Abstract:
The present invention provides systems, methods and software predicting drug efficacy for treating a disorder in a patient, the method including providing a drug score database (DSD) based on pathway manifestation strengths (PMSs) for a plurality of biological pathways associated with the drug in the treatment of the disorder and comparing the pathway manifestation strengths of the plurality of biological pathways of the patient with the drug score database to provide a predictive indication if the patient is a responder or non-responder to the drug to determine whether the drug should be used in treating the patient.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates generally to systems and methods of analysis of gene signaling pathways, and more specifically to systems and methods for improving efficacy and safety of drug combinations in a patient. 
       BACKGROUND OF THE INVENTION 
       [0002]    In the twentieth century, enormous strides were made in combatting infectious diseases, in their detection and drugs to treat them. The major problem in the medical world has thus shifted from treating acute diseases to treating chronic diseases. Over the last few decades, with the advent of genetic engineering, much research and funding has been invested in genomics and gene-based personalized medicine. A need has arisen to develop diagnostic tools for use in the characterization of personalized aspects of chronic diseases and diseases associated with aging. 
         [0003]    Novel methods have been developed for screening for drugs that can minimize the difference between the various cellular or tissue states in a variety of tissues, while also taking into accounting for toxicity and adverse effect of the drug. 
         [0004]    Intracellular signaling pathways (SPs) regulate numerous processes involved in normal and pathological conditions including development, growth, aging and cancer. Many bioinformatic tools have been developed, which analyze SPs. 
         [0005]    The information relating to signaling pathway activation (SPA) can be obtained from the massive proteomic or transcriptomic data. Although the proteomic level may be somewhat closer to the biological function of SPA, the transcriptomic level of studies today is far more feasible in terms of performing experimental tests and analyzing the data. 
         [0006]    US2008254497A provides a method of determining whether tumor cells or tissue is responsive to treatment with an ErbB pathway-specific drug. In accordance with the invention, measurements are made on such cells or tissues to determine values for total ErbB receptors of one or more types, ErbB receptor dimers of one or more types and their phosphorylation states, and/or one or more ErbB signaling pathway effector proteins and their phosphorylation states. These quantities, or a response index based on them, are positively or negatively correlated with cell or tissue responsiveness to treatment with an ErbB pathway-specific drug. In one aspect, such correlations are determined from a model of the mechanism of action of a ErbB pathway-specific drug on an ErbB pathway. Preferably, methods of the invention are implemented by using sets of binding compounds having releasable molecular tags that are specific for multiple components of one or more complexes formed in ErbB pathway activation. After binding, molecular tags are released and separated from the assay mixture for analysis. 
         [0007]    U.S. Pat. No. 8,623,592 discloses methods for treating patients which methods comprise methods for predicting responses of cells, such as tumor cells, to treatment with therapeutic agents. These methods involve measuring, in a sample of the cells, levels of one or more components of a cellular network and then computing a Network Activation State (NAS) or a Network Inhibition State (NIS) for the cells using a computational model of the cellular network. The response of the cells to treatment is then predicted based on the NAS or NIS value that has been computed. The invention also comprises predictive methods for cellular responsiveness in which computation of a NAS or NIS value for the cells (e.g., tumor cells) is combined with use of a statistical classification algorithm. Biomarkers for predicting responsiveness to treatment with a therapeutic agent that targets a component within the ErbB signaling pathway are also provided. 
         [0008]    There thus remains a need for systems and methods, which can predict drug efficacy of drug combinations in a patient. There further remains a need for systems and methods, which can predict drug combination adverse effects. There also remains a need for systems and methods, which can predict and maximize drug combination positive pathway activation. 
       SUMMARY OF THE INVENTION 
       [0009]    It is an object of some aspects of the present invention to provide systems and methods, for improving efficacy and safety of drug combinations in a patient. 
         [0010]    It is a further object of some aspects of the present invention to provide systems and methods, which provide an indication if a drug combination is likely to be effective in a geriatric or aging patient. 
         [0011]    It is a further object of some aspects of the present invention to provide systems and methods, which provide an indication if a drug combination is likely to induce adverse effects in a geriatric or aging patient. 
         [0012]    It is another further object of some aspects of the present invention to provide systems and methods, which provide an indication if a drug combination is likely to induce positive pathway activation in a geriatric or aging patient. 
         [0013]    The present invention provides methods for screening for new drug candidates and for re-purposing the approved drugs and combinations by estimating their ability to suppress pathologically activated or down-regulated biological pathways. 
         [0014]    The present invention also provides methods for screening for drugs that can minimize the difference between the various cellular or tissue states in a variety of tissues, while also taking into accounting for toxicity and adverse effect of the drug. 
         [0015]    The present invention further provides methods for screening for combinations of drugs that can minimize the difference between the various cellular or tissue states in a variety of tissues, while also taking into accounting for toxicity and adverse effect of the drug. 
         [0016]    The present invention further provides methods for screening for new drug candidates and for re-purposing the approved drugs and combinations by estimating their ability to suppress pathologically activated or down-regulated biological pathways, while activating beneficial pathways in the patient. 
         [0017]    The present invention further provides methods for providing an individual with a drug or drug combination, which is personalized to minimize adverse effects and maximize beneficial pathway activation in that specific patient. 
         [0018]    In general, the methods of the present invention are operative to:
       a) Obtain many tissues from a patient and generate tissue-specific gene expression data.   b) Optionally obtain nucleic acids data from at least one blood sample from the patient.   c) Compare gene expression data from the patient with at least one of:
           i. healthy patients gene expression data;   ii. young patients gene expression data; and   iii. results of the previous tissue-specific gene expression analysis from the same patient.   
           d) Compare pathway activation profiles (PAS) on a tissue specific level using methods described in Buzdin et al., 2014.   e) Analyze which pathways are pathological and which pathways are good/beneficial for the cell/the patient.   f) For the pathways that are pathological, an optimal combination of drugs is determined. This may be performed by using drugs with known or predicted molecular targets or effects on transcriptomes, which minimize the difference between the different pathway activation states.   g) The optimal combination of drugs from the previous step is further analyzed to minimize toxicity and adverse effects, such that combinations which work best in that specific patient and have minimal toxicity and adverse effects in most tissues (especially in the long-lived cells) are chosen for the specific patient. When screening for geroprotector (drugs that slow or prevent the pathologic age-related changes or repair accumulated damage) the combination must work well in long-lived cells and tissues like the brain, muscle, stem cells.       
 
