Systems and methods for developing alternative drug therapies using characteristics of existing drug therapies 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 characteristics of existing drug therapies to produce similar pathway behavior. [Background technology]

[0002] Today, drug therapies are used to treat pathogens and diseases. Drug therapies work by attacking specific pathways in the pathogen. Generally speaking, a pathway is a causal chain of interactions that results in an alteration of the pathogen's normal function, initiated by the drug chemically interacting with targetable biological elements of the pathogen.

[0003] Many drug therapies exist, but much research is being conducted to find new drug therapies to combat pathogens for which no drug therapy yet exists, or to replace current inadequate drug therapies. Drug therapies can be inadequate for several reasons.

[0004] First, some medications do not cure the disease, but merely reduce the prevalence or symptoms. Examples of such medications include those used to fight HIV or herpes viruses. In both cases, the medication reduces the viral load in a person, but neither medication completely eliminates the virus.

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

[0006] Third, drug therapies are often prone to resistance by 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 effectiveness due to the evolution of the target population. Resistance occurs when a subset of the targeted organisms or cells survives exposure due to a specific trait of that subset and then passes on that resistance trait to the next generation.

[0007] Fourth, some medications can be expensive to make. Drug synthesis is a multi-step process, and each step can have a significant impact on the cost of producing a drug. For example, in 2011, 4-phenyl-1, cost $260 to produce just 50 grams.

[0008] A general strategy for the rational computer-aided development of new drugs is to first identify new interactions between biological elements that may be important for cellular function, or entirely new biological elements. The most 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 the target cell in a desired manner.

[0009] However, such methods pose significant problems. Discovery of new biological elements or interactions within known elements is either very time-consuming and resource-intensive, or results in many false-positive interactions. Discovery of interactions or biological elements important for cellular function in initial laboratory tests also fails to answer the central question: will disruption of the target biological element have 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 features of existing drug therapies to produce similar pathway behavior. Summary of the Invention

[0011] A method for finding a set of parameters for a new drug therapy such that the new drug therapy produces similar outcomes to existing drug therapies. In a first step, the method can include replacing, within a mathematical model, any intervention function associated with the existing drug therapy with an untreated node function associated with the mathematical model, if the mathematical model has any such intervention function. In a second step, the method can include, for each of a plurality of parameters: Multiple The method can include selecting a parameter range. Then, for each permutation of the plurality of permutations, the method can include generating a new mathematical model of the progression of the treatment over time. , each time using the permutation to generate the progression over time The method can then include determining, for each permutation, whether its time progression includes a time progression signature present in the time progression of an existing treatment associated with an existing drug therapy, where the time progression signature is associated with an outcome of the existing drug therapy. The method can then include, for at least one permutation that includes the time progression signature, synthesizing a substance having kinetic properties that substantially match the kinetic parameters of the one permutation to produce a new drug therapy.

[0012] A method for developing a new drug therapy using characteristics of an existing drug therapy includes developing a mathematical model of a new therapy for a target biological network and synthesizing the drug therapy based on the mathematical model of the new therapy. The mathematical model of the new therapy can generate a time progression of the new therapy, including a time progression signature found in the time progression of the existing therapy in the mathematical model of the existing therapy for the target biological network. The time progression signature can be related to an outcome of the target biological network. The mathematical model of the new therapy can include a new therapy intervention function that models each new therapy intervention node in the set of new therapy intervention nodes and a first set of untreated node rate functions that model all other nodes in the mathematical model of the new therapy. Each of the new therapy intervention functions can include another new therapy intervention constant from the set of new therapy intervention constants. The mathematical model of the existing therapy can include an existing therapy intervention equation that models each existing therapy intervention node in the set of existing therapy intervention nodes and an untreated rate equation that model all other nodes in the mathematical model of the existing therapy. Each of the existing intervention equations can include existing intervention constants from the set of existing intervention constants. The set of nodes for the new treatment need not be identical to the set of nodes for the existing treatment. Furthermore, the set of nodes for the new treatment can include at least one node that is not in the set of nodes for the existing treatment. Furthermore, the set of nodes for the existing treatment can include at least one node that is not in the set of nodes for the new treatment. A drug regimen can be synthesized for each intervention constant for the new treatment, and the drug regimens together can become a new drug therapy. [Brief explanation of the drawings]

[0013] [Figure 1] Show drug therapies interacting with biological networks.

