Predicting DILI liability by transcription factor profiling

PROJECT SUMMARYDrug induced liver injuryDILIis the main reason for drug attrition during development and a leading cause of post market drug withdrawalHerewe propose a systems biology approach to detect drug candidates with DILI liabilities at early stages of developmentThis approach is based on the assessment of drug induced perturbations of multiple signal transduction pathways in hepatocytic cellsFor thatwe use Attagene multiplexed reporter technologythe FACTORIALthat enables quantitative assessment of the activity of multiple transcription factorsTFsproteins that regulate gene transcriptionThe FACTORIAL has been extensively validated by screening thousands of environmental toxicants for the U SEPA ToxCast projectThrough this effortwe discovered specificTF signaturesfor many classes of biological activitiesIn preliminary studieswe evaluated TF signatures for a small panel of drugs with DILI liabilities and found a common patternWithin certain concentration rangedrugsandaposTF signatures reflected their primary activitiesHoweverat some inflection pointsCOFFthese signatures transformed into distinctoff targetsignaturesWe found common off target TF signatures shared by different classes of DILI drugs and identified underlying mechanisms for some of those common TF signaturesincluding mitochondrial malfunctionDNA damageand lipid peroxidationBased on these findingswe developed a simple model wherein DILI mechanism is inferred from the off target TF signaturewhilst DILI probability is defined by the CMAX COFF ratiowhere CMAX is the maximal therapeutic drug concentrationMost remarkablyour data suggest the feasibility of using this model to predict idiosyncratic DILIthe task unattainable with existing technologiesThe overarching objective of this proposal is to establish TF profiling as a tool for DILI predictionTo do thatwe will obtain TF signatures of a collection ofdrugs classified by the FDA as DILI and no DILI concern drugsThese signatures will be used as a training setWe will identify clusters of common DILI specific off target TF signatures and annotate the underlying biological activitiesusing ATTAGENE DB of reference TF signaturesTo validate the off target TF signatures as potential bioactivity markerswe will compare these with data by functional assays for known DILI mechanismsFurthermorewe will determine the predictive value for the CMAX COFF parameter for stratifying DILI from non DILI drugsThe predictive values of obtained DILI specific TF signatures and the CMAX COFF parameter will be optimized using a validation set of exhaustively characterized in functional assays drug candidatesprovided by pharmaceutical industry and DILI sim consortia PROJECT NARRATIVE Drug induced liver injuryDILIis the main reason for drug attrition during development and a leading cause of post market drug withdrawalHerewe propose a novel approach to early prediction of DILIUsing proprietary Attagene technologywe describe cell response to a drug by aTF signaturecharacterizing perturbations of multiple gene regulatory pathwaysIn preliminary studieswe evaluated a panel of drugs with DILI liabilities in hepatocytic cells and found that TF signatures provide straightforward information about the probability and mechanisms of DILIIn proposed research we expand the preliminary studies to establish TF profiling approach as a validated tool for DILI risk prediction