Systems and methods

Abstract

A system for generating a combination therapy regimen for a patient suffering from a disease or condition, the system comprising at least one data processing device having at least one processor, the system configured to: receive an identification of a combination therapy suitable for treating the disease or condition, receive desired patient endpoints and patient aspects defined in association with the desired patient endpoints, store a patient-related dataset, the dataset including one or more patient data based on patient-related measures, process the dataset, the patient aspects, and the desired patient endpoints to generate a regimen for the combination therapy, and store the regimen in a database.

Description

[Technical Field]

[0001] The present invention relates to methods and systems suitable for use in identifying and providing personalized medicine to patients in need thereof, particularly when combination therapy is used in which two or more dual dosage regimens are provided. The methods and systems can be used to assess the efficacy of dosage regimens in patients using various data inputs to provide personalized medicine. The appropriate dosage regimen for a particular patient can also be predicted. [Background technology]

[0002] Patients are routinely prescribed medications by their healthcare providers for the treatment of various diseases and conditions. The medications are to be taken by the patient according to instructions provided by the healthcare provider, which together form a dosing regimen. The dosing regimen is based on clinical trials conducted on a group of patients that compare the effects of one medication with another. Clinical trials provide dosing regimens that are generic and not personalized to the particular patient requesting treatment.

[0003] Additionally, patients typically do not have immediate access to a healthcare provider, which means they often have to wait weeks for a new appointment before they can discuss their treatment with their healthcare provider and modify their regimen.

[0004] To exacerbate this situation, combination therapy regimens have been much less studied than monotherapies, leading healthcare providers to often prescribe therapies that include drug combinations that have not undergone rigorous clinical trials. Furthermore, NICE guidelines for medications are typically condition-specific, yet patients typically suffer from comorbidities.

[0005] This means that patients prescribed combination therapy may receive a lower level of care than can be provided by a treatment regimen tailored specifically for the individual patient. This can increase health complications, delay or prevent successful treatment of the disease or condition, and lead to decreased patient compliance because patients do not feel the treatment is working or appropriate for them.

[0006] In addition to the above, there are other reasons why patients may not adhere to medication. These include patients forgetting to take their medication, unpleasant side effects, lack of specific efficacy of medication, dosing frequencies greater than once a day, patients being unable to understand complex medication instructions, and patients exercising their discretion for various personal or social reasons.

[0007] Furthermore, over time, the effectiveness of a particular medication, or a patient's perception of its effectiveness, may decline due to changes in the patient caused by factors unrelated to the medication itself. For example, changes in a patient's lifestyle may affect the patient's perception of the medication's efficacy, or the medication's actual efficacy. This may discourage the patient from continuing with the course of treatment. Currently, there is no way to capture the complex interdependence between a patient's overall condition and the efficacy of their medication, nor is there a way to determine or predict how changes in a patient's condition will affect the actual or perceived efficacy of a medication.

[0008] In view of the above, there is a need in the art to provide methods and systems for providing personalized medicine that address one or more of the above problems. There is also a need to provide methods and systems for monitoring and providing combination therapy to "at risk" individuals, such as those with co-morbidities, drug rehabilitation, psychological vulnerabilities, or weakened immune systems. Summary of the Invention [Problem to be solved by the invention]

[0009] The present invention provides a reliable and efficient means for providing personalized medicine to patients in need thereof. [Means for solving the problem]

[0010] According to a first aspect of the present invention there is provided a method of generating a combination therapeutic regimen for a patient suffering from a disease or condition, the method comprising: a) establishing desired patient endpoints; b) identifying a patient position associated with a desired patient endpoint; c) generating or modifying a patient-related dataset based on one or more patient-related measurements; d) processing the dataset, patient aspects and desired patient endpoints to generate a combination therapy regimen.

[0011] The method of the first aspect of the present invention provides personalized combination therapy regimens by accurately predicting an appropriate combination therapy regimen for treating a disease in a particular patient based on data related to that patient.

[0012] According to a second aspect of the present invention there is provided a method of treating a patient suffering from a disease or condition, the method comprising: a) selecting an appropriate combination therapy to treat the disease or condition; b) establishing desired patient endpoints; c) identifying a patient aspect associated with a desired patient endpoint; d) generating or modifying a patient-related data set based on the one or more patient-related measurements; e) processing the dataset, patient aspects and desired patient endpoints to generate a regimen for combination therapy; f) administering the combination therapy to the patient according to the regimen.

[0013] A second aspect of the present invention provides a personalized method of treating a patient suffering from a disease or condition. This can be in the form of an iterative process in which a combination therapy is administered to a patient and then additional data related to the patient is processed to provide a modified combination therapy. This helps to maintain optimal treatment of the disease or condition in a dynamic patient environment.

[0014] According to a third aspect of the present invention, there is provided a system for generating a combination therapeutic regimen for a patient suffering from a disease or condition, the system comprising at least one data processing device having at least one processor, the system comprising: receiving an identification of a suitable combination therapy for treating the disease or condition; receiving a desired patient endpoint and a patient aspect defined in relation to the desired patient endpoint; storing a patient-related dataset, the dataset including one or more patient data based on the patient-related measurements; processing the dataset, patient aspects, and desired patient endpoints to generate a regimen for combination therapy; and It is configured to output a regimen.

[0015] The third aspect of the present invention provides a system capable of collecting data related to a patient's condition and analyzing this data to characterize the patient's condition. Based on this characterization, a combination therapy regimen can be generated and output by the system, and this regimen is personalized to the patient's current condition. The system can repeatedly reassess the patient's condition and update the combination therapy regimen as needed. This helps maintain optimal treatment of the disease or condition in a dynamic patient environment. [Effects of the Invention]

[0016] The present invention can provide benefits to patients, particularly with regard to medication adherence and the patient's actual and perceived health. It also provides benefits to healthcare providers by providing treatment regimens where various factors may determine the suitability of therapy. For example, it is known that both CBT and exercise therapy, as well as certain medications such as neuromodulators (e.g., tricyclic antidepressants), are useful in managing chronic pain, and that the timing and duration of each are ideally tailored to each other.

[0017] The claimed methods and systems may also be beneficial to a wide variety of practices in clinical care, particularly in "complex" or "at-risk" patients where traditionally prescribed drug treatments may potentially result in toxicity or suboptimal therapy. The present invention may also alleviate problems associated with healthcare providers relying on prior experience to individualize therapeutic therapy in complex situations where specific dosing recommendations are lacking.

[0018] A particular advantage of the present invention is its value in administering non-pharmaceutical therapies, such as behavioral interventions. Medications typically have a reasonably predictable conversion from pharmacodynamic response to efficacy on the day they are administered in the clinic, i.e., effects are realized within three days or three weeks when taken at home. Prior to the present invention, the way patients interpreted and responded to non-pharmaceutical interventions, such as behavioral therapy, could vary depending on how and by whom the therapy was delivered. For example, it could be affected by whether it was delivered by a clinician in the clinic, delivered at a later time (when treatment / motivation waned), or delivered electronically daily, hourly, or minute-by-minute. It could also depend on mental state, location and situation, history, and / or time of other pharmaceutical and non-pharmaceutical therapies, as well as the amount and duration of therapy delivery, among other factors.

[0019] Other features and advantages of all aspects of the present invention will become apparent from the following detailed description of the invention, when taken in conjunction with the accompanying drawings and examples which illustrate principal aspects of the invention. [Brief explanation of the drawings]

[0020] [Figure 1] 1 illustrates a system suitable for implementing any of the methods described herein, according to embodiments of the present invention. [Figure 2] 2 illustrates a method that may be performed by one or more components of the system of FIG. 1 according to an embodiment of the present invention. [Figure 3] 2 illustrates a method that may be performed by one or more components of the system of FIG. 1 according to another embodiment of the present invention. [Figure 4] 10 illustrates a method that may be performed by one or more components of the system of FIG. 1 according to a further embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0021] The present invention provides personalized medicine to patients, particularly to treat the disease or condition from which they suffer. The personalized medicine may be provided in the form of a combination therapy, which may include one or more pharmacological therapies and / or one or more non-pharmacological therapies.

[0022] The method of the first aspect of the invention may be used to generate or create a personalized medicine for a patient, the personalized medicine comprising a combination therapeutic regimen suitable for use in treating a disease or condition from which the patient suffers.