         [0029]    There is thus provided according to an embodiment of the present invention, a method for improving drug efficacy and safety for treating a disorder in a patient, the method including;
       a. providing a drug score database (DSD) based on pathway manifestation strengths (PMSs) for a plurality of biological pathways associated with the drug in the treatment of the disorder; and   b. comparing the pathway manifestation strengths of the plurality of biological pathways of the patient with the drug score database to provide a predictive indication if the patient is a responder or non-responder to the drug to determine whether the drug should be used in treating the patient.       
 
         [0032]    Furthermore, according to an embodiment of the present invention, the providing a drug score database (DSD) step includes;
       c. obtaining proliferative bodily samples and healthy bodily samples from patients;   d. applying the drug to the patients; and   e. determining responder and non-responder patients to the drug.       
 
         [0036]    Additionally, according to an embodiment of the present invention, the determining step includes comparing gene expression in selected signaling pathways. 
         [0037]    Moreover, according to an embodiment of the present invention, the selected signaling pathways are associated with the drug. 
         [0038]    Further, according to an embodiment of the present invention, the determining step further includes determining a drug score at least one pathway manifestation strength (PMS) value for each pathway in the responder and the non-responder patients. 
         [0039]    Yet further, according to an embodiment of the present invention, the determining step further includes determining a drug score for the drug based on the at least one pathway manifestation strength (PMS) value. 
         [0040]    Furthermore, according to an embodiment of the present invention, the bodily samples are selected from the group consisting of a tissue sample, a cell culture, an individual single cell, a bodily sample, an organism sample and a microorganism sample. 
         [0041]    Notably, according to an embodiment of the present invention the biological pathways are signaling pathways. 
         [0042]    Furthermore, according to an embodiment of the present invention, the biological pathways are metabolic pathways. 
         [0043]    Additionally, according to an embodiment of the present invention, the gene expression includes quantifying expression of plurality of gene products. 
         [0044]    Further, according to an embodiment of the present invention, the gene products include a set of at least five gene products. 
         [0045]    Furthermore, according to an embodiment of the present invention, the method further includes;
       d. calculating a pathway activation strength (PAS), indicative of the pathway activation of each of the biological pathways.       
 
