Systems and methods for developing alternative medications using characteristics of existing medications to produce similar pathway behavior

Abstract

A method for developing new drug therapies using characteristics of existing drug therapies, the method comprising: developing a mathematical model of a new treatment for a targeted biological network; and synthesizing a drug therapy based on the mathematical model of the new treatment. The mathematical model of the new treatment can generate a time progression of the new treatment that includes a time progression signature found in the time progression of the existing treatment in the mathematical model of the existing treatment for the targeted biological network. The time progression signature can be related to an outcome of the targeted biological network. A drug regimen can be synthesized for each intervention constant of the new treatment, and the drug regimens together can be a new drug therapy.

Description

Technical Field

[0001] The present disclosure relates to systems and methods for developing alternative drug therapies using the characteristics of existing drug therapies to produce similar pathway behaviors.

Background Art

[0002] Today, drug therapies are used to treat pathogens and diseases. Drug therapies act by attacking along specific pathways of the pathogen. Generally speaking, a pathway is a causal chain of interactions that results in a change in the normal function of a pathogen initiated by a drug therapy that chemically interacts with a targetable biological element of the pathogen.

[0003] Although there are many drug therapies, more research is being done to find new drug therapies to fight pathogens for which there are still no drug therapies or to replace current inadequate drug therapies. Drug therapies can be inadequate for several reasons.

[0004] First, some drug therapies do not cure the disease but only reduce morbidity or symptoms. Examples of such drug therapies include those used to fight HIV or the herpes virus. In both cases, the drug therapy reduces the amount of virus in the human, but neither drug therapy completely eliminates the virus.

[0005] Second, some drug therapies have side effects ranging from mild to severe and in some cases are life-threatening. The biological element targeted by the drug therapy causes a change in the pathway behavior of the targeted element, which in turn results in its interaction with other biological elements as a result of a chain reaction. However, these altered pathway behaviors can have a significant adverse effect on the biological network. Furthermore, the therapeutic molecule can interact with known or unknown non-target elements within the network, which can also result in negative overall pathway behavior within the target network as described above.

[0006] Thirdly, drug therapies are often resistant to pathogens that evolve over time. Specifically, in the case of therapeutic agents targeting pathogenic organisms such as bacteria, viruses, parasites, or cancer cells, the therapeutic agent may lose its effectiveness as the target population evolves. Resistance occurs when a subset of the targeted organism or cell set survives exposure resulting from a particular trait of that subset and then passes on such resistant traits to the next generation.

[0007] Fourth, some drug therapies can be expensive to produce. Drug synthesis is a multi-step process, and each step can significantly impact the cost of manufacturing the drug. For example, in 2011, it cost $260 to produce just 50 grams of 4-phenyl-1.

[0008] A common strategy for the rational computer-aided development of new drugs is to first identify novel interactions between biological elements that may be important to cellular function, or entirely novel biological elements. Highly promising biological elements are then structurally characterized at the molecular level, along with their interactions with potential therapeutic agents. The goal is to target specific biological elements that have a high probability of significantly altering the function of target cells in the desired manner.

[0009] However, such methods raise significant problems. Discovering new biological elements or interactions within known elements is extremely time-consuming, resource-intensive, or prone to false positives in interaction results. Even the discovery of interactions or biological elements crucial to cellular function in initial laboratory tests fails to answer the central question: will disrupting the target biological element produce the desired effect on the entire organism via a chain reaction mechanism?

[0010] Therefore, it would be advantageous to have a system and method for designing novel drug therapies that target pathogen pathways using the characteristics of existing drug therapies in order to produce similar pathway behavior. [Overview of the Initiative]

[0011] A method for finding a set of parameters for a new drug therapy so that it produces similar outcomes to existing drug therapies. In the first step, the method may include replacing any intervention function related to existing drug therapies within a mathematical model, where the mathematical model has any such intervention function, with an untreated node function related to the mathematical model. In the next step, the method may include selecting a parameter range for each of several parameters. Next, the method may include generating a time-course progression of the mathematical model of the new therapy for each of several permutations, using permutations to generate a time-course progression. Next, the method may include determining, for each permutation, whether its time-course progression contains a time-course progression signature that exists in the time-course progression of existing therapies related to existing drug therapies, where the time-course progression signature is related to the outcomes of existing drug therapies. Next, the method may include synthesizing a substance having kinetic properties substantially consistent with the kinetic parameters of at least one permutation containing a time-course progression signature, in order to produce a new drug therapy.