[0023] As used herein, a "combination therapy regimen," "regimen for combination therapy," or any similar term refers to a course of two or more (i.e., at least two) therapies administered to a patient with the intent of treating a disease or condition. The method may include two, three, four, five, six, seven, or more different therapies. The regimen may include associated amounts, intensities, and / or frequencies (which may be related to the administration or time of a previous dosage) for each individual therapy. Preferably, the combination therapy includes different therapies. This means that the therapies are preferably not of the same type, e.g., they are not two types of drugs acting on the opiate pathway intended to treat the same disease or condition. It is particularly preferred that the therapies do not have the same mechanism of action on the patient. For example, in this particular case, the combination therapy may provide inflammatory relief, but the combination therapy may not include two NSAID therapies. In certain embodiments, the combination therapy is not all hormone-based therapy.

[0024] The therapies can be administered sequentially or concomitantly, and by any route, with dosage intervals between the same and / or different therapies forming part of a combination therapy regimen. For example, a combination therapy regimen may require that two therapies be administered to a patient consecutively every day or every other day. Alternatively, one or more therapies can be administered according to the patient's needs (i.e., "on-demand") or when data related to the patient indicates that a therapy should be administered. Those skilled in the art will know and understand the range of therapies that can be administered to a patient suffering from a particular disease or condition.

[0025] The term "one or more" means that at least one of the items following the term must be present, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more.

[0026] "Therapy" may be based on pharmacological therapies, such as pharmacological drug therapy, or non-pharmacological therapies, such as cognitive behavioral therapy (CBT), light therapy, exercise therapy, hypnosis, massage, reflexology, and meditation. Thus, the term "therapy" should be interpreted broadly and includes any course of action that is or may be suitable for use in the treatment of a disease or condition.

[0027] The term "cognitive behavioral therapy" refers to any therapy that influences or changes the way a patient thinks and / or behaves. Its typical form is as a "talking therapy" for mental health issues such as anxiety and depression, but it is understood in the art that a similar approach to changing the way a patient thinks and behaves can be applied to multiple other conditions, such as chronic pain, functional impairment, COPD, and diabetes, to how patients interpret their medical condition, how they interpret their interactions with the world and the future, how they control their attention, and how they behave, including their sleep / wake cycle, movement patterns, and diet. CBT can also help patients cope with overwhelming problems in a more positive way by breaking them down into smaller parts. CBT is based, in part, on the concept that a patient's thoughts, emotions, physical sensations, and behaviors are interconnected, and that negative thoughts and emotions can cause or worsen certain diseases and symptoms. CBT has been well-documented in treating depression, anxiety, obsessive-compulsive disorder, panic disorder, post-traumatic stress disorder, phobias, eating disorders such as anorexia and bulimia, sleep disorders such as insomnia (in which case it is sometimes called cognitive behavioral therapy for insomnia (CBTi)), and problems associated with drug and alcohol misuse. CBT can also be used to treat patients with long-term health conditions such as chronic pain, COPD, diabetes, headaches, and fatigue such as irritable bowel syndrome (IBS) and chronic fatigue syndrome (CFS). While it is generally believed that CBT alone cannot cure the physical symptoms of IBS and CFS, it can help people cope better with their conditions.

[0028] The term "administered" is one of the art and means that a therapy is provided or given to a patient. For the present invention, how the therapy is administered to a patient may not be important. For example, the therapy may be administered to a patient by a healthcare provider or another third party. The therapy may be administered automatically by an electronic device, such as a smartphone or other handheld device, or directly in response to user input from the patient, healthcare provider, or another third party. Alternatively, the patient may administer the therapy himself or herself, such as by taking a pill or meditating. The electronic device may operate with instructions provided by a second electronic device located remotely from the electronic device, such as a cloud-based server, and such instructions are transmitted to the electronic device over a network, e.g., the Internet or a cellular network.

[0029] When the therapy is a pharmacological therapy, any suitable route can be used to administer the therapy. Preferably, the route of administration is oral, rectal, nasal, topical (including buccal and sublingual), transdermal, intrathecal, transmucosal, or parenteral (including subcutaneous, intramuscular, intravenous, and intradermal) administration. Pharmaceutical compositions useful for pharmacological therapy can be formulated into unit dosage forms, such as tablets and sustained-release capsules, and liposomes. Alternatively, pharmaceutical compositions can be provided as pre-administered gels, liquids, and syrups that are administered (by the patient, a third party, or an automated administration device) prior to administration. Dosage forms useful in the context of the present invention can be prepared by any method well known in the art of pharmacy. It is envisioned and preferred that the dosage forms can be provided in a "smart pack," i.e., a device that monitors the administration of medication to a patient. Such smart packs can be used to provide patients and / or third parties (e.g., healthcare providers) with data regarding patient compliance with a combination therapy regimen. This data may also be used in the present invention to modify the dataset as outlined below.

[0030] In some cases, a patient may only have access to a unit dosage form containing a specific amount of active pharmaceutical ingredient. In such cases, the combination therapy produced by the first aspect of the present invention may "round" the dosing regimen to the increment available to the patient. For example, if a patient has access to unit dosages containing 0.2 mg and 0.5 mg of melatonin, the method may produce a combination therapy regimen limited to increments of 0.2 mg and 0.5 mg of melatonin, e.g., 0.2, 0.5, 0.7, 0.9, 1.0, 1.2, 1.4, 1.5, 1.7, 1.9, and 2.0 mg of melatonin.

[0031] When the therapy is a non-pharmacological therapy, any suitable route can be used to administer the therapy. Preferably, the non-pharmacological therapy is administered by an electronic device such as a computer, smartphone, or another handheld device. The non-pharmacological therapy can be administered automatically or in direct response to user input from the patient, a healthcare provider, or another third party. The electronic device can operate on instructions provided by a second electronic device located remotely from the electronic device, such as a cloud-based server, and such instructions are transmitted to the electronic device over a network, e.g., the Internet or a cellular network.

[0032] The term "treatment" includes amelioration of a disease or condition, or its symptom(s). Treatment also includes amelioration of the side effects of another therapy, such as pharmacological therapy. Treatment also includes reduction of a patient's dependency on another drug or behavior. "Improvement" is an improvement, or perceived improvement, in a patient's condition, or a change in a patient's condition that makes it more tolerable, or a side effect.

[0033] With respect to a patient suffering from a disease or condition, the term "suffering" includes a patient who is suffering from the disease or condition, and also covers a patient who expects to suffer from the disease or condition, for example, when the method is used as a preventative measure.

[0034] The terms "comprises" and variations thereof do not have a limiting meaning where these terms appear in the description and claims. Such terms are understood to mean the inclusion of stated steps or elements or groups of steps or elements but not the exclusion of other steps or elements or groups of steps or elements.

[0035] The term "consisting of" means inclusive of and limited to what follows the phrase "consisting of." Thus, the phrase "consisting of" indicates that the listed elements are required or essential, and that other elements may not be present for that particular function.

[0036] The term "cloud," or equivalently, "cloud-based," should be understood to refer to one or more configurable computing resources that can be called upon as needed to perform tasks. The computing resources are located remotely from a user or a data processing device associated with a user, and are accessible over a network, such as the Internet or a cellular network.

[0037] The term "machine-readable" means a format that can be processed by a data processing device, where processing includes, but is not limited to, one or more of identifying and displaying one or more data items stored in a machine-readable data structure on a display device, and extracting one or more data items stored in a machine-readable data structure and performing one or more calculations on the data items.

[0038] Step (a) of the first aspect of the present invention involves establishing a desired patient endpoint. The desired patient endpoint can be a goal expected to be achieved by administering a combination therapy regimen to a patient. The desired patient endpoint can be established by a healthcare provider, a patient, or a combination thereof. The desired patient outcome can be specific to a particular disease or condition and / or patient. It can be a single goal or a group of goals. The endpoint can be one that forms an optimal balance between the beneficial effects and side effects of the therapy, as determined by the clinician, the patient, or ideally both.

[0039] The desired patient endpoints should be represented and stored in a manner that is accessible to and readable by the data processing device so that they can be used in processing step (d). The desired patient endpoints can be stored either by the patient or by the healthcare provider. It will be understood that storing can thus include inputting the desired patient endpoints into the data processing device using its user interface, and the data processing device creating a machine-readable representation of the desired patient endpoints and storing this representation in a non-volatile storage medium. The non-volatile storage medium is preferably cloud-based and accessible via a network, such as the Internet or a cellular network.

[0040] The machine-readable representation of the desired patient endpoint may be stored in a structured format, such as an element in a database, or in a semi-structured format, such as an element node in an XML document. The representation of the desired patient endpoint may include one or more data types, including, but not limited to, one or more strings, integers, double-precision values, floating-point values, Boolean values, and combinations thereof. The appropriate representation for the desired patient endpoint will be determined by a skilled person, given the specific circumstances of any particular scenario.