[0014] [Figure 2] 1 illustrates an exemplary reaction model of a biological network.

[0015] [Figure 3] It shows the progression of biological networks over time, specifically the actual progression over time.

[0016] [Figure 4] We present a mathematical model of a biological network, specifically the pre-treatment mathematical model.

[0017] [Figure 5] Shows progression over time before treatment.

[0018] [Figure 6] Response models for existing treatments are shown.

[0019] [Figure 7] Response models for existing treatments are shown.

[0020] [Figure 8] A mathematical model of existing treatments is presented.

[0021] [Figure 9] 1 shows a first set of existing intervention functions.

[0022] [Figure 10] A set of existing intervention constants is shown.

[0023] [Figure 11] A set of existing intervention concentrations is shown.

[0024] [Figure 12] Untreated node velocity is shown.

[0025] [Figure 13] Shows progress over time with existing treatments.

[0026] [Figure 14] Presents a new treatment response model.

[0027] [Figure 15] A mathematical model of the new treatment is presented.

[0028] [Figure 16] A second set of new therapeutic intervention functions is presented.

[0029] [Figure 17] Denote the set of intervention constants for the new treatment.

[0030] [Figure 18] A set of intervention concentrations for the new treatment is presented.

[0031] [Figure 19] 10 shows a second set of untreated node velocity functions.

[0032] [Figure 20] Shows the progression of new treatments over time. DETAILED DESCRIPTION OF THE INVENTION

[0033] Described herein are systems and methods for developing alternative drug therapies using features of existing drug therapies to effect similar pathway behavior. The following description is presented to enable any person skilled in the art to make and use the claimed invention, and is provided in the context of specific examples described below, variations of which will be readily apparent to those skilled in the art. For clarity, not all features of an actual implementation are described herein. It will be understood that in the development of any such actual implementation (as in any development project), design decisions must be made to achieve the designer's particular goals (e.g., compliance with system and business-related constraints), and that these goals will vary from implementation to implementation. It will also be appreciated that such a development effort may be complex and time-consuming, but would nevertheless be a routine undertaking for one of ordinary skill in the pertinent art having the benefit of this disclosure. Therefore, the claims appended hereto are not intended to be limited by the disclosed embodiments, but are to be accorded the widest scope consistent with the principles and features disclosed herein.

[0034] FIG. 1 illustrates a drug therapy 101 interacting with a biological network 102. Within the context of the present disclosure, the biological network 102 may be a target biological network (TBN) 102a or a non-target biological network (non-TBN) 102b. The TBN 102a may include, but is not limited to, all or part of a pathogen or disease. The TBN 102a may be multicellular, single-cell, or RNA or DNA. Exemplary categories of TBN 102a may include parasites, bacteria, viruses, or fungi. Specific examples may include E. coli, COVID-19, or cancer. A non-TBN 102b, for purposes of the present disclosure, is a biological network 102 within a host or organism that has a symbiotic relationship with the host.

[0035] The present disclosure describes systems and methods for developing one or more drug therapies 101 that disrupt TBN 101a. A drug therapy 101 is any one or more drug regimens 103, other than food, used to prevent, diagnose, treat, or alleviate the symptoms of a disease or abnormal condition. Furthermore, for purposes of this disclosure, a drug regimen 103 can be defined by a substance 104. A substance 104, for purposes of this disclosure, is a specific type of substance with uniform properties. Furthermore, a drug regimen 103 can be defined by a dosage 105 and a schedule 106. The schedule 106, in one embodiment, can be defined by a period and / or duration (e.g., every 8 hours for 3 days). In some embodiments of a drug regimen 103, the dosage 105 can vary with the schedule 106, such as increasing or decreasing over time. For purposes of this disclosure, the dose 105 may be described in absolute terms, in terms of an amount meant to be scaled by other patient-specific information such as weight, age, maturity, etc., as an intended concentration, or any other method of describing a dose known in the art.

