Artificial intelligence systems and methods for securing a pharmaceutical therapy

Abstract

The present disclosure relates to using an artificial intelligence to assist a user in structuring a pharmaceutical distribution agreement. An artificial intelligence engine incorporating generative artificial intelligence can process user-driven queries in combination with proprietary and third party models and data to provide a response that can suggest an improvement to a computer-executable pharmaceutical distribution model. The artificial intelligence engine can be trained on both real world and simulation data for pharmaceutical distribution agreements.

Description

ARTIFICIAL INTELLIGENCE SYSTEMS AND METHODS FOR SECURING A PHARMACEUTICAL THERAPYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application Number 63 / 594675, filed October 31, 2023, the contents of which have been incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present specification relates to the use of artificial intelligence in the distribution of a pharmaceutical therapy.BACKGROUND

[0003] Terms of distribution for pharmaceutical therapies vary widely based on a number of complex factors, including the ability to diagnose the condition, characteristics of the patient population, patient eligibility and patient access factors, patient adherence to therapy, availability of competing therapies, geographic considerations, production capacity, distribution logistics, and other factors. Distinct inquiries and analytics may be required to assess each of the foregoing factors. Moreover, gauging the needs and risk preferences of other parties can be particularly challenging, both due to a lack of visibility into their objectives as well uneven access to information among parties. Furthermore, differences in experience levels among those charged with arranging a distribution agreement can also result in agreements that are hard to achieve or place particular parties at a disadvantage once an agreement is implemented, which can be detrimental to patients in need of a pharmaceutical therapy.

[0004] In view of the foregoing, there is a need for techniques to improve the ability to form an agreement to distribute a pharmaceutical therapy, including improvements in the analysis of data, more effective accounting for the preferences of different parties, and techniques to reduce the impact of differences in experience levels between negotiators.BRIEF SUMMARY

[0005] The present disclosure relates to the use of artificial intelligence techniques that assist a user in structuring multiparty agreements to distribute a pharmaceutical therapy. In the broadest sense, recent advances in artificial intelligence tools such as generative artificial intelligence can improve the analysis of pharmaceutical distribution agreements by identifying one or more contexts that connect otherwise disparate considerations and data.Resulting embodiments may provide, for example, a method for making a pharmaceutical therapy available to at least one patient. In certain embodiments, for example, the method may comprise receiving input associated with the pharmaceutical therapy via a user interface. In certain embodiments, for example, the method may comprise generating a computerexecutable pharmaceutical distribution model for the pharmaceutical therapy. In certain embodiments, for example, the method may comprise analyzing the computer-executable pharmaceutical distribution model with an analytic engine to obtain computational results. In certain embodiments, for example, the method may comprise submitting the input and the computational results to an artificial intelligence engine. In certain embodiments, for example, the method may comprise obtaining a response to the submitting that comprises a recommended pharmaceutical distribution constraint. In certain embodiments, for example, the method may comprise providing at least a portion of the response to a user (for example, via the user interface).

[0006] A. In certain embodiments, for example, the pharmaceutical therapy may comprise a diagnosis. In certain embodiments, for example, the pharmaceutical therapy may comprise a code (for example a diagnosis code, a regimen code, or a billing code). In certain embodiments, for example, the pharmaceutical therapy may be associated with a therapy type (for example monotherapy, a combination therapy, a drug therapy such as a small molecule therapy or a biologic therapy, a T-Cell therapy, a gene therapy, etc.). In certain embodiments, for example, the pharmaceutical therapy may be associated with a market. In certain embodiments, for example, the pharmaceutical therapy may comprise a treatment (for example administration of a drug) to treat a condition (for example a disease). In certain embodiments, for example, the pharmaceutical therapy may comprise a treatment to prevent a condition. In certain embodiments, for example, the pharmaceutical therapy may comprise a treatment regimen.

[0007] In certain embodiments, for example, the pharmaceutical therapy may comprise a single pharmaceutical drug. In certain embodiments, for example, the pharmaceutical therapy may comprise multiple pharmaceutical drugs. In certain embodiments, for example, the pharmaceutical therapy may comprise a single dose of a pharmaceutical drug. In certain embodiments, for example, the pharmaceutical therapy may comprise multiple does of one or more pharmaceutical drugs. In certain embodiments, for example, the pharmaceutical therapy may comprise therapy (for example, radiation therapy, physical therapy, etc.). In certain embodiments, for example, the pharmaceutical therapy may comprise a combination of therapy and one or more doses of one or more pharmaceutical drugs. In certainembodiments, for example, the pharmaceutical therapy may comprise a combination of two or more of the foregoing.

[0008] In certain embodiments, for example, all or part of the treatment is approved by a regulatory body. In certain embodiments, for example, all or part of the treatment is in the process of seeking regulatory approval. In certain embodiments, for example, the pharmaceutical therapy may be a pharmaceutical therapy for a patient in need of the pharmaceutical therapy (for example a patient suffering from a condition that may be treated with the pharmaceutical therapy). In certain embodiments, for example, a patient may be a person having or diagnosed with a condition that is treatable with the treatment. In certain embodiments, a patient is an eligible patient that meets, for example, one or more additional criteria, such as being a person in a particular region, not having another condition that might be worsened by the treatment, not taking a particular pharmaceutical drug that interacts with the treatment, being at least a certain age that the treatment is approved for, having insurance or a particular insurance, or a combination thereof.

[0009] In certain embodiments, for example, the treatment regimen may comprise a dosing schedule for a drug. In certain embodiments, for example, the dosing schedule may comprise a start cycle, a cycle length, an end cycle, and a dosing for all days in each of the foregoing cycles. In certain embodiments, for example, the dosing may comprise a number of administrations of a product (for example a pharmaceutical drug) per day and a dosage amount per administration. In certain embodiments, for example, the dosing schedule may comprise a dosing for all days In certain embodiments, for example, the pharmaceutical therapy may comprise a new drug therapy. In certain embodiments, for example, the pharmaceutical therapy has an associated product pack. In certain embodiments, for example, the product pack may be based on an average dosage of a pharmaceutical compound to be administered to a patient within a pre-determined period of time.

[0010] B. In certain embodiments, for example, the input may comprise text, an image (for example a diagram, drawing, and / or photograph), a voice recording, a presentation (for example a PowerPoint presentation or a poster), or a combination of two or more of the foregoing. In certain embodiments, for example, the input may comprise a question, a request, a list, or a combination of two or more of the foregoing. In certain embodiments, for example, the input may comprise a geographic detail(s) (for example a region, country, state, and / or country where the pharmaceutical therapy may be manufactured, distributed from, or provided to). In certain embodiments, for example, the input may comprise a performancemetric (for example a measure of distribution of the pharmaceutical therapy or a measure of profit or cost for distribution of the pharmaceutical therapy).

[0011] In certain embodiments, for example, the input may comprise a minimum supply of a treatment per year. In certain embodiments, for example, the input may comprise a time horizon (for example, a period of months or years) over which a pharmaceutical distribution agreement is to be analyzed. In certain embodiments, for example, the input may comprise or point to one or more discount factors for future cash flows (for example a discount factor, an interest rate, a term structure of interest rates, etc.). In certain embodiments, for example, the input may comprise one or more currency exchange rates or a term structure thereof. In certain embodiments, for example, the input may comprise an anticipated number of patients per year. In certain embodiments, for example, the input may comprise a minimum number of patients that must be less than or equal to the anticipated number of patients. In certain embodiments, for example, the input may comprise an equality. In certain embodiments, for example, the input may comprise an inequality.

[0012] In certain embodiments, for example, the input may comprise a plurality of inputs. In certain embodiments, for example, the input may comprise a supply amount as a function, at least in part, of a size of a patient population (for example a time-dependent size of a patient population). In certain embodiments, for example, the input may comprise a supply amount as a function, at least in part, of a number of treatments per patient (for example a number of treatments per patient over an expected course of the pharmaceutical therapy, a number of treatments per patient during a given time, a number of treatment cycles per year, etc.). In certain embodiments, for example, the input may comprise a supply amount as a function, at least in part, of patient adherence to the pharmaceutical therapy (for example, the percentage of patients in a patient population that complete a prescribed number of treatment cycles of the pharmaceutical therapy; the percentage of patients in a patient population that adhere to a treatment schedule for the pharmaceutical therapy; the percentage of patients in a patient population that adhere to a treatment schedule within a threshold of time such as within 30 minutes, one hour, two hours, 12 hours, or 24 hours of a scheduled treatment time; the percentage of patients in a patient population that adhere to a treatment schedule and complete a prescribed number of treatment cycles; the he percentage of patients in a patient population that the percentage of patients in a patient population that adhere to a treatment schedule within a threshold of time and complete a prescribed number of treatment cycles; etc.).

[0013] In certain embodiments, for example, the input may comprise a constraint on one or more funding amounts for the distribution of the pharmaceutical therapy. In certain embodiments, for example, the input may comprise an upfront payment amount (for example an up front payment on a per-patient basis). In certain embodiments, for example, constraints on one or more funding amounts may comprise a further payment that is contingent upon a patient response to the pharmaceutical therapy (for example a positive per-patient response or a measure of an aggregate response to the pharmaceutical therapy by a plurality of patients). In certain embodiments, for example, the further payment may be larger than the upfront payment. In certain embodiments, for example, the input may comprise a rebate amount (for example a rebate paid after purchase of a pharmaceutical therapy) that is contingent upon a patient response to the pharmaceutical therapy (for example a negative per-patient response or a measure of an aggregate response to the pharmaceutical therapy by a plurality of patients). In certain embodiments, for example, the rebate may be smaller than the upfront payment. In certain embodiments, for example, the input may comprise a discount (for example an upfront discount that reduces a funding amount paid) to a funding amount of the one or more funding amounts as a function of a total number of patients that receive the pharmaceutical therapy. In certain embodiments, for example, the discount may be applied on a per-patient basis. In certain embodiments, for example, availability of the discount may require a specified level of patient adherence to the pharmaceutical therapy. In certain embodiments, for example, the discount may be tiered for different patients in a tier (for example tiers defined by geographic location of a patient, patient age, patient comorbidities, etc.). In certain embodiments, for example, the input may comprise a discount to a funding amount of the one or more funding amounts as a function of a previous funding provided during a prior specified time period (for example, a discount applied to future fundings during a calendar year when previous fundings exceeds a predetermined amount within the calendar year).

[0014] In certain embodiments, for example, the input may comprise a cap on funding during a specified time period (for example a cap on funding expenditures during a calendar year on a per-patient basis). In certain embodiments, for example, the cap may be a cap on total funding during a specific time period. In certain embodiments, for example, the cap may be a limit on payments to payments incurred during a specified portion of a time period (for example, a limit on payments for receipt of the pharmaceutical therapy to those payments incurred during the first 10 months of a calendar year). In certain embodiments, for example, the cap may be applied on a per patient basis. In certain embodiments, for example, the capmay be an aggregate cap for a group of patients (for example a population of patients, such as a population of patients in a geographic region). In certain embodiments, for example, the input may further comprise a rebate that is triggered once the one or more funding amounts reaches the aggregate cap. In certain embodiments, for example, the rebate may be effective for a specified incremental amount above the aggregate cap (for example, if the aggregate cap is $100 million and the incremental funding amount is $30 million, then a recipient of the pharmaceutical therapy in the about of $131 million is responsible for funding the first $100 million and the last $1 million for a total of $101 million).

[0015] In certain embodiments, for example, the constraint on one or more funding amounts may comprise a periodic funding amount for a specified number of periods (for example a specified funding amount for each year of five years). In certain embodiments, for example, the periodic funding amount for the specified number of periods may fund treatment of an unlimited number of patients with the pharmaceutical therapy during the specified number of periods. In certain embodiments, for example, the constraints on one or more funding amounts may comprise a specified number of free treatments (for example an initial number of free treatments or an additional number of free treatments following funding of an initial number of treatments) with the pharmaceutical therapy. In certain embodiments, for example, the number of free treatments may be determined from a number of funded treatments. In certain embodiments, for example, the constraint on one or more funding amounts may comprise a deferral of funding for a set of treatments with the pharmaceutical therapy until the pharmaceutical therapy receives regulatory approval. In certain embodiments, for example, the funding may be at a discounted rate relative to a price of the pharmaceutical therapy that is established based on approval of the pharmaceutical therapy.

[0016] In certain embodiments, for example, the input may comprise a bounding value (for example a maximum value or a minimum value) for a supply amount of the pharmaceutical therapy. In certain embodiments, for example, the bounding value for the supply amount may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to a supply amount of the pharmaceutical therapy relative to an initially specified supply mount of the pharmaceutical therapy.

[0017] In certain embodiments, for example, the bounding value may be a function, at least in part, of an effectiveness of the pharmaceutical therapy (for example a known effectiveness, an average effectiveness, a predicted effectiveness, a simulated effectiveness, or an optimized effectiveness).

[0018] In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the supply amount that is triggered by a threshold level of deviation of the effectiveness from an initial expectation of the effectiveness of the pharmaceutical therapy.

[0019] In certain embodiments, for example, the bounding value may be a function, at least in part, of a realized size of a patient population for the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the supply amount that is triggered by a threshold level of deviation of the realized size from an initial expectation of the size of the patient population.

[0020] In certain embodiments, for example, the bounding value may be a function, at least in part, of a number of treatments per patient. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the supply amount that is triggered by a threshold level of deviation of the realized number of treatments per patient from an initial expectation of the number of treatments per patient.

[0021] In certain embodiments, for example, the bounding value may be a function, at least in part, of a number of discontinuation recommendations for the pharmaceutical therapy.

[0022] In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the supply amount that is triggered by a threshold level of deviation of the number of discontinuation recommendations from an initial expectation of the effectiveness of the number of discontinuation recommendations.

[0023] In certain embodiments, for example, the bounding value may be a function, at least in part, of an available supply of the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the supply amount that is triggered by a threshold level of deviation of the available supply from an initial expectation of the available supply.