[0041] Preferably, the machine-readable representation of the desired patient endpoint is securely stored to protect patient confidentiality. One or more authentication credentials are preferably provided to access the stored representation of the desired patient endpoint. The representation of the desired patient endpoint may additionally or alternatively be stored in an encrypted format. Such techniques are known per se and therefore will not be described in detail here.

[0042] The desired patient endpoint may include successful treatment of a disease or condition such that the patient is no longer afflicted by the disease or condition or its pathology. It may also include amelioration of side effects of the disease or condition, side effects resulting from pharmaceuticals such as those administered to the patient to treat the disease or condition, and / or side effects of non-pharmacological therapies. In this case, the patient may have input in defining the desired patient endpoint, such as with regard to acceptable side effects. The desired patient endpoint may be the patient achieving a particular value on a known disease condition scale, such as the pain value defined by the Wong-Baker Faces Pain Scale.

[0043] The desired patient endpoint may depend largely on the disease or symptom to be treated.If insomnia is the disease or symptom, then the desired patient endpoint may include the patient having at least about 4 hours of sleep, for example, at least about 5 hours of sleep, preferably at least about 6 hours of sleep, more preferably at least about 7 hours of sleep in 24 hours, preferably at night.It may include the patient not waking up before a certain time in the morning, such as before 7:00 AM, for example, before 6:00 AM, preferably before 5:00 AM, and / or not experiencing difficulty falling asleep.The desired patient endpoint may be the patient feeling that he or she is getting sufficient quality sleep over the course of 5 days.

[0044] If diabetes is the disease or condition, then the desired patient endpoint may include patients having preprandial blood glucose levels below a certain amount, such as about 4.0 to about 7.0 mmol / L, preferably about 4.0 to about 5.9 mmol / L, before meals. The desired patient endpoint may include treatment or amelioration of side effects of having diabetes, such as fatigue or burning feet. It may also include reducing side effects from medications used to treat diabetes, such as metformin-related intestinal side effects.

[0045] When hypertension is a disease or condition, a desired patient endpoint may include a patient having a resting systolic blood pressure of about 110 to about 130 mmHg, and optionally a diastolic blood pressure of about 60 to about 85 mmHg. It may also include a reduction in symptoms associated with hypertension, such as a reduction in headaches. Conversely, recognizing that blood pressure often fluctuates throughout the day and from day to day in any given individual, it may be desirable to minimize the frequency of symptoms associated with orthostatic hypotension in patients who experience daytime periods of lower blood pressure. The reduction in symptoms may be a reduction in a specific frequency of occurrence.

[0046] When opiate dependence is a disease or condition, a desired patient endpoint may include the patient ceasing opiate use, which may include a decrease in tolerance to opiate-like substances and / or a reduction or amelioration of withdrawal symptoms, such as nausea, diarrhea, sleep disturbances / insomnia, tremors, sweating, recurrence of pain, or depressed mood.

[0047] It is contemplated that the desired patient endpoint may change as treatment of a disease or condition progresses. For example, the patient and / or healthcare provider may determine that the endpoint may not be achievable or is not necessary. For example, a desired patient endpoint for insomnia may be for the patient to have 7 hours of sleep in a 24-hour period; however, the patient may feel that 6 hours of sleep in a 24-hour period is adequate. In this case, the desired patient endpoint may be modified accordingly. Similarly, if the desired endpoint is for the patient to have 6 hours of sleep in a 24-hour period and the patient feels that 7 hours of sleep in a 24-hour period is achievable, the desired patient endpoint may be modified accordingly.

[0048] Step (b) of the first aspect of the present invention involves identifying a patient aspect associated with the desired patient endpoint. The patient aspect should be relevant to the desired patient endpoint. For example, if the desired patient endpoint is a patient with a systolic resting blood pressure of about 110 to about 130 mmHg, the patient aspect may be the patient's current resting blood pressure. If the desired patient endpoint is a patient with a preprandial blood glucose level of about 4.0 to about 5.9 mmol / L, the patient aspect may be the patient's current preprandial blood glucose level.

[0049] The difference between the patient phase and the desired patient endpoint can be used to define the therapeutic range expected to be delivered by a combination therapy regimen. For example, if the desired patient endpoint is a patient with a resting blood pressure of about 110 to about 130 mmHg systolic, and the patient's current resting blood pressure is about 170 mmHg systolic, the treatment goal would be to reduce the patient's resting blood pressure to about 40 to about 50 mmHg. In situations where the patient's mean blood pressure is 125 mmHg, the patient may experience a drop to 115 mmHg or 145 mmHg over the course of a day. While the 115 mmHg case has been associated with bothersome dizziness, if a mean blood pressure of 135 mmHg is the goal and periods of symptomatic hypotension do not occur, the 135 mmHg target may be preferred by both the patient and the clinician.

[0050] In a further variation, the initial target may be adjusted to 135 mmHg systolic, but over the course of further time, e.g., three months, the patient's vasculature may adapt to now being able to tolerate 115 mmHg systolic, and therefore the new average systolic target is 125 mmHg.

[0051] The patient aspect associated with the desired patient endpoint should be stored in the same manner as described above for the desired patient endpoint so that it can be used in processing step (d). Preferably, the machine-readable representation of the patient aspect is stored in the same format as the machine-readable representation of the desired patient endpoint, which may enable or assist in calculation of the difference between the patient aspect and the desired patient endpoint by the data processing device.

[0052] Step (c) of the first aspect of the invention involves generating or modifying a patient-related data set based on one or more patient-related measurements.

[0053] As used herein, a "dataset" is a machine-readable collection of information or data made up of separate elements that can be manipulated individually or collectively by a processor, such as a processor in a data processing device. The information or data in a dataset is related to a patient. Datasets can take many forms, including structured, semi-structured, or unstructured datasets.

[0054] Without being bound by theory, it is understood that the patient-related measurements are expected to be useful in generating appropriate combination therapy regimens for use in treating the disease or condition to which the method is relevant. Thus, the dataset may be useful in predicting a patient's susceptibility to treatment of a particular disease with a combination therapy.

[0055] The combination of patient sensitivity to treatment and the range of treatment defined by patient aspects related to desired patient endpoints can be used to provide a combination therapy regimen.

[0056] It is envisioned that the claimed methods may be used to help predict the efficacy and / or suitability of a combination therapy regimen for a particular patient.

[0057] If a dataset related to the patient is not available, perhaps because the patient has just been enrolled in the system, then the dataset is generated. This involves creating a dataset containing the relevant data, as discussed below, and linking the dataset to the patient, such as using a patient code or other unique identifier. If a suitable dataset related to the patient is already available on the data processing device, then the dataset may be modified by adding one or more relevant data entries to the dataset and / or replacing one or more relevant data entries already present in the dataset, as discussed below.

[0058] Patient-related measurements are a) one or more physiological measurements; b) one or more patient-centered outcomes; c) one or more environmental measurements local to the patient, such as temperature, humidity, and / or light intensity; and / or d) one or more behavioral factor measures.

[0059] The term "patient-related measurement" refers to data related to a patient. A patient-related measurement can be a "physiological measurement," e.g., resting heart rate, resting systolic blood pressure, blood glucose level, biomarker concentration in the blood, or other data directly related to the patient. Such measurements can be taken by the patient, a healthcare provider, or by an electronic device, such as a smartphone or other handheld device. In either case, the one or more patient-related measurements used in the methods of the invention can depend on the particular disease or condition being treated.

[0060] The patient-related measure may be a patient-centered outcome.

[0061] "Patient-centered outcomes" are assessments of patients' beliefs, opinions, and needs, optionally in conjunction with the expertise of healthcare providers, related to their treatment. Patient-centered outcomes can include an indication of whether a patient is receiving adequate relief from one or more symptoms of the disease or condition for which they are being treated. For example, it can be an indication of whether a treatment is providing the patient with sufficient pain relief or adequate sleep. It can also include negative effects of a treatment, including various side effects and a favorable trade-off between the beneficial effects and side effects of a treatment.

[0062] Patient-centered outcomes may be reported and / or recorded by the patient, the healthcare provider, or an electronic device such as a smartphone or other handheld device. When a patient-centered outcome is reported and / or recorded by the patient, it may be referred to as a patient-reported outcome.

[0063] Patient-centered outcomes, and therefore patient-reported outcomes, can be qualitative or quantitative. Patient-centered outcomes, particularly patient-reported outcomes, may need to be mapped onto a predefined scale to create a mapped patient-centered outcome. This is desirable when the patient-centered outcome (or patient-reported outcome) is qualitative. It may be desirable for the patient-reported outcome to be provided via a questionnaire. Furthermore, certain predefined scales may be individualized for the patient.