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

[0037] FIG. 2 illustrates an exemplary response model 200, specifically a pre-treatment response model 200a of a biological network 102. For purposes of this disclosure, the pre-treatment model 200a is a response model of the biological network 102 when the biological network 102 is not being treated with a drug therapy 101. As shown in FIG. 2, the response model 200 represents a network of nodes 107, each of which is a chemical interconversion of a chemical 201 within the biological network 102. Such chemical interconversions are often facilitated by proteins 202. The proteins 202 may be, and often are, enzymes. Additionally, the chemical 201 may be a non-enzyme protein 202. The illustrated response model 200 represents a biological network 102 that includes seven nodes 107, as follows: a. Node 1: Chemical A is converted to chemical B, facilitated by protein 1. b. Nodes 2 and 3: Chemical B is converted into chemical C, facilitated by proteins 2 and 3. c. Node 4: Chemical C is converted to chemical D, facilitated by protein 4. d. Node 5: Chemical C is converted to chemical D, facilitated by protein 5. e. Node 6: Chemical B is converted to chemical A and chemical E by protein 6. f. Node 7: Chemicals D and F are converted together to chemical A, facilitated by protein 7. g. Node X E : Chemical E is converted to chemical F without the modeled protein.

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

[0039] 3 illustrates a time progression 300, specifically an actual time progression 300z, of a biological network 102. For purposes of this disclosure, the actual time progression 300z is the time progression of the experimentally measured concentration of a chemical substance 201.

[0040] FIG. 4 illustrates a mathematical model 400 of a biological network 102, specifically a pre-treatment mathematical model 400a. For purposes of this disclosure, the mathematical model 400 is associated with the response model 200 and includes equations 401 that collectively describe the behavior of the biological network 102 with sufficient accuracy so that the response model 200 can accurately predict the behavior of the associated biological network 102. Furthermore, for purposes of this disclosure, the pre-treatment mathematical model 400a is associated with the pre-treatment response model 200a and includes equations 401 that collectively describe the behavior of the biological network 102 when not being treated with any medication 101. The equations 401, in one embodiment, may include concentration change equations 401a that respectively describe the rate of change of concentration of particular chemicals 201 within the response model 200. For example, the concentration change equation 301a for chemical 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 the node 107 and is shown in FIG. 4 as a function of various variables, the nodal velocity function V nFor example, variables A to F represent the concentrations 403 of corresponding chemical substances 201A to F in the reaction model 201. V nmax represents the maximum nodal velocity 404 at which the protein 202 can convert reactants to products. Each nodal velocity function 402 can be used to determine a nodal velocity, which can be used to calculate the change in concentration of the chemical 201. Those skilled in the art will recognize that nodal velocities can be modeled using the Michaelis-Menten equation. As an example, nodal velocity V1 can be calculated using the equation V1=(V 1max *A) / (k A1 + A) In this equation, k A1 is the rate constant 405 specific to chemical A and protein 1. Those skilled in the art will recognize that V 1max and K. A1 It will be appreciated that both Λ and Λ can be determined experimentally. These values ​​can also be estimated. Additionally, proteins 202 can operate on or output multiple chemicals, resulting in more complex equations. Similarly, some nodes may require multiple proteins, which can also result in more complex equations.

[0042] 5 illustrates 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 purposes of this disclosure, a pre-treatment velocity function 501 is a node velocity function that models a node 107 when such node is not intervened by a drug regimen 103.

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

[0044] The time progression 300 includes measured or modeled chemical concentration levels of the chemical 201 in the biological network 102 as a function of time. The pre-treatment time progression 300a models the chemical concentration levels of the chemical 201 in the biological network 102 as a function of time before any treatment with a drug therapy 101. In the absence of treatment, the concentration of the chemical 201 may vary within a cycle, but otherwise typically remains within a predictable, constrained level for a sustained period within the life cycle of the biological network 102. This does not mean that the concentration remains constrained throughout the entire life cycle, but rather that during a period, the concentration remains constrained and remains substantially predictable across changes from one period to the next. An important function of the pre-treatment time progression 400a is its ability to establish a baseline dynamics of the biological network 102.

[0045] 7 illustrates an existing treatment response model 200b. For purposes of this disclosure, the existing treatment response model 200b is a response model that models how an existing drug therapy 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 nodes 107 that are not intervened are modeled as untreated nodes 107b with a pre-treatment rate function 501. Note that the existing drug therapy 101 is not limited to only drug therapies 101 that have reached the market, but includes any drug therapy that has previously been considered for use on the biological network 102 and that produces an outcome. For a target biological network 102a, example outcomes include, but are not limited to, killing the target biological network 102a, rendering the target biological network 102a unable to replicate, or substantially impairing the target biological network 102a such that other conditions or forces, such as the immune system, can kill the target biological network 102a. For a non-target biological network 102b, example outcomes may include strengthening the non-target biological network 102b or destroying the non-target biological network 102b's resistance or immunity to some condition.