[0024] In certain embodiments, for example, the bounding value may be a function, at least in part, of a regulatory status of the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) tothe regulatory status of the pharmaceutical therapy that is triggered by a threshold level of deviation of the regulatory status from an initial expectation of the regulatory status.

[0025] In certain embodiments, for example, the bounding value may be a function, at least in part, of a geographical demand (for example demand by a region, country, state, county, etc.) for the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the geographical demand of the pharmaceutical therapy that is triggered by a threshold level of deviation of the geographical demand from an initial expectation of the geographical demand.

[0026] In certain embodiments, for example, the bounding value may be a function, at least in part, of a legal constraint (for example an export ban, import ban, tariff, etc.) on the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the legal constraint that is triggered by a threshold level of deviation of the legal constraint from an initial expectation of the legal constraint or lack thereof.

[0027] In certain embodiments, for example, the bounding value may be a function, at least in part, of a governmental factor (for example a government subsidy). In certain embodiments, for example, the bounding value may be computed based on a change to a governmental factor (for example the allowance or withdrawal of a government subsidy for the pharmaceutical therapy, or a government reimbursement rate for the pharmaceutical therapy).

[0028] In certain embodiments, for example, the bounding value may be a function, at least in part, of a reimbursement rate for the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from a change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the reimbursement rate of the pharmaceutical therapy.

[0029] In certain embodiments, for example, the input may comprise a discount that depends on the total number of the one or more patients to which the pharmaceutical therapy is distributed.

[0030] In certain embodiments, for example, the input may comprise one or more parameters. In certain embodiments, for example, the one or more parameters may comprise a coefficient of a variable in the pharmaceutical distribution constraint. In certain embodiments, for example, the one or more parameters may comprise a binary variable (forexample a variable that multiplies each other term in the pharmaceutical distribution constraint. In certain embodiments, for example, the one or more parameters may comprise may correspond to another variable that is associated with the input. In certain embodiments, for example, the another variable may be a continuous variable. In certain embodiments, for example, the another variable may be a discrete variable. In certain embodiments, for example, the input may comprise a bound on the another variable (for example, an upper bound and / or a lower bound).

[0031] In certain embodiments, for example, the input may comprise a value for a parameter. In certain embodiments, for example, the input may comprise specification of a technique for generating a value for a parameter. In certain embodiments, for example, the input may specify that a value can be generated using a computer-implemented statistical model. In certain embodiments, for example, the value may be one of a set of values corresponding to the parameter at different instances in time. In certain embodiments, for example, the value of one of a set of values corresponding to multiple scenarios for the parameter. In certain embodiments, for example, the parameter may comprise a coefficient of a variable in a pharmaceutical distribution constraint, as described in great detail below. In certain embodiments, for example, the parameter may be one of a plurality of parameters (for example fixed values or variables) associated with a plurality of pharmaceutical distribution constraints. In certain embodiments, for example, the parameter may comprise a binary variable that multiplies a pharmaceutical distribution constraint.

[0032] In certain embodiments, for example, the parameter may be a region where the pharmaceutical therapy is to be distributed. In certain embodiments, for example, the parameter may be a dosage of a pharmaceutical drug in the treatment. In certain embodiments, for example, the parameter may be a number of doses of a pharmaceutical drug in the treatment required or anticipated per patient. In certain embodiments, for example, the parameter may be a number of persons in a region diagnosed with a condition that is treatable using the pharmaceutical therapy. In certain embodiments, for example, the parameter may be a distributor’s cost per treatment.

[0033] In certain embodiments, for example, the input may comprise an equality, an inequality, all equalities, all inequalities, or a combination of equalities and inequalities. In certain embodiments, for example, the input may comprise an instruction to an artificial intelligence (for example a command to prepare, recommend, modify, change, etc.). In certain embodiments, for example, the input may comprise request to an artificial intelligence (for example a request to prepare, recommend, modify, change, etc.). In certainembodiments, for example, the input may comprise a negative command, negative filter, and / or negative request to an artificial intelligence, for example a request not to do something or not to use certain information. In certain embodiments, for example, the input may comprise image data, such as a compressed or uncompressed image or a compressed of uncompressed video (for example an image or video describing a pharmaceutical therapy, a plot of data, pharmaceutical drug trials, patient files, a term sheet or other papers for a pharmaceutical distribution agreement, etc.

[0034] In certain embodiments, for example, the input may comprise a pharmaceutical distribution constraint as further described herein.

[0035] C. In certain embodiments, for example, the user interface may comprise a user interface to a chatbot. In certain embodiments, for example, the user interface may be a graphical user interface (for example a graphical user interface on a mobile device such as a smart phone). In certain embodiments, for example, the user interface may comprise an initial user interface that allows a user to provide input and a secondary user interface that allows a user to view output. In certain embodiments, for example, the user interface may comprise an interface to a sandbox through, for example, a graphical user interface (GUI) that allows the users to generate a new model or modify an existing model in development, by for example, adding, removing, or adjusting distribution constraints within the model. The application of the methods, systems, and products disclosed herein is expressly contemplated with respect to the implementation of the embodiments disclosed in International Patent Application No. PCT / US2023 / 031565 (“Systems and Methods for Implementing an Exchange to Expedite Negotiations”), which is hereby incorporated by reference herein in its entirety and for all purposes. Similarly, the application of the methods, systems, and products disclosed herein is expressly contemplated with respect to the implementation of the embodiments disclosed in U.S. Patent Application No. 16 / 914,008 (“System and Methods for Securing a Drug Therapy”), which is hereby incorporated by reference herein in its entirety and for all purposes.

[0036] In certain embodiments, for example, the user interface may comprise an initial user interface that allows a user to make selections to define part or all of a new computerexecutable pharmaceutical distribution model. In certain embodiments, for example, the user interface may comprise a number of interactive fields that allow the user or another party to input values for different parameters (for example, model assumptions) for a new computerexecutable pharmaceutical distribution model. In certain embodiments, for example, the user interface may comprise interface elements for different pricing parameters, such as drop-down menu that the user or another party can interact with to select a pricing strategy (for example, a per patient price strategy), a check box or toggle element that the user or another party can interact with to select a pricing type (for example, tiered or volume), or the like. In certain embodiments, for example, the initial user interface may request information or other selections from the user or another party, such as a selection of a particular treatment or set of treatments for the new model, one or more distributors for the treatment, one or more suppliers of the treatment, or the like. In certain embodiments, for example, after making such a selection, a third-party computing system may provide the user or another party with an interactive interface, through which the user or another party can set initial computerexecutable pharmaceutical distribution model constraints, parameters, and / or values for the computer-executable pharmaceutical distribution model.

[0037] In certain embodiments, for example, the user interface may be generated by a local application. In certain embodiments, for example, the user interface may be part of a distributed application. In certain embodiments, for example, the user interface may be generated by an artificial intelligence.

[0038] D. In certain embodiments, for example, the computer-executable pharmaceutical distribution model may comprise a pharmaceutical distribution constraint. In certain embodiments, for example, the pharmaceutical distribution constraint may be derived from the input. In certain embodiments, for example, the computer-executable pharmaceutical distribution model may comprise a plurality of pharmaceutical distribution constraints, some of which may be derived, at least in part, from the input and others which may be determined independently of the input.

[0039] In certain embodiments, for example, information used to generate the computerexecutable pharmaceutical distribution model (including one or more pharmaceutical distribution constraints) may be obtained, at least in part, from (or derived from) one or more external sources of information (for example, an external source of information such as a database of a medical insurance provider, an academic database storing medical information, etc.), one or more internal sources (for example an internal source of information such as information relied on for previously generated models, medical data received from one or more distributors of treatments, medical data received from one or more suppliers of treatments, etc.), or a combination of external and internal sources.

[0040] In certain embodiments, for example, the computer-executable pharmaceutical distribution model may comprise a pharmaceutical distribution constraint (or a plurality of such constraints) that specifies a minimum supply of a treatment per year. In certainembodiments, for example, the computer-executable pharmaceutical distribution model may comprise a pharmaceutical distribution constraint that specifies a time horizon (for example, a period of months or years) over which a pharmaceutical distribution agreement is to be analyzed. In certain embodiments, for example, the computer-executable pharmaceutical distribution model may comprise a pharmaceutical distribution constraint that comprises or points to one or more discount factors for future cash flows (for example a discount factor, an interest rate, a term structure of interest rates, etc.). In certain embodiments, for example, the computer-executable pharmaceutical distribution model may comprise a pharmaceutical distribution constraint that comprises one or more currency exchange rates or a term structure thereof. In certain embodiments, for example, the computer-executable pharmaceutical distribution model may comprise a pharmaceutical distribution constraint that specifies an anticipated number of patients per year. In certain embodiments, for example, the computerexecutable pharmaceutical distribution model may comprise a pharmaceutical distribution constraint that specifies that a minimum number of patients must be less than or equal to the anticipated number of patients. In certain embodiments, for example, the pharmaceutical distribution constraint may be an equality. In certain embodiments, for example, the pharmaceutical distribution constraint may be an inequality.

[0041] In certain embodiments, for example, for example, the pharmaceutical distribution constraint may comprise a supply amount as a function, at least in part, of a size of a patient population (for example a time-dependent size of a patient population). In certain embodiments, for example, the pharmaceutical distribution constraint may comprise a supply amount as a function, at least in part, of a number of treatments per patient (for example a number of treatments per patient over an expected course of the pharmaceutical therapy, a number of treatments per patient during a given time, a number of treatment cycles per year, etc.). In certain embodiments, for example, the pharmaceutical distribution constraints may comprise a supply amount as a function, at least in part, of patient adherence to the pharmaceutical therapy (for example, the percentage of patients in a patient population that complete a prescribed number of treatment cycles of the pharmaceutical therapy; the percentage of patients in a patient population that adhere to a treatment schedule for the pharmaceutical therapy; the percentage of patients in a patient population that adhere to a treatment schedule within a threshold of time such as within 30 minutes, one hour, two hours, 12 hours, or 24 hours of a scheduled treatment time; the percentage of patients in a patient population that the percentage of patients in a patient population that adhere to a treatment schedule and complete a prescribed number of treatment cycles; the he percentage of patientsin a patient population that the percentage of patients in a patient population that adhere to a treatment schedule within a threshold of time and complete a prescribed number of treatment cycles; etc.).

[0042] In certain embodiments, for example, the pharmaceutical distribution constraints may comprise constraints on one or more funding amounts for the distribution of the pharmaceutical therapy. In certain embodiments, for example, the constraints on one or more funding amounts may comprise an upfront payment (for example an up front payment on a per-patient basis). In certain embodiments, for example, constraints on one or more funding amounts may comprise a further payment that is contingent upon a patient response to the pharmaceutical therapy (for example a positive per-patient response or a measure of an aggregate response to the pharmaceutical therapy by a plurality of patients). In certain embodiments, for example, the further payment may be larger than the upfront payment. In certain embodiments, for example, constraints on one or more funding amounts may comprise a rebate (for example a rebate paid after purchase of a pharmaceutical therapy) that is contingent upon a patient response to the pharmaceutical therapy (for example a negative per-patient response or a measure of an aggregate response to the pharmaceutical therapy by a plurality of patients). In certain embodiments, for example, the rebate may be smaller than the upfront payment. In certain embodiments, for example, the constraints on one or more funding amounts may comprise a discount (for example an upfront discount that reduces a funding amount paid) to a funding amount of the one or more funding amounts as a function of a total number of patients that receive the pharmaceutical therapy. In certain embodiments, for example, the discount may be applied on a per-patient basis. In certain embodiments, for example, availability of the discount may require a specified level of patient adherence to the pharmaceutical therapy. In certain embodiments, for example, the discount may be tiered for different patients in a tier (for example tiers defined by geographic location of a patient, patient age, patient comorbidities, etc.). In certain embodiments, for example, the constraints on one or more funding amounts comprise a discount to a funding amount of the one or more funding amounts as a function of a previous funding provided during a prior specified time period (for example, a discount applied to future fundings during a calendar year when previous fundings exceeds a predetermined amount within the calendar year).

[0043] In certain embodiments, for example, the constraints on one or more funding amounts may comprise a cap on funding during a specified time period (for example a cap on funding expenditures during a calendar year on a per-patient basis). In certain embodiments,for example, the cap may be a cap on total funding during a specific time period. In certain embodiments, for example, the cap may be a limit on payments to payments incurred during a specified portion of a time period (for example, a limit on payments for receipt of the pharmaceutical therapy to those payments incurred during the first 10 months of a calendar year). In certain embodiments, for example, the cap may be applied on a per patient basis. In certain embodiments, for example, the cap may be an aggregate cap for a group of patients (for example a population of patients, such as a population of patients in a geographic region). In certain embodiments, for example, the constraints on one or more funding amounts may further comprise a rebate that is triggered once the one or more funding amounts reaches the aggregate cap. In certain embodiments, for example, the rebate may be effective for a specified incremental amount above the aggregate cap (for example, if the aggregate cap is $100 million and the incremental funding amount is $30 million, then a recipient of the pharmaceutical therapy in the about of $131 million is responsible for funding the first $100 million and the last $1 million for a total of $101 million).

[0044] In certain embodiments, for example, the constraints on one or more funding amounts comprise a periodic funding amount for a specified number of periods (for example a specified funding amount for each year of five years). In certain embodiments, for example, the periodic funding amount for the specified number of periods may fund treatment of an unlimited number of patients with the pharmaceutical therapy during the specified number of periods. In certain embodiments, for example, the constraints on one or more funding amounts may comprise a specified number of free treatments (for example an initial number of free treatments or an additional number of free treatments following funding of an initial number of treatments) with the pharmaceutical therapy. In certain embodiments, for example, the number of free treatments may be determined from a number of funded treatments. In certain embodiments, for example, the constraints on one or more funding amounts may comprise a deferral of funding for a set of treatments with the pharmaceutical therapy until the pharmaceutical therapy receives regulatory approval. In certain embodiments, for example, the funding may be at a discounted rate relative to a price of the pharmaceutical therapy that is established based on approval of the pharmaceutical therapy.