[0012] A method for developing a new pharmacotherapy using the characteristics of an existing pharmacotherapy, the method comprising the steps of developing a mathematical model of the new therapy for a targeted biological network, and synthesizing a pharmacotherapy based on the mathematical model of the new therapy. The mathematical model of the new therapy can generate a time course of the new therapy, which includes time course progression signatures found in the time course of the existing therapy in the mathematical model of the existing therapy for the targeted biological network. The time course progression signatures may be related to the outcome of the targeted biological network. The mathematical model of the new therapy may include a new therapy intervention function that models each of the new therapy intervention nodes in a set of new therapy intervention nodes, and a first set of untreated node rate functions that model all other nodes of the mathematical model of the new therapy. Each of the new therapy intervention functions may include another new therapy intervention constant from a set of new therapy intervention constants. The mathematical model of the existing therapy may include an existing therapy intervention equation that models each of the existing therapy intervention nodes in a set of existing therapy intervention nodes, and an untreated rate equation that models all other nodes of the mathematical model of the existing therapy. Each of the existing therapeutic intervention equations may include existing therapeutic intervention constants from a set of existing therapeutic intervention constants. The set of nodes for a new treatment does not have to be identical to the set of nodes for an existing treatment. Furthermore, the set of nodes for a new treatment may include at least one node that is not present in the set of nodes for an existing treatment. Furthermore, the set of nodes for an existing treatment may include at least one node that is not present in the set of nodes for a new treatment. A drug regimen can be synthesized for each new therapeutic intervention constant, and these drug regimens together may constitute a new drug therapy. [Brief explanation of the drawing]

[0013] [Figure 1] This demonstrates drug therapies that interact with biological networks.

[0014] [Figure 2] This shows an exemplary response model of a biological network.

[0015] [Figure 3] Shows the time-course progression of a biological network, specifically the actual time-course progression.

[0016] [Figure 4] Mathematical model a of a biological network, particularly the pre-treatment mathematical model.

[0017] [Figure 5] Shows the time-course progression before treatment.

[0018] [Figure 6] Shows the response model of an existing treatment.

[0019] [Figure 7] Shows the response model of an existing treatment.

[0020] [Figure 8] Shows the mathematical model of an existing treatment.

[0021] [Figure 9] Shows the first set of existing treatment intervention functions.

[0022] [Figure 10] Shows the set of existing treatment intervention constants.

[0023] [Figure 11] Shows the set of existing treatment intervention concentrations.

[0024] [Figure 12] Shows the untreated node velocity.

[0025] [Figure 13] Shows the time-course progression of an existing treatment.

[0026] [Figure 14] Shows the response model of a new treatment.

[0027] [Figure 15] This presents a mathematical model for a new treatment.

[0028] [Figure 16] Here is a second set of intervention functions for new treatments.

[0029] [Figure 17] This shows a set of constants for intervention in new treatments.

[0030] [Figure 18] This shows a set of intervention concentrations for the new treatment.

[0031] [Figure 19] A second set of untreated node rate functions is shown.

[0032] [Figure 20] This shows the temporal progression of the new treatment. [Modes for carrying out the invention]

[0033] Described herein are systems and methods for developing alternative pharmacotherapy using features of existing pharmacotherapy to produce similar pathway behavior. The following description is provided to enable those skilled in the art to create and use the claimed invention, provided in the context of the specific embodiments described below, whose variations will be readily apparent to those skilled in the art. For clarity, not all features of the actual embodiments are described herein. It will be understood that in the development of any such actual implementation (such as in any development project), design decisions must be made to achieve the designer's specific goals (e.g., compliance with system and business-related constraints), and these goals will differ from implementation to implementation. It will also be understood that such development efforts may be complex and time-consuming, but nevertheless, are routine work for those skilled in the art in the appropriate field who are interested in this disclosure. Accordingly, the claims attached herein are not intended to be limited by the disclosed embodiments, but should be given the broadest scope consistent with the principles and features disclosed herein.

[0034] Figure 1 shows a drug therapy 101 interacting with a biological network 102. In the context of this disclosure, the biological network 102 may be a targeted biological network (TBN) 102a or a non-targeted biological network (non-TBN) 102b. A TBN 102a may include, but is not limited to, all or part of a pathogen or disease. A TBN 102a may be multicellular, single-celled, or RNA or DNA. Exemplary categories of TBN 102a may include parasites, bacteria, viruses, or fungi. Specific examples include Escherichia coli, COVID-19, or cancer. A non-TBN 102b is, for the purposes of this disclosure, a biological network 102 within a host or organism that is in a symbiotic relationship with the host.

