A system and method for developing alternative drug therapies that produce similar pathway behavior using existing drug therapy characteristics

Abstract

A method for developing novel drug therapies utilizing properties of existing drug therapies, the method comprising the steps of: developing a novel therapeutic mathematical model of a target biological network and synthesizing a drug therapy based on the novel therapeutic mathematical model. The novel therapeutic mathematical model is capable of generating novel therapeutic time-course progressions, which include time-course progression features found in existing therapeutic time-course progressions of existing therapeutic mathematical models of the target biological network. The time-course progression features can be correlated with the outcomes of the target biological network. Each new therapeutic intervention constant can be used to synthesize a drug regimen, and these drug regimens collectively constitute the novel drug therapy.

Description

Background Technology

[0001] This invention relates to a system and method for developing alternative drug therapies that utilize the properties of existing drug therapies to produce similar pathway behaviors.

[0002] Today, drug therapy is used to treat pathogens and diseases. Drug therapy works by attacking specific pathways of the pathogen. Generally, pathways are causal chains of interactions that lead to alterations in the normal function of the pathogen, said alterations being triggered by drug therapy through chemical interactions with targetable biological elements of the pathogen.

[0003] While many drug therapies exist, further research is underway to find new drug treatments to combat pathogens for which there are currently no drug therapies or to replace those that are inadequate. Drug therapies may be insufficient for a variety of reasons.

[0004] First, some drug therapies do not cure the disease, but only reduce its prevalence or symptoms. Examples of such therapies include those used to combat HIV and herpes simplex virus. In both cases, while drug therapy can reduce the viral load in the body, no single drug therapy can completely eliminate the virus.

[0005] Second, some drug therapies have side effects ranging from mild to severe, and in some cases, can be fatal. The biological elements targeted by drug therapies alter the pathway behavior of those target elements, leading to interactions between the targeted biological elements and other biological elements due to cascading reactions. However, these altered pathway behaviors can have significant negative impacts on biological networks. Furthermore, therapeutic molecules can interact with known or unknown non-target elements within the network, which can also generate negative overall pathway behavior within the target network, as described above.

[0006] Third, over time, drug therapies often become susceptible to resistance from evolving pathogens. Specifically, if a therapeutic agent targets pathogens such as bacteria, viruses, parasites, or even cancer cells, the agent may lose its effectiveness due to the evolution of the target population. Resistance develops when a subset of target organisms or cells survives exposure due to specific characteristics of that subset and then passes that resistance on to the next generation.

[0007] Fourth, the manufacturing cost of some drug therapies can be very high. Drug synthesis is a multi-step process, and one step can have a significant impact on the cost of manufacturing a drug. For example, in 2011, it cost $260 to prepare just 50 grams of 4-phenyl-1.

[0008] A common strategy in rational computer-aided drug development is to first identify novel interactions between biological elements, or entirely new biological elements that may be crucial for cellular function. Then, the promising biological elements are structurally characterized at the molecular level, along with their interactions with potential therapeutic agents. The goal is to target a specific biological element that is likely to significantly alter the function of the target cell in the desired manner.

[0009] However, these methods present significant problems. Discovering new biological elements or interactions within known elements is extremely time-consuming and resource-intensive, or fraught with false positives from interaction outcomes. Even identifying interactions or biological elements crucial for cellular function in initial laboratory tests fails to answer a core question: Will disruption of a target biological element have the intended effects on the entire organism through a chain reaction mechanism?

[0010] Therefore, it is advantageous to have a system and approach that uses features of existing drug therapies to design novel drug therapies that target pathogen pathways to produce similar pathway behavior. Summary of the Invention

[0011] A method for finding a set of parameters for a new drug therapy to produce results similar to existing drug therapies. In a first step, the method may include: if the mathematical model has any intervention function associated with an existing drug therapy, replacing any intervention function associated with the existing drug therapy within the mathematical model with a no-treatment-node function associated with the mathematical model. In a next step, the method may include: selecting a range for each of a plurality of parameters. The method may then include: generating a temporal progression of the new treatment mathematical model for each of a plurality of permutations, each time the temporal progression is generated using the permutation. The method may then include: determining for each permutation whether its temporal progression includes a temporal progression feature present in the temporal progression of an existing treatment associated with the existing drug therapy, the temporal progression feature being correlated with the outcome of the existing drug therapy. The method may then include: for at least one permutation containing the temporal progression feature, synthesizing a substance having kinetic properties substantially matching the kinetic parameters of that permutation to produce the new drug therapy.

