schedule via a pump mechanism (shown in another example) communicatively coupled to the controller. For example, the controller may generate an activation control signal at the start time in a new interval to expel the updated basal delivery dosage from a reservoir. The new interval being a day in the modified basal delivery schedule. The generated activation signal may be output to a pump mechanism coupled to the reservoir and the controller (as described with reference to <FIG>).

The example routine <NUM> is implemented utilizing data from the basal delivery history alone because of the consistent blood glucose measurement values of the particular individual. However, other individuals that may need more flexibility may rely on an algorithm that inputs additional data when determining an adjusted basal need.

<FIG> illustrates a routine <NUM> in accordance with another example of the disclosed subject matter. The routine <NUM> provides additional robustness by utilizing data from different histories of the user.

In block <NUM>, a controller of a drug delivery device, when executing programming code that implements routine <NUM>, may be configured to access in a memory coupled to the controller a basal delivery history, a bolus delivery history, a blood glucose measurement history, and a meal announcement history for a period of time.

In the example of routine <NUM>, the basal delivery history may be a data structure containing GLP-<NUM> Therapeutic amount data related to basal doses of the GLP-<NUM> Therapeutic delivered at respective times over the course of the basal delivery history based on an algorithm executed by the controller. For example, the GLP-<NUM> Therapeutic amount data may be related to a GLP-<NUM> Therapeutic and may be in units of GLP-<NUM> Therapeutic, such as <NUM> Units (U), <NUM> U, <NUM> U or the like, and a time of the delivery may be stored as a respective time for each delivery, such as <NUM> (<NUM> am), <NUM> (<NUM>:<NUM> am) , <NUM> (<NUM> am), <NUM> (<NUM>:<NUM> pm), <NUM> (<NUM> pm) or the like. Alternatively, other drugs may be administered. Because the basal delivery history tracks background or basal doses deliveries of the drug, the basal delivery history may have multiple deliveries (e.g., <NUM>-<NUM> instances) and relatively small amounts of the GLP-<NUM> Therapeutic (in comparison to the bolus dosages) delivered (e.g., <NUM> U versus <NUM> U) per each instance throughout an interval.

The bolus delivery history may, for example, be a data structure containing bolus delivery time data related to delivery of bolus doses of the GLP-<NUM> Therapeutic and an amount of the GLP-<NUM> Therapeutic delivered in each bolus dose. For example, the bolus delivery history may be arranged similar to the basal delivery history and may have an amount of the GLP-<NUM> Therapeutic delivered.

In addition, the blood glucose measurement history may, for example, be a data structure of blood glucose measurement values (blood glucose measurement values) in which each blood glucose measurement value has a corresponding time when the blood glucose measurement value was obtained. For example, the data structure may have a blood glucose measurement value in one column, such as <NUM>/dL, and in another column a time, such as <NUM> (<NUM> am), or the like.

In some examples, the controller may access the meal announcement history, which, may be, for example, a data structure of times when a meal announcement notification was received by the controller. In some examples, the meal announcement notification may be received from a user interface device, such as a switch, push button, keyboard, touchscreen display, a microphone, or the like. The controller may, for example, store the meal announcement notification with a time that the notification was received, for example, the meal announcement notification may be a binary indication, such as Yes or No, or some other type of flag, and the time associated with the meal announcement notification may be a time, such as <NUM> (6AM), <NUM> (12PM) and <NUM> (6PM), or the like. At a time when a meal announcement is not received, the controller may set the indication to No and log the time which corresponds to other times stored in the other data structures for the basal delivery history, bolus delivery history, and the blood glucose measurement history.

In block <NUM>, the controller when executing routine <NUM> filters respective data in each of the basal delivery history, the bolus delivery history, the blood glucose measurement history, and the meal announcement history, according to predetermined filter settings related to a time interval of the period of time. For example, a low pass filter with a cutoff frequency of approximately <NUM> millihertz (<NUM> cycle/<NUM> hours) may be applied to the respective data in each of the basal delivery history (which may be referred to as a(t)), the bolus delivery history (which may be referred to as b(t)), the blood glucose measurement history (which may be referred to as g(t)), and the meal announcement history (which may be referred to as m(t)). The controller may be configured to divide the filtered data into <NUM><NUM>-hour (<NUM>-day) periods. In other examples, cutoff frequencies other than <NUM> cycle/<NUM> hours may be chosen, such as <NUM> cycle/<NUM> hours or the like. Furthermore, the filters for the bolus delivery history, the blood glucose measurement history, and the meal announcement history may be chosen with different shapes and durations to match the expected insulin/meal absorption curves, insulin-on-board (IOB)/carbohydrates-on-board (COB) curves, or the like.

In block <NUM>, routine <NUM> calculates a daily basal profile using the filtered respective data from each of the basal delivery history, the bolus delivery history, the blood glucose measurement history, and the meal announcement history. The daily basal profile may be, for example, a time schedule for delivering respective basal delivery dosages and times at which a basal delivery dosage is to be delivered. The respective basal delivery dosages and times in the daily basal profile may correspond to a respective adjusted basal need calculated for a respective time in the interval.

The example process at block <NUM> may include the controller determining a series of time settings for times in the time schedule based on the predetermined filter settings from block <NUM>. For example, each time in the series of time settings may be selected based on the time interval (i.e., interval) of the period of time. The controller may access a memory to obtain an amount of the drug from the drug amount data in the basal delivery history a(t) and at a time (t) in the amount data that corresponds to each time in the series of time settings. The amount of the GLP-<NUM> Therapeutic from time t may be referred to as a "GLP-<NUM> Therapeutic basal delivery history factor" for the respective time t. The controller may further be configured to obtain, from the bolus delivery history b(t), a respective amount of the GLP-<NUM> Therapeutic delivered in a bolus dose at each time (t) in the series of time settings. The controller may be further configured to determine a number of blood glucose measurement factors from blood glucose measurement values g(t) in the blood glucose measurement history and that corresponds to a respective time (t) in the series of time settings. Based on these calculated factors (i.e., delivery history factors, blood glucose measurement factors, the bolus delivery history factor), a basal dose of the GLP-<NUM> Therapeutic may be determined.