[0035] This disclosure describes a system and method for developing one or more pharmacotherapys 101 that disrupt TBN 101a. Pharmacotherapy 101 is any one or more non-food drug regimens 103 used to prevent, diagnose, treat, or alleviate symptoms of a disease or abnormal condition. Furthermore, for the purposes of this disclosure, drug regimens 103 can be defined by substance 104. Substance 104 is a particular type of substance having uniform properties for the purposes of this disclosure. Furthermore, drug regimens 103 can be defined by dosage 105 and schedule 106. In one embodiment, schedule 106 can be defined by duration and / or period (e.g., 8 hours every 3 days). In some embodiments of drug regimens 103, dosage 105 may vary by schedule 106, such as increasing or decreasing over time. For the purposes of this disclosure, dose 105 may be described as an absolute amount, meaning an amount that is scaled by other patient-specific information such as body weight, age, and maturity, as an intended concentration, or as any other method known in the art to describe a dose.

[0036] The biological network 102 comprises nodes 107. For the purposes of this disclosure, nodes 107 are aspects of the biological network 102 in which pharmacotherapy 101 can potentially intervene, such as by accelerating, slowing, preventing, or initiating chemical interconversions within the biological network 102. Furthermore, for the purposes of this disclosure, nodes 107 of TBN 102a are target nodes 107a, and nodes 107 of non-TBN 102b are non-target nodes 107b.

[0037] Figure 2 shows an exemplary reaction model 200, in particular a pre-treatment reaction model 200a of the biological network 102. For the purposes of this disclosure, the pre-treatment model 200a is a reaction model of the biological network 102 when the biological model 102 is not being treated with drug therapy 101. As shown in Figure 2, the reaction model 200 represents a network of nodes 107, each of which is a chemical interconversion of chemical substances 201 within the biological network 102. Such chemical interconversions are often facilitated by proteins 202. Proteins 202 may be enzymes, and are often enzymes. Furthermore, chemical substances 201 may be non-enzymatic proteins 202. The reaction model 200 shown represents a biological network 102 containing seven nodes 107, as follows: a. Node 1: Chemical substance A is converted to chemical substance B through the action of protein 1. b. Nodes 2,3: Chemical substance B is converted to chemical substance C through the action of proteins 2 and 3. c. Node 4: Chemical substance C is converted to chemical substance D through the action of protein 4. d. Node 5: Chemical substance C is converted to chemical substance D through the action of protein 5. e. Node 6: Chemical substance B is converted to chemical substances A and E by protein 6. f. Node 7: Chemicals D and F are converted together to chemical A through the action of protein 7. g.NodeX E Chemical substance E is converted to chemical substance F without a modeled protein.

[0038] Those skilled in the art will recognize that not all chemical interconversions occurring within the biological network 102 need to be represented in the reaction model 200. Furthermore, within each chemical interconversion represented in the reaction model 200, not all chemical substances 201 or proteins 202 involved in the chemical interconversion need to be modeled in the reaction model 200. For example, in process 1, A + Z1 → B could actually be A + x1 + Z1 → B + y1, where x1 is the set of unmodeled reactants in process 1 and y1 is the set of unmodeled products in process 1.

[0039] Figure 3 shows the temporal progression 300 of the biological network 102, specifically the actual temporal progression 300z. For the purposes of this disclosure, the actual temporal progression 300z is the temporal progression of the experimentally measured concentration of the chemical substance 201.

[0040] Figure 4 shows a mathematical model 400 of the biological network 102, in particular a pre-treatment mathematical model 400a. For the purposes of this disclosure, the mathematical model 400 is related to the response model 200 and includes equations 401 that describe the behavior of the biological network 102 with sufficient accuracy so that the response model 200 can accurately predict the behavior of the biological network 102 to which it is related. Furthermore, for the purposes of this disclosure, the pre-treatment mathematical model 400a includes equations 401 that describe the behavior of the biological network 102 when it has not received any treatment with any drug therapy 101, with respect to the pre-treatment response model 200a. In one embodiment, equations 401a may include concentration change equations 401a that describe the rate of change of the concentration of a particular chemical substance 201 in the response model 200, respectively. For example, the concentration change equation 401a for chemical substance A is as follows: dA / dt=V6(B,V6max,kB6)+V7(D,F,V7max,kD7,kF7)-V1(V1max, A,kA1).