[0045] In certain embodiments, for example, the pharmaceutical distribution constraints may comprise a bounding value (for example a maximum value or a minimum value) for a supply amount of the pharmaceutical therapy. In certain embodiments, for example, the bounding value for the supply amount may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximumreduction) to a supply amount of the pharmaceutical therapy relative to an initially specified supply mount of the pharmaceutical therapy.

[0046] In certain embodiments, for example, the bounding value may be a function, at least in part, of an effectiveness of the pharmaceutical therapy (for example a known effectiveness, an average effectiveness, a predicted effectiveness, a simulated effectiveness, or an optimized effectiveness). In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the supply amount that is triggered by a threshold level of deviation of the effectiveness from an initial expectation of the effectiveness of the pharmaceutical therapy.

[0047] In certain embodiments, for example, the bounding value may be a function, at least in part, of a realized size of a patient population for the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the supply amount that is triggered by a threshold level of deviation of the realized size from an initial expectation of the size of the patient population.

[0048] In certain embodiments, for example, the bounding value may be a function, at least in part, of a number of treatments per patient. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the supply amount that is triggered by a threshold level of deviation of the realized number of treatments per patient from an initial expectation of the number of treatments per patient.

[0049] In certain embodiments, for example, the bounding value may be a function, at least in part, of a number of discontinuation recommendations for the pharmaceutical therapy.

[0050] In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the supply amount that is triggered by a threshold level of deviation of the number of discontinuation recommendations from an initial expectation of the effectiveness of the number of discontinuation recommendations.

[0051] In certain embodiments, for example, the bounding value may be a function, at least in part, of an available supply of the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction)to the supply amount that is triggered by a threshold level of deviation of the available supply from an initial expectation of the available supply.

[0052] In certain embodiments, for example, the bounding value may be a function, at least in part, of a regulatory status of the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the regulatory status of the pharmaceutical therapy that is triggered by a threshold level of deviation of the regulatory status from an initial expectation of the regulatory status.

[0053] In certain embodiments, for example, the bounding value may be a function, at least in part, of a geographical demand (for example demand by a region, country, state, county, etc.) for the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the geographical demand of the pharmaceutical therapy that is triggered by a threshold level of deviation of the geographical demand from an initial expectation of the geographical demand.

[0054] In certain embodiments, for example, the bounding value may be a function, at least in part, of a legal constraint (for example an export ban, import ban, tariff, etc.) on the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from an incremental change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the legal constraint that is triggered by a threshold level of deviation of the legal constraint from an initial expectation of the legal constraint or lack thereof.

[0055] In certain embodiments, for example, the bounding value may be a function, at least in part, of a governmental factor (for example a government subsidy). In certain embodiments, for example, the bounding value may be computed based on a change to a governmental factor (for example the allowance or withdrawal of a government subsidy for the pharmaceutical therapy, or a government reimbursement rate for the pharmaceutical therapy).

[0056] In certain embodiments, for example, the bounding value may be a function, at least in part, of a reimbursement rate for the pharmaceutical therapy. In certain embodiments, for example, the bounding value may be computed from a change (for example a minimum increase, a minimum reduction, a maximum increase, or a maximum reduction) to the reimbursement rate of the pharmaceutical therapy.

[0057] In certain embodiments, for example, a constraint of the plural distribution constraints may comprise a discount that depends on the total number of the one or more patients to which the pharmaceutical therapy is distributed.

[0058] In certain embodiments, for example, a pharmaceutical distribution constraint may comprise one or more parameters. In certain embodiments, for example, the one or more parameters may comprise a coefficient of a variable in the pharmaceutical distribution constraint. In certain embodiments, for example, the one or more parameters may comprise a binary variable (for example a variable that multiplies each other term in the pharmaceutical distribution constraint. In certain embodiments, for example, the one or more parameters may comprise may correspond to another variable that is associated with the pharmaceutical distribution constraint. In certain embodiments, for example, the another variable may be a continuous variable. In certain embodiments, for example, the another variable may be a discrete variable. In certain embodiments, for example, the pharmaceutical distribution constraint may define a bound on the another variable (for example, an upper bound and / or a lower bound).

[0059] In certain embodiments, for example, generating the computer-executable pharmaceutical distribution model may comprise setting a value for a parameter in the pharmaceutical distribution constraint. In certain embodiments, for example, the value may be generated using a computer-implemented statistical model. In certain embodiments, for example, the value may be one of a set of values corresponding to the parameter at different instances in time. In certain embodiments, for example, the value of one of a set of values corresponding to multiple scenarios for the parameter. In certain embodiments, for example, generating the computer-executable pharmaceutical distribution model may comprise setting a value corresponding to a parameter that is associated with a pharmaceutical distribution constraint. In certain embodiments, for example, the parameter may comprise a coefficient of a variable in the pharmaceutical distribution constraint. In certain embodiments, for example, the parameter may be one of a plurality of parameters (for example fixed values or variables) associated with a plurality of pharmaceutical distribution constraints. In certain embodiments, for example, the parameter may comprise a binary variable that multiplies a pharmaceutical distribution constraint.

[0060] In certain embodiments, for example, the parameter may be a region where the pharmaceutical therapy is to be distributed. In certain embodiments, for example, the parameter of the plurality of parameters may be a dosage of a pharmaceutical drug in the treatment. In certain embodiments, for example, the parameter may be a number of doses ofa pharmaceutical drug in the treatment required or anticipated per patient. In certain embodiments, for example, the parameter may be a number of persons located in a region and diagnosed with a condition that is treatable using the pharmaceutical therapy. In certain embodiments, for example, the parameter may be a distributor’s cost per treatment.

[0061] In certain embodiments, for example, the pharmaceutical distribution constraint may be one of a plurality of archetypal constraints. In certain embodiments, for example, a first archetypal constraint of the plurality of archetypal constraints may be mutually exclusive to a second archetypal constraint of the plurality of archetypal constraints.

[0062] In certain embodiments, for example, the pharmaceutical distribution constraints may be an equality or an inequality. In certain embodiments, for example, the plurality of pharmaceutical distribution constraints may comprise an equality, an inequality, all equalities, all inequalities, or a combination of equalities and inequalities.

[0063] In certain embodiments, for example, generating the computer-executable pharmaceutical distribution model may comprise generating source code for the computerexecutable pharmaceutical distribution model. In certain embodiments, for example, the computer-executable pharmaceutical distribution model may be implemented on a server. In certain embodiments, for example, the computer-executable model may be configured to be executed by the analytic engine as further described below.

[0064] E. In certain embodiments, for example, the analytic engine may comprise one or more computer-executable programs configured to simulate the computer-executable pharmaceutical distribution model. In certain embodiments, for example, the analytic engine may perform a computer simulation (or multiple computer simulations) (for example using software implemented using a programming language such as JAVA) with respect to variables and / or parameters of the computer-executable pharmaceutical distribution model. In certain embodiments, for example, the computer simulation may track one or more targets (for example a goal such as a distribution goal for the pharmaceutical therapy) over a prospective time horizon.

[0065] In certain embodiments, for example, the analytic engine may compute a timevarying behavior of at least one parameter (for example a parameter of a plurality of parameters) of the computer-executable pharmaceutical distribution model to obtain computational results.

[0066] In certain embodiments, for example, the calculating the time-varying behavior may comprise a dynamic simulation. In certain embodiments, for example, the calculating the time-varying behavior may comprise calculating the time-varying behavior of the at leastone parameter for a scenario. In certain embodiments, for example, the calculating the timevarying behavior may comprise calculating the time-varying behavior of the at least one parameter for a plurality of scenarios. In certain embodiments, for example, the calculating the time-varying behavior may comprise a Monte Carlo simulation.

[0067] In certain embodiments, for example, the calculating the time-varying behavior may be performed during an optimization process (for example optimization of one or more parameters of the at least one parameter with respect to a performance metric, such as maximizing or minimizing the performance metric through the selection of the one or more parameters). In certain embodiments, for example, the optimization process may comprise at least partially solving a linear program. In certain embodiments, for example, the optimization process may comprise at least partially solving a nonlinear program. In certain embodiments, for example, the optimization process may comprise at least partially solving an integer program. In certain embodiments, for example, the optimization process may comprise at least partially solving an integer-linear program. In certain embodiments, for example, the optimization process may comprise at least partially solving a mixed integer- nonlinear program. In certain embodiments, for example, the optimization process may comprise simulated annealing. In certain embodiments, for example, the optimization process may at least partially optimizes the at least one parameter. In certain embodiments, for example, the parameter may be a variable associated with the plurality of pharmaceutical distribution constraints. In certain embodiments, for example, the parameter may be a coefficient of a variable that may be associated with the plurality of pharmaceutical distribution constraints. In certain embodiments, for example, the parameter may be a binary parameter that multiples another parameter that is associated with the plurality of pharmaceutical distribution constraints.

[0068] In certain embodiments, for example, the time-varying behavior may comprise a change (for example an instantaneous change or a cumulative change) in a distribution quantity of the pharmaceutical therapy. In certain embodiments, for example, the timevarying behavior may comprise a change in a patent adherence to the pharmaceutical therapy. In certain embodiments, for example, the time-varying behavior may comprise a change in a residual of a distribution constraint of the plurality of pharmaceutical distribution constraints. In certain embodiments, for example, the time-varying behavior may comprise a change in a supply of the pharmaceutical therapy. In certain embodiments, for example, the time-varying behavior may comprise a change in a number of discontinuation recommendations for the pharmaceutical therapy. In certain embodiments, for example, the time-varying behaviormay comprise a change in an available supply for the pharmaceutical therapy. In certain embodiments, for example, the time-varying behavior may comprise a change in a production rate for the pharmaceutical therapy. In certain embodiments, for example, the time-varying behavior may comprise a change in a regulatory status (for example regulatory approval or regulatory rejection) for the pharmaceutical therapy. In certain embodiments, for example, the time-varying behavior may comprise a change in a legal status for the pharmaceutical therapy. In certain embodiments, for example, the time-varying behavior may comprise a change in a geographic factor (for example a geographic supply factor or a geographic demand factor) for the pharmaceutical therapy. In certain embodiments, for example, the time-varying behavior may comprise a change in a governmental factor (for example a government subsidy) for the pharmaceutical therapy. In certain embodiments, for example, the time-varying behavior may comprise a change in a reimbursement rate (for example an insurance reimbursement rate for the pharmaceutical therapy.

[0069] In certain embodiments, for example, calculating the time-varying behavior may comprise the analytic engine performing calculations to enforce a distribution constraint of the plurality of distribution constraints. In certain embodiments, for example, the calculations to enforce the distribution constraint may comprise determining a value for a variable that allows the distribution constraint to be enforced. In certain embodiments, for example, calculating the time-varying behavior may comprise performing calculations to determine a time series of values for a variable present in a distribution constraint of the plurality of distribution constraints.

[0070] In certain embodiments, for example, the analytic engine may generate one or more distributions of performance results.

[0071] F. In certain embodiments, for example, the computational results may be simulation results. In certain embodiments, for example, the computational results may comprise a residual of a distribution constraint of the plurality of pharmaceutical distribution constraints. In certain embodiments, for example, the computational results may comprise a selection of constraints from the plurality of pharmaceutical distribution constraints. In certain embodiments, for example, the computational results may comprise residual values for a constraint of the plurality of pharmaceutical distribution constraints. In certain embodiments, for example, the computational results may comprise a time series of values for a variable associated with a pharmaceutical distribution constraint (or plurality of pharmaceutical distribution constraints).

[0072] In certain embodiments, for example, the computational results may comprise values for a performance metric. In certain embodiments, for example, the computational results may comprise results of backtesting computations (for example, values for a performance metric computed for backtesting simulations). In certain embodiments, for example, the computational results may comprise a difference between computational results performed on a set of data used to train a model and computational results performed on a set of data that was not used to train a model.

[0073] In certain embodiments, for example, the computational results may comprise results for a plurality of time-dependent scenarios that are a function of a parameter (for example a variable or a fixed coefficient) of the computer-executable pharmaceutical distribution model. In certain embodiments, for example, the computational results may comprise a solution to a mathematical program that optimizes a parameter so as to maximize or minimize the value of a performance metric, subject to one or more pharmaceutical distribution constraints of the computer-executable pharmaceutical distribution model. In certain embodiments, for example, the computational results may comprise a sensitivity analysis with respect to the parameter of the computer-executable pharmaceutical distribution model.

[0074] G. In certain embodiments, for example, the artificial intelligence engine may comprise an artificial neural network. In certain embodiments, for example, the artificial neural network may comprise a feedforward artificial neural network, a recurrent neural network, a convolutional neural network, or a combination of two or more of the foregoing.

[0075] In certain embodiments, for example, the artificial intelligence engine may comprise a generative artificial intelligence model. In certain embodiments, for example, the generative artificial intelligence model may comprise a large language model (“LLM”). In certain embodiments, for example, the generative artificial intelligence model may comprise a self-attention mechanism (for example a self-attention mechanism based on a transformer model or a neural machine translation model. In certain embodiments, for example, the generative artificial intelligence model may comprise a naive Bayes model. In certain embodiments, for example, the generative artificial intelligence model may comprise a Latent Dirichlet Allocation (“LDA”) model. In certain embodiments, for example, the generative artificial intelligence model may comprise a Gaussian Mixture Model (“GMM”). In certain embodiments, for example, the generative artificial intelligence model may comprise a deep learning method. In certain embodiments, for example, the deep learning method may comprise a Restricted Boltzmann Machine (“RBM”). In certain embodiments, for example,the deep learning method may comprise a Deep Belief Network (“DBN”). In certain embodiments, for example, the deep learning method may comprise a Variational Autoencoder (“VAE”). In certain embodiments, for example, the deep learning method may comprise a Generative Adversarial Network (“GAN”).

[0076] In certain embodiments, for example, the artificial intelligence engine may comprise one or more computer processors that are specialized for neural network processing. In certain embodiments, for example, the artificial intelligence engine may comprise one or more graphics processing units (“GPUs”), field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”), or a combination of two or more of the foregoing. In certain embodiments, for example, the artificial intelligence engine may comprise one or more core on system-on-a-chips (“SoCs”) (for example an SoC that implements one or more GPUs, FPGAs, and / or ASICs).