In block <NUM>, routine <NUM> generates, by the controller, a control signal for delivery of a basal dosage at a scheduled time from the daily basal profile.

In block <NUM>, routine <NUM> causes the controller to output the control signal to a pump mechanism of the drug delivery device communicatively coupled to the controller.

In other examples, the calculation of the daily basal profile at block <NUM> may be performed using different processes. An adjusted basal need may be an individual entry in the daily basal profile. The adjusted basal need may, in one example, be obtained based on a number of different factors as discussed in the respective examples below.

In other examples, the controller may be configured to account for additional data that is collected based on the assumption that a user is not paying close attention to blood glucose measurement values, in which case the adjusted basal need discussed above may be used in combination with other factors. The controller may determine the adjusted basal need according to different equations depending upon the example. The respective equations may utilize different factors, such as a delivery history factor obtained from the basal delivery history, a bolus factor obtained from the bolus delivery history, a blood glucose measurement factor obtained from the blood glucose measurement history, and a meal announcement factor based on a meal announcement history. In other examples, a proportionality factor may be incorporated into the equation to further optimize the basal dosage settings.

For example, the delivery history factor may be an amount of basal GLP-<NUM> Therapeutic delivered at a respective time t taken from the basal delivery history. The amounts of the basal GLP-<NUM> Therapeutic delivered in the basal delivery history a(t).

In another example, the controller may be configured to determine a blood glucose measurement factor for each respective time (i.e., t) of a number of preselected times from the blood glucose history. For example, the controller may obtain, by accessing a memory, a blood glucose measurement value from the blood glucose measurement history stored in the memory that corresponds to the respective time (t). The controller, in the example, may use the respective time (t) in the following equation to determine the blood glucose measurement factor: <MAT> where:.

In evaluating Eq. (<NUM>), the controller may determine a numerator, specific to the respective time (t) of the preselected times, by subtracting the fasting blood glucose target setting from the obtained blood glucose measurement value g(t) corresponding to the respective time. The denominator may be determined by multiplying a duration of insulin action (DIA) value by an estimated correction factor. The denominator may remain constant over the period of time, such as two weeks, but may be updated based on changes to a user's physical attributes (e.g., the consistency of blood glucose measurement value) or habits (e.g., diet or physical activity).

In Eq. (<NUM>), the estimated correction factor CF may be an estimate of a specific user's sensitivity to the GLP-<NUM> Therapeutic and how efficiently the user processes the GLP-<NUM> Therapeutic. The fasting blood glucose target setting in this example is <NUM>, but different values may also be used, such as <NUM>/dL, <NUM>/dL, <NUM>/dL or the like. In some examples, the fasting blood glucose target setting may be user dependent. For example, the controller may obtain the fasting blood glucose target setting from user preference settings (described with reference to a later example) or from a clinical setting common for the physical attributes (e.g., height, weight, age, resting heart rate and/or the like) of the user that are stored in the memory accessible by the controller. Alternatively, it may be obtained from clinical data sources and input into the memory coupled to the controller.

For example, the controller may determine individual adjusted basal need for each respective time (t) in the daily basal profile based on the blood glucose measurement factor that corresponds to each time in the series of time settings may be calculated, for example, using the obtained blood glucose measurement value (g(t)), a fasting blood glucose target setting (e.g., <NUM>), a duration of insulin action value (DIA), and a correction factor (CF).

In addition, the controller may also determine a meal announcement factor that corresponds to each time in the series of time settings based on the meal announcement history. The controller may also determine a drug ratio related to meal content in which the drug ratio related to meal content may be a GLP-<NUM> Therapeutic-to-carbohydrate ratio. The meal announcement factor may be determined by retrieving a meal announcement value corresponding to a respective time in the series of time settings. In the example, the meal announcement value may be an estimate of a number of grams of carbohydrates in the meal that triggered a particular meal announcement notification. A meal announcement notification may be received via user input, an automatic meal detection algorithm, historical data or the like. For example, the estimated number of grams of carbohydrates may be input by the user via a user interface to a drug delivery device. Alternatively, the estimated number of carbohydrates may be determined by a meal detection algorithm that utilized historical data of changes in blood glucose measurement values to generate a meal announcement notification. In another alternative, a meal detection algorithm may utilize inputs from various sensors (e.g., camera or microphone, global positioning sensors) and computer applications (e.g., calendar applications, travel direction applications, recipe applications, or the like). Based on one or more of the meal detection or meal announcement processes, the controller may determine or estimate a GLP-<NUM> Therapeutic-to-carbohydrate ratio. The controller may use the GLP-<NUM> Therapeutic-to-carbohydrate ratio (GC) as the divisor in a division operation in which the numerator is the meal announcement value, the quotient of the division may be referred to as the meal announcement factor. For example, an equation (Eq. (<NUM>)) for the meal announcement factor may be: <MAT> where:.

In these more detailed calculations, the adjusted basal need of routine <NUM> may have been calculated utilizing only the obtained amount of the GLP-<NUM> Therapeutic from the basal delivery history a(t). However, as more factors are used in the routine <NUM>, the controller may, for example, determine "an aggregate factor. " The controller may be configured to sum the obtained amount of the GLP-<NUM> Therapeutic from the basal delivery history a(t) from the amount data, the respective blood glucose measurement factor, and the obtained respective amount of the GLP-<NUM> Therapeutic delivered in the bolus dose to provide the aggregate factor. The aggregate factor, in this example, for a respective time (t) may generally refer to the sum of values from the respective time (t) in the basal delivery history and the bolus delivery history, and the blood glucose measurement factor based on the respective time (t). For example: <MAT> where:.