[0012] A method for developing new drug therapies using characteristics of existing drug therapies, the method comprising the steps of: developing a new therapeutic mathematical model of a target biological network and synthesizing a drug therapy based on the new therapeutic mathematical model. The new therapeutic mathematical model is capable of generating new therapeutic time-course progressions, including time-course progression features found in existing therapeutic time-course progressions of existing therapeutic mathematical models of the target biological network. The time-course progression features are correlated with the outcome of the target biological network. The new therapeutic mathematical model may include: modeling a new therapeutic intervention function for each of a set of new therapeutic intervention nodes, and a first set of no-treatment node velocity functions modeling all other nodes of the new therapeutic mathematical model. Each new therapeutic intervention function may include one or more new therapeutic intervention constants from a set of new therapeutic intervention constants. The existing therapeutic mathematical model may include: existing therapeutic intervention equations modeling each of a set of existing therapeutic intervention nodes, and no-treatment velocity equations modeling all other nodes of the existing therapeutic mathematical model. Each existing therapeutic intervention equation may include existing therapeutic intervention constants from a set of existing therapeutic intervention constants. The set of new therapeutic nodes may differ from the set of existing therapeutic nodes. Furthermore, the new set of treatment nodes may include at least one node that is not in the existing set of treatment nodes. Furthermore, the existing set of treatment nodes may include at least one node that is not in the new set of treatment nodes. A drug regimen can be synthesized for each of the new treatment intervention constants, and the drug regimens can collectively constitute a new drug therapy. Attached Figure Description

[0013] Figure 1 Drug therapies that interact with biological networks are demonstrated.

[0014] Figure 2 An exemplary response model of a biological network is shown.

[0015] Figure 3 It shows the temporal progression of biological networks, especially the actual temporal progression.

[0016] Figure 4 A mathematical model of a biological network is shown, specifically a pre-treatment mathematical model.

[0017] Figure 5 This shows the progression of the treatment timeline.

[0018] Figure 6 This demonstrates an existing treatment response model.

[0019] Figure 7 This demonstrates an existing treatment response model.

[0020] Figure 8 An existing mathematical model for treatment is presented.

[0021] Figure 9 The first set of existing treatment intervention functions is shown.

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

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

[0024] Figure 12 The speed of the untreated node is displayed.

[0025] Figure 13 It shows the timeline progression of existing treatments.

[0026] Figure 14 A new treatment response model was shown.

[0027] Figure 15 The new therapeutic mathematical model was shown.

[0028] Figure 16 The second set of new therapeutic intervention functions is shown.

[0029] Figure 17 A new set of therapeutic intervention constants was shown.

[0030] Figure 18 A new set of therapeutic intervention concentrations was shown.

[0031] Figure 19 The velocity function of the second group of untreated nodes is shown.

[0032] Figure 20 It shows the timeline progression of the new treatment. Detailed Implementation

[0033] This document describes systems and methods for developing alternative pharmaceutical therapies that utilize features of existing pharmaceutical therapies to produce similar pathway behavior. The following description is intended to enable any person skilled in the art to make and use the invention, and is provided in the context of the specific examples discussed below, variations of which will be apparent to those skilled in the art. For clarity, this specification does not describe all features of actual implementation. It will be understood that in any such actual implementation of development (as in any development project), design decisions must be made to achieve specific goals of the designer (e.g., compliance with system and business-related constraints), and these goals will vary from implementation to implementation. It should also be understood that such development work can be complex and time-consuming, but remains routine for those skilled in the art who will benefit from the invention. Therefore, the embodiments disclosed herein are not intended to limit the scope of the appended claims, which are consistent with the widest scope of the principles and features disclosed herein.

[0034] Figure 1 A drug treatment 101 interacting with a biological network 102 is shown. In the context of this disclosure, the biological network 102 can be a target biological network (TBN) 102a or a non-target biological network (non-TBN) 102b. TBN 102a can include, but is not limited to, all or part of a pathogen or disease. TBN 102a can be multicellular, single-celled, or even RNA or DNA. Examples of the TBN 102a category can include parasites, bacteria, viruses, or fungi. Specific examples include Escherichia coli, COVID-19, or cancer. For the purposes of this invention, non-TBN 102b refers to the biological network 102 within a host or an organism with a reciprocal relationship to the host.