         [0047]    Furthermore, according to an embodiment of the present invention, the calculating step includes adding concentrations of the set of the at least five gene products of the sample and comparing to a same set in the at least one control sample. 
         [0048]    Moreover, according to an embodiment of the present invention, the gene products provide at least one function in the biological pathway. 
         [0049]    Additionally, according to an embodiment of the present invention, the at least one function includes an activation function and a suppressor function. 
         [0050]    Further, according to an embodiment of the present invention, the at least one function includes an up-regulating function and a down-regulating function. 
         [0051]    Yet further, according to an embodiment of the present invention, the determining step includes at least one of profiling gene expression, RNA profiling, RNA sequencing, DNA profiling, DNA sequencing, protein profiling, amino acid sequencing, at least one immunochemical methodology, a mass spectrometry analysis, a microarray technology, a quantitative PCR methodology and combinations thereof. 
         [0052]    Furthermore, according to an embodiment of the present invention, the method is quantitative. Additionally or alternatively, the method is qualitative. 
         [0053]    Further, according to an embodiment of the present invention, A the patients are sick. 
         [0054]    Furthermore, according to an embodiment of the present invention, the sick subject suffers from a proliferative disease or disorder. 
         [0055]    Importantly, according to an embodiment of the present invention, the proliferative disease or disorder is cancer. 
         [0056]    Furthermore, according to an embodiment of the present invention, the proliferative disease or disorder is colon cancer. 
         [0057]    Additionally, according to an embodiment of the present invention, the drug is a monoclonal antibody (mAb). 
         [0058]    Furthermore, according to an embodiment of the present invention, the monoclonal antibody (mAb) is Bevacizumab. 
         [0059]    Moreover, according to an embodiment of the present invention, the pathway is selected from the group consisting of a Caspase Cascade pathway; a CREB pathway; a GPCR pathway; a CSK3 pathway; an HIF1Alpha pathway; an HIF1Alpha Pathway VEGF pathway; an ILK pathway; an IP3 pathway; a mAb pathway; a PPAR pathway; a VEGF pathway; and combinations thereof. 
         [0060]    There is thus provided according to an embodiment of the present invention, a computer software product, the product configured for predicting drug efficacy for treating a disorder in a patient, the product including a computer-readable medium in which program instructions are stored, which instructions, when read by a computer, cause the computer to;
       a. provide a drug score database (DSD) based on pathway manifestation strengths (PMSs) for a plurality of biological pathways associated with the drug in the treatment of the disorder; and   b. compare the pathway manifestation strengths of the plurality of biological pathways of the patient with the drug score database to provide a predictive indication if the patient is a responder or non-responder to the drug to determine whether the drug should be used in treating the patient.       
 
         [0063]    There is thus provided according to another embodiment of the present invention, a system for predicting drug efficacy for treating a disorder in a patient the system including;
       a. a processor adapted to activate a computer-readable medium in which program instructions are stored, which instructions, when read by a computer, cause the processor to;
           i. provide a drug score database (DSD) based on pathway manifestation strengths (PMSs) for a plurality of biological pathways associated with the drug in the treatment of the disorder; and   ii. compare the pathway manifestation strengths of the plurality of biological pathways of the patient with the drug score database to provide a predictive indication if the patient is a responder or non-responder to the drug to determine whether the drug should be used in treating the patient;   
           b. a memory for storing the drug score database (DSD); and   c. a display for displaying data associated with the predictive indication of the patient.       
 
         [0069]    Furthermore, according to an embodiment of the present invention, the drug, previously used for a first indication is used for a new second indication. 
         [0070]    Importantly, according to an embodiment of the present invention, the drug is at least one of repurposed and repositioned. 
         [0071]    The present invention will be more fully understood from the following detailed description of the preferred embodiments thereof, taken together with the drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0072]    The invention will now be described in connection with certain preferred embodiments with reference to the following illustrative figures so that it may be more fully understood. 
           [0073]    With specific reference now to the figures in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of the preferred embodiments of the present invention only and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the invention. In this regard, no attempt is made to show structural details of the invention in more detail than is necessary for a fundamental understanding of the invention, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice. 
           [0074]    In the drawings: 
           [0075]      FIG. 1A  is a simplified schematic illustration of a system for improving efficacy and safety of drug or drug combinations in a patient, in accordance with an embodiment of the present invention; 
           [0076]      FIG. 1B  is a schematic showing further details of drug profile database and transcriptomic database of  FIG. 1A , in accordance with an embodiment of the present invention; 
           [0077]      FIGS. 2A-2D  are simplified schematic steps in a method for improving efficacy and safety of a drug or drug combination in a patient, in accordance with an embodiment of the present invention; and 
           [0078]      FIGS. 3A-3B  are simplified diagrams of effects of a drug on up-regulating and down-regulating signaling and metabolic pathways, respectively, in accordance with embodiments of the present invention. 
       
    
    