In an alternative example, during the steps of block <NUM>, the controller may be configured to determine the number of blood glucose measurement factors from the blood glucose measurement history using different techniques in different examples. In the specific example, the controller may be operable to calculate an adjusted basal need for each respective time (t) in the daily basal profile using the following equation: <MAT> where:.

In the example, each calculated adjusted basal need may be an individual entry in the schedule. For example, the controller may be configured, for each respective time of the plurality of preselected times, to provide an aggregate factor, which is a sum of the obtained amount of the GLP-<NUM> Therapeutic at the respective time, the obtained respective amount of the GLP-<NUM> Therapeutic delivered in the bolus dose, and the respective blood glucose measurement factor. The controller is further configured to subtract from the aggregate factor the meal announcement factor from the respective time and output the result of the summing and subtracting as the adjusted basal need for a respective time (t) of the plurality of preselected times.

In yet another example of calculating the adjusted basal need for the time schedule of the daily basal profile as part of the processing at block <NUM>, the adjusted basal need for a respective time of the number of preselected times may be determined using another process that accounts for the user's correction bolus bc(t) at respective time t that may be associated with meal ingestions depending on the user's use cases. The drug delivery device may be manually commanded by the user to deliver the correction bolus. Alternatively, or in addition, the controller may, based on user preference settings, be permitted to deliver the correction bolus. In one exemplary example, correction boluses bc(t) can also be processed with a low pass filter having a cutoff frequency of approximately <NUM> or another cutoff frequency setting. A proportion X of these manual correction bolus bc(t) may be considered to be relevant to meals and subtracted to address the user's basal needs with further specificity.

For example, the controller may be configured to determine the adjusted basal need for each respective time of the number of preselected times, based on the following equation: <MAT> where:.

In one example, the proportionality factor X may be considered <NUM>, which is an assumption that half of the manual boluses used by the user over the course of the interval may be associated with meals (e.g., the user manually attempting to compensate for meal consumption). Of course, values other than <NUM> may be set for the proportionality factor X, such as <NUM>, <NUM>, or the like. Users who exhibit keto diets may assume a lower proportionality factor than <NUM>, as they may not need a significant amount of the GLP-<NUM> Therapeutic associated with meals. Users who are highly active may not need as much of the GLP-<NUM> Therapeutic associated with basal delivery and may assume a higher proportionality factor than <NUM>. This value can be potentially fixed to an individualized value per user and per recommendation from the physician, adjusted occasionally by the user and/or the physician manually, or adjusted every fixed period of time, such as <NUM>-<NUM> days, or adjusted automatically based on the proportion of GLP-<NUM> Therapeutic delivery as basal versus bolus in the available basal delivery history every fixed period of time.

In yet another example, rather than subtracting the GLP-<NUM> Therapeutic deliveries from the aggregate factor as described in the equations above, the summation of all GLP-<NUM> Therapeutic deliveries can be adjusted by a proportion of bolus GLP-<NUM> Therapeutic deliveries versus the automated GLP-<NUM> Therapeutic deliveries. Allowances may be made to more heavily bias towards an equal split between basal GLP-<NUM> Therapeutic delivery needs and bolus GLP-<NUM> Therapeutic delivery needs.

In this example, the controller may access a memory to obtain an amount of the GLP-<NUM> Therapeutic from the amount data in the basal delivery history (a(t)) and at a time (t) (which is a respective time or may be the "time interval") in the amount data that corresponds to each time in the series of time settings. The controller may further be configured to obtain, from the bolus delivery history (b(t)), a respective amount of the GLP-<NUM> Therapeutic delivered in a bolus dose at each time (t) in the series of time settings. The controller may be further configured to determine a number of blood glucose measurement factors from blood glucose measurement values (g(t)) in the blood glucose measurement history and that corresponds to a respective time (t) in the series of time settings. Each respective blood glucose measurement factor (g(t)) of the number of blood glucose measurement factors may correspond to a respective time (t) in the series of time settings. The controller may be further configured to calculate a blood glucose measurement factor that corresponds to each time in the series of time settings using the obtained blood glucose measurement value, a fasting blood glucose target setting, a duration of GLP-<NUM> Therapeutic action (DIA) value, and an estimated correction factor (CF). All of the factors below may be calculated as described with reference to the examples of <FIG> above.

In this specific example, the routine <NUM> may configure the controller, when determining the adjusted basal need for each of the number of preselected times, to summing the obtained amount of the GLP-<NUM> Therapeutic, the respective blood glucose measurement factor, and the obtained respective amount of the GLP-<NUM> Therapeutic delivered in the bolus dose to provide an aggregate factor. The controller may determine a proportion of an amount of the GLP-<NUM> Therapeutic provided via bolus dosages to an amount provided via the aggregate factor. The proportion may be modified based on a contribution factor.

In more detail, the controller may implement the following equation, and based on a(t), b(t), and g(t), the total GLP-<NUM> Therapeutic atot(t) may be calculated (in a manner similar to the aggregate factor above) as: <MAT>.

In an example, the amount of GLP-<NUM> Therapeutic delivery for basal needs (referred to as ab(t) (below)) can be estimated based on the proportion of bolus versus total GLP-<NUM> Therapeutic as: <MAT> where the contribution factor may be a weighting factor that can range between approximately <NUM> to approximately <NUM>, or approximately <NUM> to approximately <NUM>, depending on the overall proportion of the GLP-<NUM> Therapeutic delivery for boluses versus total delivery.

In more detail, the contribution factor may be determined: <MAT>.

The total GLP-<NUM> Therapeutic atot(t) (referred to as "the aggregate factor" in earlier examples) may be multiplied by the modified proportion to determine a resultant. The controller may be configured to output the resultant of the multiplying of atot(t) and contribution factor as the adjusted basal need for a respective time of the plurality of preselected times.