[0064] The patient-related measurements may relate to the patient's environment, such as the patient's local environment. In this case, environmental measurements may be taken. Suitable environmental measurements may include temperature, humidity, and / or light intensity, such as daily light exposure, daily average temperature, daily maximum / minimum temperature, and daily rainfall. The environmental measurements may be reported and / or recorded by the patient, a healthcare provider, or an electronic device, such as a smartphone or other handheld device.

[0065] The patient-related measurements can be behavioral factor measurements. These are measurements of specific patient behaviors, such as total steps per day, minutes of cardiovascular training performed per day, or units of alcohol consumed per week. Behavioral factor measurements can be qualitative or quantitative. Behavioral factor measurements may need to be mapped onto a predefined scale to create a mapped behavioral factor measurement, which is desirable when the behavioral factor measurement is qualitative.

[0066] As noted above, patient-related measures useful in the methods of the invention may depend on the disease or condition it is intended to treat. Again, patient-related measures are known that may be expected to be useful in generating appropriate combination therapy regimens for use in treating the disease or condition.

[0067] Data may be entered directly into the dataset in raw form, or may be processed before being entered into the dataset. Such processing may include obtaining the data and modifying or evaluating one or more of the constituent data points of the data before entering it into the dataset. For example, a patient may provide information in the form of a patient-centered outcome, such as the level of pain they are experiencing, by pointing to faces on the Wong-Baker Faces Pain Scale, which is converted into a number according to the scale and entered into the dataset.

[0068] Patient-related measurements may be taken by the patient and / or a healthcare provider. For example, a patient may take their own blood pressure, heart rate, or blood glucose level. Alternatively, a healthcare provider may take the patient's blood pressure, heart rate, or blood glucose level. Patient-related measurements may require input from multiple people. For example, a patient may provide a blood sample at a specific time, which is analyzed for a particular biomarker concentration, and the concentration is entered into a dataset. Measurements may be actively obtained, for example, when the patient and / or healthcare provider takes a specific action to obtain a measurement, such as providing a blood sample at a specific time. Measurements may also be passively obtained, such as via wearable technology, preferably linked to an electronic device such as a smartphone or other handheld device.

[0069] Patient-related measurements may be obtained from other sources, such as online databases or third parties. For example, data may be taken from an online weather website to estimate daily exposure for a patient based on their location.

[0070] Each of the patient-related measures used in the methods of the present invention should be expected to have an impact on the combination therapy regimen, although it will be apparent that the level of impact may depend on the particular patient-related measure, the patient and / or the disease or condition to which the method relates. It is therefore envisaged that the step of generating or modifying a patient-related dataset may include the step of applying a weighting factor to each of one or more patient-related measures.

[0071] Step (d) of the first aspect of the present invention involves processing the dataset, patient aspects and desired patient endpoints to generate a combination therapy regimen. Processing may be performed using a processor, such as a processor in a data processing device.

[0072] Without being bound by theory, the difference between the patient aspects and the desired patient endpoints may define the range of treatments delivered to the patient, and the dataset may be used to predict the patient's susceptibility to treatment. Thus, the dataset, patient aspects, and desired patient endpoints may be processed to provide a patient-specific dosing regimen (personalized medicine) that is expected to treat a particular disease or condition.

[0073] In the processing step, the dataset, patient aspects, and desired patient endpoints may be processed using a rules-based system to generate a regimen for the combination therapy. Alternatively, the dataset, patient aspects, and desired patient endpoints may be processed using one or more machine learning algorithms to generate a regimen for the combination therapy. As a further alternative, a hybrid approach is contemplated in which both a rules-based system and one or more machine learning algorithms are used to process the dataset, patient aspects, and desired patient endpoints.

[0074] The term "rule-based system" refers to a system that operates according to a set of one or more predefined rules. The one or more rules may be encoded in a computer-interpretable format, such as one or more modules of program code. The one or more rules may be encoded to utilize known or hypothesized relationships between patient aspects and their desired patient endpoints, and / or observations of changes in patient behavior, health, or other such parameters as treatment progresses, to generate recommended regimens for combination therapy. Other factors unrelated to the patient's condition, such as regulatory constraints, may additionally or alternatively be encoded into the one or more rules.

[0075] One or more rules may be modified or deleted as necessary to take into account new observations, hypotheses, and / or knowledge. One or more new rules may be added to an existing set of one or more rules, perhaps with one or more new rules being introduced to take into account new observations, hypotheses, and / or knowledge, and / or changes in the regulatory framework.

[0076] A rule may reference another entity, such as a dataset discussed above. A rule may specify that a particular action is or is not performed based on a property of the entity, such as the value of a data point in the dataset. A rule may include instructing a data processing device to perform a calculation, possibly including or based on a property of the entity, with the action performed as a result of the rule depending on the output of the calculation. A rule may reference one or more external data sources, such as a medical institution database, with the action specified by the rule depending on data retrieved from that database.

[0077] The term "machine learning algorithm" takes its ordinary meaning in the art and includes any algorithm that employs a now-known or later-developed machine learning technique or techniques. Examples of machine learning algorithms include, but are not limited to, neural networks, support vector machines, naive Bayes classifiers, K-means algorithms, etc. Deep learning techniques may be used. Machine learning algorithms may employ supervised, semi-supervised, and / or unsupervised learning techniques.

[0078] In the context of the present disclosure, at least one machine learning algorithm is used together with or instead of the rule-based system discussed above, with the purpose of creating a regimen for combination therapy. One or more machine learning algorithms can use one or more data points from the dataset discussed above to input into a model, and the output of the model is a regimen for combination therapy. The model can be trained using one or more data points from the dataset discussed above. The training of machine learning models and the use of trained models are known per se in the art, and therefore will not be discussed in detail here.

[0079] The combination therapy may include a pharmacological therapy, a non-pharmacological therapy, or a mixture thereof. Specifically, the combination therapy used in the methods of the present invention may include: (i) two or more pharmacological therapies; (ii) one or more pharmacological therapies and one or more non-pharmacological therapies, preferably cognitive behavioral therapies; or (iii) Two non-pharmacological therapies, preferably at least one of which is cognitive behavioral therapy.

[0080] In a second aspect of the present invention, there is provided a method of treating a patient suffering from a disease or condition, the method comprising: a) selecting an appropriate combination therapy to treat the disease or condition; b) establishing desired patient endpoints; c) identifying a patient aspect associated with a desired patient endpoint; d) generating or modifying a patient-related data set based on the one or more patient-related measurements; e) processing the dataset, patient aspects and desired patient endpoints to generate a regimen for combination therapy; f) administering the combination therapy to the patient according to the regimen.

[0081] As will be appreciated, steps (b) to (e) of the second aspect of the invention correspond to steps (a) to (d) of the first aspect of the invention, and the definitions therefor for the first aspect of the invention apply equally to the second aspect of the invention.

[0082] Further to the above, step (a) of the second aspect of the present invention involves selecting an appropriate combination therapy to treat the disease or condition. The range of combination therapies that may be appropriate for use in treating a particular disease or condition will be known, particularly to healthcare providers.

[0083] Step (f) of the second aspect of the invention comprises administering the combination therapy to the patient according to the regimen. As noted above, it is within the scope of the invention that the combination therapy be administered to the patient according to the regimen in any suitable manner.

[0084] It is envisioned that successful treatment of a disease or condition may require multiple (i.e., two or more) treatment cycles. A treatment cycle may include each of steps (a) through (f) of the second aspect of the invention. Thus, a treatment method according to the second aspect of the invention may include performing steps (a) through (f) and then repeating steps (a) through (f) at least one time, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.

[0085] In the treatment method according to the second aspect of the invention, it is preferred that the combination therapy is not changed between treatment cycles, and therefore the method comprises multiple treatment cycles, each treatment cycle preferably comprising steps (b) to (f).

[0086] When multiple treatment cycles are used, the frequency of the cycles may depend on the specific therapy administered to the patient. In particular, the duration between two consecutive cycles of processing step (e) of the second aspect of the present invention (i.e., processing the dataset, patient aspects, and desired patient endpoints to create a regimen for combination therapy) may depend on the time frame in which the patient is expected to respond to the therapy. For example, if the patient is expected to have a fast response time to a treatment, such as the use of insulin to treat diabetes, processing step (e) may be performed at least about 1 hour, such as at least about 2 hours, e.g., at least about 3 hours, e.g., at least about 4 hours, after processing step (e) was last performed. If the patient is expected to have an intermediate response time to a treatment, such as the use of melatonin to treat insomnia, processing step (e) may be performed at least about 1 day, such as at least about 2 days, e.g., at least about 3 days, e.g., at least about 4 days, after processing step (e) was last performed. If the patient is expected to have a slow response time to treatment, such as the use of cognitive behavioral therapy in the treatment of opiate addiction, treating step (e) may be performed at least about one week, such as at least two weeks, e.g., at least about three weeks, e.g., at least about four weeks, after treating step (e) was last performed.