[0077] In certain embodiments, for example, part or all of the input and the computational results may be submitted to the generative artificial intelligence as one or more prompts. In certain embodiments, for example, one or more prompts may be engineered using the input and the computational results (for example using a prompt engine that receives the input and the computational results out outputs one or more prompts in a form suitable to be submitted to the artificial intelligence engine).

[0078] In certain embodiments, for example, the prompt engine may use the input and the computational results to form a question, a request, or both. In certain embodiments, for example, the prompt engine may augment the input and the computational results with information from a library of stored prompt patterns to form a prompt. In certain embodiments, for example, the prompt engine may analyze the input and the computational results and request additional information (for example via the user interface and / or from the user). In certain embodiments, for example, the additional information may be used to discriminate between alternative prompt patterns.

[0079] H. In certain embodiments, for example, the recommendation may comprise change to a pharmaceutical distribution constraint present in the computer-executable pharmaceutical distribution model. In certain embodiments, for example, the recommendation may comprise a change to a pharmaceutical distribution constraint (for example adjusting or removing a pharmaceutical distribution constraint) that was provided by the user as part or all of the user input. In certain embodiments, for example, the recommendation may comprise a proposed term sheet. In certain embodiments, for example,the recommendation may comprise a restriction (for example an additional pharmaceutical distribution constraint).

[0080] I. In certain embodiments, for example, the user may be a user of a supplier computing system. In certain embodiments, for example, the user may be a user of a distributor computing system. In certain embodiments, for example, the user may be a supplier (and / or a manufacturer). In certain embodiments, for example, the user may be a distributor. In certain embodiments, for example, the user may be a broker. In certain embodiments, for example, the user may be a regulatory agent.

[0081] J. In certain embodiments, for example, the response may comprise a global forecast and rollout plan for the pharmaceutical therapy. In certain embodiments, for example, the input may comprise an assumption of a rebate off of a list price of the pharmaceutical therapy. In certain embodiments for example, the rebate off of a list price of the pharmaceutical therapy may be in the range of between 20% and 80%, for example in the range of between 20%-50%, in the range of between 20% to 40%, in the range of between 25% and 75%, in the range of between 50% to 75%, or the rebate off of a list price of the pharmaceutical therapy by be in the range of between 40% and 60% (for example 50%).

[0082] In certain embodiments, for example, the input may comprise an assumption of a global rollout to a predetermined number of geographic locations per quarter, for example a predetermined number of equal to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114,115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, or 128 geographic locations per quarter.

[0083] In certain embodiments, for example, the computer-executable pharmaceutical distribution model may comprise a plurality of alternative archetypal constraints (for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more archetypal constraints). In certain embodiments, for example, the computational results may comprise a comparison (for example based on a performance metric) of the effect of two (or more) alternative archetypal constraints of the plurality of alternative archetypal constraints in a predetermined geographic location.

[0084] In certain embodiments, for example, the computational results may comprise an optimal combination (for example based on a performance metric as described herein) of (a)a rebate off of a list price of the pharmaceutical therapy and (b) a cap on a price of the pharmaceutical therapy in the predetermined geographic location.

[0085] K. In certain embodiments, for example, the computer-executable pharmaceutical distribution model may be associated with historical data for the performance of the computer-executable pharmaceutical distribution model. In certain embodiments, for example, the computational results may comprise an evaluation of the computer-executable pharmaceutical distribution model based on the historical data. In certain embodiments, for example, at least a portion of the response may comprise a recommended change to the computer-executable pharmaceutical distribution model based on a changes in the value of a performance metric (for example a change in revenue or distribution amount of the pharmaceutical therapy based on the recommended change to the computer-executable pharmaceutical distribution model). In certain embodiments, for example, the response may comprise image data, such as a compressed or uncompressed image or a compressed of uncompressed video (for example a description such as a chart describing performance of a pharmaceutical distribution agreement with respect to a performance metric, an image showing a term sheet (or a markup of a term sheet) or other papers for a pharmaceutical distribution agreement, etc.

[0086] L. In certain embodiments, for example, the pharmaceutical therapy may be a gene therapy. In certain embodiments, for example, the pharmaceutical distribution agreement may comprise a renewal period for the gene therapy. In certain embodiments, for example, the analytic engine may optimize a performance measure (for example an amortized cost) that is evaluated over a fixed time period based on the renewal period. In certain embodiments, for example, the performance metric may comprise a cost of the pharmaceutical therapy that is amortized over a fixed time period associated with the renewal period. In certain embodiments, for example, the performance metric may comprise a cost of the pharmaceutical therapy that is amortized over a fixed time period after which the gene therapy may be discontinued.

[0087] M. In certain embodiments, for example, the computational results may comprise a comparison of the pharmaceutical therapy with an alternative pharmaceutical therapy (or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more alternative pharmaceutical therapies) based on a performance metric (for example a performance metric that compares patient outcomes or costs between two or more pharmaceutical therapies). In certain embodiments, for example, the user may be a payer for the pharmaceutical therapy. In certain embodiments, for example, the performance metric may comprise a measure of anoutcome for the pharmaceutical therapy. In certain embodiments, for example, the performance metric may comprise a measure of the cost of the pharmaceutical therapy.

[0088] In certain embodiments, for example, the recommendation may comprise a term for a pharmaceutical distribution agreement that will trigger renegotiation of a pharmaceutical distribution agreement for the pharmaceutical therapy if a performance metric for a second pharmaceutical therapy exceeds, falls below, or exceeds and / or falls below a threshold value for the performance metric.

[0089] Certain embodiments may provide, for example, a method of generating one or more suggestions using an artificial intelligence to change a pharmaceutical distribution agreement. In certain embodiments, for example, the method may comprise receiving a term of the pharmaceutical distribution agreement via a user interface. In certain embodiments, for example, the method may comprise associating a performance metric with the term. In certain embodiments, for example, the method may comprise forming a prompt that comprises the term and the performance metric. In certain embodiments, for example, the method may comprise submitting the prompt to an artificial intelligence engine that comprises a generative artificial intelligence. In certain embodiments, for example, the method may comprise obtaining a response to the prompt from the artificial intelligence engine that comprises a suggestion to change the term. In certain embodiments, for example, the method may comprise providing the suggestion to a user.

[0090] A. In certain embodiments, for example, receiving a term for a pharmaceutical distribution agreement comprises the user uploading a term sheet associated with the pharmaceutical distribution agreement via the user interface.

[0091] B. In certain embodiments, for example, the performance metric may indicate violation or compliance with a pharmaceutical distribution constraint. In certain embodiments, for example, the performance metric may comprise a metric indicating that a pharmaceutical distribution constraint is met or is not met over time (for example, through the duration of the distribution agreement). In certain embodiments, for example, the performance metric may comprise a metric indicating the distance (for example, Euclidean distance) between a first pharmaceutical distribution constraint and (i) a second pharmaceutical distribution constraint or (ii) one or more predetermined values (for example, vector of values indicating a desired or minimum number of patients at different points in time). In certain embodiments, for example, the performance metric may comprise a metric indicating whether a first pharmaceutical distribution constraint is within a threshold (for example, threshold value, threshold percentage, etc.) from (i) a second pharmaceuticaldistribution constraint or (ii) one or more predetermined values. In certain embodiments, for example, the performance metric may comprise a metric indicating a likelihood that a pharmaceutical distribution constraint will be met or not. In certain embodiments, for example, the performance metric may comprise a metric indicating a likelihood that a pharmaceutical distribution constraint will be met or not over time (for example, through the duration of the distribution agreement), etc. In certain embodiments, for example, the performance metric may comprise a metric based on multiple pharmaceutical distribution constraints. In certain embodiments, for example, the performance metric may comprise a metric indicating an overall performance for a particular scenario across all pharmaceutical distribution constraints or a select subset of pharmaceutical distribution constraints (for example, pharmaceutical distribution constraints that a user of the distributor computing system and / or of the supplier computing system has indicated belong to a set of more relevant pharmaceutical distribution constraints, pharmaceutical distribution constraints that the third- party computing system determines are most often negotiated or disputed per its past models or stored data, etc.). In certain embodiments, for example, the performance metric may comprise a metric indicating overall simulated performance of the pharmaceutical distribution agreement over time for a particular scenario across all pharmaceutical distribution constraints or a select subset of pharmaceutical distribution constraints.

[0092] In certain embodiments, for example, the performance metric may comprise a predicted outcome. In certain embodiments, for example, the performance metric may comprise a number of patients that use the pharmaceutical therapy. In certain embodiments, for example, the performance metric may comprise a probability of a successful negotiation.

[0093] In certain embodiments, for example, the associating a performance metric with the term comprises an analytic engine selecting the performance metric from a plurality of performance metrics (for example from a library of predetermined performance metric types). In certain embodiments, for example, the associating a performance metric with the term comprises performing at least one simulation of the term.

[0094] Certain embodiments may provide, for example, a method for changing the performance of an artificial intelligence engine. In certain embodiments, for example, the method may comprise obtaining information associated with a plurality of computerexecutable pharmaceutical distribution models from a first database. In certain embodiments, for example, the method may comprise training the artificial intelligence engine using at least the information. In certain embodiments, for example, the method may comprise selecting a computer-executable pharmaceutical distribution model. In certain embodiments, forexample, the method may comprise processing the computer-executable pharmaceutical distribution model in an analytic engine to obtain computational results that include a simulated performance of the computer-executable pharmaceutical distribution model over a period of time. In certain embodiments, for example, the method may comprise retrieving historical data that quantifies the performance of the computer-executable pharmaceutical distribution model over the period of time from a second database. In certain embodiments, for example, the method may comprise comparing the simulated performance to the historical data. In certain embodiments, for example, the method may comprise re-training the artificial intelligence engine based upon the comparing.

[0095] A. In certain embodiments, for example, the information may comprise a dataset for persons with a particular condition (for example, diagnosis) or having been treated with a particular therapy and can meet one or more criteria. In certain embodiments, for example, the one or more criteria may comprise data from a particular region, data from at least a minimum number of persons or patients, data for a particular dosage of a pharmaceutical drug, includes data for a particular number of cycles / packs / doses of a pharmaceutical drug per person for treating a particular condition, data collected that is no older than a particular time from the current time, or a combination of two or more of the foregoing. In certain embodiments, for example, the information may comprise historical performance of a pharmaceutical distribution agreement based on one or more performance metrics. In certain embodiments, for example, the information may comprise historical performance of a pharmaceutical distribution constraint based on one or more performance metrics. In certain embodiments, for example, the information may comprise a time series of distribution amounts for the pharmaceutical therapy. In certain embodiments, for example, part or all of the information may be simulation results.

[0096] In certain embodiments, for example, the information and / or the historical information may comprise a proprietary dataset under control of the user or a counterparty (for example a customer) of the user. In certain embodiments, for example, the information and / or the historical information may comprise a contract repository. In certain embodiments, for example, the information and / or the historical information may comprise a publicly available dataset. In certain embodiments, for example, the information and / or the historical information may comprise one or more publicly available contracts. In certain embodiments, for example, the information and / or the historical information may comprise publicly available contract-related information.

[0097] In certain embodiments, for example, the information and / or the historical information may comprise a proprietary dataset (for example a proprietary deal catalog or a proprietary database such as a database pertaining to metallurgy, cancer imaging, medical diagnosis, etc.). In certain embodiments, for example, the information and / or the historical information may comprise a confidential dataset. In certain embodiments, for example, the information and / or the historical information may comprise a first dataset (for example a proprietary and / or confidential dataset) and a second dataset (for example a dataset that includes pharmaceutical distribution agreement information for a customer who elects to participate in the training and or re-training of the artificial intelligence engine.

[0098] B. In certain embodiments, for example, training the artificial intelligence engine may comprise data cleaning. In certain embodiments, for example, training the artificial intelligence engine may comprise data normalization. In certain embodiments, for example, training the artificial intelligence engine may comprise data supplementation. In certain embodiments, for example, training the artificial intelligence engine may comprise data orthogonalization. In certain embodiments, for example, training the artificial intelligence engine may comprise transforming the data.

[0099] In certain embodiments, for example, training the artificial intelligence engine may comprise initializing one or more parameters of an artificial intelligence model. In certain embodiments, for example, the one or more parameters may be initialized with a random value (for example, each parameter being initialized may be initialized with a separate random value or all with the same random value). In certain embodiments, for example, the random value may be generated by a random number generator. In certain embodiments, for example, the one or more parameters may be initialized by transfer learning from another artificial intelligence model (for example using one or more pretrained weights). In certain embodiments, for example, the one or more parameters may be initialized based on user input (for example in response to a request for initialization values). In certain embodiments, a plurality of parameters may be initialized by a combination of two or more of the foregoing initialization techniques.

[0100] In certain embodiments, for example, training the artificial intelligence engine may comprise making predictions using the artificial intelligence model and comparing the predictions to known results. In certain embodiments, for example, the comparison may comprise measuring the performance of the model according to a loss function. In certain embodiments, for example, the loss function may be an Li measure of prediction error, an L2 measure of prediction error norm, an Leo measure of prediction error, a log loss measure ofprediction error , a categorical cross-entry loss, a support vector machine loss, a Huber loss, a Poisson loss, a Kullback-Leibler divergence, or a combination of two or more of the foregoing.

[0101] C. In certain embodiments, for example, the historical data may comprise a dataset for persons with a particular condition (for example, diagnosis) or having been treated with a particular therapy and can meet one or more criteria. In certain embodiments, for example, the one or more criteria may comprise data from a particular region, data from at least a minimum number of persons or patients, data for a particular dosage of a pharmaceutical drug, includes data for a particular number of cycles / packs / doses of a pharmaceutical drug per person for treating a particular condition, data collected that is no older than a particular time from the current time, or a combination of two or more of the foregoing. In certain embodiments, for example, the historical data may comprise historical performance of a pharmaceutical distribution agreement based on one or more performance metrics. In certain embodiments, for example, the historical data may comprise historical performance of a pharmaceutical distribution constraint based on one or more performance metrics. In certain embodiments, for example, the historical data may comprise a time series of distribution amounts for the pharmaceutical therapy. In certain embodiments, for example, the historical data may comprise part of the same data as the information. In certain embodiments, for example, the historical data may not comprise part of the same data as the information.