[0041] The concentration change equation 401a describes the behavior of node 107, and the node velocity function V is shown in Figure 4 as a function of various variables. n()402 can be included. For example, variables A to F each represent the concentrations 403 of the corresponding chemical substances 201A to F in reaction model 201. nmax V1 represents the maximum nodal rate 404 at which protein 202 can convert reactants into products. Each nodal rate function 402 can be used to determine the nodal rate, which can then be used to calculate the change in concentration of chemical 201. Those skilled in the art will recognize that the nodal rate can be modeled by the Michaelis-Menten equation. As an example, the nodal rate V1 is given by the equation V1=(V 1max *A) / (k A1 It can be modeled as +A). In such an equation, k A1 This is a rate constant of 405 specific to chemical substance A and protein 1. Those skilled in the art will understand V 1max and K A1 It will be recognized that both of these can be determined experimentally. These values ​​can also be estimated. Furthermore, protein 202 can operate with or output multiple chemicals, leading to more complex equations. Similarly, some nodes may require multiple proteins, which can also lead to more complex equations.

[0042] Figure 5 shows a set of pre-treatment node velocity functions 500 for a pre-treatment mathematical model 400a, which is a model of an untreated biological network 102, where each node velocity function is a pre-treatment velocity function 501. For the purposes of this disclosure, the pre-treatment velocity function 501 is a node velocity function that models a node 107 when such a node is not intervened by a drug regimen 103.

[0043] Figure 6 shows the pre-treatment time course 300a. One objective of the pre-treatment mathematical model 300a is to generate the pre-treatment time course 300a. For the purposes of this disclosure, the pre-treatment time course is the time course of the pre-treatment mathematical model 300a and is intended to model the actual time course 300z with sufficient accuracy.

[0044] The time course progression 300 includes the level of chemical concentration of chemical 201 within the measured or modeled biological network 102 as a function of time. The pre-treatment time course progression 300a models the level of chemical concentration of chemical 201 within the biological network 102 as a function of time before any treatment with pharmacotherapy 101. In the absence of treatment, the concentration of chemical 201 may change within the cycle, but otherwise, it typically remains within a predictable and constrained level over the duration of the biological network 102's life cycle. This does not mean that the concentration remains constrained throughout the entire life cycle, but rather that during the period, the concentration remains constrained and substantially predictable over changes from one period to the next. A key function of the pre-treatment time course progression 400a is that it can establish baseline dynamics of the biological network 102.

[0045] Figure 7 shows the existing treatment response model 200b. For the purposes of this disclosure, the existing treatment response model 200b is a response model that models how existing pharmacotherapy interacts with the biological network 102. The existing treatment response model 200b includes at least one existing treatment intervention node 107a. The existing treatment intervention node 107a is intervened by a drug regimen 103. The remaining unintervened node 107 is modeled as untreated node 107b with a pre-treatment rate function 501. It should be noted that existing pharmacotherapy 101 is not limited to pharmacotherapy 101 that has reached the market, but includes any pharmacotherapy whose use on the biological network 102 has been previously considered and which yields an outcome. In the case of a targeted biological network 102a, examples of outcomes include, but are not limited to, killing the targeted biological network 102a, preventing the targeted biological network 102a from replicating, or substantially impairing the targeted biological network 102a so that other conditions or forces, such as the immune system, can kill it. In the case of a non-targeted biological network 102b, examples of outcomes may include strengthening the non-targeted biological network 102b, or disrupting a non-targeted biological network 102b that is resistant to or immune to a particular condition.

[0046] Figure 8 shows the mathematical model 400b of the existing treatment. In the mathematical model 400b of the existing treatment, each node rate function 402 that models the existing treatment intervention node 107a can be called the intervention function 402a. The types of intervention functions are classified into two main categories: inhibitory functions and accelerating functions. Inhibitory functions model the delay or substantial cessation of chemical interconversion at the existing treatment intervention node 107a. Conversely, accelerating functions model the initiation or acceleration of chemical interconversion at the existing treatment intervention node 107a.

[0047] Figure 9 shows a first set 900 of existing therapeutic intervention functions. As shown in Figure 9, the first set 900 can be a set of one or more existing therapeutic intervention functions.

[0048] Figure 10 shows a set of existing treatment intervention constants 1000. Each intervention function 402a may have one or more intervention constants 1001 related to a drug regimen 103 of an existing drug therapy 101 related to a mathematical model 400b of the existing treatment. The intervention constant 1001 is a number that represents the effect that the related drug regimen 103 has on the node velocity of node 107.

[0049] Figure 11 shows a set of existing therapeutic intervention concentrations 1100. Each intervention function 402a may include concentration constants 1102 that model the concentrations of a single drug regimen 103 of an existing pharmacotherapy 101. The set of existing therapeutic intervention concentrations 1100 includes these concentration constants 1102.

[0050] Figure 12 shows a first set 1200 of untreated node velocity functions. Within the mathematical model 400b of the existing treatment, the untreated node 107b is modeled with a pre-treatment velocity function 501. The first set 1200 of untreated node velocity functions includes each of these pre-treatment velocity functions 501.