[0087] Notwithstanding the above, an advantage of the present invention is that a patient's combination therapy regimen can be changed within a period shorter than that required by two consecutive visits to a healthcare provider (a first visit to provide the patient with the combination therapy regimen and a second visit to modify the combination therapy regimen based on the patient's response to the combination therapy regimen). The frequency of visits to a healthcare provider may depend on the type of therapy administered to the patient, and therefore, the period between two consecutive cycles of treatment step (e) of the second aspect of the present invention is preferably shorter than the frequency of such visits. For example, treatment step (e) may be performed less than about 8 weeks, e.g., less than about 6 weeks, e.g., less than about 5 weeks, or less than about 10 weeks after treatment step (e) was last performed. In certain cases, treatment step (e) may be performed less than about 2 weeks, e.g., less than about 1 week, e.g., less than about 4 days, i.e., less than about 2 days, or less than about 4 weeks after treatment step (e) was last performed.

[0088] In an exemplary embodiment, in step (d), the dataset may be modified based on one or more patient-related measurements. However, step (e) is not performed until an elapsed time has elapsed, where the elapsed time is equal to the length of time between the generation of the regimen for combination therapy in step (e) and the latest modification to the dataset in step (d). Essentially, step (d) may be performed multiple times until a certain time has elapsed since the generation of the regimen for combination therapy in step (e) occurred, and then step (e) is performed. The elapsed time may be the above-mentioned time between two consecutive cycles of processing step (e).

[0089] If multiple treatment cycles are used, the method may include an additional step after step (e) of adjusting the regimen for the combination therapy based on the difference between the regimen provided in step (e) of the previous cycle and the regimen provided in step (e). In this case, the adjusted regimen for the combination therapy may be adjusted by 60, 70, 80, or 90% of the difference. For example, if an 80% threshold is employed and the regimen provided in the previous cycle included 100 mg of a drug and the new regimen includes 200 mg of a drug, the method may revert to the 180 mg regimen administered to the patient in step (f). This additional step reduces the sensitivity of the method to changes in the patient data set and helps prevent a patient's combination therapy regimen from fluctuating between too high and too low a drug dose to achieve the desired patient endpoint.

[0090] The processing steps may also limit the maximum and minimum amounts that one or more drugs may be administered to a patient and / or the amount by which a regimen for a combination therapy is altered based on regulatory considerations, patient or healthcare provider orders, or other factors.

[0091] Furthermore, since it may not be necessary to re-establish the desired patient endpoint, the method of the second aspect of the invention may include multiple treatment cycles, where a treatment cycle comprises steps (c) through (f).

[0092] Also envisaged in the present invention is a combination therapy for use in treating a patient suffering from a disease or condition, the combination therapy being provided by a method comprising the steps of the first aspect of the invention and all its embodiments.

[0093] The combination therapy produced or provided by the methods of the invention can be used to treat or prevent any disease or condition, including prediabetes, diabetes, cardiovascular disease, neurodegenerative diseases such as mild cognitive impairment (MCI), Alzheimer's disease, Parkinson's disease, atrial fibrillation, attention deficit hyperactivity disorder (ADHD), autoimmune diseases such as ulcerative colitis, lupus erythematosus, Crohn's disease, celiac disease, Hashimoto's thyroiditis, bipolar disorder, cerebral palsy such as movement disorders and athetosis, chronic graft-versus-host disease, hepatitis, chronic kidney disease, arthritis and chronic bone and joint diseases such as osteoarthritis and rheumatoid arthritis, cancer, obesity, asthma, sinusitis, cystic fibrosis, and the like. pulmonary fibrosis, pain including chronic pain syndromes, depression, eating disorders, polycystic ovary syndrome, epilepsy, fibromyalgia, viral diseases such as HIV / AIDS, Huntington's disease, hypotension, hypertension, allergic rhinitis, multiple sclerosis, fatigue conditions including chronic fatigue syndrome, insomnia, narcolepsy, osteoporosis, periodontal disease, postural orthostatic tachycardia syndrome, sickle cell anemia and other hemoglobin disorders, sleep apnea, thyroid disease, and reflux including gastroesophageal reflux disease. diarrhea, vomiting, irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), peptic ulcers, acute urticaria, atopic dermatitis, contact dermatitis, seborrheic dermatitis, headaches such as migraine, cluster headache, tension headache, drug addiction, especially opiate addiction, cocaine, alcohol or nicotine addiction, and their chronic use, thromboembolic disease, hair loss, hormone replacement therapy, psychiatric disorders such as psychosis, anxiety, depression, endocrine dysfunction including growth hormone deficiency, hypothyroidism, and coagulation factors. hematological disorders including deficiencies or low levels of white or red blood cells; neurodevelopmental delay (NDD) disorders such as autism spectrum disorder (ASD), Smith-Magenis syndrome, and ADHD; parasomnias including REM and NREM parasomnias and nightmare disorder; sleep movement disorders such as restless legs syndrome and periodic limb movement disorder; circadian rhythm disorders (including disorders caused by shift work and / or jet lag); chorea; and tic disorders, both acute and chronic.

[0094] Diseases and conditions for which the present invention is particularly useful are insomnia, obesity, diabetes, particularly type II diabetes, hypertension, and opiate dependence.

[0095] Data collected when the disease or symptom is insomnia may relate to one or more of the data considered for insomnia in the table below.

[0096] [Table 1-1]

[0097] [Table 1-2]

[0098] The data collected when the disease or condition is type II diabetes may relate to one or more of the data considered for type II diabetes in the table below.

[0099] [Table 2]

[0100] As mentioned above, suitable combination therapies for treating diseases and conditions are well known. However, when the disease or condition is insomnia, the combination therapy may advantageously consist of two therapies, the first of which includes melatonin and the second of which includes cognitive behavioral therapy for insomnia (CBTi).

[0101] When the disease or condition is diabetes, particularly type II diabetes, the combination therapy may advantageously consist of two therapies, the first comprising metformin and the second comprising cognitive behavioral therapy.

[0102] When the disease or condition is diabetes, particularly type II diabetes, the combination therapy may advantageously consist of two therapies, the first comprising metformin and the second comprising a GLP-1 agonist.

[0103] When the disease or condition is diabetes, particularly type II diabetes, the combination therapy may advantageously consist of two therapies, the first comprising a GLP-1 agonist and the second comprising cognitive behavioral therapy.

[0104] When the disease or condition is diabetes, particularly type II diabetes, the combination therapy may advantageously consist of three therapies, with a first therapy comprising metformin, a second therapy comprising cognitive behavioral therapy, and a third therapy comprising a GLP-1 agonist.

[0105] When the disease or condition is hypertension, the combination therapy may advantageously consist of two therapies, the first comprising amlodipine and the second comprising cognitive behavioral therapy.

[0106] When the disease or condition is opiate-dependent, the combination therapy may advantageously consist of two or three therapies. When the combination therapy consists of two therapies, it is preferred that the first therapy includes morphine, and the second therapy includes an alpha-2 agonist or cognitive behavioral therapy. When the combination therapy consists of three therapies, it is preferred that the first therapy includes morphine, the second therapy includes an alpha-2 agonist, and the third combination therapy includes cognitive behavioral therapy. In each case, it is preferred that the alpha-2 agonist is clonidine.

[0107] A suitable system 100 for performing any of the methods described above is shown in Figure 1. System 100 includes a data processing device 105 communicatively coupled to a database 110 that stores a dataset, as previously discussed herein. Database 110 is stored on a storage medium, for example, a cloud-based storage medium.

[0108] The data processing device 105 comprises at least one processor and is configured to perform any of the methods described herein, or one or more steps thereof. The data processing device 105 may optionally operate according to one or more rules stored in the database 110, and / or the data processing device 105 may be configured to perform one or more machine learning tasks. A machine learning task may include any combination of training a model using data from a dataset stored in the database 110 and / or using the trained model to classify input, such as data from a dataset stored in the database 110.

