Development of a Molecular Fingerprint for Predicting Drug-induced Cholestasis

Cholestasis Classification by logistic regression

Jake Reske, ScitoVation graduate intern in toxicogenomics, will explains a meta-analysis strategy used to develop a preliminary classifier of hepatic cholestasis through public transcriptomic data sets. Cholestasis is a hepatic disease that results from bile acid metabolic dysregulation. We describe our approach to using transcriptomics, statistical modeling, and machine learning to identify molecular signatures of cholestasis. What you’ll learn: 

  • Data-driven approaches to identify molecular signatures of cholestasis
  • A meta-analysis strategy is applied to develop a model-based classifier of cholestasis from public gene expression data
  • Cholestatic potential of a compound can be predicted from in vitro expression of 9 genes with 88% estimated accuracy

Click below to watch the recording.

About our Speaker: 
Jake Reske is a graduate intern in Computational Toxicology at ScitoVation and Ph.D. candidate in Genetics and Genome Sciences at Michigan State University. He is interested in research, development, and application of genomic and high-throughput sequencing technologies, bioinformatics, and data-driven strategies in healthcare and safety spaces.

Cholestasis is a hepatic disease resulting from impaired bile acid metabolism and transport. Select drugs are well-established causal agents of cholestasis. Predicting the potential for a small molecule to cause cholestasis is a priority in the drug safety space due to the potential of these effects to derail development. Transcriptomic and statistical modeling approaches to identify molecular signatures of cholestasis have not yet been implemented on a wide scale. Here, we explain a meta-analysis strategy used to develop a preliminary classifier of hepatic cholestasis through public transcriptomic data sets. Briefly, 199 experimental transcriptome samples, corresponding to either control, cholestatic, or non-cholestatic hepatotoxic labels, were extracted from 8 published studies in human, rat, and mouse and from both RNA-seq and array-based platforms. Experiments included genetically, pharmacologically, surgically, and dietary induced models of disease. Using a uniform computational pipeline, we calculated the relative expression for each gene, across the transcriptome, as well as differential gene expression between experimentally controlled cholestasis and control samples. The resulting standardized transcriptomic data were used to construct a model-based sample classifier of cholestasis through multiple feature selection and machine learning statistical approaches. Our final logistic regression model predicted cholestatic status from expression of 9 genes with 88% accuracy by Monte Carlo cross-validation. These studies support experimental meta-analysis as biological informative for defining cholestasis gene regulatory networks, even with diverse organisms and model systems. Ongoing work seeks to validate performance of the predictive model by independent drug treatment experiments in the HepaRG® human hepatocyte model.

About ScitoVation:
ScitoVation helps clients assess chemical compound safety using innovative science, next-generation technology, and professional expertise. ScitoVation is known for partnership, flexibility, and proven success in its work to develop safer and more effective pharmaceuticals, food ingredients, agricultural chemicals, commodity chemicals and consumer products. A spin-off of the former The CIIT and The Hammer Institutes for Chemical & Drug Safety Sciences, ScitoVation is an industry leader of New Approach Methods (NAMS) for chemical/drug discovery & development in the rapidly evolving global regulatory landscape.