[0046] 8 shows a mathematical model of an existing treatment 400b. In the mathematical model of an existing treatment 400b, each node rate function 402 that models an existing intervention node 107a can be an intervention function 402a. The types of intervention functions fall into two main categories: inhibitory functions or acceleration functions. Inhibitory functions model the slowing or substantial cessation of chemical interconversion at an existing intervention node 107a. Conversely, acceleration functions model the initiation or speeding up of chemical interconversion at an existing intervention node 107a.

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

[0048] 10 shows a set of existing treatment intervention constants 1000. Each intervention function 402a can have one or more intervention constants 1001 associated with a drug regimen 103 of an existing medical treatment 101 associated with the mathematical model of existing treatment 400b. The intervention constants 1001 are numbers that represent the effect that the associated drug regimen 103 has on the velocity of the node 107.

[0049] 11 shows a set of existing intervention concentrations 1100. Each intervention function 402a can include concentration constants 1102 that model the concentrations of one drug regimen 103 of an existing medical therapy 101. The set of existing intervention concentrations 1100 includes these concentration constants 1102.

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

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

[0052] In one embodiment, a set of parameters for a new drug therapy can be found so that the new drug therapy produces similar outcomes to existing drug therapies. In a first step, the method includes: determining, within the mathematical model, any intervention functions associated with the existing drug therapy, if the mathematical model has any such intervention functions, by: The relevantIn a next step, the method may include substituting an untreated node function associated with the mathematical model for each of the plurality of parameters. Multiple The method can include selecting a parameter range. Then, for each permutation of the plurality of permutations, the method can include generating a new mathematical model of the progression of the treatment over time. , each time using the permutation to generate the progression over time The method can then include determining, for each permutation, whether its time progression includes a time progression signature present in the time progression of an existing treatment associated with an existing drug therapy, where the time progression signature is associated with an outcome of the existing drug therapy. The method can then include, for at least one permutation that includes the time progression signature, synthesizing a substance having kinetic properties that substantially match the kinetic parameters of the one permutation to produce a new drug therapy.

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

[0054] 14 illustrates a new treatment response model 200c. For purposes of this disclosure, the new treatment response model 200c is a response model that models how a new medication 101 may interact with the biological network 102. The new treatment response model 200c includes at least one new treatment intervention node 107a. The new treatment intervention node 107a is intervened by the drug regimen 103 of the new medication 101. The remaining nodes 107 that are not intervened are modeled as untreated nodes 107b with a pre-treatment rate function 501.

[0055] 15 shows a mathematical model 400c of a new treatment. In the mathematical model 400c of a new treatment, each node speed function 402 that models a new treatment intervention node 107a can be an intervention function 402a. The types of intervention functions fall into two main categories: inhibitory functions or acceleration functions. Inhibitory functions model the slowing or substantial cessation of chemical interconversion at the new treatment intervention node 107a. Conversely, acceleration functions model the initiation or speeding up of chemical interconversion at the new treatment intervention node 107a.

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

[0057] 17 shows a set of new treatment intervention constants 1700. Each intervention function 402a can have one or more intervention constants 1001 associated with a drug regimen 103 of the new medical treatment 101 associated with the new treatment mathematical model 400c. The intervention constants 1001 are numbers that represent the effect that the associated drug regimen 103 has on the node's velocity 107.

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

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

[0060] 20 illustrates a new treatment progression over time 300c. The new treatment progression over time 300c can include a progression over time signature 1301 that is predictive of and common to, or associated with, or present in the existing treatment progression over time 300b.

[0061] A method for developing a new drug therapy using characteristics of an existing drug therapy includes developing a mathematical model of a new therapy for a target biological network and synthesizing the drug therapy based on the mathematical model of the new therapy. The mathematical model of the new therapy can generate a time progression of the new therapy, including a time progression signature found in the time progression of the existing therapy in the mathematical model of the existing therapy for the target biological network. The time progression signature can be related to an outcome of the target biological network. The mathematical model of the new therapy can include a new therapy intervention function that models each new therapy intervention node in the set of new therapy intervention nodes and a first set of untreated node rate functions that model all other nodes in the mathematical model of the new therapy. Each of the new therapy intervention functions can include another new therapy intervention constant from the set of new therapy intervention constants. The mathematical model of the existing therapy can include an existing therapy intervention equation that models each existing therapy intervention node in the set of existing therapy intervention nodes and an untreated rate equation that model all other nodes in the mathematical model of the existing therapy. Each of the existing intervention equations can include existing intervention constants from the set of existing intervention constants. The set of nodes for the new treatment need not be identical to the set of nodes for the existing treatment. Furthermore, the set of nodes for the new treatment can include at least one node that is not in the set of nodes for the existing treatment. Furthermore, the set of nodes for the existing treatment can include at least one node that is not in the set of nodes for the new treatment. A drug regimen can be synthesized for each intervention constant for the new treatment, and the drug regimens together can become a new drug therapy.