[0109] The data processing device 105 can be configured to perform tasks including receiving data from the patient device 115, generating data sets for storage in the database 110, appending data to existing data sets stored in the database 110, transmitting information and / or commands to the patient device 115 and / or the clinician data processing device 130, etc. The data processing device 105 can be a server that hosts a website or portal accessible to one or both of the patient device 115 and the clinician data processing device 130.

[0110] In the illustrated embodiment, the patient device 115 is a smartphone, optionally including a sensor 120. However, the invention is not limited in this respect and the patient device 115 can take many other forms, including, but not limited to, a mobile phone, a tablet computer, a desktop computer, a voice-activated computing system, a laptop, a gaming system, a vehicle computing system, a wearable device, a smart watch, a smart television, an Internet of Things device, a medication dispensing device, and a device including a medication pump.

[0111] The patient device 115 is communicatively coupled to the data processing device 105 via a network 125. In the illustrated embodiment, the network 125 is the Internet, although the invention is not limited in this respect and the network 125 can be any network that allows communication between the patient device 115 and the data processing device 105, such as a cellular network or a combination of the Internet and a cellular network.

[0112] The patient device 115 is configured to collect data related to the patient and / or the patient's immediate environment and transmit at least a portion of the collected data to the data processing device 105. The patient device 115 may collect data using sensors 120, which may be any combination of light sensors such as cameras, temperature sensors, acoustic sensors such as microphones, accelerometers, air pressure sensors, airborne particulate sensors, global positioning sensors, humidity sensors, electric field sensors, magnetic field sensors, moisture sensors, air quality sensors, and Geiger counters, and / or any other such sensors capable of determining characteristics of the patient and / or the patient's immediate environment.

[0113] Alternatively, the sensor 120 can be omitted from the patient device 115. In that case, information about the patient and / or the patient's immediate environment can be obtained through other mechanisms, including manual data entry using the human interface devices of the patient device 115.

[0114] It will be understood that system 100 may include two or more patient devices similar to patient device 115. It is contemplated that a single patient may use two or more patient devices to collect data and provide it to system 100.

[0115] The patient device 115 may have one or more applications installed on a storage medium associated with the patient device (not shown), one or more applications configured to control data acquisition via the sensors 120 and / or to assist the patient in providing data related to their current symptoms and / or their immediate environment.

[0116] System 100 optionally includes a clinician data processing device 130 communicatively coupled to data processing device 105 via network 125. Clinician data processing device 130 is generally similar to patient device 115 and offers a similar set of functionality. In particular, clinician data processing device 130 enables data relating to the patient and / or the patient's immediate environment to be collated and transmitted to data processing device 105. It is contemplated that clinician data processing device 130 will be physically located on the clinician's premises in use, such as a doctor's surgery, a pharmacy, or other medical facility, e.g., a hospital. Clinician data processing device 130 may include one or more sensors, such as sensor 120, and / or may be configured to control one or more separate sensors, such as sensor 120, that are capable of collecting information about the patient and / or their local environment.

[0117] It is also contemplated that the clinician data processing device 130 will typically be used by a medically trained person with appropriate data security clearance, and as a result, more advanced functionality may be available than via the patient device 115. For example, the clinician data processing device 130 may be able to access a patient's medical history, generate prescriptions for the patient, order medications, etc. Access to functionality may be controlled by security policies enforced by the data processing device 105.

[0118] It is contemplated that the system 100 may omit the patient device 115 entirely, in which case all reporting of data to the data processing device 105 would be handled by the clinician data processing device 130. This configuration may find particular utility in situations where the patient is unable to provide data to the data processing device via the patient device, for example, due to their current medical condition or non-compliance.

[0119] FIG. 2 illustrates a method that may be performed by the data processing device 105 according to an embodiment of the present invention.

[0120] In step 200, the data processing device 105 receives a desired patient endpoint. The desired patient endpoint may be received from the patient device 115 or the clinician data processing device 130. The data processing device 105 may store the desired patient endpoint in a machine-processable format in the database 110. The desired patient endpoint may be provided by a clinician via a user interface of the clinician data processing device 130 or by a patient via a user interface of the patient device 115.

[0121] The data processing device 105 also receives, in step 200, an identification of a combination therapy suitable for treating a disease or condition from which the patient is suffering that corresponds to the desired patient endpoint. The data processing device 105 may store the identification in a machine-processable format in the database 110. The identification may be provided by a clinician via a user interface of the clinician data processing device 130, or may be retrieved from the database 110 or another data source (e.g., a medical institution database) based on the identification of the disease or condition from which the patient is suffering or based on a patient unique identifier.

[0122] In step 205, the data processing device 105 identifies a patient aspect associated with the desired patient endpoint. To identify the patient aspect, the data processing device 105 may receive patient-related information from one or both of the patient device 115 and the clinician data processing device 130.

[0123] Patient-related information includes, but is not limited to, information entered by the patient using the user interface of the patient device 115, data collected by the sensors 120 of the patient device 115, if present, information entered by the clinician or other medical professional using the user interface of the clinician data processing device 130, and / or data collected by the sensors of the clinician data processing device, if present.

[0124] When sensor data is provided, the data processing device 105 preferably identifies patient aspects by processing the patient-related information using one or more rules stored in the database 110 and / or using trained machine learning models stored in the database 110.

[0125] In step 210, the data processing device 105 stores a patient-related dataset, the dataset including one or more patient data based on patient-related measurements, including, but not limited to, measurements entered by the patient using a user interface of the patient device 115, measurements performed by sensors 120 of the patient device 115, if present, measurements entered by a clinician or other medical professional using a user interface of the clinician data processing device 130, and / or measurements performed by sensors of the clinician data processing device, if present.

[0126] If a dataset associated with the patient already exists in database 110, the data processing device preferably appends the patient data to this existing dataset as part of the storing operation. If a dataset associated with the patient is not found in database 110, storing preferably includes creating a blank dataset, assigning a unique patient identifier to the blank dataset, and populating the blank dataset with the patient data. The unique patient identifier is associated with the patient and can be generated according to known unique identifier generation schemes.

[0127] The data processing device 105 may be configured to apply a weighting factor to each of the patient-related measurements received in step 210 when generating the patient data. The weighting factor represents the relative importance of a particular patient-related measurement relative to other patient-related measurements. The data processing device 105 may generate a separate weighting factor for each of the patient-related measurements. A given weighting factor may have the same value as another weighting factor or a different value.

[0128] The weighting factors may be defined by a clinician in collaboration with the patient. Preferably, the data processing device 105 first receives a range for each weighting factor, followed by a value for each weighting factor that falls within the respective range. Selection within the range may be based on patient preferences, such as, for example, the level of desire for a particular benefit and / or the desire to avoid a particular side effect.

[0129] Preferably, each weighting coefficient is selected to minimize the expected time for the patient to move from a patient phase to a desired patient endpoint. A probabilistic prediction of the patient's condition, e.g., Bayesian prediction, can be used to predict the patient's future condition using current and past patient measurements as a function of each weighting coefficient. A set of weighting coefficients is selected based on the prediction. A set of weighting coefficients is preferably selected that minimizes the expected time for the patient to move from a patient phase (i.e., their current state) to a desired patient endpoint. One or more weighting coefficients can be adjusted as needed during the course of treatment if the patient's actual progress deviates significantly from the patient's predicted progress.

[0130] In step 215, the data processing device 105 processes the dataset, patient aspects, and desired patient endpoints to generate a combination therapy regimen. This step may include processing the dataset, patient aspects, and desired patient endpoints using one or more rules and / or using one or more machine learning algorithms. Regardless of the technique used to generate the combination therapy regimen, the result of step 215 is a combination therapy regimen that is predicted, suggested, or otherwise believed to be likely to be effective in moving the patient toward the desired patient endpoint.

[0131] In cases where the combination therapy includes one or more drugs provided in a fixed dosage format, e.g., a component requiring the patient to administer a pill containing a set amount of active ingredient, step 215 preferably includes a comparison of the dosing requirements of the dosage form of the generated combination therapy regimen to the dosages of the associated drug(s) available to the patient.

[0132] In the event that the patient is unable to administer the associated drug(s) in the amounts required by the generated combination therapy regimen, the data processing device 105 may adjust the generated combination therapy regimen to call for amounts of the associated drug(s) that minimize the difference between the amounts required by the originally generated regimen and the possible combinations of dosages that can be administered by the patient.

[0133] For example, consider the case where a patient is required to administer Drug X as part of a combination therapy. The patient has access to pills containing Drug X, each pill containing 10 mg of Drug X. Data processing device 105 initially generates a combination therapy regimen calling for 32 mg of Drug X. Because the patient is unable to administer exactly 32 mg, data processing device 105 adjusts the combination therapy regimen to call for 30 mg of Drug X, which can be administered by the patient taking three 10 mg pills.