[0102] In certain embodiments, for example, back testing a populated agreement may comprise using an analytical tool to measure potential performance of the agreement (for example, budget impact performance, patient access performance, etc.). In certain embodiments, for example, back testing may comprise testing the populated against historical data to show what would have happened if an agreement had been performed at a previous point in time (for example, the previous three years if the agreement has a duration of three years, the previous year if the agreement has a duration of one year, etc.). In certain embodiments, for example, back testing results may inform possible future agreements and evaluate the effectiveness of an existing agreement.

[0103] Certain embodiments may provide, for example, a method for making a pharmaceutical therapy available to at least one patient, comprising: (i) receiving input associated with the pharmaceutical therapy via a user interface; (ii) generating a computerexecutable pharmaceutical distribution model for the pharmaceutical therapy; (iii) analyzing the computer-executable pharmaceutical distribution model with an analytic engine to obtaincomputational results; (iv) submitting the input and the computational results to an artificial intelligence engine; (v) obtaining a response to the submitting that comprises a recommended pharmaceutical distribution constraint; and (vi) providing at least a portion of the response to a user.

[0104] Certain embodiments may provide, for example, a method of generating one or more suggestions using artificial intelligence to change a pharmaceutical distribution agreement, comprising: (i) receiving a term of the pharmaceutical distribution agreement via a user interface; (ii) associating a performance metric with the term; (iii) forming a prompt that comprises the term and the performance metric; (vi) submitting the prompt to an artificial intelligence engine that comprises a generative artificial intelligence; (v) obtaining a response to the prompt from the artificial intelligence engine that comprises a suggestion to change the term; and (vi) providing the suggestion to a user.

[0105] Certain embodiments may provide, for example, a method for changing the performance of an artificial intelligence engine, comprising: (i) obtaining information associated with a plurality of computer-executable pharmaceutical distribution models from a first database; (ii) training the artificial intelligence engine using at least the information; (iii) selecting a computer-executable pharmaceutical distribution model; (iv) processing the computer-executable pharmaceutical distribution model in an analytic engine to obtain computational results that include a simulated performance of the computer-executable pharmaceutical distribution model over a period of time; (v) retrieving historical data that quantifies the performance of the computer-executable pharmaceutical distribution model over the period of time from a second database; (vi) comparing the simulated performance to the historical data; and (vii) re-training the artificial intelligence engine based upon the comparing.

[0106] Certain embodiments may provide, for example, a product for distributing a pharmaceutical therapy to at least one patient, the product comprising a non-transitory computer-readable storage medium having computer-readable program code executable by a computing device to perform computing operations, the computing operations comprising: (i) receiving input associated with the pharmaceutical therapy via a user interface; (ii) generating a computer-executable pharmaceutical distribution model for the pharmaceutical therapy; (iii) analyzing the computer-executable pharmaceutical distribution model with an analytic engine to obtain computational results; (iv) submitting the input and the computational results to an artificial intelligence engine; (v) obtaining a response to the submitting that comprises arecommended pharmaceutical distribution constraint; and (vi) providing at least a portion of the response to a user.

[0107] Certain embodiments may provide, for example, a product for distributing a pharmaceutical therapy to at least one patient, the product comprising a non-transitory computer-readable storage medium having computer-readable program code executable by a computing device to perform computing operations, the computing operations comprising: (i) receiving a term of the pharmaceutical distribution agreement via a user interface; (ii) associating a performance metric with the term; (iii) forming a prompt that comprises the term and the performance metric; (vi) submitting the prompt to an artificial intelligence engine that comprises a generative artificial intelligence; (v) obtaining a response to the prompt from the artificial intelligence engine that comprises a suggestion to change the term; and (vi) providing the suggestion to a user.

[0108] Certain embodiments may provide, for example, a product for distributing a pharmaceutical therapy to at least one patient, the product comprising a non-transitory computer-readable storage medium having computer-readable program code executable by a computing device to perform computing operations, the computing operations comprising: (i) obtaining information associated with a plurality of computer-executable pharmaceutical distribution models from a first database; (ii) training the artificial intelligence engine using at least the information; (iii) selecting a computer-executable pharmaceutical distribution model; (iv) processing the computer-executable pharmaceutical distribution model in an analytic engine to obtain computational results that include a simulated performance of the computer-executable pharmaceutical distribution model over a period of time; (v) retrieving historical data that quantifies the performance of the computer-executable pharmaceutical distribution model over the period of time from a second database; (vi) comparing the simulated performance to the historical data; and (vii) re-training the artificial intelligence engine based upon the comparing.

[0109] Certain embodiments may provide, for example, a system for distributing a pharmaceutical therapy to at least one patient, the system comprising one or more computing systems configured to perform computing operations comprising: (i) receiving input associated with the pharmaceutical therapy via a user interface; (ii) generating a computerexecutable pharmaceutical distribution model for the pharmaceutical therapy; (iii) analyzing the computer-executable pharmaceutical distribution model with an analytic engine to obtain computational results; (iv) submitting the input and the computational results to an artificial intelligence engine; (v) obtaining a response to the submitting that comprises a recommendedpharmaceutical distribution constraint; and (vi) providing at least a portion of the response to a user.

[0110] Certain embodiments may provide, for example, a system for distributing a pharmaceutical therapy to at least one patient, the system comprising one or more computing systems configured to perform computing operations comprising: (i) receiving a term of the pharmaceutical distribution agreement via a user interface; (ii) associating a performance metric with the term; (iii) forming a prompt that comprises the term and the performance metric; (vi) submitting the prompt to an artificial intelligence engine that comprises a generative artificial intelligence; (v) obtaining a response to the prompt from the artificial intelligence engine that comprises a suggestion to change the term; and (vi) providing the suggestion to a user.[OHl] Certain embodiments may provide, for example, a system for distributing a pharmaceutical therapy to at least one patient, the system comprising one or more computing systems configured to perform computing operations comprising: (i) obtaining information associated with a plurality of computer-executable pharmaceutical distribution models from a first database; (ii) training the artificial intelligence engine using at least the information; (iii) selecting a computer-executable pharmaceutical distribution model; (iv) processing the computer-executable pharmaceutical distribution model in an analytic engine to obtain computational results that include a simulated performance of the computer-executable pharmaceutical distribution model over a period of time; (v) retrieving historical data that quantifies the performance of the computer-executable pharmaceutical distribution model over the period of time from a second database; (vi) comparing the simulated performance to the historical data; and (vii) re-training the artificial intelligence engine based upon the comparing.BRIEF DESCRIPTION OF THE DRAWINGS

[0112] FIG. l is a schematic diagram of an embodiment of a neural network.

[0113] FIG. 2 is a schematic diagram of an embodiment of a neuron.

[0114] FIG. 3 is a schematic diagram of a self-attention-based neural machine translation process.

[0115] FIG. 4 is a schematic diagram of a training a generative adversarial network.

[0116] FIG. 5 is a schematic diagram of a system for distributing a pharmaceutical therapy.

[0117] FIG. 6 is a flow chart illustrating exemplary computing operations that may be associated with computer-readable program code in a product in accordance with certain embodiments disclosed herein.

[0118] FIG. 7 is a flow chart illustrating exemplary computing operations that may be associated with computer-readable program code in a product in accordance with certain embodiments disclosed herein.

[0119] FIGS. 8A-B is a flow chart illustrating exemplary computing operations that may be associated with computer-readable program code in a product in accordance with certain embodiments disclosed herein.DETAILED DESCRIPTION

[0120] Certain embodiments may provide, for example, use of an artificial intelligence engine in methods and systems to make a pharmaceutical therapy available to at least one patient. The artificial intelligence engine can include an artificial neural network that comprises a plurality of artificial neurons. Artificial neurons within a neural network are often organized into layers. FIG. 1 illustrates a neural network 100 including an input layer comprising input neurons 102A-C, a hidden layer comprising input neurons 104A-D, and an output layer comprising output neurons 106A-B. While the neural network 100 illustrated in FIG. 1 consists of nine neurons (102A-C, 104A-D, and 106A-B) organized into three layers, artificial neural networks may comprise any desirable number and / or arrangement of neurons, including any number of layers. The input layer may not include full neurons, but rather can include values in a data record, for example, that constitute inputs to the next layer of neurons. The activity of input neurons may represent raw information that is fed into the neural network.

[0121] As explained above, an artificial neural network can include one or more “hidden” layers. As shown in FIG. 1, the second layer is a hidden layer that includes four neurons 104A-D between the input layer 102A-C and the output layer 106A-B. Unlike the input layer 102A-C and the output layer 106A-B, the hidden layer 104A-D may not be connected externally. A hidden layer can be used to recode, or provide a representation for, the inputs provided by the input layer. The activity of each hidden neuron 104A-D can be determined by the activities of the input neurons 102A-C and the weights applied to the connections between the input and hidden neurons. An artificial neural network can include multiple hidden layers. However, some artificial neural networks do not contain any hidden layers . Various other layers may be included in place of, or in addition to, one or more hidden layers.For example, an artificial neural network can include a linear summer layer, which provides as output the sum of the inputs to the layer, possibly offset or modified in some way.

[0122] In neural network 100 illustrated in FIG. 1, the far-right layer is an output layer. A single sweep through the network from left to right results in the assignment of a value to each output node 106A-B in the output layer.

[0123] The individual neurons that make up a neural network can be based on any number of models. Neurons, including those in the neural network 100, can have a variety of configurations. As illustrated in FIG. 2, a neuron 200 can include a number of inputs 202A-E and associated weights 206A-E that are fed into a processing module 204 which uses the weighted inputs to calculate a single output 212. For example each input can be multiplied by its corresponding weight 206A-E to form a weighted input into to the processing module. A neuron can be configured to calculate, at least in part, an output by summing its inputs, or by summing inputs multiplied by one or more weights.

[0124] The processing module 204 acts as a logical operator based on an activation function. A purpose of the activation function is to limit the amplitude of the output 212 to some finite value. One kind of neuron is termed a perceptron model, wherein weighted inputs are fed into a linear summation module and summed together. Optionally, the summation can further include a bias term 208 (for example a numerical value “1”) multiplied by a weight 210. The resulting linear sum is then fed through an activation function to produce an output 212.

[0125] An activation function can have any desirable design. Table I below provides a nonexclusive list of commonly-used activation functions. In addition, an activation function can comprise a polynomial function (for example a first, second, third, fourth, fifth or higher- order polynomial activation function). Furthermore, an artificial intelligence network can use a combination of two or more different types of activation functions.TABLE I

[0126] In certain embodiments, the artificial intelligence engine may comprise a generative artificial intelligence. For example, the artificial intelligence engine can comprise a self-attention mechanism that is configured to weigh the importance of different elements in a sequence when processing that sequence. This approach is particularly effective when the prompt comprises words in a phrase or pixels in an image.

[0127] In certain embodiments, for example, a self-attention mechanism can be implement as a neural machine translation model. In certain embodiments, for example, a self-attention mechanism can be implemented according to a “Transformer” architecture discussed in “Attention Is All You Need" by Vaswani et al. (2017), which is hereby incorporated by reference herein in its entirety for all purposes. For example, a self-attention mechanism can receive an input sequence (e.g., a sentence or an image) and break it into elements (e.g., words or pixels) represented as vectors. For each pair of elements in the sequence, self-attention computes a score that represents the relationship or similarity between the elements. This score is calculated using a weighted dot product of the vectors. Scores are then normalized using a softmax function in order to convert scores into a probability distribution. These probabilities are called attention weights and indicate how much each element should attend to the others. Finally, the attention weights are used to compute a weighted sum of the vectors in the sequence. This weighted sum represents the “attended” information for each element. An objective of a self-attention mechanism is its ability to capture dependencies and relationships between elements in a sequence regardless of their position. In certain cases, self-attention mechanisms can capture long-range dependencies more effectively than recurrent neural networks (RNNs).

[0128] In an artificial intelligence model, multiple self-attention layers can be stacked together, and the model learns to weigh information from different parts of the input sequence adaptively. This makes it highly suitable for tasks like machine translation, textgeneration, and many other NLP tasks, as well as computer vision tasks such as image captioning.

[0129] An exemplary neural machine translation transformer model 300 utilizing selfattention is illustrated in FIG. 3. The transformer model 300 is an encoder-decoder model based on the attention mechanism. In an encoder, several identical self-attention layers are stacked on top of each other. Each layer contains two sub-layers, a multi-headed self-care sub-layer (multi-head attention layer) and a position-feed-forward layer (position-wise feedforward layer), which are connected to the layer standardization by a residual connection.

[0130] The decoder uses a similar layer architecture as the encoder except that masking is used to avoid processing of the unpredicted words. The decoder is also composed of 6 layers. Each layer of the decoder comprises two sub-layers, and a multi-headed attention sub-layer, receiving output from the corresponding encoder.

[0131] The attention mechanism can be represented by the following form:The multi -head Attention mechanism projects Q, K and V through h different linear transformations, and finally splices the results. There are three applications of the attention mechanism in the transformer: 1) Encoder attention: the self-attention mechanism, query, key and value are all from the same place; 2) Decoder attention: the self-attention mechanism, query, key and value are all from the same place; and 3) Encoder-decoder attention: the query is from the decoder, the key and the value are from the encoder. The feedforward network layer is realized by adopting a full connection layer plus Relu function

[0132] A multi-domain neuro-machine translation (NMT) system may be trained using a joint learning domain vector approach. The learned domain vector representation is merged with the word vector representation into the encoding and decoding side of the NMT. In this way, the NMT model is forced to encode and decode semantic information and domain information simultaneously, and a single NMT model is used to perform translation tasks for multiple domains.