The foregoing examples described a daily basal profile or a time schedule. The respective daily basal profile or time schedule may be the same for every day of the week. However, it is further envisioned that the controller may be configured to evaluate more broadly than single day and modify basal based on day of week, or day of year, such as large meals on Friday night or Sunday morning; or intense exercise on Saturdays, for example.

It may be helpful to describe an example of a system that may be configured to implement the above described examples as well as additional examples.

<FIG> illustrates a functional block diagram of an exemplary drug delivery system suitable for implementing the example processes and techniques described herein.

The drug delivery system <NUM> may implement (and/or provide functionality for) a medication delivery algorithm, such as an artificial pancreas (AP) application, to govern or control automated delivery of GLP-<NUM> Therapeutic to a user (e.g., to maintain euglycemia - a normal level of glucose in the blood). The drug delivery system <NUM> may be an automated drug delivery system that may include a wearable automatic drug delivery device <NUM>, an analyte sensor <NUM>, and a management device (PDM) <NUM>. The drug delivery system <NUM> may be an automatic drug delivery system that is configured to deliver a dosage of the GLP-<NUM> Therapeutic without any user interaction, or in some examples, limited user interaction, such as depressing a button to announce ingestion of a meal or the like.

The system <NUM>, in an optional example, may also include a smart accessory device <NUM>, such as a smartwatch, a personal assistant device or the like, which may communicate with the other components of system <NUM> via either a wired or wireless communication links <NUM>-<NUM>.

The management device <NUM> may be a computing device such as a smart phone, a tablet, a personal diabetes management device, a dedicated diabetes therapy management device, or the like. In an example, the management device (PDM) <NUM> may include a processor <NUM>, a management device memory <NUM>, a user interface <NUM>, and a communication device <NUM>. The management device <NUM> may contain analog and/or digital circuitry that may be implemented as a processor <NUM> for executing processes based on programming code stored in the management device memory <NUM>, such as the AP algorithm or application (APP) <NUM>, to manage a user's blood glucose levels and for controlling the delivery of the drug, medication, or therapeutic agent according to a time schedule, basal drug profile, and the like as discussed above. The management device <NUM> may be used to initially set up, adjust settings, and/or control operation of the wearable automatic drug delivery device <NUM> and/or the analyte sensor <NUM> as well as the optional smart accessory device <NUM>.

The processor <NUM> may also be configured to execute programming code stored in the management device memory <NUM>, such as the MDA APP <NUM>. The MDA APP <NUM> may be a computer application that is operable to deliver the GLP-<NUM> Therapeutic based on information received from the analyte sensor <NUM>, the cloud-based services <NUM> and/or the management device <NUM> or optional smart accessory device <NUM>. The memory <NUM> may also store programming code to, for example, operate the user interface <NUM> (e.g., a touchscreen device, a camera or the like), the communication device <NUM> and the like. The processor <NUM> when executing the MDA APP <NUM> may be configured to implement indications and notifications related to meal ingestion, blood glucose measurements, and the like. The user interface <NUM> may be under the control of the processor <NUM> 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 above.

In a specific example, when the memory <NUM> stores an MDA APP, such as an artificial pancreas application, the processor <NUM> may also be configured to execute a diabetes treatment plan (which may be stored in the memory <NUM>) that is managed by the MDA APP <NUM> stored in memory <NUM>. In addition to the functions mentioned above, when the MDA APP <NUM> is an AP application, it may further provide functionality to enable the processor <NUM> to determine a basal dosage according to an adjusted basal need, a basal profile or the like as described with respect to the examples of <FIG>. In addition, the MDA APP <NUM> provides functionality to enable the processor <NUM> to output signals to the wearable automatic drug delivery device <NUM> to deliver the basal GLP-<NUM> Therapeutic dosages described with reference to the examples of <FIG>.

The communication device <NUM> may include one or more transceivers such as Transceiver A <NUM> and Transceiver B <NUM> and receivers or transmitters that operate according to one or more radio-frequency protocols. In the example, the transceivers <NUM> and <NUM> may be a cellular transceiver and a Bluetooth® transceiver, respectively. For example, the communication device <NUM> may include a transceiver <NUM> or <NUM> configured to receive and transmit signals containing information usable by the MDA APP <NUM>.

The drug delivery device <NUM>, in the example system <NUM>, may include a user interface <NUM>, a controller <NUM>, a pump mechanism <NUM>, a communication device <NUM>, a memory <NUM>, a power source <NUM>, device sensors <NUM>, and a reservoir <NUM>. The wearable automatic drug delivery device <NUM> may be configured to perform and execute the processes described in the examples of <FIG> without input from the management device <NUM> or the optional smart accessory device <NUM>.

The controller <NUM> alone may implement the processes of <FIG>, <FIG> and <FIG> to determine a daily basal profile or an adjusted basal need as described with respect to the other examples, based on an input from the analyte sensor <NUM>. The controller <NUM> of the wearable automatic drug delivery device <NUM> may be operable to implement delivery of the GLP-<NUM> Therapeutic to the user according to a diabetes treatment plan or other delivery regimen stored in the memory <NUM> as other programs <NUM> (Other). For example, the controller <NUM> may be operable to execute programming code and be configured when executing non-transitory programming code of a medication delivery application or algorithm, such as MDA APP <NUM> and other programs <NUM>, to perform the functions that implement the example routines and processes described herein. In an operational example, the controller <NUM>, when executing the programming code implementing MDA APP <NUM>, may be configured to output a control signal causing actuation of the drive mechanism <NUM> (the drive mechanism <NUM> may also be referred to as a pump mechanism) to deliver time-dependent basal dosages or the like as described with reference to the examples of <FIG>.