[0051] Figure 13 shows the progression over time of an existing treatment 300b. The existing progression over time 300b may include a progression over time signature 1301 that predicts the outcome as described above. The progression over time signature 1301 may include one or more attributes. In one embodiment, the progression over time signature 1301 may be that the concentration 403a of a first chemical 201 reaches a threshold 1302. In another embodiment, the progression over time signature 1301 may include a series of events. For example, a series of events may be defined at least in part by a first chemical 201a having a first chemical concentration 403a that satisfies or exceeds a first threshold 1302a, followed by a second chemical 201b having a second chemical concentration 403b that satisfies or exceeds a second threshold 1302b. As another example, a series of events may be at least partially defined by a first chemical 201a having a first chemical concentration 403a that satisfies or exceeds a first threshold, followed by a first chemical concentration a that satisfies or exceeds a second threshold 1302b. In another embodiment, the time-series progression signature 1301 may include a first chemical concentration 403a of the first chemical 201a that satisfies or exceeds a first threshold, and a second chemical concentration 403b of the second chemical 201 that satisfies or exceeds a second threshold 1302b. In another embodiment, the time-series progression signature may include chemical concentrations 403 of chemical 201 in a set of chemicals 1303 such that the chemical concentrations together do not deviate by more than a deviation threshold from the set of target concentrations 1304 for each of the chemicals 201. In such an embodiment, the deviation between the set of chemical concentrations 1303 and the set of target concentrations 1304 can be determined using root mean square calculation.

[0052] In one embodiment, a set of parameters for a new pharmacotherapy can be found such that the new pharmacotherapy yields similar outcomes to existing pharmacotherapys. In a first step, the method may include replacing any intervention function related to existing pharmacotherapys within the mathematical model, where the mathematical model has any such intervention function, with an untreated node function related to the mathematical model. In a next step, the method may include selecting a parameter range for each of a plurality of parameters. Next, the method may include using permutations to generate a progression over time, for each of a plurality of permutations, a progression over time of the mathematical model of the new therapy. Next, for each permutation, the method may include determining whether its progression over time contains a progression over time signature that exists in the progression over time of existing pharmacotherapys, where the progression over time signature is related to the outcomes of existing pharmacotherapys. Next, the method may include synthesizing a substance having kinetic properties substantially consistent with the kinetic parameters of at least one permutation containing a progression over time signature, in order to produce a new pharmacotherapy.

[0053] In one embodiment, the parameter range may include a designation of a set of nodes that can be intervened. In another embodiment, the parameter range may include a method of intervention, such as accelerating or decelerating a chemical intervention within a node. In such an embodiment, the parameter range may include multiple intervention equations to be considered. In yet another embodiment, the parameter range may include a range of one or more intervention constants for one or more intervention equations. In yet another embodiment, the parameter range may include a range of acceptable intervention concentrations. In yet another embodiment, the parameter range may include spatial considerations.

[0054] Figure 14 shows a novel therapy response model 200c. For the purposes of this disclosure, the novel therapy response model 200c is a response model that models how a novel drug therapy may interact with a biological network 102. The novel therapy response model 200c includes at least one novel therapy intervention node 107a. The novel therapy intervention node 107a is intervened by a drug regimen 103 of the novel drug therapy 101. The remaining unintervened node 107 is modeled as an untreated node 107b with a pre-treatment rate function 501.

[0055] Figure 15 shows the mathematical model 400c of the new treatment. In the mathematical model 400c of the new treatment, each node rate function 402 that models the intervention node 107a of the new treatment can be called the intervention function 402a. The types of intervention functions are classified into two main categories: inhibitory functions and accelerating functions. Inhibitory functions model the delay or substantial cessation of chemical interconversion at the intervention node 107a of the new treatment. Conversely, accelerating functions model the initiation or acceleration of chemical interconversion at the intervention node 107a of the new treatment.

[0056] Figure 16 shows a second set 1600 of intervention functions for the new treatment. As shown in Figure 16, the second set 1600 can be a set of one or more intervention functions for the new treatment.

[0057] Figure 17 shows a set of intervention constants 1700 for the new treatment. Each intervention function 402a may have one or more intervention constants 1001 related to a drug regimen 103 of the new pharmacotherapy 101 associated with the mathematical model 400c of the new treatment. The intervention constant 1001 is a number that represents the effect that the associated drug regimen 103 has on the node's velocity at node 107.

[0058] Figure 18 shows a set of 1800 intervention concentrations for new therapies. Each intervention function 402a may include concentration constants 1102 that model the concentration of a single drug regimen 103 for a new drug therapy 101. The set of 1800 existing therapy intervention concentrations includes these concentration constants 1102.