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

[0036] Biological network 102 includes nodes 107. For the purposes of this disclosure, nodes 107 are aspects of biological network 102 that drug therapy 101 can potentially intervene in, for example, by accelerating, decelerating, preventing, or initiating chemical interconversions within biological network 102. Furthermore, for the purposes of this invention, nodes 107 of TBN 102a are target nodes 107a, and nodes 107 of non-TBN 102b are non-target nodes 107b.

[0037] Figure 2 An exemplary response model 200 is shown, specifically a pre-treatment response model 200a of the biological network 102. For the purposes of this invention, the pre-treatment model 200a is a response model of the biological network 102 when the biological model 102 has not received treatment with the drug therapy 101. Figure 2 As shown, reaction model 200 represents a network of nodes 107, where each node represents the chemical interconversion of chemical substances 201 in biological network 102. This chemical interconversion is typically facilitated by protein 202. Protein 202 can and often is an enzyme. Furthermore, chemical substance 201 can be a non-enzymatic protein 202. As shown in the figure, reaction model 200 represents a biological network 102 comprising seven nodes 107, as follows:

[0038] a. Node 1: With the help of protein 1, chemical substance A is converted into chemical substance B.

[0039] b. Nodes 2 and 3: With the help of protein 2 and protein 3, chemical substance B is transformed into chemical substance C.

[0040] c. Node 4: With the help of protein 4, chemical substance C is converted into chemical substance D.

[0041] d. Node 5: With the help of protein 5, chemical substance C is converted into chemical substance D.

[0042] e. Node 6: With the assistance of protein 6, chemical substance B is converted into chemical substance A.

[0043] f. Node 7: With the help of protein 7, chemical substances D and F are converted together into chemical substance A.

[0044] g. Node X E Chemical substance E is converted into chemical substance F without modeling the protein.

[0045] 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. For example, in process 1, A + Z1 → B could actually be A + x1 + Z1 → B + y1, where x1 is a set of unmodeled reactants of process 1 and y1 is a set of unmodeled products of process 1.

[0046] Figure 3 The temporal progression 300 of biological network 102 is shown, specifically the actual temporal progression 300z. For the purposes of this invention, the actual temporal progression 300z is the temporal progression of the experimentally measured concentration of chemical substance 201.

[0047] Figure 4 A mathematical model 400 of the biological network 102 is shown, particularly a pre-treatment mathematical model 400a. For the purposes of this invention, the mathematical model 400 is associated with the response model 200 and includes several equations 401 that collectively describe the behavior of the biological network 102 with sufficient precision to accurately predict the behavior of the biological network 102 associated with the response model 200. Furthermore, for the purposes of this disclosure, the pre-treatment mathematical model 400a is associated with the pre-treatment response model 200a and includes several equations 401 that collectively describe the behavior of the biological network 102 when not receiving any drug therapy 101. In one embodiment, equations 401 may include concentration change equations 401a, each equation 401a describing the rate of change in concentration of a specific chemical substance 201 within the response model 200. For example, the concentration change equation 301a related to chemical A is as follows: dA / dt=V6(B,V6max,kB6)+V7(D,F,V7max,kD7,kF7)–V1(V1max,A,kA1).

[0048] Concentration change equation 401a may include a node velocity function Vn()402 describing the behavior of node 107 and in Figure 4 The expression is represented as a function of various variables. For example, variables A to F each represent the concentration 403 of the corresponding chemical substances 201A-F in reaction model 201. n max This represents the maximum node velocity 404 at which protein 202 can convert reactants into products. Each node velocity function 402 can be used to determine a node velocity, and the node velocity can be used to calculate changes in the concentration of chemical substance 201. Those skilled in the art will recognize that node velocities can be modeled using Michaelis-Menten equations. For example, a node velocity V1 can be represented by the equation V1 = (V... 1max *A) / (k A1 +A) Modeling. In this equation, k A1It is a rate constant of 405 specific to chemical substance A and protein 1. Those skilled in the art will recognize V 1max and k A1 All of these can be determined experimentally. These values ​​can also be estimated. Furthermore, protein 202 can manipulate or output multiple chemicals, leading to more complex equations. Similarly, some nodes may require multiple proteins, which also results in more complex equations.