[0133] Embodiments include multi-domain neural machine translation based on domain- aware self-attention mechanisms (DSA). In DSA, each word has a semantic vector of words and a domain vector. The domain vector is a vectorized representation of the domain from which the word comes, while the domain vector is also used as a key and value for the domain-aware self-attention mechanism. To learn the domain vector, three methods are proposed: 1) Supervised learning is performed using domain signals; 2) Unsupervisedlearning via a domain attention network; and 3) guided learning of the loss of assistance. The first method is implemented at the sentence level, while the other two methods are implemented at the word level. By these three methods, the DSA model is able to handle a variety of multi-domain translated configurations. When both training and test data know the domain ID, a method of supervised learning using domain signals is applicable. If there is a word-level domain signal on the training data, a guided learning method may be used, which may be provided by an external domain detector. The unsupervised domain learning method does not require any prior knowledge of the domain structure of the training and testing data. It may even learn the domain structure to which the word corresponds while training the NMT model.

[0134] In order for the NMT model to have different attention results, or to get attention information from different presentation subspaces, multi-headed self-attention mechanisms are used.

[0135] Other generative artificial intelligence models are provided. For example, naive Bayes is an example of a generative model that is more often used as a discriminative model. Other examples of generative models include Latent Dirichlet Allocation (“LDA) and the Gaussian Mixture Model (“GMM”). Deep learning methods can be also used as generative models, including a Restricted Boltzmann Machine (“RBM”) and a Deep Belief Network (“DBN”). Two modem examples of deep learning generative modeling algorithms include the Variational Autoencoder (“VAE”) and the Generative Adversarial Network (“GAN”).

[0136] GANs are a deep-leaming-based generative model. More generally, GANs are a model architecture for training a generative model. The GAN model architecture involves two sub-models: a generator model for generating new examples and a discriminator model for classifying whether generated examples are real, from the domain, or fake, generated by the generator model. The generator model is used to generate new plausible examples from the problem domain. The discriminator model is used to classify examples as real (from the domain) or fake (generated).

[0137] The generator model takes a fixed-length random vector as input and generates a sample in the domain. The vector is drawn from randomly from a Gaussian distribution, and the vector is used to seed the generative process. After training, points in this multidimensional vector space will correspond to points in the problem domain, forming a compressed representation of the data distribution. This vector space is referred to as a latent space, or a vector space comprised of latent variables. Latent variables, or hidden variables, are those variables that are important for a domain but are not directly observable.

[0138] Latent variables, or a latent space, may be a projection or compression of a data distribution. That is, a latent space provides a compression or high-level concepts of the observed raw data such as the input data distribution. In the case of GANs, the generator model applies meaning to points in a chosen latent space, such that new points drawn from the latent space can be provided to the generator model as input and used to generate new and different output examples. After training, the generator model is kept and used to generate new samples.

[0139] The discriminator model takes an example from the domain as input (real or generated) and predicts a binary class label of real or fake (generated). The real example comes from the training dataset. The generated examples are output by the generator model. The discriminator is a normal (and well understood) classification model. After the training process, the discriminator model is discarded as we are interested in the generator. Sometimes, the generator model can be repurposed as it has learned to effectively extract features from examples in the problem domain. Some or all of the feature extraction layers can be used in transfer learning applications using the same or similar input data.

[0140] Generative modeling is an unsupervised learning problem, although a property of the GAN architecture is that the training of the generative model is framed as a supervised learning problem whereby the generator and discriminator are trained together.

[0141] FIG. 4 illustrates a process for training generative models utilized in embodiments provided herein. The training provides a random input vector 402 to a generator model 404. The generator model 404 generates a batch of examples 406, and these, along with real examples 408 from the domain, are provided to a discriminator model 408 and then classified as real or fake. The discriminator model 410 is then updated to get better at discriminating real and fake samples in the next round, and importantly, the generator model 404 is updated based on how well, or not, the generated samples “fooled” the discriminator.

[0142] The generator model 404 and the discriminator model 410 competing against each other resulting in the generator model being trained. When the binary classifier 412 determines that the discriminator model 410 has successfully identified real and fake samples, it is rewarded or no change is needed to the model parameters, whereas the generator model 404 is penalized with large updates to model parameters. Alternately, when the generator model 404 “fools” the discriminator, it is rewarded, or no change is needed to the model parameters, but the discriminator model 404 is penalized and its model parameters are updated.

[0143] In principle, if the generator model 404 generated perfect replicas from the input domain every time then the discriminator could tell the difference and would predict “unsure” (e.g. 50% for real and fake) in every case. In practice, one of ordinary skill in the art would understand how to generate a useful generator model in real-world applications, such as character level, word level, or sentence level predictions, etc.

[0144] GANs can be configured to work with image data (including video data), for example by incorporating CNNs in the generator and / or discriminator models. Modeling image data means that the latent space, the input to the generator, provides a compressed representation of the set of images or photographs used to train the model. It also means that the generator generates new images or photographs, providing an output that can be easily viewed and assessed by developers or users of the model.

[0145] GAN can also be configured to conditionally generating an output. The generative model can be trained to generate new examples from the input domain, where the input, the random vector from the latent space, is provided with (conditioned by) some additional input. The additional input could be a class value, such as two competing term sheets for a pharmaceutical distribution agreement used to generate simulation results.

[0146] The discriminator can also be conditioned, meaning that it can be provided both with input that is either real or fake and the additional input. In the case of a classification label type conditional input, the discriminator would then expect that the input would be of that class, in turn teaching the generator to generate examples of that class in order to fool the discriminator. In this way, a conditional GAN can be used to generate examples from a domain of a given type.

[0147] A GAN model can be conditioned on an example from the domain, such as historical performance of a pharmaceutical distribution agreement, allowing GANs to be used to provide responses regarding the provisions of the pharmaceutical distribution agreement or a provision within the pharmaceutical distribution agreement.

[0148] In the case of conditional GANs to provide recommendations, the discriminator is provided examples of real and generated performance as input. The generator is provided with a random vector from a scenario in which the pharmaceutical distribution agreement is not entered into.

[0149] One of the many major advancements in the use of deep learning methods in domains such as computer vision is a technique called data augmentation. Data augmentation results in better performing models, both increasing model skill and providing a regularizing effect, reducing generalization error. It works by creating new, artificial but plausibleexamples from the input problem domain on which the model is trained. The techniques are primitive in the case of image data, involving crops, flips, zooms, and other simple transforms of existing images in the training dataset.

[0150] Successful generative modeling provides an alternative and potentially more domain-specific approach for data augmentation. In fact, data augmentation is a simplified version of generative modeling, although it is rarely described this way. In complex domains or domains with a limited amount of data, generative modeling provides a path towards more training for modeling. GANs have seen much success in this use case in domains such as deep reinforcement learning.

[0151] Generative artificial intelligence (Al) is a computer technology that generative a probabilistic output that is varied and non-deterministic. Generative artificial intelligence differs from discriminative modeling - where instead of predicting a class label given some input features (e.g., predicting a dog based on the features of barking and having a tail), generative modeling predicts features given a label. Instead of learning the boundary in feature space that separates one class from another (for example, what features separate successful from unsuccessful pharmaceutical distribution agreements in a particular country), as in discriminative modeling, generative modeling learns what features probabilistically occur together, which allows for the learning of new features associated with a given class. Two types of generative artificial intelligence models are generative adversarial networks (GANs) and transformer-based models.

[0152] Large language models (LLMs) are a particular type of generative artificial intelligence. LLMs are a machine learning technology for generating human-readable text and solving complex problems via deep neural networks. LLMs are trained on many parameters - in some cases on billions or trillions of parameters in order to create appropriate outputs that are realistic and accurate.

[0153] Some prior methods of training LLMs include using massive amounts of unstructured data to train a model via self-supervised learning. These methods can be used to create “off-the-shelf’ or pretrained models that can be utilized for example, by a chatbot.

[0154] Because generative artificial intelligence probabilistically generates output, the same input may give two different results if the model is run twice for the same input. The probability of one word (or pixel or other information) following another is based on the probability of the words occurring together or near each other in the training data. Naturally, this creates a problem where words may appear together often in a particular data set, but the same words do not often appear together in the data set used to train the model. For example,in a text completion task to provide a response to an inquiry regarding a computer-executable pharmaceutical distribution model, an LLM is pretrained on a large dataset and used by a generative artificial intelligence to complete the text of a prompt (e.g., “tell me what type of treatment a patient under the age of 35 should receive”).

[0155] The first feature is the provision of a generative artificial intelligence that is finetuned for specific tasks related to making pharmaceutical therapies available to patients. A pre-trained model is used as a starting point, but an expanded training set is generated that leverages a proprietary database of previous distribution models and / or publicly available data to refine the model for specific tasks. Unfortunately, the complex world of negotiating contracts, particularly in the field of pharmaceuticals, requires more than fine tuning a prebuilt LLM. Users of a computing platform for crafting such negotiations and planning for their eventual execution and success depend on complex modeling and simulation of pricing structures, financial incentives, timelines, planning for different contingencies, and so forth. These types of complex simulations may prohibit a casual user from even attempting to use the platform due to the steep learning curve.

[0156] The second feature is the minimization of this additional training or skill set by providing an analytic engine that performs any of the necessary simulations to fully and completely respond to a user’s query to the generative artificial intelligence. The generative artificial intelligence and analytic engine work together to translate a user’s input (e.g., a text query, an incomplete deal sheet, a complete deal sheet) into an output and whether prediction of the output requires a new simulation or extrapolation from an older simulation. This combination of features provides a robust generative artificial intelligence that can quickly and realistically respond to a user’s query while giving the user more flexibility and time to craft and experiment their inquiry or distribution model.

[0157] Certain embodiments may provide, for example, a method (for example a method using artificial intelligence tools), or a system or a computer program product for implementing said method, for making a pharmaceutical therapy available to at least one patient. In certain embodiments, for example, the method may comprise receiving input associated with a pharmaceutical therapy via a user interface. The user interface is, in certain embodiments, a graphical user interface (GUI).

[0158] In certain embodiments, a user interface can include an interface area that includes a number of interactive fields where a user can input values for different parameters (for example, model assumptions) for a new computer-executable pharmaceutical distribution model (discussed in more detail below) if the pharmaceutical therapy. Interface areas ofother embodiments include various interface elements for different pricing parameters, such as drop-down menu that a user can interact with to select a pricing strategy (for example, a per patient price strategy), a check box or toggle element that a user can interact with to select a pricing type (for example, tiered or volume), or the like.

[0159] In certain embodiments, for example, the method, or system or computer program product for implementing said method, may comprise generating a computer-executable pharmaceutical distribution model for the pharmaceutical therapy.

[0160] An initial user interface can be presented to a user (for example on a computer screen / display) and can request information or other selections from the user, such as a selection of a particular treatment or set of treatments for a new distribution model, one or more distributors for the pharmaceutical therapy, one or more suppliers of the pharmaceutical therapy, or the like. After making such a selection, the user is provided with an interactive interface, through which the user can set initial pharmaceutical distribution constraints, parameters, and / or values for the generated model.

[0161] In at least one embodiment, computer-executable pharmaceutical distribution models, such as the generated model discussed above, are models for the distribution of pharmaceutical therapies. Each computer-executable pharmaceutical distribution model includes a set of pharmaceutical distribution constraints that specify, for example, terms for the supply and distribution of a particular pharmaceutical therapy. The pharmaceutical distribution constraints may be represented by one or more formulas, parameters (for example variables and / or coefficients such as fixed coefficients) of model parameters, and / or values of model values.

[0162] The model values are assigned, for example, to correspond to at least a portion of the model parameters automatically or based on information received from one or more external sources. As an example, for a parameter indicating the success rate of a Drug A in treating a Condition X, a third-party computing system automatically calculates a value based on statistical data on the success rate of Drug A in treating Condition X obtained from one of a plurality of data sources and based on a success rate provided by the supplier computing system 104. The success rate provided by a supplier computing system, for example, may be determined at the supplier computing system using, for example, confidential statistical data indicating the success rate of Drug A in treating Condition X.

[0163] In certain embodiments, for example, a method may comprise analyzing a computer-executable pharmaceutical distribution model, such as the generated model described above, with an analytic engine to obtain computational results.

[0164] The analytic engine can analyze a generated computer-executable pharmaceutical distribution model as described above. The analytic engine can include one or more programs configured to analyze computer-executable pharmaceutical distribution models. The analytic engine may analyze a computer-executable pharmaceutical distribution model by, for example, dynamically simulating the model. As an example, the analytic engine can perform a simulation for each of multiple scenarios for the generated model (for example, generated by the analytic engine of the third-party computing system). In a simulation, a parameter can be assigned a first value in a first scenario and a second (possibly different value) in a second scenario. As another example, a parameter can be assigned a value based on (for example derived from) information obtained from a distributor computing system and / or the supplier computing system described above. As another example, a parameter can be assigned a value based on information received from an external source. Additionally or alternatively, assigning a value or values to a particular parameter includes assigning a value or set of values to the particular parameter as a set (or fixed) value (for example, based on a selection made by a user of the distributor computing system and / or the supplier computing system).

[0165] A simulation may indicate, for example, a performance of the generated model through a set of performance metrics. These performance metrics may indicate pharmaceutical distribution constraint violations. In more detail, the performance metrics can include, for example, a metric indicating whether a pharmaceutical distribution constraint of the generated model is met or not, a metric indicating whether a pharmaceutical distribution constraint of the generated model is met or not over time (for example, through the duration of the distribution agreement), a metric indicating the distance (for example, Euclidean distance) between a first pharmaceutical distribution constraint of the generated model (for example, indicating anticipated performance in one area such as the anticipated number of patients over time) and (i) a second pharmaceutical distribution constraint of the generated model (for example, indicating a desired or minimum performance in one area such as the minimum number of patients over time) or (ii) one or more predetermined values (for example, vector of values indicating a desired or minimum number of patients at different points in time), a metric indicating whether a first pharmaceutical distribution constraint of the generated model is within a threshold (for example, threshold value, threshold percentage, etc.) from (i) a second pharmaceutical distribution constraint of the generated model or (ii) one or more predetermined values, a metric indicating a likelihood that a pharmaceutical distribution constraint of the generated model will be met or not, a metric indicating alikelihood that a pharmaceutical distribution constraint of the generated model will be met or not over time (for example, through the duration of the distribution agreement), etc.

