DToxS

The Drug Toxicity Signature Generation Center links cellular drug responses to adverse events

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About

The Drug Toxicity Signature Generation Center is a Systems Pharmacology research center at the Icahn School of Medicine. The proteomics experiments for the center are conducted at the Center for Advanced Proteomics, Rutgers-New Jersey Medical School. The overall goal of DToxS is to use genomic and proteomic high-throughput measurements coupled with medium-throughput experimental measurement of protein states as the basis for computational analysis that integrates network analyses with structural constraints and dynamical models in multiple cell types to identify signatures that predict toxicity induced by individual drugs and mitigation of this toxicity by drug combinations. To anchor the signatures in observable human disease and therapeutics, we leverage the strategy employed in our recent study, in which we searched the FDA-Adverse Event Reporting System Database (FAERS) and found nearly thousands of drug combinations used in humans where a second drug mitigates serious toxicity associated with first drug. We hypothesize that we can use these observations to improve our capability to predict toxicity of drugs and mitigation by drug pairs.

Goals

  1. Experimentally obtain expression patterns of mRNA, proteins and protein states (e.g. phosphorylation) for hundreds of individual drugs and two -drug combinations identified in the FAERS whereby the second drug mitigates serious toxicities induced by the first drug and shown in FAERS to cause one of three serious toxicities-cardiotoxicity; hepatic toxicity or peripheral neuropathy. We use primary or established human cell lines and cell types directly differentiated from human induced pluripotent cells (hIPSC) obtained from normal subjects. For each drug combination and the two constituent drugs we obtain mRNA, proteomic data, and dynamic protein state from multiple cell lines.
  2. Computationally we utilize the experimental data for multi-tier analyses that combines statistical and network models using the human interactome and Gene Ontology with structural model based filtering and dynamical multi-compartment ODE models to obtain sets of relational signatures for each drug combination. For this we combine the perturbagen induced changes in mRNA levels and protein levels to develop networks that will be constrained by structural modeling to identify new off-targets and dynamical models using the protein state data. From the subnetworks we infer pathways involved in toxicity and its mitigation, and the nodes in these pathways will be quantitatively weighted by global sensitivity analysis of the dynamical models to develop signatures grounded in mechanisms and cellular phenotypes inferred by the MEP and HMS LINCS Centers. This integrative approach generate sets of experimentally-observed (EOS), network-inferred (NIS) and dynamical model weighted (wEOS & wNIS) signatures for both drug combinations and individual drugs at signatures per year from our Center’s experiments.
  3. We will extensively share our data and resources. We will provide the raw and processed data with annotations to the BD2K-LINCS Data Coordination and Integration Center for dissemination to the larger community. We will provide our hiPSC-derived cardiomyocytes, hepatocytes and neurons to all other LINCS centers for use in their assay systems and to the biomedical research community. We will develop computational models that integrate our toxicity data with those from other LINCS centers to develop molecular signature based efficacy to toxicity ratios that could be broadly useful in drug development. We will run web-based courses using Coursera for data utilization and development of signature–based research projects and conduct personalized workshops online to enable academic researchers to utilize our signatures to develop research projects that can compete for individual research grant funding.