[0049] Figure 5 A set of pre-treatment node velocity functions 500 is shown for the pre-treatment mathematical model 400a of the untreated biological network 102, each node velocity function being a pre-treatment velocity function 501. For the purposes of this invention, the pre-treatment velocity function 501 is a node velocity function that models node 107 when it has not been intervened by the drug regimen 103.

[0050] Figure 6 The pre-treatment time progression 300a is shown. One purpose of the pre-treatment mathematical model 300a is to generate the pre-treatment time progression 300a. For the purposes of this disclosure, the pre-treatment time progression is the time progression of the pre-treatment mathematical model 300a and is intended to simulate the actual time progression 300z with sufficient accuracy.

[0051] The pretreatment time progression 300a includes the chemical concentration level of chemical substance 201 within the measured or modeled biological network 102 as a function of time. Prior to any treatment of drug therapy 101, the pretreatment time progression 300a models the chemical concentration level of chemical substance 201 within the biological network 102 as a function of time. In the absence of treatment, the concentration of chemical substance 201 may vary over periods, but generally remains within predictable, constrained levels for a duration over the lifespan of the biological network 102. This does not mean that the concentration will remain constrained throughout the entire lifespan, but rather that the concentration will remain constrained for a period of time, and the variation from one period to the next remains largely predictable. An important function of the pretreatment time progression 400a is that it can establish the baseline dynamics of the biological network 102.

[0052] Figure 7A prior art treatment response model 200b is shown. For the purposes of this invention, prior art treatment response model 200b is a response model simulating how existing drug therapies interact with biological network 102. Prior art treatment response model 200b includes at least one prior art treatment intervention node 107a. Prior art treatment intervention node 107a is intervened by drug regimen 103. The uninterrupted remaining node 107 is modeled as an untreated node 107b with a pre-treatment velocity function 501. It should be noted that prior art drug therapies 101 are not limited to drug therapies already on the market, but include any drug therapies previously considered for use in biological network 102 that result in a consequence. In the case of target biological network 102a, examples of consequences include, but are not limited to: causing the death of target biological network 102a; rendering target biological network 102a unable to replicate; or substantially destroying target biological network 102a, such that other conditions or forces, such as the immune system, can kill target biological network 102a. In the case of a non-target biological network 102b, examples of results may include: strengthening the non-target biological network 102b; or making the non-target biological network 102b resistant or immune to certain conditions.

[0053] Figure 8 The existing treatment mathematical model 400b is shown. In the existing treatment mathematical model 400b, the velocity function 402 for each node modeling the existing treatment at intervention node 107a can be an intervention function 402a. Intervention functions fall into two main categories: inhibitory functions or accelerating functions. Inhibitory functions model the slowing down or near-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.

[0054] Figure 9 The first set of existing treatment intervention functions, 900, is displayed. (Example) Figure 9 As shown, the first group of 900 can be a group of one or more existing therapeutic intervention functions.

[0055] Figure 10 A set of existing treatment intervention constants 1000 is shown. Each intervention function 402a may have one or more intervention constants 1001 associated with a drug regimen 103 of an existing drug therapy 101 related to an existing treatment mathematical model 400b. An intervention constant 1001 represents the number of times the associated drug regimen 103 affects the node velocity at node 107.

[0056] Figure 11 A set of existing therapeutic intervention concentrations 1100 is shown. Each intervention function 402a may include a concentration constant 1102 that simulates the concentration of a drug regimen 103 of an existing drug therapy 101. The set of existing therapeutic intervention concentrations 1100 includes these concentration constants 1102.

[0057] Figure 12 The first set of untreated node velocity functions 1200 is shown. In the existing treatment mathematical model 400b, untreated nodes 107b are modeled using pre-treatment velocity functions 501. The first set of untreated node velocity functions 1200 includes each of these pre-treatment velocity functions 501.