       [0079]    In all the figures similar reference numerals identify similar parts. 
       DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
       [0080]    In the detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that these are specific embodiments and that the present invention may be practiced also in different ways that embody the characterizing features of the invention as described and claimed herein. 
         [0081]    Reference is now made to  FIG. 1A , which is a simplified schematic illustration of a system for improving efficacy and safety of drug combinations in a patient, in accordance with an embodiment of the present invention. 
         [0082]    System  100  typically includes a server utility  110 , which may include one or a plurality of servers and one or more control computer terminals  112  for programming, trouble-shooting servicing and other functions. Server utility  110  includes a system engine  111  and database,  191 . Database  191  comprises a user profile database  125 , a pathway cloud database  123  and a drug profile database  180 . 
         [0083]    Depending on the capabilities of a mobile device, system  100  may also be incorporated on a mobile device that synchronizes data with a cloud-based platform. 
         [0084]    The drug profile database comprises data relating to a large number of drugs for controlling and treating ageing processes. For each type of drug, the dosage values, pharmo-kinetic data and profile, pharmodynamic data and profiles are included. 
         [0085]    The drug profile database further comprises data of drug combinations, including dosage values pharmo-kinetic data and profile, pharmodynamic data and profiles. 
         [0086]    A medical professional, research personnel or patient assistant/helper/carer  141  is connected via his/her mobile device  140  to server utility  110 . The patient, subject or child  143  is also connected via his/her mobile device  142  to server utility  110 . In some cases, the subject may be a mammalian subject, such as a mouse, rat, hamster, monkey, cat or dog, used in research and development. In other cases, the subject may be a vertebrate subject, such as a frog, fish or lizard. The patient or child is monitored using a sample analyzer  199 . Sample analyzer  199 , may be associated with one or more computers  130  and with server utility  110 . Computer  130  and/or sample analyzer  199  may have software therein for predicting drug efficacy in a patient, as will be described in further details hereinbelow. 
         [0087]    Typically, gene expression data  123  ( FIG. 1 ), generated by the software of the present invention, is stored locally and/or in cloud  120  and/or on server  110 . 
         [0088]    The sample analyzer may be constructed and configured to receive a solid sample  190 , such as a biopsy, a hair sample or other solid sample from patient  143 , and/or a liquid sample  195 , such as, but not limited to, urine, blood or saliva sample. The sample may be extracted by any suitable means, such as by a syringe  197 . 
         [0089]    The patient, subject or child  143  may be provided with a drug (not shown) by health professional/research/doctor  141 . 
         [0090]    System  100  further comprises an outputting module  185  for outputting data from the database via tweets, emails, voicemails and computer-generated spoken messages to the user, carers or doctors, via the Internet  120  (constituting a computer network), SMS, Instant Messaging, Fax through link  122 . 
         [0091]    Users, patients, health care professionals or customers  141 ,  143  may communicate with server  110  through a plurality of user computers  130 ,  131 , or user devices  140 ,  142 , which may be mainframe computers with terminals that permit individual to access a network, personal computers, portable computers, small hand-held computers and other, that are linked to the Internet  120  through a plurality of links  124 . The Internet link of each of computers  130 ,  131 , may be direct through a landline or a wireless line, or may be indirect, for example through an intranet that is linked through an appropriate server to the Internet. System  100  may also operate through communication protocols between computers over the Internet which technique is known to a person versed in the art and will not be elaborated herein. 
         [0092]    Users may also communicate with the system through portable communication devices such as mobile phones  140 , communicating with the Internet through a corresponding communication system (e.g. cellular system)  150  connectable to the Internet through link  152 . As will readily be appreciated, this is a very simplified description, although the details should be clear to the artisan. Also, it should be noted that the invention is not limited to the user-associated communication devices—computers and portable and mobile communication devices—and a variety of others such as an interactive television system may also be used. 
         [0093]    The system  100  also typically includes at least one call and/or user support and/or tele-health center  160 . The service center typically provides both on-line and off-line services to users. The server system  110  is configured according to the invention to carry out the methods of the present invention described herein. 
         [0094]    It should be understood that many variations to system  100  are envisaged, and this embodiment should not be construed as limiting. For example, a facsimile system or a phone device (wired telephone or mobile phone) may be designed to be connectable to a computer network (e.g. the Internet). Interactive televisions may be used for inputting and receiving data from the Internet. Future devices for communications via new communication networks are also deemed to be part of system  100 . Memories may be on a physical server and/or in a virtual cloud. 
         [0095]    A mobile computing device may also embody a non-synced or offline copy of memories, copies of pathway cloud data, user profiles database, drug profiles database and execute the system, engine locally. 
         [0096]      FIG. 1B  is a schematic  120  showing further details of drug profile database  180  and transcriptomic database  170  of  FIG. 1A , in accordance with an embodiment of the present invention. 
         [0097]    Drug profile database  180  comprises: 
         [0098]    1. For many cell types, transcriptome-drug-transcriptome effects; 
         [0099]    2. Drug toxicity data; 
         [0100]    3. Drug adverse effects 
         [0101]    4. Drug CNR tables calculated as described in reference (1); 
         [0102]    5. Drug pathway activation strength (PAS) effects. 
         [0103]    Database of Transcriptomic Datasets  170  comprises: 
         [0104]    1. Healthy norms for every tissue; 
         [0105]    2. Transcriptomes of biopsies from age-related diseases; 
         [0106]    3. Transcriptomes of biopsies from young patients; 
         [0107]    4. Transcriptomes of biopsies from old patients; and 
         [0108]    5. Calculated PAS values from 1 &amp; 2 above and 3 &amp; 4 above. 
         [0109]    Reference is now made to  FIGS. 2A-2D  are respective simplified schematic steps  200 ,  220 ,  230  and  240  in a method for improving efficacy and safety of a drug or drug combination in a patient, in accordance with an embodiment of the present invention. 
         [0110]    Step  1  ( FIG. 2A ): For every available tissue type construct a set of pathways considered to be pathologic. Compare healthy/normal cell tissue transcriptomes  202  with disease cell/tissue transcriptomes  210  to generate pathway activation strengths  206  for a first set of pathways. 
         [0111]    Step  2  ( FIG. 2B ): Calculate the net effect of the combination on PAS by utilizing known PAS effects 
         [0112]    For each drug, such as drug A  222 , drug B  224 , up to drug X,  226  calculate the PAS effects in cell/tissues known for adverse effects and/or toxicity, the combination of drugs must minimize the signaling disturbance by minimizing PAS  208  of sets of pathological pathways without significant effects of beneficial and protective pathways. Compare young patient&#39;s cell/tissue transcriptomes  204  with old patient&#39;s disease cell/tissue transcriptomes  212  to generate pathway activation strengths  208  for a second set of pathways. 
         [0113]    Step  3  ( FIG. 2C ): Evaluate the net toxicity and adverse effects for each prospective combination. 
         [0114]    Step  4  ( FIG. 2D ): Rank the combinations in a ranking step  242  by the ability to minimize PAS differences, adverse effects and toxicity. 
         [0115]    The steps in the method of the present invention may optionally include any one or more of the following: For many tissues from a patient, tissue-specific gene expression data are determined N nucleic acids are extracted from blood.
       1. Gene expression data with (three cases): a. healthy patients b. young patients c. results of the previous tissue-specific gene expression analysis from the same patient are compared.   2. The pathway activation profiles (PAS) on a tissue specific level using methods described in (Buzdin et al., Front. Mol. Biosci. 1:8. 2014, Borisov et al., 2014).   3. It is determined which pathways are pathologic and what pathways are good for the cell.   4. For the pathways that are pathologic, the optimal combination of drugs are determined (drugs with known or predicted molecular targets or effects on transcriptomes), which minimize the difference between the different pathway activation states.   5. Minimize, where possible, toxicity and adverse effects.   6. The drug combinations, which work best and have least toxic and adverse effects in most tissues (especially in the long-lived cells) are determined as being the best for the individual patient.       
 