The memory <NUM> may store programming code executable by the controller <NUM>. The programming code, for example, may enable the controller <NUM> to control expelling the GLP-<NUM> Therapeutic from the reservoir <NUM> and control the administering of doses of the GLP-<NUM> Therapeutic based on signals from the MDA APP <NUM> or, external devices, when the drug delivery device <NUM> is configured to receive and respond to the external control signals. The memory <NUM> may also be configured to store other data and programming code, such as other programs <NUM>. For example, the memory may be configured to store the data structures discussed above with respect to the examples of <FIG> with data, such as the basal delivery history, bolus delivery history, a blood glucose measurement history and a meal announcement history, a correction bolus delivery history as well as data related to the time-dependent basal dosages, such as a daily basal profile, or a time schedule. In some instances, all glucose and GLP-<NUM> Therapeutic histories are stored in the management device <NUM>, which may be a smartphone, or smart accessory <NUM>, and the cloud-based services <NUM>, which may include data storage. The drug delivery device <NUM> and CGM upload their histories to the management device <NUM>, when in communication. The management device <NUM>, in turn uploads the histories to the cloud-based services <NUM> periodically. When the drug delivery device <NUM> or analyte sensor <NUM> is disposed of, no history will be lost because the respective history is stored in the cloud by cloud-based services <NUM>. Similarly, when a management device <NUM> or smart accessory <NUM> is replaced, no history will be lost. The basal profile adaptive computation as discussed in the example routines <NUM> and <NUM> may be performed on the management device <NUM> and transmitted to the drug delivery device <NUM> at activation of the drug delivery device <NUM> via a communication link, such as <NUM> or <NUM>. Alternatively, the computation can be done in the cloud, downloaded to the management device <NUM>, and then transmitted to the pod when activated.

The reservoir <NUM> may be configured to store the GLP-<NUM> Therapeutic suitable for automated delivery.

The device sensors <NUM> may include one or more of a pressure sensor, a power sensor, or the like that are communicatively coupled to the controller <NUM> and provide various signals. In an example, the controller <NUM> or a processor, such as <NUM>, may be operable to use the various signals in the determination of an amount of the GLP-<NUM> Therapeutic delivered, a daily basal profile or an adjusted basal need as described with respect to the other examples. In such an example, the controller <NUM> may have a clock, a counter or the like that maintains or enables maintaining a time. An initial time may be set when the wearable drug delivery device <NUM> is initially set up by a user or HCP.

In an example, the wearable automatic drug delivery device <NUM> includes a communication device <NUM>, which may be a receiver, a transmitter, or a transceiver that operates according to one or more radio-frequency protocols, such as Bluetooth, Wi-Fi, a near-field communication standard, a cellular standard, or the like. The controller <NUM> may, for example, communicate with a personal diabetes management device <NUM> and an analyte sensor <NUM> via the communication device <NUM>.

The wearable automatic drug delivery device <NUM> may be attached to the body of a user, such as a patient or diabetic, at an attachment location and may deliver, according to an automatic delivery algorithm, any therapeutic agent, including any drug or medicine, such as the GLP-<NUM> Therapeutic or the like, to a user at or around the attachment location. A surface of the wearable automatic drug delivery device <NUM> may include an adhesive to facilitate attachment to the skin of a user as described in earlier examples. The controller <NUM> may be operable to maintain a basal delivery history of the delivered therapeutic drug, such as the GLP-<NUM> Therapeutic, by storing delivered basal dosages and bolus dosages in memory <NUM>. The memory <NUM> may also store a basal delivery history that may be a data structure containing amount data related to background doses of the GLP-<NUM> Therapeutic delivered at respective times in the basal delivery history, a bolus delivery history that may be a data structure containing bolus delivery time data related to delivery of bolus doses of the GLP-<NUM> Therapeutic and an amount of the GLP-<NUM> Therapeutic delivered in each bolus dose, a blood glucose measurement history that may be a data structure of blood glucose measurement values in which each blood glucose measurement value has a corresponding time when the blood glucose measurement value was obtained, and a meal announcement history that may be a data structure of times when a meal announcement notification was received by the controller.

The wearable automatic drug delivery device <NUM> may, for example, include a reservoir <NUM> for storing the GLP-<NUM> Therapeutic, a patient interface (not shown), for example, a needle or cannula, for delivering the drug into the body of the user (which may be done subcutaneously, intraperitoneally, or intravenously), and a drive mechanism <NUM> for transferring the GLP-<NUM> Therapeutic from the reservoir <NUM> through a needle or cannula and into the user. The drive mechanism <NUM> may be fluidly coupled to reservoir <NUM>, and communicatively coupled to the controller <NUM>.

The wearable automatic drug delivery device <NUM> may further include a power source <NUM>, such as a battery, a piezoelectric device, other forms of energy harvesting devices, or the like, for supplying electrical power to the drive mechanism <NUM> and/or other components (such as the controller <NUM>, memory <NUM>, and the communication device <NUM>) of the wearable automatic drug delivery device <NUM>.

In some examples, the wearable automatic drug delivery device <NUM> and/or the management device <NUM> may include a user interface <NUM>, respectively, such as a keypad, a touchscreen display, levers, light-emitting diodes, buttons on a housing of the management device <NUM>, a microphone, a camera, a speaker, a display, or the like, that is configured to allow a user to enter information and allow the management device <NUM> to output information for presentation to the user (e.g., alarm signals or the like). The user interface <NUM> may provide inputs, such as a voice input, a gesture (e.g., hand or facial) input to a camera, swipes to a touchscreen, or the like, to processor <NUM> which the programming code interprets.

When configured to communicate to an external device, such as the PDM <NUM> or the analyte sensor <NUM>, the wearable automatic drug delivery device <NUM> may receive signals over the wired or wireless link <NUM> from the management device <NUM> or <NUM> from the analyte sensor <NUM>. The controller <NUM> of the wearable automatic drug delivery device <NUM> may receive and process the signals from the respective external devices (e.g., cloud-based services <NUM>, smart accessory device <NUM>, or management device <NUM>) to implement delivery of a drug to the user according to a daily basal profile, a time schedule, a modified basal drug delivery schedule stored in the memory <NUM> as other programs <NUM> (Other).