[0134] In another example, data processing device 105 initially generates a combination therapy regimen calling for 38 mg of drug X. Because the patient is unable to administer exactly 38 mg, data processing device 105 adjusts the combination therapy regimen to call for 40 mg of drug X, which can be administered by the patient taking four 10 mg pills.

[0135] Alternatively, the data processing device 105 can be configured to adjust the generated combination therapy regimen to call for an amount of the associated drug(s) equal to the closest value administrable by the patient, not exceeding the dosage originally generated by the data processing device 105.

[0136] Under this alternative implementation, using the Drug X example above, if the data processing device initially generates a combination therapy regimen calling for 38 mg of Drug X, the combination therapy regimen may be adjusted to call for 30 mg of Drug X, which can be administered by a patient taking three 10 mg pills. This alternative implementation may be preferred in situations where exceeding the recommended dosage is deemed undesirable.

[0137] Information regarding dosage forms available to the patient may be provided to the data processing device 105 by the patient device 115 and / or the clinician data processing device 130. This information may be stored in a data set associated with the patient as part of step 210.

[0138] As part of step 215, the data processing device 105 can additionally or alternatively be configured to check whether the change in dosage of one or more components of the combination therapy is greater than a threshold level. The threshold change can be expressed as a percentage change in dosage of a recently generated combination therapy regimen, i.e., the regimen currently followed by the patient. The threshold level is preferably set based on an estimate of the maximum change in dosage that the patient can safely tolerate. The threshold level can be received by the data processing device 105 from a clinician, possibly via the clinician's data processing device 130.

[0139] In the event that the change in dosage is greater than the threshold level, the data processing device 105 is configured to adjust the combination therapy regimen so that the dosage equals the threshold level. This adjustment can be performed in addition to, or instead of, adjusting based on the dosage available to the patient. This adjustment has the effect of ensuring that the patient does not comply with a combination therapy regimen that prohibits a change in dosage that is deemed too great for the patient to tolerate.

[0140] If the change in dosage is below a threshold level, the data processing device 105 is configured not to adjust the combination therapy regimen.

[0141] In step 220, the data processing device 105 stores the combination therapy regimen generated in step 215. The combination therapy regimen may be stored in the database 110, preferably associated with the patient, and more preferably in a data set associated with the patient. Metadata such as the date and time the combination therapy regimen was generated may also be stored along with the combination therapy regimen.

[0142] In the event that an adjustment to the generated combination therapy regimen of the type discussed above is made in step 215, an indication that this adjustment was made may also be stored by data processing device 105 as part of step 220, e.g., in metadata associated with the combination therapy regimen. A notification may additionally or alternatively be transmitted by data processing device 105 to patient device 115 and / or clinician data processing device 130 to notify one or both parties that an adjustment to the combination therapy regimen has been made.

[0143] The timing of the transmission can be optimized based on regiment and current patient data (eg, patient location or state of mind).

[0144] Step 220 may also include transmitting the combination therapy regimen to one or both of the patient device 115 and the clinician data processing device 130, possibly for display on a display of one or both of these devices. Additional actions that the data processing device 105 may perform as part of step 220 include any combination of generating a prescription for the patient based on the combination therapy regimen, instructing the patient to follow the combination therapy regimen, and controlling a medication administration device to administer at least one medication associated with the combination therapy regimen to the patient. The data processing device 105 may affect these additional actions by transmitting control commands to other devices, including, but not limited to, the patient device 115 and / or the clinician data processing device 130.

[0145] Preferably, the data processing device 105 is configured to make a determination as to whether the combination therapy regimen generated in step 215 complies with, for example, the requirements, guidelines, etc. of the relevant regulatory framework. Checking for regulatory compliance may include, for example, checking that the recommended dosages of the drugs that are part of the combination therapy are within a dosage range that has regulatory approval. If the recommended dosages are non-compliant, corrective action by the data processing device may be taken, such as setting the dosages of the drugs to values ​​that have regulatory approval and that are closest to the recommended values, and / or transmitting a message to the clinician data processing device 130 requesting further instructions.

[0146] The database 110 may store a regulatory data table that identifies, for each drug, dosage ranges that have regulatory approval, for use by the data processing device 105 when checking the combination therapy regimen generated in step 215 for regulatory compliance.

[0147] It will be appreciated that steps 200-220 can be performed multiple times for a single patient by the data processing device 105. In this manner, a dynamic combination therapy is provided that adapts to changes in the patient's condition as treatment progresses. Without being bound by theory, it is believed that adapting the combination therapy as treatment progresses may result in a more effective treatment for the patient. For example, the patient may achieve or approach achieving a desired patient endpoint, possibly in a relatively rapid manner.

[0148] It will also be appreciated that in some cases it may be appropriate to keep the desired patient endpoints and concomitant therapies constant over the course of treatment. In such cases, for second and subsequent iterations of the process of Figure 2, the data processing device 105 may omit step 200 because the identification of the desired patient endpoints and appropriate concomitant therapies remains unchanged.

[0149] 2 , it is contemplated that data processing device 105 may receive one or more pieces of information in a human-understandable format that is not suitable, or at least not optimized, for storage in a data set stored in database 110. For example, the patient-related measurements may include patient-reported outcomes provided in the form of natural language or as values ​​within a constrained response framework. In such cases, data processing device 105 is preferably configured to map the patient-reported outcomes onto a predefined scale to create a mapped patient-reported outcome. In this way, the “messy” information received by data processing device 105 can be transformed into “clean” data before being stored in database 110.

[0150] It is also contemplated that a variant of step 200 may be performed in second or subsequent iterations of the process of Figure 2 where only the desired patient endpoints are received by data processing device 105. This variant is particularly suitable for use where the combination therapy remains constant but the desired patient endpoints may change over time.

[0151] An exemplary embodiment in which the data processing device 105 performs two or more iterations is shown in Figure 3. Steps 300-320 are the same as steps 200-220, respectively, and therefore will not be described in detail again here. The following presents additional considerations that are preferably present in an iterative process such as that of Figure 3.

[0152] In step 325, the data processing device 105 receives additional patient-related information.

[0153] The patient-related information may be of the type discussed earlier in this specification and is received after the patient-related information received in connection with step 205 .

[0154] In step 330, the data processing device 105 updates the patient-related dataset discussed above in connection with step 210. The updating may include appending patient data based on the additional patient-related information received in step 325 to the patient-related dataset, or overwriting some or all of the existing contents of the patient-related dataset with patient data based on the additional patient-related information received in step 325.

[0155] At this point, processing loops back to step 315. In this case, the data processing device 105 processes the data sets, patient aspects, and desired patient endpoints to generate a combination therapy regimen in the manner previously described in connection with step 215.

[0156] The combination therapy regimen generated by this second iteration of step 315 can be the same as or different from the combination therapy regimen generated by the first iteration of step 315. Any differences result from the data processing device 105 utilizing a patient data set that includes or is revised based on the additional patient-related information received in step 325.

[0157] The data processing device 105 utilizes the revised patient data set to provide a recommended combination therapy regimen when performing a second iteration of step 315 that is responsive to the patient's actual condition. It will thus be appreciated that steps 315-330 can be repeated multiple times in the manner shown in FIG. 3 to enable a dynamic combination therapy regimen to be devised, the dynamic regimen being responsive to the patient's actual condition.

[0158] It will be appreciated that the data processing device 105 may receive a new desired patient endpoint at any time, for example, from the patient device 115 or the clinician data processing device 130. In response to receiving the new desired patient endpoint, the data processing device 105 is configured to replace the existing desired patient endpoint with the new desired patient endpoint. Thus, in the next iteration of the process of Figure 3, a combination therapy regimen is generated based on the new desired patient endpoint.

[0159] Another exemplary embodiment in which the data processing device 105 performs two or more iterations is shown in Figure 4. Steps 400-430 are the same as steps 300-330, respectively, and therefore will not be described in detail again here. The following presents additional considerations that are preferably present in an iterative process such as that of Figure 4.

[0160] In step 435, the data processing device 105 calculates whether the elapsed time associated with the additional patient-related information exceeds a threshold. The elapsed time is equal to the amount of time between the most recent update to the combination therapy regimen (415) and the most recent update to the patient dataset (420). The time at which the patient-related measurement was made can be established by generating a timestamp when the measurement was made, which can be appended to or otherwise associated with the measurement.