[0166] The performance metrics can additionally or alternatively include metrics based on multiple pharmaceutical distribution constraints, such as, as a metric indicating an overall performance of the generated model for a particular scenario across all pharmaceutical distribution constraints or a select subset of pharmaceutical distribution constraints (for example, pharmaceutical distribution constraints that a user of the distributor computing system and / or of the supplier computing system has indicated belong to a set of more relevant pharmaceutical distribution constraints, pharmaceutical distribution constraints that the third- party computing system determines are most often negotiated or disputed per its past models or stored data, etc.), an overall performance of the generated model over time for a particular scenario across all pharmaceutical distribution constraints or a select subset of pharmaceutical distribution constraints, etc.

[0167] As an example, in performing a simulation of the generated model described above, the analytic engine may use the assigned values of parameters of the generated model to simulate the performance of the generated model over time (for example, over the three- year duration of the agreement for the distribution of 100 mg of Drug A starting on January 1, 2024). In more detail, the analytic engine may simulate the anticipated number of eligible patients over time. The analytic engine can use, for example, (i) a pharmaceutical distribution constraint for calculating the number of eligible patients over time and (ii) different sets of values assigned to the parameters of the pharmaceutical distribution constraint that correspond to different scenarios to simulate the number of eligible patients that the distributor can expect over time for each of the different scenarios.

[0168] In some implementations, the analytic engine simulates the generated model using randomized or pseudo-randomized analysis techniques. For example, the analytical engine performs one or more Monte Carlo simulations by randomly sampling from subsets of data in a repository of medical data corresponding to different pharmaceutical distribution constraints. The medical data may, in some examples, be stored in a public data repository, a private data repository, or be generated from a combination of public and private data.

[0169] In more detail, the analytic engine can identify from the medical data a first subset of data (for example, can generate a data set from the medical data) indicating the number of persons in U.S. diagnosed with a condition that is treatable by 100 mg of Drug A over the past five years and a second subset of data indicating the number of persons in Canada diagnosed with a condition that is treatable by 100 mg of Drug A over the past five years. Inperforming the one or more Monte Carlo simulations to, for example, simulate the anticipated number of treatments required over time, the analytic engine can randomly sample from the first subset of data and the second subset of data. Each sample combination can represent a different scenario that is being simulated.

[0170] In some implementations, the analytic engine uses the computational results produced from simulating the generated model to calculate performance metrics for the generated model. As an example, after simulating the generated model, the analytic engine uses the simulation results to calculate one or more performance metrics (for example, one or more performance metrics for the corresponding parameter value(s), for a particular pharmaceutical distribution constraint, for the generated model as a whole for a particular scenario, for the generated model as a whole across multiple scenarios, etc.).

[0171] In some implementations, the pharmaceutical distribution constraints include performance rules. These rules may be adjustable or the rules may not adjustable. The performance rules can be provided by the supplier computing system, the distributor computing system, and / or the third-party computing system. As an example, the distributor computing system transmits a pharmaceutical distribution constraint to the third-party computing system that the third-party computing system uses to analyze and / or update the generated model. The pharmaceutical distribution constraint can be a performance rule, such as a rule that the minimum number of treatments supplied by the supplier must be less than or equal to the estimated number of treatments. In evaluating the performance of the generated model, the analytic engine can generate a performance metric based on a comparison of the performance results from one or more simulations of the generated model to the pharmaceutical distribution constraint that indicates, for example, whether the generated model met the rule, did not meet the rule, a likelihood of success in the generated model meeting the rule, a likelihood of failure in the generated model meeting the rule, a score indicating how often the generated model met or did not meet the rule for different scenarios, etc.

[0172] For example, the computational results produced from simulating the generated model can include results indicating the anticipated number of treatments of the 100 mg dosage of Drug A required over time. The performance results may indicate, for example, that an average of 4500 treatments are anticipated per year under a first scenario, an average of 4000 treatments are anticipated per year under a second scenario, an average of 3700 treatments are anticipated per year under a third scenario, an average of 3500 treatments are anticipated per year under a fourth scenario, and an average of 3300 treatments are anticipateper year under a fifth scenario. As an example, based on these performance results, the analytic engine can calculate a value of 0.4 for a performance metric, indicating that the rule was met in only 40% of the tested scenarios. As another example, based on these performance results, the analytic engine 132 can determine a value of 0 for a performance metric, indicating that the rule was not met (for example, based on a number of treatments averaged over the different scenarios, 3800 treatments, being less than the minimum 4000 treatments; based on the rule not being met in a single scenario; based on the rule not being met in a threshold number or percent of scenarios; etc.). As another example, based on these performance results, the analytic engine can determine a value of 200 for a performance metric, indicating that the difference between the minimum number of treatments per year and the average estimated number of treatments per year is -200 treatments. As another example, based on these performance results, the analytic engine can determine a vector of values of [0, 0.05] for a performance metric, indicating (i) through the first value that the rule was not met and (ii) through the second value that the average estimated number of treatments per year was 5% less than the minimum number of treatments.

[0173] In some implementations, in simulating the generated model, the analytic engine generates one or more distributions of performance results. The analytic engine can then calculate the performance metrics for the generated model using the one or more distributions. As an example, the analytic engine can generate a distribution of performance results for a particular pharmaceutical distribution constraint or parameter such as the anticipated number of treatments over time for multiple, different scenarios. The distribution results can indicate, for example, that more than 4300 treatments are anticipated at the end of the first year of the agreement (for example, at January 1, 2025) under a first scenario, that between 3800 and 3900 treatments are anticipated at the end of the first year of the agreement under five other scenarios, that between 3600 and 3800 treatments are anticipated at the end of the first year of the agreement under ten other scenarios, that between 3300-3400 treatments are anticipated at the end of the first year of the agreement under four other scenarios, and that less than 3200 treatments are anticipated at the end of the first year of the agreement under a final scenario.

[0174] The analytic engine may use a portion of the distribution to calculate the performance metrics for this pharmaceutical distribution constraint or parameter of the generated model. For example, the analytic engine may evaluate only those results that are within a standard deviation of the distribution, resulting in at least the results from the first scenario and the final scenario being removed from the evaluation. The analytic engine canthen use the subset of performance results to calculate one or more performance metrics, such as a value of 0 for a performance metric indicating that 100% of the performance results in the subset failed to meet the pharmaceutical distribution constraint.

[0175] In some implementations, the third-party computing system makes available at least a portion of the performance results to one or more of the parties of the generated model. For example, after simulating the generated model for five different scenarios and obtaining five sets of performance results, the third-party computing system can transmit the five sets of performance results to the distributor computing system and the supplier computing system or otherwise make those results accessible. The results may be displayed, at least in part, using one or more graphs corresponding to different pharmaceutical distribution constraints, different scenarios, or the like. As an example, the performance results can indicate which, the number of, or percent of supplier-defined pharmaceutical distribution constraints that were met or not met. Similarly, the performance results can, for example, indicate which, the number of, or percent of distributor-defined pharmaceutical distribution constraints that were met or not met.

[0176] In certain embodiments, a method includes calculating a time-varying behavior of at least one parameter of the new distribution model to obtain computational results. As an example, the third-party computing system described above can calculate a time-varying behavior of one or more parameters (for example, an anticipated number of patients, a rate of patient increase, a rate of patient dropout, a cost of therapy, etc.) over one or more periods of time. The period of time can be a scheduled time that the model is to be in effect (for example, over a period of time defined by a particular start and end date for the model), a time between a scheduled start time that the model is take effect and threshold amount of time thereafter (for example, a month after a start date for the model, six months after the start date for the model, a year after the start date for the model, etc.), or the like. The one or more periods of time can include multiple periods of time, such as each month that the model is scheduled to be in effect, each year the model is to be in effect (for example, first, second, and third years following a start date for a model having a duration of three years), or the like.

[0177] In certain embodiments, for example, a method may comprise submitting (for example via a prompt) the user input and computational results to an artificial intelligence engine. The artificial intelligence engine may, in certain examples, be accessed through a user interface as described above. The user interface may be a GUI including a chatbot, whereby the chatbot receives the user input and provides at least a portion of the user input to the artificial intelligence engine. For example, a user may access the GUI via a computerhaving a display screen, whereby the chatbot is configured to receive keyboard entries (e.g., alphanumeric input) forming a prompt to be transmitted to the artificial intelligence engine. In some examples, the artificial intelligence engine implements a generative artificial intelligence to receive the prompt, process the prompt, and generate an output. The output may be utilized for further processing by the artificial intelligence engine or at least a portion of the output may be returned to the user interface via the chatbot or other areas of the GUI.

[0178] The user interface may include, for example, an area for the user to upload a term sheet of a negotiation for a distribution agreement for a particular pharmaceutical therapy. In other examples, the user interface may include various graphical elements for the user to input specific terms of a term sheet that establishes a particular distribution model of a pharmaceutical therapy.

[0179] In certain embodiments, a method may comprise providing user input to the analytic engine to obtain computational results. The user input and / or the computational results are then provided to the artificial intelligence engine.

[0180] The artificial intelligence engine (or a separate prompt engine) may modify the user input and / or the computational results before submitting them to the generative artificial intelligence to increase the specificity of a user prompt and thereby improve the relevancy of a resulting response to the prompt. For example, a user may perform a number of different simulations using the analytic engine by changing one or more values corresponding to terms of a term sheet (e.g., an anticipated number of treatments). The user may then enter the following into the chatbot described above: “what would improve this proposed agreement?”. The analytic engine may determine that the user has modified a specific term for each simulation, and then generate the following prompt for the generative artificial intelligence described above: “what value of anticipated number of treatments would improve this proposed agreement?”

[0181] Embodiments herein are not limited to this example and the artificial intelligence engine is capable of modifying prompts to include computational results from the analytic engine. In an example, the artificial intelligence engine may generate computational results from the user’s simulations in a current session of working with the user interface and / or previously stored simulations that it determines are relevant to the user’s distribution model being developed.

[0182] In certain embodiments, for example, a method may comprise obtaining a response to the submitting that comprises a recommended pharmaceutical distribution constraint. The response may include the output of the generative artificial intelligenceresponsive to the generative artificial intelligence processing the prompt. For example, the response to the prompt “what value of anticipated number of treatments would improve this proposed agreement?” may be “To increase the likelihood of this proposed agreement being accepted, the anticipated number of treatments should be increased by 2 treatments per year.”

[0183] In certain embodiments, for example, the method may comprise providing at least a portion of the response to a user. For example, the response to the prompt “what value of anticipated number of treatments would improve this proposed agreement?” may be “increase the anticipated number of treatments by 2.”

[0184] FIG. 5 is a schematic depiction of a system 500 implementing the above-described methods that provides responses to prompts received from a user 502 (for example a negotiator) via a computer interface 504 over a network 506 (for example, the public Internet).

[0185] System 500 comprises a generative artificial intelligence engine 508 that is trained, at least in part, by fitting model parameters (for example model parameter in a selfattention model) to training data 510 associated with a plurality of pharmaceutical distribution transactions. As an additional training process, one or more computer-executable pharmaceutical distribution models (actual or hypothetical) is selected from a computerexecutable pharmaceutical distribution model database 512 and computational results for the one or more computer-executable pharmaceutical distribution models generated using an analytic engine 516 (for example, generating a simulation of the computer-executable pharmaceutical distribution model(s) from the present to a horizon date in the future). The computational results are then compared to historical data associated with the computerexecutable pharmaceutical distribution model(s) obtained from the distribution model database 512 to generate further computational results. The further computational results are used to further train the generative artificial intelligence engine 508. Furthermore, to facilitate the training and / or further training, system 500 can ingest additional training data from one or more external sources 514 (for example, data from a system associated with the negotiator 502, publicly available data, data from a third party, or data associated with a counterparty to the negotiator). As shown in FIG. 5, the data can be obtained via network 506 or by some other internal or external network.

[0186] System 500 can generate a summary (for example a term sheet) or a draft (partial or complete) of a pharmaceutical distribution agreement. In one mode, for example, the system 500 can generate a de novo draft of a summary or a draft of a pharmaceutical distribution agreement (for example a based on a prompt received from the user 502). Inanother mode, the user 502 can supply (for example via upload over network 506) a starting draft of a summary or draft of a pharmaceutical distribution agreement term sheet and the system 502 can generate one or more suggested modifications (inclusive of additions and / or deletions) to the starting draft of a summary or draft of a pharmaceutical distribution agreement term sheet. In addition, the system 500 can provide informative parameters associated with the generated summary or draft of the pharmaceutical distribution agreement, including a performance metric (for example a predicted number of patients that will gain access to the pharmaceutical therapy under the terms of the summary or draft pharmaceutical distribution agreement, probability of success of a negotiation based on the summary or draft of the pharmaceutical distribution agreement, etc.).

[0187] System 500 or portions thereof can also be deployed in or interfaced with a chatbot mode to answer questions from a user 502 pertaining to a pharmaceutical therapy. The system 500 can process a user 502 question to generate a prompt using a prompt generation engine 518, and submit the prompt to the generative artificial intelligence engine 508. Generation of the prompt can include identification of a computer-executable pharmaceutical distribution model associated with the pharmaceutical therapy (for example using data form the distribution model database 512) and analyzing the computer-executable pharmaceutical distribution model in the context of the pharmaceutical therapy using the analytic engine 516 to generate computational results. The prompt generation engine 518 processes the user question and the computational results to generate a prompt that is processed by the generative artificial intelligence. In response to the prompt, the generative artificial intelligence provides a response (for example a recommended pharmaceutical distribution constraint), and the system 500 provides a response to the user 502.

[0188] The model database 512 includes computer-executable pharmaceutical distribution models. For example, the model database can contain computer-executable pharmaceutical distribution models that were previously generated based on user input as well as models derived from a term sheet or other deal documentation. Different models can involve the same or different suppliers and / or distributors. As an example, a model in the model database 512 can include a model for the distribution of Drug A in the U.S. and Canada, and can involve a first supplier (for example, Supplier X) having the supplier computing system 104 and a first distributor (for example, Distributor X) having the distributor computing system 102.