[0059] Figure 19 shows a second set of untreated node velocity functions 1900. Within the mathematical model 400b of the new treatment, the untreated node 107b is modeled with a pre-treatment velocity function 501. The first set of untreated node velocity functions 1200 contains each of these pre-treatment velocity functions 501.

[0060] Figure 20 shows the time-course progression 300c of the new treatment. The new time-course progression 300c may include time-course progression signatures 1301 that predict, are common to, or are associated with, or are present in, time-course progression 300b of the existing treatment.

[0061] A method for developing a new pharmacotherapy using the characteristics of an existing pharmacotherapy, the method comprising the steps of developing a mathematical model of the new therapy for a targeted biological network, and synthesizing a pharmacotherapy based on the mathematical model of the new therapy. The mathematical model of the new therapy can generate a time course of the new therapy, which includes time course progression signatures found in the time course of the existing therapy in the mathematical model of the existing therapy for the targeted biological network. The time course progression signatures may be related to the outcome of the targeted biological network. The mathematical model of the new therapy may include a new therapy intervention function that models each of the new therapy intervention nodes in a set of new therapy intervention nodes, and a first set of untreated node rate functions that model all other nodes of the mathematical model of the new therapy. Each of the new therapy intervention functions may include another new therapy intervention constant from a set of new therapy intervention constants. The mathematical model of the existing therapy may include an existing therapy intervention equation that models each of the existing therapy intervention nodes in a set of existing therapy intervention nodes, and an untreated rate equation that models all other nodes of the mathematical model of the existing therapy. Each of the existing therapeutic intervention equations may include existing therapeutic intervention constants from a set of existing therapeutic intervention constants. The set of nodes for a new treatment does not have to be identical to the set of nodes for an existing treatment. Furthermore, the set of nodes for a new treatment may include at least one node that is not present in the set of nodes for an existing treatment. Furthermore, the set of nodes for an existing treatment may include at least one node that is not present in the set of nodes for a new treatment. A drug regimen can be synthesized for each new therapeutic intervention constant, and these drug regimens together may constitute a new drug therapy.

[0062] This disclosure teaches a novel pharmacotherapy designed using one of the methods described above. For example, this disclosure teaches a pharmacotherapy having multiple drug regimens, each regimen being a substance having parameters confirmed using the methods described above. Furthermore, the parameters are such that the novel pharmacotherapy has common outcomes with existing pharmacotherapy. Furthermore, each drug regimen intervenes a node, and together these nodes form a set of nodes, such a set of nodes not common with a second set of nodes intervened by existing pharmacotherapy.

[0063] Novel drug development and design applications that can be stored in memory can provide properties and characteristics of theoretical therapeutic compounds that can be used to identify corresponding real-world potential therapeutic compounds. High-resolution models developed from identified biochemical or biological networks that interact with known therapeutic compounds are generated and processed using drug development and design systems. Such methods are sometimes referred to in this disclosure as the DASS method. The DASS method can provide models and information regarding the effects of known therapeutics having identified biochemical or biological networks. In one embodiment, the DASS method can also be used to provide models and information regarding identified biochemical or biological networks without known therapeutics. After processing with the DASS method, properties and characteristics of theoretical therapeutic compounds that affect the identified biochemical or biological network can be identified in the same way that the original therapeutics affect the network. Similarly, theoretical therapeutics for specific target cells in an identified biochemical or biological network can be found using this method without using known therapeutics.

[0064] In one embodiment of the DASS method, the system outputs theoretical properties and characteristics of theoretical therapeutic compounds that can be used to interact with identified biochemical or biological networks. These properties and characteristics of theoretical therapeutic compounds can be compared to real therapeutic compounds to identify potential therapeutic compounds that can be tested on the identified biochemical or biological network, resulting in pathway behavior similar to that of the biological or biochemical network or at least the target cell.

[0065] In another embodiment, potential therapeutic compounds are simulated against an identified biochemical or biological network to check whether the potential therapeutic compound could be a high-quality drug candidate. A high-quality drug candidate is an identified potential therapeutic compound that can produce the same or better effect on target cells as the original therapeutic compound, and causes minimal perturbation to non-target cells or produces overall similar pathway behavior against the identified biochemical or biological network, as the original therapeutic agent does.

[0066] In another embodiment, modeling and simulation of biochemical or biological networks are performed by a drug development and design system, and all information relating to any modeling performed can be stored in the system's data storage device.

[0067] A system performing the method described herein may have a typical networking environment comprising multiple electronic devices and servers connected via a network. Examples of electronic devices may include, but are not limited to, computers, smartphones, and / or tablets. In one embodiment, the electronic devices and servers can communicate with each other. The network 107 may be wired, wireless, or a combination of both. An example of a LAN is a network within a single building. An example of a WAN is the internet.