[0161] As an example, if the processing device 105 generates an updated combination therapy regimen at 9:00 AM and measures their blood glucose level at 12:00 PM on the same day, the elapsed time associated with the measurement is 3 hours.

[0162] The threshold is set in accordance with the considerations discussed earlier in this specification relating to the time frame in which the patient is expected to respond to therapy, e.g., the time between two successive cycles of treatment step (e) in accordance with the second aspect of the present invention. As noted above, preferably, the threshold is set to be equal to or greater than the duration of the time frame in which the patient is expected to respond to therapy, and less than the time between two successive visits by the patient to a healthcare provider.

[0163] The threshold value may be fixed at the beginning of the course of treatment and remain static throughout, or the threshold value may be varied as the course of treatment progresses, i.e., from iteration to iteration of the process of Figure 4. Variations may be based on an analysis of the time frame in which a particular patient will respond to a particular combination therapy, which analysis may be performed by the data processing device 105 on the patient data set established in step 310.

[0164] If the elapsed time is calculated to be less than the threshold, processing returns to step 425, where the data processing device 105 awaits further patient-related information. Without being bound by theory, it is believed that, at least in some cases, it may be counterproductive to adjust a combination therapy regimen over a time frame significantly shorter than the expected time frame for a patient to respond to the therapy. Thus, an "overshoot / undershoot" scenario, in which the regimen fluctuates for some time before reaching a stable level, may be avoided, or at least the time spent fluctuating may be reduced.

[0165] In the event that the elapsed time is calculated to be greater than the threshold time, processing loops back to step 415 and proceeds as described with respect to Figure 3. Any patient-related information collected during one or more iterations in which the elapsed time is calculated to be less than the threshold time is preferably used in generating the combination therapy regimen in step 415. In this manner, patient-related information collected too quickly to be immediately processed is still utilized.

[0166] As with the process of Figure 3, it will be appreciated that the data processing device 105 may receive a new desired patient endpoint at any time, for example, from the patient device 115 or the clinician data processing device 130. In response to receiving the new desired patient endpoint, the data processing device 105 is configured to replace the existing desired patient endpoint with the new desired patient endpoint. Thus, at the next iteration of the process of Figure 4 where the elapsed time is greater than the threshold, a combination therapy regimen will be generated based on the new desired patient endpoint.

[0167] It will be understood that any of the methods described herein, or portions thereof, can be encoded by computer-readable instructions and stored on a non-transitory computer-readable medium. Thus, any portion of the invention described above can be implemented by a computer executing appropriate instructions stored on a non-transitory computer-readable medium. Thus, computer-readable media storing such instructions are also within the scope of the invention.

[0168] The foregoing discussion discloses embodiments in accordance with the present invention. As will be understood, the approaches, methods, techniques, materials, devices, and the like disclosed herein may be embodied in additional embodiments as will be understood by those skilled in the art, and it is the intent of this application to encompass and include such variations. Accordingly, the present disclosure is illustrative and should not be construed as limiting the scope of the following claims.

Claims

1. 1. A system for generating a combination therapeutic regimen for a patient suffering from a disease or condition, the system comprising: at least one data processing device having at least one processor; a) receiving an identification of a suitable combination therapy to treat said disease or condition; b) receiving patient-related information including a desired patient endpoint and processing the patient-related information to identify a patient aspect defined in association with the desired patient endpoint; c) storing a dataset related to the patient, the dataset including one or more patient data based on patient-related measurements; d) processing the dataset, the patient aspects, and the desired patient endpoints to generate a regimen for the combination therapy; e) storing said regimen in a database; f) receiving additional patient-related information; and g) calculating whether the elapsed time associated with said additional patient-related information exceeds a threshold value relating to the time frame for said patient to respond to said combination therapy; In the affirmative, h) updating the data set associated with the patient based on the additional patient-related information; i) processing the updated dataset, the patient aspects, and the desired patient endpoints to generate an updated regimen for the combination therapy; and j) storing said updated regimen in said database; In negation, k) updating the dataset associated with the patient based on the additional patient-related information and maintaining the combination therapy regimen; the combination therapy includes one or more pharmacological therapies and one or more non-pharmacological therapies; and the one or more non-pharmacological therapies include cognitive behavioral therapy; system.

2. The system comprises: The system of claim 1 , configured to output the one or more non-pharmacological therapies via the patient's electronic device.

3. The system comprises: receiving sensor data collected by at least one sensor; and The system of claim 1 or 2, further configured to determine at least one of the one or more patient data based at least in part on the received sensor data.

4. The system of claim 3 , wherein the at least one sensor is an environmental sensor and / or a physiological sensor.

5. 5. The system of claim 4, wherein the environmental sensors are any combination of light sensors, temperature sensors, acoustic sensors, accelerometers, air pressure sensors, airborne particulate sensors, global positioning sensors, humidity sensors, electric field sensors, magnetic field sensors, moisture sensors, air quality sensors, sensors capable of detecting proximity to a WiFi transmitter and / or a cellular network base station, and Geiger counters.

6. The system of claim 4 or 5, wherein the physiological sensor is any biological or endpoint-based biomarker sensor.

7. The system of any one of claims 1 to 6, further comprising a human interface device, wherein the system is configured to output the regimen using the human interface device.

8. 8. The system of claim 1, wherein the system is configured to process the datasets, the patient aspects, and the desired patient endpoints using a rules-based system and / or machine learning algorithms to generate the regimen for the combination therapy.

9. The system according to any one of claims 1 to 8, wherein the disease and the combination therapy are selected from the group consisting of: a) the disease or condition is diabetes and the combination therapy comprises metformin and cognitive behavioral therapy; b) the disease or condition is diabetes or obesity and the combination therapy comprises a GLP-1 agonist and cognitive behavioral therapy; c) the disease or condition is diabetes or obesity and the combination therapy comprises a GLP-1 agonist, metformin and cognitive behavioral therapy.

10. The system according to any one of claims 1 to 8, wherein the disease and the combination therapy are selected from the group consisting of: the disease or condition is insomnia and the combination therapy comprises melatonin and cognitive behavioral therapy for insomnia (CBTi); The disease or condition is opiate addiction and the combination therapy comprises: (i) morphine and cognitive behavioral therapy; or (ii) Morphine, alpha-2 agonists and cognitive behavioral therapy.

11. The system of any one of claims 1 to 8, wherein the disease or condition is hypertension and the combination therapy comprises amlodipine and cognitive behavioral therapy.

12. 12. The system of any one of claims 1 to 11, wherein the desired patient endpoint is improvement in the disease or condition, improvement in symptoms associated with the disease or condition, improvement in side effects of pharmacological therapy, and / or improvement in side effects of non-pharmacological therapy.

13. the one or more patient-related measurements a) one or more physiological measurements; b) one or more patient-centered outcomes; c) one or more environmental measurements local to the patient, such as temperature, humidity, and / or light intensity; and / or d) one or more behavioral factor measures.

14. The system of any one of claims 1 to 13, wherein the one or more patient-centered outcomes include one or more patient-reported outcomes.

15. 15. The system of claim 14, wherein the system is further configured to map the one or more patient-reported outcomes onto a predefined scale to create a mapped patient-reported outcome, and wherein the one or more patient data stored in the dataset is based at least in part on the mapped patient-reported outcome.

16. The system of any one of claims 1 to 15, wherein the system is configured to apply a weighting factor to each of the patient-related measurements to generate the patient data.

17. 1. A method for generating a combination therapeutic regimen for a patient suffering from a disease or condition, said method comprising: a) establishing desired patient endpoints; b) receiving patient-related information including the desired patient endpoint and processing the patient-related information to identify patient aspects associated with the desired patient endpoint; c) generating or modifying a data set associated with said patient based on one or more patient-related measurements; d) processing the dataset, the patient aspects, and the desired patient endpoints to generate a combination therapy regimen; e) storing said regimen in a database; f) receiving additional patient-related information; and g) calculating whether the elapsed time associated with said additional patient-related information exceeds a threshold value relating to the time frame for said patient to respond to a combination therapy; In the affirmative, h) updating the data set associated with the patient based on the additional patient-related information; i) processing the updated dataset, the patient aspects, and the desired patient endpoints to generate an updated regimen for the combination therapy; and j) storing said updated regimen in said database; In negation, k) updating the dataset associated with the patient based on the additional patient-related information and maintaining the combination therapy regimen; the combination therapy includes one or more pharmacological therapies and one or more non-pharmacological therapies; and the one or more non-pharmacological therapies include cognitive behavioral therapy; method.

18. 18. The method of claim 17, wherein the method comprises a plurality of treatment cycles, each of the treatment cycles comprising steps f) through k).

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