In an operational example, the controller <NUM>, when executing the MDA APP <NUM>, may output a control signal operable to actuate the drive mechanism <NUM> to deliver a carbohydrate-compensation dosage of the GLP-<NUM> Therapeutic, a correction bolus, a revised basal dosage or the like as described with reference to the examples of <FIG>.

The smart accessory device <NUM> may be, for example, an Apple Watch®, other wearable smart device, including, for example, eyeglasses, provided by other manufacturers, a global positioning system-enabled wearable, a wearable fitness device, smart clothing, or the like. Similar to the management device <NUM>, the smart accessory device <NUM> may also be configured to perform various functions including controlling the wearable automatic drug delivery device <NUM>. For example, the smart accessory device <NUM> may include a communication device <NUM>, a processor <NUM>, a user interface <NUM>, a sensor <NUM>, and a memory <NUM>. The user interface <NUM> may be a graphical user interface presented on a touchscreen display of the smart accessory device <NUM>. The sensor <NUM> may include a heart rate sensor, a blood oxygen saturation sensor, a pedometer, a gyroscope, a combination of these sensors, or the like. The memory <NUM> may store programming code to operate different functions of the smart accessory device <NUM> as well as an instance of the MDA APP <NUM>. The processor <NUM> that may execute programming code, such as the MDA APP <NUM> for controlling the wearable automatic drug delivery device <NUM> to implement the <FIG> examples described herein.

In an operational example, the drug delivery device <NUM> may be configured, when the controller <NUM> executes programming code stored in the memory, such as other programs <NUM> and MDA APP <NUM>, to obtain, from the basal delivery history stored in the memory, basal drug dosage data. The obtained basal drug dosage data obtained from the basal delivery history may include information related to a plurality of basal dosages delivered daily over a number of weeks. The controller may process the obtained basal dosage data using a low pass filter to reduce any outlier data points that may skew an average of the amounts of the GLP-<NUM> Therapeutic delivered during a basal dosage. For example, the controller may be further configured to identify basal dosages within a same day of the processed basal dosage data that have similar amounts of the GLP-<NUM> Therapeutic delivered in a basal dosage; and aggregate the identified basal dosages to reduce a number of different basal dosages to be accounted for in the daily basal profile. The drug delivery device <NUM> may generate from the processed basal dosage data, a daily basal profile that includes a number of basal dosages and a respective time for each of the basal dosages of the number of basal dosages to be delivered during a day. The controller may generate a control signal according to the daily basal profile. The controller may apply the control signal to the pump mechanism to deliver a respective basal dosage at the basal dosages respective time in the daily basal profile.

In addition to the above operational example, the controller <NUM> may be operable or configured to execute programming code embodying the routine <NUM> of <FIG> and routine <NUM> of <FIG>.

The analyte sensor <NUM> may include a controller <NUM>, a memory <NUM>, a sensing/measuring device <NUM>, a user interface <NUM>, a power source/energy harvesting circuitry <NUM>, and a communication device <NUM>. The analyte sensor <NUM> may be communicatively coupled to the processor <NUM> of the management device <NUM> or controller <NUM> of the wearable automatic drug delivery device <NUM>. The memory <NUM> may be configured to store information and programming code, such as an instance of the MDA APP <NUM>.

The analyte sensor <NUM> may be configured to detect multiple different analytes, such as lactate, ketones, uric acid, sodium, potassium, alcohol levels, hormone levels, or the like, and output results of the detections, such as measurement values or the like. The analyte sensor <NUM> may, in an example, be configured to measure a blood glucose value at a predetermined time interval, such as every <NUM> minutes, or the like. The communication device <NUM> of analyte sensor <NUM> may have circuitry that operates as a transceiver for communicating the measured blood glucose values to the management device <NUM> over a wireless link <NUM> or with wearable automatic drug delivery device <NUM> over the wireless communication link <NUM>. While called an analyte sensor <NUM>, the sensing/measuring device <NUM> of the analyte sensor <NUM> 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 <NUM> 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 <NUM>), or any combination thereof.

Similar to the controller <NUM>, the controller <NUM> of the analyte sensor <NUM> may be operable to perform many functions. For example, the controller <NUM> may be configured by the programming code stored in the memory <NUM> to manage the collection and analysis of data detected the sensing and measuring device <NUM>.

Although the analyte sensor <NUM> is depicted in <FIG> as separate from the wearable automatic drug delivery device <NUM>, in various examples, the analyte sensor <NUM> and wearable automatic drug delivery device <NUM> may be incorporated into the same unit. That is, in various examples, the sensor <NUM> may be a part of the wearable automatic drug delivery device <NUM> and contained within the same housing of the wearable automatic drug delivery device <NUM> (e.g., the sensor <NUM> or, only the sensing/measuring device <NUM> and memory storing related programming code may be positioned within or integrated into, or into one or more components, such as the memory <NUM>, of, the wearable automatic drug delivery device <NUM>). In such an example configuration, the controller <NUM> may be able to implement the process examples of <FIG> alone without any external inputs from the management device <NUM>, the cloud-based services <NUM>, the optional smart accessory device <NUM>, or the like.

The communication link <NUM> that couples the cloud-based services <NUM> to the respective devices <NUM>, <NUM>, <NUM> or <NUM> of system <NUM> may be a cellular link, a Wi-Fi link, a Bluetooth link, or a combination thereof. Services provided by cloud-based services <NUM> may include data storage that stores anonymized data, such as blood glucose measurement values, basal delivery history, bolus delivery history, time data, and other forms of data. In addition, the cloud-based services <NUM> may process the anonymized data from multiple users to provide generalized information related to clinical diabetes-related data and the like.