[0189] As another example, a different model in the model database 512 can be directed to the distribution of pharmaceutical therapy in the U.S. and Canada that includes both DrugA and Drug B, and involves the Distributor X and multiple suppliers, such as the Supplier X to supply Drug A and a second supplier (for example, Supplier Y) to supply Drug B.

[0190] As another example, a different model in the model database 512 can be directed to the distribution of Drug A in the U.S., Canada, and Mexico and involves the Supplier X to supply Drug A and multiple distributors, Distributor X to distribute the therapy in U.S. and Canada and a second distributor (for example, Distributor Y) to distribute the therapy in Mexico.

[0191] The analytic engine 516 can include one or more programs designed to analyze computer-executable pharmaceutical distribution models. The analytic engine 516 can analyze the computer-executable pharmaceutical distribution model by, for example, dynamically simulating the model. As an example, the analytic engine 516 can perform a simulation for each of multiple scenarios for the model. A simulation may indicate, for example, a performance of the model through a set of performance metrics.

[0192] As an example, in performing a simulation of the model, the analytic engine 516 may use the assigned values of parameters of the model to simulate the performance of the model over time (for example, over the three-year duration of the agreement for the distribution of 100 mg of Drug A starting on January 1, 2024). In more detail, the analytic engine 516 may simulate the anticipated number of eligible patients over time. The analytic engine 516 can use, for example, (i) a pharmaceutical distribution constraint for calculating the number of eligible patients over time and (ii) different sets of values assigned to the parameters of the pharmaceutical distribution constraint that correspond to different scenarios to simulate the number of eligible patients that the distributor can expect over time for each of the different scenarios.

[0193] In some implementations, the analytic engine 516 simulates the model using randomized or pseudo-randomized analysis techniques. For example, the analytic engine 516 can perform one or more Monte Carlo simulations by randomly sampling from subsets of data in medical data corresponding to different pharmaceutical distribution constraints. In more detail, the analytic engine 516 can identify from the medical data a first subset of data (for example, can generate a data set from the medical data) indicating the number of persons in U.S. diagnosed with a condition that is treatable by 100 mg of Drug A over the past five years and a second subset of data indicating the number of persons in Canada diagnosed with a condition that is treatable by 100 mg of Drug A over the past five years. In performing the one or more Monte Carlo simulations to, for example, simulate the anticipated number of treatments required over time, the analytic engine 516 can randomly sample from the firstsubset of data and the second subset of data. Each sample combination can represent a different scenario that is being simulated.

[0194] In some implementations, the analytic engine 516 uses the results from simulating the model to calculate performance metrics for the model. As an example, after simulating the model, the analytic engine 516 uses the simulation results to calculate one or more performance metrics (for example, one or more performance metrics for the corresponding parameter value(s), for a particular pharmaceutical distribution constraint, for the model as a whole for a particular scenario, for the model as a whole across multiple scenarios, etc.).

[0195] In some implementations, the pharmaceutical distribution constraints include performance rules. These rules may be set such that they are adjustable or set so that they are not adjustable. The performance rules can be provided by a supplier computing system, a distributor computing system, and / or a third-party computing system. As an example, the distributor computing system transmits a pharmaceutical distribution constraint to the third- party computing system that the third-party computing system uses to analyze and / or update the model. The pharmaceutical distribution constraint can be a performance rule, such as a rule that the minimum number of treatments supplied by the supplier must be less than or equal to the estimated number of treatments. In evaluating the performance of the model, the analytic engine 516 can generate a performance metric based on a comparison of the performance results from one or more simulations of the model to the pharmaceutical distribution constraint that indicates, for example, whether the model met the rule, did not meet the rule, a likelihood of success in the model meeting the rule, a likelihood of failure in the model meeting the rule, a score indicating how often the model met or did not meet the rule for different scenarios, etc.

[0196] For example, the simulation results from simulating the model can include results indicating the anticipated number of treatments of the 100 mg dosage of Drug A required over time. The performance results may indicate, for example, that an average of 4500 treatments are anticipated per year under a first scenario, an average of 4000 treatments are anticipated per year under a second scenario, an average of 3700 treatments are anticipated per year under a third scenario, an average of 3500 treatments are anticipated per year under a fourth scenario, and an average of 3300 treatments are anticipate per year under a fifth scenario. As an example, based on these performance results, the analytic engine 516 can calculate a value of 0.4 for a performance metric, indicating that the rule was met in only 40% of the tested scenarios. As another example, based on these performance results, the analytic engine 516 can determine a value of 0 for a performance metric, indicating that therule was not met (for example, based on a number of treatments averaged over the different scenarios, 3800 treatments, being less than the minimum 4000 treatments; based on the rule not being met in a single scenario; based on the rule not being met in a threshold number or percent of scenarios; etc.). As another example, based on these performance results, the analytic engine 516 can determine a value of 200 for a performance metric, indicating that the difference between the minimum number of treatments per year and the average estimated number of treatments per year is -200 treatments. As another example, based on these performance results, the analytic engine 516 can determine a vector of values of [0, 0.05] for a performance metric, indicating (i) through the first value that the rule was not met and (ii) through the second value that the average estimated number of treatments per year was 5% less than the minimum number of treatments.

[0197] In some implementations, in simulating the model, the analytic engine 516 generates one or more distributions of performance results. The analytic engine 516 can then calculate the performance metrics for the model using the one or more distributions. As an example, the analytic engine 516 can generate a distribution of performance results for a particular pharmaceutical distribution constraint or parameter such as the anticipated number of treatments over time for multiple, different scenarios. The distribution results can indicate, for example, that more than 4300 treatments are anticipated at the end of the first year of the agreement (for example, at January 1, 2025) under a first scenario, that between 3800 and 3900 treatments are anticipated at the end of the first year of the agreement under five other scenarios, that between 3600 and 3800 treatments are anticipated at the end of the first year of the agreement under ten other scenarios, that between 3300-3400 treatments are anticipated at the end of the first year of the agreement under four other scenarios, and that less than 3200 treatments are anticipated at the end of the first year of the agreement under a final scenario. The analytic engine 516 may use a portion of the distribution to calculate the performance metrics for this pharmaceutical distribution constraint or parameter of the model. For example, the analytic engine 516 may evaluate only those results that are within a standard deviation of the distribution, resulting in at least the results from the first scenario and the final scenario being removed from the evaluation. The analytic engine 516 can then use the subset of performance results to calculate one or more performance metrics, such as a value of 0 for a performance metric indicating that 100% of the performance results in the subset failed to meet the pharmaceutical distribution constraint.

[0198] A nonexclusive list of external data sources 514 includes, for example, an external source of information such as a database of a medical insurance provider, a external datasource containing pharmaceutical distribution information (for example, documentation of historical pharmaceutical distribution agreements, or a historical or projected performance of pharmaceutical distribution agreements, an academic database storing medical information, a governmental database, etc.). An external data source among the external data sources 514 can be public domain, licensed, owned, or purchased.

[0199] The methods and systems disclosed herein may be implemented in part or in whole by non-transitory computer-readable storage medium having computer-readable program code executable by a computing device. FIGS. 6, 7, and 8A-B illustrate exemplary computing operations in accordance with certain embodiments disclosed herein.

[0200] The subject matter described herein can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structural means disclosed in this specification and structural equivalents thereof, or in combinations of them. The subject matter described herein can be implemented as one or more computer program products, such as one or more computer programs tangibly embodied in an information carrier (e.g., in a machine readable storage device), or embodied in a propagated signal, for execution by, or to control the operation of, data processing apparatus (e.g., a programmable processor, a computer, or multiple computers). A computer program (also known as a program, software, software application, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file. A program can be stored in a portion of a file that holds other programs or data, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

[0201] The processes and logic flows described in this specification, including the method steps of the subject matter described herein, can be performed by one or more programmable processors executing one or more computer programs to perform functions of the subject matter described herein by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus of the subject matter described herein can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0202] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processor of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. Information carriers suitable for embodying computer program instructions and data include all forms of nonvolatile memory, including by way of example semiconductor memory devices, (e.g., EPROM, EEPROM, and flash memory devices); magnetic disks, (e.g., internal hard disks or removable disks); magneto optical disks; and optical disks (e.g., CD and DVD disks). The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0203] To provide for interaction with a user, the subject matter described herein can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, (e.g., a mouse or a trackball), by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0204] The subject matter described herein can be implemented in a computing system that includes a back end component (e.g., a data server), a middleware component (e.g., an application server), or a front end component (e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described herein), or any combination of such back end, middleware, and front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local-area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet.

[0205] It is to be understood that the disclosed subject matter is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the drawings. The disclosed subject matter iscapable of other embodiments and of being practiced and carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.

[0206] As such, those skilled in the art will appreciate that the conception, upon which this disclosure is based, can readily be utilized as a basis for the designing of other structures, methods, and systems for carrying out the several purposes of the disclosed subject matter. It is important, therefore, that the claims be regarded as including such equivalent constructions insofar as they do not depart from the spirit and scope of the disclosed subject matter.

[0207] Although the disclosed subject matter has been described and illustrated in the foregoing exemplary embodiments, it is understood that the present disclosure has been made only by way of example, and that numerous changes in the details of implementation of the disclosed subject matter can be made without departing from the spirit and scope of the disclosed subject matter, which is limited only by the claims which follow.

Claims

CLAIMS1. A method for making a pharmaceutical therapy available to at least one patient, comprising: i) receiving input associated with the pharmaceutical therapy via a user interface; ii) generating a computer-executable pharmaceutical distribution model for the pharmaceutical therapy; iii) analyzing the computer-executable pharmaceutical distribution model with an analytical engine to obtain computational results; iv) submitting the input and the computational results to an artificial intelligence engine; v) obtaining a response to the submitting that comprises a recommended pharmaceutical distribution constraint; and vi) providing at least a portion of the response to a user.

2. The method of claim 1, wherein the artificial intelligence engine comprises a generative artificial intelligence.

3. The method of claim 1, wherein the analyzing comprises a simulation of the computerexecutable pharmaceutical distribution model to produce at least a portion of the computational results.

4. The method of claim 1, wherein the user interface is an interface to a chatbot.

5. The method of claim 1, further comprising: prompt engineering to form a prompt comprising the input and the computational results, wherein the submitting comprises submitting the prompt to the artificial intelligence engine.

6. The method of claim 1, wherein the input comprises characteristics for a patient population exhibiting a condition for the pharmaceutical therapy.

7. The method of claim 1, wherein the response comprises a global forecast and rollout plan for the pharmaceutical therapy.

8. The method of claim 7, wherein the input comprises an assumption of a rebate off of a list price of the pharmaceutical therapy.

9. The method of claim 7, wherein the input comprises an assumption of a global rollout to a predetermined number of geographic locations per quarter.

10. The method of claim 7, wherein the computer-executable pharmaceutical distribution model comprises a plurality of alternative archetypal constraints.

11. The method of claim 10, wherein the computational results comprise a comparison of the effect of two alternative archetypal constraints of the plurality of alternative archetypal constraints in a predetermined geographic location.

12. The method of claim 10, wherein the computational results comprise an optimal combination of (a) a rebate off of a list price of the pharmaceutical therapy and (b) a cap on a price of the pharmaceutical therapy in the predetermined geographic location.

13. The method of claim 1, wherein the computer-executable pharmaceutical distribution model is associated with historical data for the performance of the computer-executable pharmaceutical distribution model.

14. The method of claim 13, wherein the computational results comprise an evaluation of the computer-executable pharmaceutical distribution model based on the historical data.

15. The method of claim 14, wherein the at least a portion of the response comprises a recommended change to the computer-executable pharmaceutical distribution model based on a changes in the value of a performance metric.

16. The method of claim 1, wherein the pharmaceutical therapy is a gene therapy.

17. The method of claim 16, wherein the analytic engine optimizes a performance measure that is evaluated over a fixed time period associated with a renewal period for the gene therapy.

18. The method of claim 17, wherein the performance measure comprises a cost of the pharmaceutical therapy that is amortized over a fixed time period associated with the renewal period.

19. The method of claim 1, wherein the computational results comprise a comparison of the pharmaceutical therapy with an alternative pharmaceutical therapy based on a performance metric.

20. The method of claim 19, wherein the user is a payer for the pharmaceutical therapy.

21. The method of claim 20, wherein the performance metric comprises a measure of an outcome for the pharmaceutical therapy.

22. The method of claim 20, wherein the performance metric comprises a measure of the cost of the pharmaceutical therapy.

23. The method of claim 20, wherein the recommendation comprises a term for a pharmaceutical distribution agreement that will trigger renegotiation of a pharmaceutical distribution agreement for the pharmaceutical therapy if a performance metric for a second pharmaceutical therapy exceeds or falls below a threshold value for the performance metric.

24. A product for distributing a pharmaceutical therapy to at least one patient, the product comprising a non-transitory computer-readable storage medium having computer-readable program code executable by a computing device to perform computing operations, the computing operations comprising: i) receiving input associated with the pharmaceutical therapy via a user interface; ii) generating a computer-executable pharmaceutical distribution model for the pharmaceutical therapy; iii) analyzing the computer-executable pharmaceutical distribution model with an analytical engine to obtain computational results; iv) submitting the input and the computational results to an artificial intelligence engine; v) obtaining a response to the submitting that comprises a recommended pharmaceutical distribution constraint; and vi) providing at least a portion of the response to a user.

25. A system for distributing a pharmaceutical therapy to at least one patient, the system comprising one or more computing systems configured to perform computing operations comprising: i) receiving input associated with the pharmaceutical therapy via a user interface; ii) generating a computer-executable pharmaceutical distribution model for the pharmaceutical therapy; iii) analyzing the computer-executable pharmaceutical distribution model with an analytical engine to obtain computational results; iv) submitting the input and the computational results to an artificial intelligence engine; v) obtaining a response to the submitting that comprises a recommended pharmaceutical distribution constraint; and vi) providing at least a portion of the response to a user.

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