         [0122]    When screening for gero-protector (drugs that slow or prevent the pathologic age-related changes or repair accumulated damage) the combination must work well in long-lived cells and tissues like the brain, muscle, stem cells. Some non-limiting examples of these exact algorithms for these combinations are described below. 
         [0123]    1. Drug Scoring for their Ability to Compensate the Pathological Changes in the Signaling Pathways 
         [0124]    The following method is used for predictive assessment of drug efficiency for individual patients based on their ability to compensate the pathological changes in the plethora of signaling pathways (signalome). For example, for the inhibitor drugs the following scheme was proposed. 
         [0000]    
       
         
           
             
               
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                         ; 
                         
                           protein 
                            
                           
                               
                           
                            
                           n 
                            
                           
                               
                           
                            
                           is 
                            
                           
                               
                           
                            
                           more 
                            
                           
                               
                           
                            
                           likely 
                            
                           
                               
                           
                            
                           a 
                            
                           
                               
                           
                            
                           signal 
                            
                           
                               
                           
                            
                           activator 
                            
                           
                               
                           
                            
                           in 
                            
                           
                               
                           
                            
                           a 
                            
                           
                               
                           
                            
                           pathway 
                            
                           
                               
                           
                            
                           p 
                         
                       
                     
                   
                 
                 
                   
                     
                       1 
                       ; 
                       
                         protein 
                          
                         
                             
                         
                          
                         n 
                          
                         
                             
                         
                          
                         is 
                          
                         
                             
                         
                          
                         a 
                          
                         
                             
                         
                          
                         signal 
                          
                         
                             
                         
                          
                         activator 
                          
                         
                             
                         
                          
                         in 
                          
                         
                             
                         
                          
                         a 
                          
                         
                             
                         
                          
                         pathway 
                          
                         
                             
                         
                          
                         p 
                       
                     
                   
                 
               
             
           
         
       
     
         [0127]    AMCF (activation-to-mitosis conversion factor) is a discrete flag 
         [0000]    
       
         
           
             
               AMCF 
               p 
             
              
             
               { 
               
                 
                   
                     
                       
                         - 
                         1 
                       
                       , 
                       
                           
                       
                        
                       
                         pathway 
                          
                         
                             
                         
                          
                         activation 
                          
                         
                             
                         
                          
                         is 
                          
                         
                             
                         
                          
                         anti 
                          
                         
                           - 
                         
                          
                         mitotic 
                       
                     
                   
                 
                 
                   
                     
                       1 
                       , 
                       
                         pathways 
                          
                         
                             
                         
                          
                         activation 
                          
                         
                             
                         
                          
                         is 
                          
                         
                             
                         
                          
                         pro 
                          
                         
                           - 
                         
                          
                         mitotic 
                       
                     
                   
                 
               
             
           
         
       
     
         [0128]    The action of a (protein activity inhibitor) drug was described using the discrete drug-target index: 
         [0000]    
       
         
           
             
               DTI 
               dt 
             
             = 
             
               { 
               
                 
                   