The wireless communication links <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM> may be any type of wireless link operating using known wireless communication standards or proprietary standards. As an example, the wireless communication links <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM> 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 devices <NUM>, <NUM>, <NUM> and <NUM>.

In some instances, the basal GLP-<NUM> Therapeutic may comprise a co-formulation of GLP-<NUM> and insulin and, more specifically, rapid-acting insulin. In certain examples, reservoir <NUM> in wearable drug delivery device <NUM> may be prefilled with a co-formulation of GLP-<NUM> and a rapid-acting insulin. Rapid-acting insulin is sold under various tradenames, for example, Humalog®, NovoLog®, Admelog®, Apidra®, Fiasp® and Lyumjev®, among others. The GLP-<NUM> may be, for example, liraglutide, which may be co-formulated with U100 rapid-acting insulin in a ratio of <NUM>-<NUM>/ml of insulin, with the preferred co-formulation of <NUM>/ml of insulin. In other examples, the GLP-<NUM> may be lixisenatide or exenatide, which may be co-formulated with U100 rapid-acting insulin in a ratio of <NUM>-<NUM>/ml of insulin, with a preferred co-formulation of <NUM>/ml of insulin.

According to the invention, the GLP-<NUM> Therapeutic includes GLP-<NUM> and co-formulations of GLP-<NUM> and rapid-acting insulin. In an example, the GLP-<NUM> Therapeutic does not include long-acting insulin. Using rapid-acting insulin instead of long-acting insulin in a co-formulation with GLP-<NUM> yields particular advantages. GLP-<NUM> receptor agonists stimulate insulin secretion and inhibit glucagon secretion, thereby lowering blood glucose levels. Given this known mechanism , it is expected that a co-formulation with rapid-acting insulin, given in response to rising glucose levels, as disclosed herein, would be more effective and have a greater glucose lowering effect than a co-formulation with long-acting insulin could provide without glucose feedback (e.g., via a CGM). The co-formulation delivered by continuous or basal subcutaneous infusion would work synergistically to reduce fasting hyperglycemia as well as postprandial glucose excursions, as the co-formulation is being delivered in a more physiological manner compared to delivery of a long-acting insulin or a co-formulation including long-acting insulin, which would be delivered, for example, once weekly given the nature and instructions for use of a long-acting insulin. Further, incretins are naturally released after eating and therefore the advantageous mode of delivery of a GLP-<NUM> Therapeutic disclosed herein more closely emulates physiological responses, i.e., larger amounts (e.g., bolus) of GLP-<NUM> Therapeutic are delivered upon food consumption and smaller amounts (e.g., basal) of GLP-<NUM> Therapeutic are delivered when fasting or not consuming food.

In some examples, reservoir <NUM> of wearable drug delivery device <NUM> may be fillable, or in some examples refillable, by the user, who may obtain premixed co-formulations of GLP-<NUM> and the rapid-acting insulin and transfer the co-formulation into reservoir <NUM>. In other examples, drug delivery device <NUM> may comprise a second reservoir (not shown) and second pump mechanism (not shown) wherein reservoir <NUM> contains GLP-<NUM> and wherein the second reservoir contains the rapid-acting insulin, such that the co-formulation of GLP-<NUM> and insulin may be formulated on the fly under the direction of MDA <NUM>. In yet other examples, the user may wear two drug delivery devices <NUM>, with the reservoir <NUM> of the first drug delivery device <NUM> filled with GLC-<NUM> and the reservoir <NUM> of the second drug delivery device <NUM> filled with the rapid-acting insulin. In such an example, the first and second drug delivery devices <NUM> would communicate wirelessly with each other to coordinate the co-formulation of GLP-<NUM> and insulin to be delivered to the patient under the direction of MDA <NUM>.

In some examples, the user may be able to signal to drug delivery system <NUM> that he or she has experienced an adverse event or a side effect (e.g., nausea) using user interface <NUM> of management device <NUM>, user interface <NUM> of smart accessory device <NUM> or user interface <NUM> of drug delivery device <NUM>. For example, the user may enter a "Side Effect Mode" in which the delivery of the GLP-<NUM> Therapeutic may be suspended or reduced for a predetermined period of time or a user-defined period of time (in either case, e.g., <NUM> minutes, <NUM> minutes, <NUM> minutes, <NUM> hour, <NUM> hours, <NUM> hours, or the like), or until receiving a further input from the user, to allow the user to recover from the adverse effect and will thereafter continue as directed by MDA <NUM>. Such suspension may stop delivery of all basal, or alternatively all basal and bolus, GLP-<NUM> Therapeutic for the period of time (pre-determined or user-defined), or reduce delivery of all basal GLP-<NUM> Therapeutic for the period of time (pre-determined or user-defined) by, for example, <NUM>%, <NUM>%, <NUM>%, or the like. In any case, delivery of the GLP-<NUM> Therapeutic may resume upon expiration of the period of time or may ramp back up to the previous rate of delivery (e.g., the previous basal rate of delivery) of GLP-<NUM> Therapeutic. Such ramp up or restoration of GLP-<NUM> Therapeutic delivery after expiration of the period of time may, for example, occur approximately linearly over a period of time, e.g., <NUM> minutes, <NUM> minutes, <NUM> minutes, <NUM> hour, <NUM> hours, <NUM> hours, or the like, or over a period of time that matches the pre-determined or user-defined period of time. Alternatively, the ramp up may occur immediately upon expiration of the predetermined or user-defined period of time.