[0058] Figure 13 The existing treatment timeline progression 300b is displayed. The existing timeline progression 300b may include a timeline progression feature 1301 of the predicted outcome as described above. The timeline progression feature 1301 may include one or more attributes. In one embodiment, the timeline progression feature 1301 may be that a first chemical substance concentration 403a of a first chemical substance 201 reaches a threshold 1302. In another embodiment, the timeline progression feature 1301 may include an event sequence. For example, the event sequence may be defined at least in part as a second chemical substance 201b reaching or exceeding a second threshold 1302b after the first chemical substance 201a has a first chemical substance concentration 403a that has reached or exceeded a first threshold 1302a. In another instance, the event sequence is defined at least in part as a first chemical substance concentration reaching or exceeding a second threshold 1302b after the first chemical substance 201a has a first chemical substance concentration 403a that has reached or exceeded a first threshold. In another embodiment, the time-course progression feature 1301 may include a first chemical substance concentration 403a of a first chemical substance 201a reaching or exceeding a first threshold, while a second chemical substance concentration 403b of a second chemical substance 201 reaches or exceeds a second threshold 1302b. In another embodiment, the time-course progression feature may include the chemical concentrations 403 of a group of chemicals 201 falling within a range such that the chemical concentrations together do not deviate from (equal to or greater than) a set of target concentrations 1304 for each chemical substance 201. In such an embodiment, the deviation between the chemical concentration group 1303 and the target concentration group 1304 can be determined using root mean square (RMS) calculation.

[0059] In one implementation, a set of parameters for a new drug therapy can be found such that the new drug therapy produces results similar to those of existing drug therapies. In a first step, the method may include: if the mathematical model has any such intervention function, replacing any intervention function associated with the existing drug therapy within the mathematical model with an untreated node function associated with the mathematical model. In a next step, the method may include selecting a parameter range for each of a plurality of parameters. The method may then include generating a temporal progression of the new treatment mathematical model for each of a plurality of permutations, each time the temporal progression is generated using the permutation. The method may then include: determining for each permutation whether its temporal progression includes a temporal progression feature present in the temporal progression of an existing treatment associated with the existing drug therapy, the temporal progression feature being related to the outcome of the existing drug therapy. The method may then include: for at least one permutation containing the temporal progression feature, synthesizing a substance having kinetic properties substantially matching the kinetic parameters of that permutation to produce the new drug therapy.

[0060] In one embodiment, the parameter range includes a specified set of nodes that can be intervened upon. In another embodiment, the parameter range may include the mode of intervention, such as by accelerating or slowing down chemical intervention within a node. In this embodiment, the parameter range includes multiple intervention equations for consideration. In another embodiment, the parameter range may include a range of one or more intervention constants of one or more intervention equations. In another embodiment, the parameter range may include a range of acceptable intervention concentrations. In another embodiment, the parameter range may include spatial considerations.

[0061] Figure 14 A novel treatment response model 200c is shown. For the purposes of this invention, the novel treatment response model 200c is a response model simulating how a novel drug therapy interacts with a biological network 102. The novel treatment response model 200c includes at least one novel treatment intervention node 107a. The novel treatment intervention node 107a is intervened by a drug regimen 103 of a novel drug therapy 101. The uninterventional remaining nodes 107 are modeled as untreated nodes 107b with a pretreatment rate function 501.

[0062] Figure 15 The new treatment mathematical model 400c is shown. In the new treatment mathematical model 400c, the velocity function 402 for each node modeling the new treatment at intervention node 107a can be an intervention function 402a. Intervention functions fall into two main categories: inhibitory functions or accelerating functions. Inhibitory functions model the slowing down or near-cessation of chemical interconversions at the existing treatment intervention node 107a. Conversely, accelerating functions model the initiation or acceleration of chemical interconversions at the existing treatment intervention node 107a.

[0063] Figure 16 The second set of new treatment intervention functions, 1600, is displayed. (Example) Figure 16 As shown, the second group 1600 can be a group of one or more new therapeutic intervention functions.

[0064] Figure 17 A set of new treatment intervention constants 1700 is shown. Each intervention function 402a may have one or more intervention constants 1001 associated with a drug regimen 103 of a new drug therapy 101 related to the new treatment mathematical model 400c. The intervention constant 1001 represents the number of times the associated drug regimen 103 affects the node velocity at node 107.

[0065] Figure 18 A set of new therapeutic intervention concentrations 1800 is shown. Each intervention function 402a may include a concentration constant 1102 that simulates the concentration of a drug regimen 103 of the new drug therapy 101. The existing set of therapeutic intervention concentrations 1800 includes these concentration constants 1102.

[0066] Figure 19 The second set of untreated node velocity functions 1900 is shown. In the new treatment mathematical model 400b, untreated nodes 107b are modeled using pre-treatment velocity functions 501. The first set of untreated node velocity functions 1900 includes each of these pre-treatment velocity functions 501.