                     
                       0 
                       , 
                       
                           
                       
                        
                       
                         drug 
                          
                         
                             
                         
                          
                         d 
                          
                         
                             
                         
                          
                         inhibits 
                          
                         
                             
                         
                          
                         protein 
                          
                         
                             
                         
                          
                         t 
                       
                     
                   
                 
                 
                   
                     
                       1 
                       , 
                       
                         drug 
                          
                         
                             
                         
                          
                         d 
                          
                         
                             
                         
                          
                         does 
                          
                         
                             
                         
                          
                         not 
                          
                         
                             
                         
                          
                         inhibit 
                          
                         
                             
                         
                          
                         protein 
                          
                         
                             
                         
                          
                         t 
                       
                     
                   
                 
               
             
           
         
       
     
         [0129]    The discrete flag of node involvement index is 
         [0000]    
       
         
           
             
               NII 
               tp 
             
             = 
             
               { 
               
                 
                   
                     
                       0 
                       , 
                       
                         pathaway 
                          
                         
                             
                         
                          
                         p 
                          
                         
                             
                         
                          
                         does 
                          
                         
                             
                         
                          
                         not 
                          
                         
                             
                         
                          
                         contain 
                          
                         
                             
                         
                          
                         the 
                          
                         
                             
                         
                          
                         protein 
                          
                         
                             
                         
                          
                         t 
                       
                     
                   
                 
                 
                   
                     
                       1 
                       , 
                       
                         pathaway 
                          
                         
                             
                         
                          
                         p 
                          
                         
                             
                         
                          
                         contains 
                          
                         
                             
                         
                          
                         the 
                          
                         
                             
                         
                          
                         protein 
                          
                         
                             
                         
                          
                         t 
                       
                     
                   
                 
               
             
           
         
       
     
         [0130]    For the activator drugs the DS 1  function should be used with the opposite (“minus”) sign before the right-hand part. 
         [0131]    Although this approach was previously proposed for the targeted drugs in oncology: monoclonal antibodies (a.k.a. mAbs), kinase inhibitors (a.k.a. nibs) etc., it can be extended to other fields of medicine, such as, e.g., geriatrics and used for scoring of geroprotectors according to their ability to restore the juvenile state of signaling pathways in the critical (bone marrow, epithelial, osteoblast etc.) cells of a given aged person. 
         [0132]    2. Possible Modifications of the Formula for Drug Scoring 
         [0133]    The formula for the DS 1  value contains three discrete flags, AMCF, NII, and ARR, which may be replaced with continuous analogs to reproduce the drug action more precisely. 
         [0134]    First, we can substitute the AMCF flags with the continuous weight factors for take into account the relative importance of different pathways in the mechanism of drug action, 
         [0000]    
       
         
           
             
               DS 
                
               
                   
               
                
               
                 3 
                 d 
               
             
             = 
             
               
                 ∑ 
                 t 
                 
                     
                 
               
                
               
                 
                   DTI 
                   dt 
                 
                  
                 
                   
                     ∑ 
                     p 
                   
                    
                   
                       
                   
                    
                   
                     
                       NII 
                       tp 
                     
                     · 
                     
                       PAS 
                       p 
                     
                     · 
                     
                       
                         w 
                         pd 
                       
                       . 
                     
                   
                 
               
             
           
         
       
     
         [0000]    The weighting coefficients, w pd , can be chosen, e.g., using the least square (or any other) fit procedure to minimize the error function, 
         [0000]    
       
         
           
             
               
                 S 
                 d 
               
               = 
               
                 
                   ∑ 
                   c 
                 
                  
                 
                     
                 
                  
                 
                   
                     ( 
                     
                       
                         DS 
                          
                         
                             
                         
                          
                         
                           3 
                           dc 
                         
                       
                       - 
                       
                         ClinFlag 
                         dc 
                       
                     
                     ) 
                   
                   2 
                 
               
             
             , 
           
         
       
     
         [0000]    where ClinFlag dc  is the discrete value that is equal to 100% and zero, respectively, if the drug application has led to the observed drug d effect in the case c, which may be either an in vitro experiment or clinical observation.
 
Second, if each drug affects the expression level of each gene in its own way, then the drug-target index, DTI, should be substituted with a continuous value, DTA dt =1 g(DTR dt ). Let the DTA, drug-target action, be a value that reflects the changes of gene expression levels where the drug-target ratio DTR dt  is the ratio of measured expression levels for the target gene t after and before the application of the drug d.
 