<FIG> show exemplary user interface screens for invoking Side Effect Mode and for allowing the user to specify the duration of the suspension of the delivery of the GLP-<NUM> therapeutic. Such screens would appear, for example, as part of user interface <NUM> or user interface <NUM>, as shown in <FIG>. Side Effect Mode may be selected from the menu of the main user screen. As shown in <FIG>, the user may tap the menu icon which will cause the menu to appear, as shown in <FIG>. The user may select the "Side Effect Mode" menu item from the menu. Upon selection of the Side Effect Mode menu item, the user may be delivered to the screen as shown in <FIG> which explains the purpose of Side Effect Mode and/or, may be taken to the screen shown in <FIG>, wherein the user may enter the duration of the suspension (or reduction in rate) of the delivery of the GLP-<NUM> therapeutic. Although the figures show a duration from <NUM> minutes to <NUM> hours, any range of times may be used. Alternatively, the screen shown in <FIG> may be shown as part of a series of instructional screens or, for example, a "user manual" built into the user interface. In alternate examples, the user may utilize user interface <NUM> on drug delivery device <NUM>, as shown in <FIG>, to invoke Side Effect Mode, in which case, Side Effect Mode may utilize a default duration (e.g., <NUM> minutes).

Because wearable drug delivery device <NUM> has controller <NUM> and a version of MDA <NUM> running thereon, it may be possible, in certain examples, for the user to use drug delivery device <NUM> independently, without management device <NUM> or smart accessory device <NUM>. In such cases, drug delivery device <NUM> may independently communicate with cloud-based services <NUM> via communication link <NUM> in addition to analyte sensor <NUM> via communication link <NUM>.

Software related implementations of the techniques described herein, such as the processes examples described with reference to <FIG> 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). In some examples, 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.

In addition, or alternatively, while the examples may have been described with reference to a closed loop algorithmic implementation, variations of the disclosed examples may be implemented to enable open loop use. The open loop implementations allow for use of different modalities of delivery of insulin such as smart pen, syringe or the like. Blood glucose measurements may be provided for closed-loop input from a blood glucose monitor, a continuous glucose monitor, or the like. A management device may maintain the data history and adjust or recommend system settings. For example, the disclosed MDA application and algorithms may be operable to perform various functions related to open loop operations, such as the generation of prompts requesting the input of information such as weight or age. Similarly, a dosage amount of insulin may be received by the MDA application or algorithm from a user via a user interface. Other open-loop actions may also be implemented by adjusting user settings or the like in an MDA application or algorithm.

Some examples of the disclosed device or processes may be implemented, for example, using a storage medium, a computer-readable medium, or an article of manufacture which may store an instruction or a set of instructions that, if executed by a machine (i.e., processor or controller), may cause the machine to perform a method and/or operation in accordance with examples of the disclosure. Such a machine may include, for example, any suitable processing platform, computing platform, computing device, processing device, computing system, processing system, computer, processor, or the like, and may be implemented using any suitable combination of hardware and/or software. The computer-readable medium or article may include, for example, any suitable type of memory unit, memory, memory article, memory medium, storage device, storage article, storage medium and/or storage unit, for example, memory (including non-transitory memory), removable or non-removable media, erasable or non-erasable media, writeable or re-writeable media, digital or analog media, hard disk, floppy disk, Compact Disk Read Only Memory (CD-ROM), Compact Disk Recordable (CD-R), Compact Disk Rewriteable (CD-RW), optical disk, magnetic media, magneto-optical media, removable memory cards or disks, various types of Digital Versatile Disk (DVD), a tape, a cassette, or the like. The instructions may include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, encrypted code, programming code, and the like, implemented using any suitable high-level, low-level, object-oriented, visual, compiled and/or interpreted programming language. The non-transitory computer readable medium embodied programming code may cause a processor when executing the programming code to perform functions, such as those described herein.

Certain examples of the present disclosure were described above. It is, however, expressly noted that the present disclosure is not limited to those examples, but rather the intention is that additions and modifications to what was expressly described herein are also included within the scope of the disclosed examples. Moreover, it is to be understood that the features of the various examples described herein were not mutually exclusive and may exist in various combinations and permutations, even if such combinations or permutations were not made express herein, without departing from the scope of the disclosed examples. In fact, variations, modifications, and other implementations of what was described herein will occur to those of ordinary skill in the art without departing from the the scope of the disclosed examples. As such, the disclosed examples are not to be defined only by the preceding illustrative description.

Claim 1:
A wearable drug delivery device comprising:
a controller (<NUM>);
a memory (<NUM>), coupled to the controller (<NUM>) and configured to store a basal delivery history and
software, for execution by the controller (<NUM>), the software implementing a medication delivery algorithm;
a single reservoir (<NUM>) configured to store GLP-<NUM> Therapeutic, wherein the GLP-<NUM> Therapeutic includes formulations of GLP-<NUM> and co-formulations of GLP-<NUM> and rapid-acting insulin, in any concentrations;
a pump mechanism (<NUM>), controlled by a control signal output by the controller when the medication delivery algorithm is executed by the controller, to deliver time-dependent basal dosages and in fluid communication with the single reservoir; and
a patient interface, in fluid communication with the pump mechanism;
wherein basal doses of the GLP-<NUM> Therapeutic are delivered to a wearer of the wearable drug delivery device in accordance with a basal delivery schedule, the medication delivery algorithm implementing a method comprising:
retrieving the basal delivery history, the basal delivery history including a predetermined number of basal dosages of GLP-<NUM> Therapeutic delivered over a period of time partitioned into intervals;
evaluating the basal dosages within each interval to obtain an interval profile for each interval indicating the amount of GLP-Therapeutic delivered in each basal dosage and a delivery time of each basal dosage;
determining an average interval profile comprising a series of average basal dosages, each having an average delivery time;
evaluating each average basal dosage with respect to other average basal dosages to determine a similarity in the amount of the GLP-<NUM> Therapeutic delivered and a similarity in the corresponding average delivery time;
aggregating average basal dosages meeting an amount similarity threshold and assigning a time range based on a time similarity threshold; and
modifying the basal delivery schedule with an updated amount of the GLP-<NUM> Therapeutic based on the aggregated average basal dosages and an updated delivery time based on the assigned time range.