[0067] Figure 20 A new treatment timeline progression 300c is shown. The new timeline progression 300c may include a timeline progression feature 1301 that predicts an existing treatment timeline progression 300b and is common to, related to, or otherwise present in the treatment timeline progression 300b.

[0068] A method for developing new drug therapies using features of existing drug therapies, the method comprising the steps of: developing a new therapeutic mathematical model of a target biological network and synthesizing a drug therapy based on the new therapeutic mathematical model. The new therapeutic mathematical model is capable of generating new therapeutic time-course progressions, including time-course progression features found in existing therapeutic time-course progressions of existing therapeutic mathematical models of the target biological network. The time-course progression features are correlated with the outcome of the target biological network. The new therapeutic mathematical model includes: modeling a new therapeutic intervention function for each of a set of new therapeutic intervention nodes, and modeling a first set of no-treatment node velocity functions for all other nodes of the new therapeutic mathematical model. Each new therapeutic intervention function includes one or more new therapeutic intervention constants from a set of new therapeutic intervention constants. The existing therapeutic mathematical model may include: existing therapeutic intervention equations modeled for each of a set of existing therapeutic intervention nodes, and no-treatment velocity equations modeled for all other nodes of the existing therapeutic mathematical model. Each of the existing therapeutic intervention equations includes existing therapeutic intervention constants from a set of existing therapeutic intervention constants. The set of new therapeutic nodes differs from the set of existing therapeutic nodes. Furthermore, the new set of treatment nodes includes at least one node that is not in the existing set of treatment nodes. Furthermore, the existing set of treatment nodes includes at least one node that is not in the new set of treatment nodes. A drug regimen can be synthesized for each of the new treatment intervention constants, and the drug regimens collectively constitute a new drug therapy.

[0069] This invention teaches novel drug therapies designed using any of the methods described above. For example, this disclosure teaches a drug therapy with multiple drug regimens, each regimen consisting of one substance having parameters determined using the methods described above. Furthermore, these parameters enable the novel drug therapy to achieve the same results as existing drug therapies. Further, each of the drug regimens intervenes in one node, and these nodes collectively constitute a set of nodes that differ from the second set of nodes intervened in by existing drug therapies.

[0070] A novel drug development and design application, which can be stored in a memory, can provide the properties and characteristics of theoretical therapeutic compounds that can be used to identify corresponding real-world potential therapeutic compounds. A high-resolution model is developed using a drug development and design system from identified biochemical or biological networks that interact with known therapeutic compounds. This method is sometimes referred to in this invention as the DASS method. The DASS method can provide a model and information about the effects of known therapies with defined biochemical or biological networks. In one embodiment, the DASS method can also be used to provide a model and information about defined biochemical or biological networks for which no known therapy exists. After processing with the DASS method, the characteristics and properties of theoretical therapeutic compounds that affect the defined biochemical or biological network can be identified in the same way that the original therapeutic affects the network. Similarly, the method can be used to identify theoretical therapeutics for specific target cells in defined biochemical or biological networks for which no known treatment exists.

[0071] 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 defined biochemical or biological networks. These properties and characteristics can be used to identify potential therapeutic compounds that produce pathway behavior similar to that of biological or biochemical networks or at least target cells, and can be tested on defined biochemical or biological networks.

[0072] In another embodiment, potential therapeutic compounds are simulated against a defined biochemical or biological network to examine whether they can serve as high-quality therapeutic candidates. A high-quality therapeutic candidate is a identified potential therapeutic compound that produces the same or better effects on target cells as the original therapeutic compound and causes minimal perturbation to non-target cells, or that generates pathway behavior on a defined biochemical or biological network that is completely similar to the original therapy.

[0073] In another embodiment, the modeling and simulation of the biochemical or biological network is performed by a drug development and design system, and all information related to any modeling performed can be stored in the system's data storage.

[0074] A system performing the methods described in this invention may include a typical network 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 may communicate with each other. 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.

[0075] Electronic devices may include local memory and a local processor. Local memory may contain local applications and local data.

[0076] A server may include server storage and a server processor. Server storage may contain server applications and server data.

[0077] In one implementation, the drug development and design application can refer to a native application where the interface, display, logic, and data are locally controlled on the electronic device. In such an implementation, memory can refer to local memory, processor can refer to a local processor, and data can refer to local data.