         [0135]    Then, the following formula for the drug score may be suggested: 
         [0000]    
       
         
           
             
               DS 
                
               
                   
               
                
               
                 4 
                 d 
               
             
             = 
             
               
                 
                   ∑ 
                   t 
                 
                  
                 
                     
                 
                  
                 
                   
                     DTA 
                     dt 
                   
                    
                   
                     
                       ∑ 
                       p 
                     
                      
                     
                         
                     
                      
                     
                       
                         NII 
                         tp 
                       
                       · 
                       
                         AMCF 
                         p 
                       
                       · 
                       
                         PAS 
                         p 
                       
                     
                   
                 
               
               = 
               
                 - 
                 
                   
                     ∑ 
                     t 
                   
                    
                   
                       
                   
                    
                   
                     1 
                      
                     
                       g 
                        
                       
                         ( 
                         
                           DTR 
                           dt 
                         
                         ) 
                       
                     
                      
                     
                       
                         ∑ 
                         p 
                         
                             
                         
                       
                        
                       
                           
                       
                        
                       
                         
                           NII 
                           tp 
                         
                         · 
                         
                           AMCF 
                           p 
                         
                         · 
                         
                           
                             ∑ 
                             n 
                           
                            
                           
                               
                           
                            
                           
                             
                               
                                 ARR 
                                 np 
                               
                               · 
                               
                                 BTIF 
                                 n 
                               
                               · 
                               1 
                             
                              
                             
                               g 
                               ( 
                               
                                 CNR 
                                 n 
                               
                               ) 
                             
                           
                         
                       
                     
                   
                 
               
             
           
         
       
     
         [0136]    Since this summation is performed twice for the logarithmic ratios, the value above is in fact the negative covariance between drug action and pathological changes in the transcriptome level that takes into account the pathway activation/suppression and their cell proliferation consequences. 
         [0137]    Third, we can replace the discrete ARR flags with the continuous functions of relative importance of each gene/gene product for the pathway activation. This leads to the following assessment, 
         [0000]    
       
         
           
             
               DS 
                
               
                   
               
                
               
                 5 
                 d 
               
             
             = 
             
               
                 ∑ 
                 t 
               
                
               
                   
               
                
               
                 
                   DTI 
                   dt 
                 
                  
                 
                   
                     ∑ 
                     p 
                   
                    
                   
                       
                   
                    
                   
                     
                       NII 
                       tp 
                     
                     · 
                     
                       AMCF 
                       p 
                     
                     · 
                     
                       
                         ∑ 
                         n 
                         
                             
                         
                       
                        
                       
                           
                       
                        
                       
                         
                           
                             w 
                             np 
                           
                           · 
                           
                             BTIF 
                             n 
                           
                           · 
                           1 
                         
                          
                         
                             
                         
                          
                         
                           g 
                           ( 
                           
                             CNR 
                             n 
                           
                           ) 
                         
                       
                     
                   
                 
               
             
           
         
       
     
         [0138]    As far as we have mentioned in (Buzdin, 2014), two ways for determination of w np  functions may be suggested. The former operates with the concept of sensitivity of the ODE system on the free parameters (Kholodenko, 2003), which is generally applied to kinetic constants (such as the dissociation constant, the Michaelis-Menten constants etc.), but may also be used for the total concentrations of certain proteins in the kinetic model of a pathway. The latter way to calculate the importance function for the genes/proteins in a pathway is related to the stiffness/sloppiness analysis (Daniels, 2008) for the effector activation upon total protein concentrations. The eigenvector components of the Hesse matrix (that is constructed for the quadratic difference function between the calculated time-courses for the activation of the pathway effector proteins and measured, using, e.g., the Western blot technique, concentrations of the activated effectors) along the stiffest direction, may be used for assessment of the w np  value. 
         [0139]    3. Assessment of Joint Action of Multiple Drugs 
         [0140]    The combined treatment using several drugs simultaneously leads to the problem of taking into account the synergistic action of different drugs. 
         [0141]    Previously (Buzdin, 2014) it has been the following simplified method has been proposed and validated, which assumes the multiplicative dependence of overall outcome of pathway activation upon the expression levels of each gene in signaling pathways. The additive functions like multiple drug scores (DS 1 -DS 5 ) emerge after taking the logarithm from this multiplicative value. 
         [0142]    If assume this simplified hypothesis for the description of joint drug action (say, drugs d 1  and d 2 ) as well, then the overall reaction of a signaling pathway that is caused by these drugs, should also multiplicative, and, consequently, the drug score should be additive: 
         [0000]      DS( d   1   +d   2 )=DS( d   1 )+DS( d   1 ). 
         [0143]    The presence of synergistic/anti-synergistic action of the drugs may be taken into account using the following cross-talk item Δ: 
         [0000]      DS( d   1   +d   2 )=DS( d   1 )+DS( d   2 )+Δ=DS( d   1 )+DS( d   2 )+ C   syn ·sign(DS( d   1 )·DS( d   2 ))√{square root over (|DS( d   1 )·DS( d   2 )|)},
 
         [0000]    where C syn  is the synergistic constant that is equal, e.q. to 3 or 2 for strong drug synergism, 1 for moderate synergism, 0 for independent drug action, −1 for moderate anti-synergism and −2 or −3 for strong anti-synergism. 
         [0144]    4. Assessment of Drug Toxicity 
         [0145]    The toxicity of a drug can be evaluated using the following method 
         [0000]    
       