[0068] An electronic device may include local memory and a local processor. Local memory may contain local applications and local data.

[0069] A server may include server memory and a server processor. Server memory may contain server applications and server data.

[0070] In one embodiment, a drug development and design application can mean a local application in which the interface, presentation, logic, and data storage are controlled locally on an electronic device. In such an embodiment, memory can mean local memory, processor can mean a local processor, and data can mean local data.

[0071] In another embodiment, the drug development and design application may mean a local application along with the server application. One example of such an embodiment is when the local application is a general-purpose (browser) application. Another example of such an embodiment is when the local application is a purpose-specific (non-browser) application.

[0072] In the first example, the browser accesses the server application via a website. In such an embodiment, the user interface is performed using an electronic device, the presentation can be performed by the local application and the server application, and the logic and data can be executed by the server. In such an example, memory may mean electronic device memory and / or server memory, processor may mean electronic device processor and / or server processor, and data may mean server data.

[0073] In the second example, a purpose-specific application accesses the server application 103b. In such an embodiment, the user interface may be on the electronic device, and the presentation, logic, and data storage may be distributed between the electronic device and the server. In such an example, memory may mean electronic device memory and / or server memory, processor may mean electronic device processor and / or server processor, and data may mean local data and / or server data.

[0074] The memory described herein stores both data and several components that can be executed by the processor. In particular, the DASS method and potentially other applications are stored in memory and can be executed by the processor. Information such as the interaction between known therapeutic compounds and target cells, kinetic characterization data between therapeutic compounds and non-target cells, and other data can also be stored in memory. Furthermore, an operating system can be stored in memory and executed by the processor.

[0075] The drug development and design systems and various other systems described herein can be implemented with software or code executed on general-purpose hardware as described above, but alternatively, they can also be implemented with dedicated hardware or a combination of software / general-purpose hardware and dedicated hardware. When implemented with dedicated hardware, each can be implemented as a circuit or state machine using any one or combination of several technologies. These technologies may include, but are not limited to, discrete logic circuits having logic gates for performing various logic functions when one or more data signals are applied, application-specific integrated circuits having appropriate logic gates, or other components. Such technologies are generally well known to those skilled in the art and are therefore not described in detail herein.

[0076] Furthermore, any logic or application described herein, including software or code, including drug development and design systems, can be embodied, for example, by an instruction execution system such as a processor in a computer system or other system, or in any computer-readable storage medium for use in connection therewith. In this sense, the logic may include statements, including instructions and declarations, which can be fetched from a computer-readable storage medium and executed by an instruction execution system.

[0077] It should be emphasized that the embodiments described above in this disclosure are merely possible examples of implementations described for the sake of a clear understanding of the principles of this disclosure. Many variations and modifications can be made to the embodiments described above without substantially departing from the spirit and principles of this disclosure. All such modifications and variations are intended to be within the scope of this disclosure and protected by the following claims.

[0078] Various modifications are possible to the details of the exemplary methods of operation without departing from the scope of the following claims. Some embodiments may combine the activities described herein as separate steps. Similarly, one or more of the steps described may be omitted depending on the specific operating environment in which the method is being implemented. It should be understood that the above description is intended to be illustrative and not limiting. For example, the embodiments described above may be used in combination with one another. Many other embodiments will be apparent to those skilled in the art upon consideration of the above description. Therefore, the scope of the present invention should be determined by reference to the appended claims, along with the entire scope of equivalents to which such claims are granted. In the appended claims, the terms “including” and “in which” are used as plain English synonyms for the terms “comprising” and “wherein,” respectively.