[0062] The present disclosure teaches new drug therapies designed using any of the aforementioned methods. For example, the present disclosure teaches drug therapies having multiple drug regimens, each regimen being a substance with parameters identified using the above methods. Furthermore, the parameters are such that the new drug therapies share outcomes with existing drug therapies. Furthermore, each drug regimen intervenes at a node, and the nodes together form a set of nodes, and such set of nodes is not common with a second set of nodes intervened by the existing drug therapies.

[0063] The new drug development and design application, which 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 the identified biochemical or biological networks that interact with known therapeutic compounds are generated and processed using the drug development and design system. Such methods are sometimes referred to as DASS methods in this disclosure. The DASS method can provide models and information regarding the effects of known therapeutics with the identified biochemical or biological networks. In one embodiment, the DASS method can also be used to provide models and information regarding the identified biochemical or biological networks without known therapeutics. After processing using the DASS method, features and characteristics of theoretical therapeutic compounds that affect the identified biochemical or biological networks in the same way that the original therapeutics affect the networks can be identified. Similarly, theoretical therapeutics for specific target cells in the identified biochemical or biological networks 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 the identified biochemical or biological networks. The properties and characteristics of the theoretical therapeutic compounds can be used to compare with real therapeutic compounds, thereby finding potential therapeutic compounds that produce similar pathway behavior as the biological or biochemical network, or at least the target cell, that can be tested on the identified biochemical or biological network.

[0065] In another embodiment, a potential therapeutic compound is simulated against the 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 perturbations on non-target cells, or produces overall similar pathway behavior for the identified biochemical or biological network as the original therapeutic does.

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

[0067] A system for performing the methods described herein may comprise a typical networking environment comprising a plurality of electronic devices and a server connected via a network. Examples of electronic devices may include, but are not limited to, a computer, a smartphone, and / or a tablet. In one embodiment, the electronic devices and the server may 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] The electronic device may include a local memory and a local processor. The local memory may include local applications and local data.

[0069] The server may include a server memory and a server processor. The server memory may include server applications and server data.

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

[0071] In another embodiment, the drug development and design application can refer to a local application along with a server application. An 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 special-purpose (non-browser) application.

[0072] In a first example, a browser accesses a server application through a website. In such an embodiment, the interface with the user occurs using the electronic device, presentation may be performed by local applications and server applications, and logic and data may be executed by the server. In such an example, memory may refer to electronic device memory and / or server memory, processor may refer to electronic device processor and / or server processor, and data may refer to server data.

[0073] In a second example, the special-purpose application accesses server application 103b. In such an embodiment, the interface with the user may occur on the electronic device, and presentation, logic, and data storage may be distributed across both the electronic device and the server. In such an example, memory may refer to electronic device memory and / or server memory, processor may refer to electronic device processor and / or server processor, and data may refer to local data and / or server data.

[0074] The memory described herein stores both data and several components executable by the processor. Specifically, stored in the memory and executable by the processor are the DASS method and potentially other applications. Information such as interactions of known therapeutic compounds with target cells, kinetic characterization data of therapeutic compounds with non-target cells, and other data may also be stored in the memory. Additionally, an operating system may be stored in the memory and executed by the processor.

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

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

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

[0078] Various changes may be made in the details of the illustrated methods of operation without departing from the scope of the following claims. Some embodiments may combine activities described herein as separate steps. Similarly, one or more of the described steps may be omitted, depending on the particular operating environment in which the method is being implemented. It is to be understood that the above description is intended to be illustrative, and not limiting. For example, the above-described embodiments may be used in combination with each other. Many other embodiments will be apparent to those skilled in the art upon reviewing the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms "including" and "in which" are used as the plain-English equivalents of 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 described in 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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