[0078] In another implementation, drug development and design applications can refer to both local and server-side applications. One example of this implementation is that the local application is a general-purpose (browser) application. Another example of this implementation is that the local application is a special-purpose (non-browser) application.

[0079] In the first embodiment, the browser accesses the server application via a website. In such an implementation, electronic devices are used to interact with the user; the display can be executed by both local and server applications, while the logic and data can be executed by the server. In such an embodiment, memory can refer to electronic device memory and / or server memory, processor can refer to electronic device processor and / or server processor, and data can refer to server data.

[0080] In a second embodiment, the dedicated application accesses server application 103b. In such an implementation, an electronic device can be used to interact with a user, while the display, logic, and data storage can be distributed across the electronic device and the server. In such an embodiment, memory refers to electronic device memory and / or server memory, processor can refer to electronic device processor and / or server processor, and data will refer to local data and / or server data.

[0081] The memory described above stores data and several components executable by the processor. Specifically, the DASS method and possible other applications are stored in the memory and executable by the processor. Information such as known interactions between therapeutic compounds and target cells, kinetic data of therapeutic compounds with non-target cells, and other data may also be stored in the memory. Furthermore, the operating system may be stored in the memory and executed by the processor.

[0082] While the drug development and design systems described herein, as well as various other systems, can be embodied in software or code executed by the general-purpose hardware described above, alternatively, they can also be embodied in dedicated hardware or a combination of software / general-purpose hardware and dedicated hardware. If embodied in dedicated hardware, each can be implemented as a circuit or state machine, employing any one or a combination of various techniques. These techniques can include, but are not limited to: discrete logic circuits with logic gates for implementing various logical functions when one or more data signals are applied; application-specific integrated circuits with appropriate logic gates; or other components, etc. Such techniques are generally well known to those skilled in the art and therefore will not be described in detail here.

[0083] Furthermore, any logic or application program containing software or code described herein, including the drug development and design system, can be embodied in any computer-readable storage medium for use by or in conjunction with an instruction execution system, such as a processor in a computer system or other system. In this sense, logic can include, for example, statements containing instructions and declarations that can be retrieved from a computer-readable storage medium and executed by an instruction execution system.

[0084] It should be emphasized that the above-described embodiments of the present invention are merely possible implementations proposed to clearly understand the principles of the present invention. Many variations and modifications can be made to the above-described embodiments without substantially departing from the spirit and principles of the present invention. All such modifications and variations are intended to be included within the scope of this disclosure and are protected by the claims.

[0085] Various changes may be made to the details of the illustrated method of operation without departing from the scope of the appended claims. Some embodiments may combine the activities described herein into separate steps. Similarly, one or more described steps may be omitted, depending on the specific operating environment in which the method is implemented. It should be understood that the above description is illustrative and not restrictive. For example, the above embodiments may be used in combination with each other. Many other embodiments will be apparent to those skilled in the art after reading the above description. Therefore, the scope of the invention should be determined with reference to the appended claims and the full scope of their equivalents. In the appended claims, the terms “comprising” and “therein” are used as simple equivalents to the corresponding terms “including” and “wherein”.