Claims

1. 1. A method for developing new drug therapies using features of existing drug therapies, said method comprising: (A) a computer system having a memory and a processor, obtaining a mathematical model of an existing treatment of a target biological network including a plurality of nodes; The mathematical model of the existing treatment a first subset of intervention nodes in the plurality of nodes, each respective intervention node in the first subset of intervention nodes including: (i) a corresponding existing treatment intervention function representing one or more chemical interconversions responsive to the existing medical therapy at the respective intervention node; and (ii) a corresponding existing treatment intervention constant from a set of existing treatment intervention constants for the respective existing treatment intervention function; a second subset of untreated nodes in the plurality of nodes, wherein each respective untreated node in the second subset of untreated nodes includes a corresponding untreated function representing one or more chemical interconversions responsive to the absence of the existing medical therapy at the respective untreated node; Including, obtaining a mathematical model of the existing treatment, the mathematical model generating a corresponding time progression of the existing treatment of the targeted biological network, the time progression including at least a first signature associated with an outcome of the existing treatment of the targeted biological network; (B) the computer system developing, for each respective permutation in the plurality of permutations, a mathematical model of each new treatment of the target biological network; each respective permutation in said plurality of permutations includes a designation of a respective set of intervening nodes; said mathematical model of each new treatment; a third subset of intervention nodes in the plurality of nodes, each respective intervention node in the third subset of intervention nodes including (i) a corresponding new therapeutic intervention function representing one or more chemical interconversions at the respective intervention node, and (ii) a corresponding new therapeutic intervention constant from a corresponding set of new therapeutic intervention constants for the respective new therapeutic intervention function; a fourth subset of untreated nodes in the plurality of nodes, each respective untreated node in the fourth subset of untreated nodes including a corresponding untreated function representing one or more chemical interconversions at the respective untreated node; Including, the mathematical model of each new treatment generates a corresponding new treatment time progression for the targeted biological network, the new treatment time progression including at least a corresponding second signature associated with an outcome of each new treatment for the targeted biological network; the first subset of intervening nodes includes at least one node that is not in the third subset of intervening nodes; developing a third subset of intervening nodes that includes at least one node that is not in the first subset of intervening nodes; (C) the computer system, for each respective permutation in the plurality of permutations, identifies a corresponding real-world therapeutic compound for each respective intervention node in the third subset of intervention nodes, and synthesizes a drug regimen including, for each respective new treatment intervention constant in the corresponding set of new treatment intervention constants, for each respective permutation in the plurality of permutations, the corresponding real-world therapeutic compound having a corresponding second signature that matches the first signature of the existing treatment's progression over time; thereby obtaining, for each permutation, a set of drug regimens that together form a new drug therapy, wherein at least one drug regimen in the set of drug regimens is not included in the existing drug therapy; and A method comprising:

2. for each respective intervention node in the third subset of intervention nodes, the corresponding new treatment's intervention function is an inhibitory function and the corresponding new treatment's intervention constant associated with the inhibitory function is an inhibitory constant; or 2. The method of claim 1, wherein for each respective intervention node in the third subset of intervention nodes, the corresponding new treatment's intervention function is an acceleration function and the corresponding new treatment's intervention constant associated with the acceleration function is an acceleration constant.

3. 2. The method of claim 1, wherein the corresponding untreated function for each untreated node is a Michaelis-Menten equation.

4. The method of claim 1 , wherein the first subset of intervening nodes consists of only one node.

5. The method of claim 1 , wherein the third subset of intervention nodes includes a plurality of intervention nodes, and there are no common nodes between the first subset of intervention nodes and the third subset of intervention nodes.

6. 2. The method of claim 1, wherein each respective node common between the first subset of intervention nodes and the third subset of intervention nodes includes a different intervention constant for an existing treatment compared to the intervention constant for a corresponding new treatment.

7. The method of claim 1, wherein developing the mathematical model of each new treatment further comprises replacing one or more intervention nodes in a first subset of intervention nodes in the mathematical model of the existing treatment with corresponding untreated nodes.

8. The method described in claim 1, wherein each respective permutation in the plurality of permutations further includes, for each intervention node in the third subset of intervention nodes, acceleration of the chemical interconversion, inhibition of the chemical interconversion, a plurality of intervention functions, a range of intervention constants, or a range of intervention concentrations.

9. the corresponding time progression of the existing treatment of the mathematical model includes, for each respective chemical in the set of chemicals of the targeted biological network at each respective time point in the corresponding plurality of time points, a respective concentration of the respective chemical; 2. The method of claim 1, wherein, for each respective permutation in the plurality of permutations, the corresponding new treatment time progression of the mathematical model of the respective new treatment includes, for each respective chemical in the set of chemicals of the targeted biological network, a respective concentration of the respective chemical at each respective time point in the corresponding plurality of time points.

10. The method of claim 1 , wherein the targeted biological network comprises a chemical, a protein, an RNA, or a DNA.

11. The method of claim 1 , wherein the targeted biological network is all or part of a pathogen or disease.

12. 2. The method of claim 1, wherein the mathematical model of the existing treatment consists of a first subset of the intervention nodes and a second subset of the untreated nodes, and the mathematical model of the new treatment consists of a third subset of the intervention nodes and a fourth subset of the untreated nodes.

13. 10. The method of claim 1, wherein the set of drug regimens includes at least one, two, three, four, five, or six drug regimens that together form the new drug therapy, each respective drug regimen in the set of drug regimens being a respective drug defined by a corresponding dosage or a corresponding schedule.

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