Interogatory cell-based assays for identifying drug-induced toxicity markers

Abstract

Described herein is a discovery Platform Technology for analyzing a drug-induced toxicity condition, such as cardiotoxicity via model building.

1 . A method for identifying a drug that causes or is at risk for causing drug-induced cardiotoxicity, comprising: comparing (i) a level of expression of one or more biomarkers present in a first cell sample obtained prior to the treatment with the drug; with (ii) a level of expression of the one or more biomarkers present in a second cell sample obtained following the treatment with the drug; wherein the one or more biomarkers is selected from the markers listed in table 2; wherein a modulation in the level of expression of the one or more biomarkers in the second sample as compared to the first sample is an indication that the drug causes or is at risk for causing drug-induced cardiotoxicity. 
     
     
         2 . A method for identifying a rescue agent that can reduce or prevent drug-induced cardiotoxicity comprising: (i) determining a normal level of expression of one or more biomarkers present in a first cell sample obtained prior to the treatment with a cardiotoxicity inducing drug; (ii) determining a treated level of expression of the one or more biomarkers present in a second cell sample obtained following the treatment with the cardiotoxicity inducing drug to identify one or more biomarkers with a change of expression in the treated cell sample; (iii) determining the level of expression of the one or more biomarkers with a changed level of expression in the cardiotoxicity inducing drug treated sample present in a third cell sample obtained following the treatment with the cardiotoxicity inducing drug and the rescue agent; and (iv) comparing the level of expression of the one or more biomarkers determined in the third sample with the level of expression of the one or more biomarkers present in the first sample; wherein the one or more biomarkers is selected from the markers listed in table 2; and wherein a normalized level of expression of the one or more biomarkers in the third sample as compared to the first sample is an indication that the rescue agent can reduce or prevent drug-induced cardiotoxicity. 
     
     
         3 . A method for alleviating, reducing or preventing drug-induced cardiotoxicity, comprising administering to a subject a rescue agent identified by the method of  claim 2 , thereby reducing or preventing drug-induced cardiotoxicity in the subject. 
     
     
         4 . The method of  claim 1 , wherein the one or more biomarkers is selected from the group consisting CCDC47, TIMP1, PTX3, HSP76, FINC, CYB5, PAI1, IBP7 (IGFBP7), 1C17, EDIL3, HMOX1, NUCB1, CS010, and HSPA4. 
     
     
         5 - 15 . (canceled) 
     
     
         16 . A method for identifying a modulator of drug-induced toxicity, said method comprising:
 (1) establishing a model for drug-induced toxicity, using cells associated with drug-induced toxicity, to represents a characteristic aspect of drug-induced toxicity;   (2) obtaining a first data set from the model for drug-induced toxicity, wherein the first data set represents one or more of genomics, lipidomics, proteomics, metabolomics, transcriptomics, and single nucleotide polymorphism (SNP) data characterizing the cells associated with drug-induced toxicity;   (3) obtaining a second data set from the model for drug-induced toxicity, wherein the second data set represents a functional activity or a cellular response of the cells associated with drug-induced toxicity;   (4) generating a consensus causal relationship network among the expression levels of the one or more of genomics, lipidomics, proteomics, metabolomics, transcriptomics, and single nucleotide polymorphism (SNP) data and the functional activity or cellular response based solely on the first data set and the second data set using a programmed computing device, wherein the generation of the consensus causal relationship network is not based on any known biological relationships other than the first data set and the second data set;   (5) identifying, from the consensus causal relationship network, a causal relationship unique in drug-induced toxicity, wherein a gene, lipid, protein, metabolite, transcript, or SNP associated with the unique causal relationship is identified as a modulator of drug-induced toxicity.   
     
     
         17 . The method of  claim 16 , wherein second data set representing the functional activity or cellular response of the cells comprises one or more of bioenergetics, cell proliferation, apoptosis, organellar function, a genotype-phenotype association actualized by functional models selected from ATP, ROS, OXPHOS, and Seahorse assays, global enzyme activity, and an effect of global enzyme activity on the enzyme metabolic substrates of cells associated with drug-induced toxicity. 
     
     
         18 . The method of  claim 17 , wherein the global enzyme activity is global kinase activity, and wherein the effect of global enzyme activity on the enzyme metabolic substrates is the phospho proteome. 
     
     
         19 . (canceled) 
     
     
         20 . The method of  claim 16 , wherein step (4) is carried out by an artificial intelligence (AI)-based informatics platform. 
     
     
         21 . (canceled) 
     
     
         22 . The method of  claim 20 , wherein the AI-based informatics platform receives all data input from the first data set and the second data set without applying a statistical cut-off point. 
     
     
         23 . The method of  claim 16 , wherein the consensus causal relationship network established in step (4) is further refined to a simulation causal relationship network, before step (5), by in silico simulation based on input data, to provide a confidence level of prediction for one or more causal relationships within the consensus causal relationship network. 
     
     
         24 . The method of  claim 16 , wherein the unique causal relationship is identified as part of a differential causal relationship network that is uniquely present in cells, and absent in the matching control cells. 
     
     
         25 - 31 . (canceled) 
     
     
         32 . The method of  claim 16 , wherein the drug is Anthracyclines, 5-Fluorouracil, Cisplatin, Trastuzumab, Gemcitabine, Rosiglitazone, Pioglitazone, Troglitazone, Cabergoline, Pergolide, Sumatriptan, Bisphosphonates, or TNF antagonists. 
     
     
         33 - 35 . (canceled) 
     
     
         36 . The method of  claim 2 , wherein the one or more biomarkers is selected from the group consisting CCDC47, TIMP1, PTX3, HSP76, FINC, CYB5, PAI1, IBP7 (IGFBP7), 1C17, EDIL3, HMOX1, NUCB1, CS010, and HSPA4.