Claims

1. A method for developing new drug therapies by utilizing the properties of existing drug therapies, the method comprising the steps of: A novel therapeutic mathematical model is developed for a target biological network comprising multiple nodes. This novel therapeutic mathematical model generates new therapeutic time-course progressions, which include new therapeutic time-course progression features found in existing therapeutic time-course progressions of existing therapeutic mathematical models of the target biological network that match existing therapeutic time-course progression features. Wherein: The new treatment timeline and the existing treatment timeline each include the chemical concentration level of the chemical substance within the target biological network as a function of time, and The existing treatment timeline progression characteristics are correlated with the existing treatment outcomes of the target biological network. The new therapeutic mathematical model includes: A set of new therapeutic intervention nodes from the plurality of nodes, each corresponding new therapeutic intervention node including a new therapeutic intervention function that models the chemical interconversion at the corresponding new therapeutic intervention node, and A set of no-treatment node velocity functions that model all other nodes in the new treatment mathematical model among the plurality of nodes; Each of the novel therapeutic intervention functions includes one or more novel therapeutic intervention constants from a set of novel therapeutic intervention constants, wherein each corresponding novel therapeutic intervention constant represents the effect of the novel drug regimen in the novel drug therapy on the rate of chemical interconversion of the corresponding novel therapeutic intervention node; The existing mathematical models for treatment include: A set of existing therapeutic intervention nodes among the plurality of nodes, each corresponding existing therapeutic intervention node including an existing therapeutic intervention equation modeling the chemical interconversion at the corresponding existing therapeutic intervention node, and A no-treatment velocity equation that models all other nodes of the existing treatment mathematical model in the plurality of nodes; Each of the existing therapeutic intervention equations includes existing therapeutic intervention constants from a set of existing therapeutic intervention constants, wherein the existing therapeutic intervention constants represent the effect of existing drug regimens in existing drug therapies on the rate of chemical interconversion at existing therapeutic intervention nodes; The new set of treatment intervention nodes is different from the existing set of treatment intervention nodes; further, the new set of treatment intervention nodes includes at least one node that is not in the existing set of treatment intervention nodes; further, the existing set of treatment intervention nodes includes at least one node that is not in the new set of treatment intervention nodes. For each of the new treatment intervention nodes in the set of new treatment intervention nodes, the corresponding real-world therapeutic compound is identified by matching the properties of the corresponding real-world therapeutic compound with the new treatment intervention constant of the corresponding new treatment intervention node; and For each of the new therapeutic intervention nodes, a drug regimen comprising the corresponding real-world therapeutic compound is synthesized, and the drug regimens together constitute a new drug therapy.

2. The method as described in claim 1, wherein, The new therapeutic timeline progression includes the chemical concentration level of each chemical substance in a set of chemicals within the target biological network; and The existing treatment timeline includes the chemical concentration level of each of the aforementioned chemical substances in the group of chemical substances.

3. The method as described in claim 2, wherein, The new treatment timeline further includes the concentration of each protein in a set of proteins within the target biological network; and The existing treatment timeline further includes the concentration of each of the proteins in the aforementioned group.

4. The method of claim 2, wherein, The existing treatment timeline progression characteristics include the concentration of the first chemical substance among the chemical substances reaching or exceeding a threshold.

5. The method of claim 2, wherein, The existing treatment timeline progression characteristics include the sequence of events.

6. The method of claim 5, wherein, The event sequence is defined, at least in part, as a second chemical substance having a concentration that reaches or exceeds a second threshold after a first chemical substance has a concentration that reaches or exceeds a first threshold.

7. The method of claim 5, wherein, The sequence of events is defined, at least in part, as a first chemical substance having a concentration that reaches or exceeds a first threshold, followed by the first chemical substance having a concentration that reaches or exceeds a second threshold.

8. The method of claim 2, wherein, The existing treatment timeline progression characteristics include a first chemical substance of the group of chemicals having a concentration that reaches or exceeds a first chemical substance concentration, while the concentration of a second chemical substance of the group of chemicals is reaching or exceeds a second chemical substance concentration.

9. The method of claim 2, wherein, The existing treatment timeline progression characteristics include the chemical concentrations of the group of chemicals entering a range such that the total concentrations of the chemicals are equal to or greater than the target concentration of each of the chemicals in the group without deviation.

10. The method of claim 9, wherein, The deviation between the chemical substance concentration and the target concentration is obtained by root mean square calculation.

11. The method of claim 1, wherein, For at least one of the new treatment intervention nodes in the set of new treatment intervention nodes, the new treatment intervention function is an inhibition function, and the intervention constant associated with the inhibition function is an inhibition constant.

12. The method of claim 1, wherein, For at least one of the new treatment intervention nodes in the set of new treatment intervention nodes, the new treatment intervention function is an acceleration function, and the intervention constant associated with the acceleration function is an acceleration constant.

13. The method of claim 1, wherein, The velocity function of the untreated node or the velocity equation of the untreated node is the Michaelis-Menten equation.

14. The method of claim 1, wherein, The aforementioned set of existing treatment intervention nodes contains only one node.

15. The method of claim 1, wherein, There are no shared nodes between the existing set of treatment intervention nodes and the new set of treatment intervention nodes.

16. The method of claim 1, wherein, Each node shared between the set of existing treatment intervention nodes and the set of new treatment intervention nodes includes an existing treatment intervention constant that is different from the corresponding new treatment intervention constant.

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