         
           
             
               
                 TOX 
                  
                 
                   ( 
                   d 
                   ) 
                 
               
               = 
               
                 
                   
                     ∑ 
                     r 
                   
                    
                   
                       
                   
                    
                   
                     
                       TOX 
                       r 
                     
                      
                     
                       ( 
                       d 
                       ) 
                     
                   
                 
                 = 
                 
                   
                     ∑ 
                     r 
                   
                    
                   
                       
                   
                    
                   
                     
                       ARD 
                       r 
                     
                     
                       1 
                        
                       
                           
                       
                        
                       g 
                        
                       
                         
                           MTC 
                            
                           
                               
                           
                            
                           
                             50 
                             r 
                           
                            
                           
                             ( 
                             d 
                             ) 
                           
                         
                         
                           CAC 
                            
                           
                               
                           
                            
                           
                             ( 
                             d 
                             ) 
                           
                         
                       
                     
                   
                 
               
             
             , 
           
         
       
     
         [0000]    when MTC50 r (d) is the maximally tolerable concentrations of a drug d according to the adverse reaction r (that causes this adverse reaction over the 50% of population),CAC (d) is the clinically acting concentration of the same drug, and ARD d  is the expert-assessed adverse reaction danger that may be equal to 1 for relatively tolerable and reversible reactions, and, e.g., 100, for instantly fatal consequences. 
         [0146]    Similarly to the assessment of joint drug action, the joint toxicity of two drugs according to the adverse reaction r, can be represented as follows, 
         [0000]        TOX   r ( d   1   +d   2 )=max(0; TOX   r ( d   1 )+ TOX   r (d 2 )+ C   syn ·√{square root over ( TOX   r ( d   1 )· TOX   r ( d   2 ))}),
 
         [0000]    when the C syn  factor depends of both reaction type r and two drugs d 1  and d 2 . 
         [0147]    Taking into account the toxicity for the drug scoring may result in the following function,
       DS6 d =w action ·DS(1÷5) d −w tox ·TOX d . The weighting factors, w action  and w tox , may be defined only a posteriori (e.g., after a least squares fitting during the comparison of a DS 6  values and clinical overall outcome of application of a drug d).       
 
       EXAMPLES 
     Example 1 
       [0149]    A set of commercially available drugs for treating a specific disease are chosen. For example, FDA approved drugs for treating a specific cancer, say kidney cancer. For each of the drugs, an in silico analysis is performed to evaluate several characteristics of each drug in database  180  ( FIG. 1A ):
       a. mechanism of action (signaling and metabolic pathways significantly dysregulated by a given drug);   b. drug resistance (a list of pathways correlated with drug resistance in cell lines);   c. biomarker identification (when gene expression dataset for certain clinical trial was available we determined what pathways could serve as biomarkers); and   d. drug repurposing (a prioritized list of different types of cancer the drug might be effective against).       
 
         [0154]    Reference is now made to  FIGS. 3A-3B , which are simplified diagrams of effects of a drug on up-regulating and down-regulating signaling pathways  300  and metabolic pathways  350 , respectively, in accordance with embodiments of the present invention A drug—say a tyrosine kinase inhibitor “DRUG A”,  308  is chosen.
       1. The signaling pathways  300  associated the target of the drug (say tyrosine kinase)” are termed herein “ON target”  302  and those not associated with the drug are termed herein “OFF target”  314  ( FIG. 3A ). Drug A,  308  is found to induce up-regulation  310  and down-regulation  312  of different pathways. For example, Drug A up-reglates a set  304  of on-target pathways and a down-regulates set  306  of on-target pathways. For example, Drug A further up-reglates a set  316  of off-target pathways and a down-regulates set  318  of off-target pathways.   2. Metabolic pathways  350  are mapped ( FIG. 3B ) for DRUG A.   3. Drug A,  358  up-reglates a set  352  of metabolict pathways and a down-regulates set  354  of metabolic pathways.   4. Toxicity data are mapped for DRUG A using data from database  180  ( FIG. 1B ) and  FIGS. 2B and 2C .   1) 5) Thereafter, positively and negatively correlated with drug resistance pathways are ranked for DRUG A, as described herein. Steps 1-5 are repeated for drugs B, C, D to . . . Z.   2) The drugs are ranked according to their drug score and the one with the highest score is suggested for use in treating the kidney cancer for a population of patients.   3) If drug A is the current drug used for treating kidney cancer, but it is found that drug B has a much higher drug score than drug A, then it is recommended that drug B is used to treat the kidney cancer.       
 
         [0162]    It is to be understood that the invention is not limited in its application to the details set forth in the description contained herein or illustrated in the drawings. The invention is capable of other embodiments and of being practiced and carried out in various ways. Those skilled in the art will readily appreciate that various modifications and changes can be applied to the embodiments of the invention as hereinbefore described without departing from its scope, defined in and by the appended claims. 
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