Interrogatory cell-based assays for identifying drug-induced toxicity markers
Inventors
Narain, Niven Rajin • Sarangarajan, Rangaprasad • VISHNUDAS, VIVEK K.
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Publication Number
US-11694765-B2
Publication Date
2023-07-04
Expiration Date
Abstract
Described herein is a discovery Platform Technology for analyzing a drug-induced toxicity condition, such as cardiotoxicity via model building.
Core Innovation
The invention provides a computer-implemented method for identifying a modulator of drug-induced toxicity by building two causal relationship networks from datasets derived from cells associated with drug-induced toxicity and from comparison cells. A first data set is obtained from the drug-induced toxicity model and includes measured genomics, lipidomics, proteomics, metabolomics, transcriptomics, and/or SNP data characterizing the cells, together with a second data set representing a measured functional activity or measured cellular response of those cells.
A first causal relationship network is generated using only the first and second data sets, without basing the generation on any known biological relationships other than the first and second data sets. The network construction includes creating a list of network fragments with variables connected by relationships, scoring each fragment probabilistically based on the first data set and/or the second data set, creating an ensemble of trial networks using different subsets of the fragments, and globally optimizing the ensemble by evolving the trial networks in parallel.
In parallel, a computer-implemented second causal relationship network is generated using only the third and fourth data sets, also without basing the generation on any known biological relationships other than those datasets. The method identifies a causal relationship unique in drug-induced toxicity by comparing the first causal relationship network and the second causal relationship network and generating a differential causal relationship network, including relationships present in one network and absent in the other, different directionality, and/or significantly different parameters.
A gene, lipid, protein, metabolite, transcript, or SNP associated with the unique causal relationship is identified as a modulator of drug-induced toxicity.
Claims Coverage
The independent claim centers on four inventive features: paired dataset acquisition, first causal relationship network generation for toxicity cells, second causal relationship network generation for comparison cells, and differential comparison to identify a unique drug-induced toxicity causal relationship and its associated modulator.
Paired dataset acquisition for toxicity and comparison cells
Obtain a first data set from cells associated with drug-induced toxicity with measured genomics, lipidomics, proteomics, metabolomics, transcriptomics, and/or SNP data, together with a second data set representing measured functional activity or measured cellular response; obtain third and fourth data sets from comparison cells with corresponding measured omics/genetic data and measured functional activity or measured cellular response.
Causal relationship network generation without known biological relationships for toxicity cells
Generate a computer-implemented first causal relationship network using only the first data set and the second data set, including creating a list of network fragments, assigning probabilistic scores to fragments, creating an ensemble of trial networks from subsets of fragments, and globally optimizing the ensemble by evolving trial networks in parallel using multiple processors.
Causal relationship network generation without known biological relationships for comparison cells
Generate a computer-implemented second causal relationship network using only the third data set and the fourth data set.
Identification of a unique causal relationship via differential causal relationship network
Identify, from a computer-implemented comparison of the first and second causal relationship networks, 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; the comparison includes generating a differential causal relationship network including relationships present in one network and absent in the other, relationships with different directionality, and/or relationships with significantly different parameters.
The inventive coverage is defined by constructing two causal relationship networks from paired multi-omics/genetic datasets and measured functional activity or cellular response, then identifying a modulator through a unique causal relationship determined by a differential network comparison.
Stated Advantages
Identifies a modulator of drug-induced toxicity by finding a causal relationship unique in drug-induced toxicity.
Provides a differential causal relationship network enabling causal relationships to be distinguished by presence/absence, directionality, or significantly different parameters between toxicity and comparison states.
Supports causal network generation without relying on known biological relationships other than the input datasets.
Documented Applications
Identifying drugs at risk and identifying rescue agents to alleviate toxicity in subjects, including drug-induced cardiotoxicity biomarker panels and a therapeutic/rescue strategy linked to normalization of biomarker expression.
Drug-induced cardiotoxicity biomarker panel use, including TIMP1, PTX3, HSP76, FINC, CYB5, PAI1, IBP7/IGFBP7, 1C17, EDIL3, HMOX1, NUCB1, CS010, and HSPA4.
Therapeutic/rescue strategy application that links to Coenzyme Q10 (CoQ10) for normalizing biomarker expression.
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