A novel computational approach for toxicogenomics biomarker discovery in drug development pipeline

In the early stage of drug development process, it is urgent to judge the toxicity effect of some common chemical compounds (CCs) that is not yet well investigated. Biomarker genes (BGs) and dose of CCs can help to draw a deduction about a drug for safety assessment. Classical toxicology method uses large number of samples to extract clinical results which is both time consuming and costly. However, conventional molecular methods can perform to identify only BGs and fail to detect source factor influencing these BGs. The aim of this study is to propose a suitable algorithm that can identify more promising and essential toxicity biomarkers related to some common CCs for safety assessment of new drugs. The glutathione is an effective metabolite of detoxification process in liver. Glutathione depletion analysis is one of the major key research areas in drug development pipeline. In this paper, we studied glutathione depletion analysis of some reported CCs (acetaminophen, methapyrilene and nitrofurazone). We develop an algorithm combining ANOVA and principal component analysis (PCA) using visualization technique to find biomarker genes and associated glutathione depleting CCs and their corresponding doses. There are numerous numbers of genes in the glutathione metabolism pathway regulated as differentially expressed (DE) genes due to the toxic effect of these CCs and proposed algorithm identify only five genes (Mgst2, Gclc, G6pd, Gsr and Srm) that are also foremost genes in the glutathione metabolism pathway. Proposed algorithm states that high dose of all the CCs are responsible for glutathione depletion, nevertheless middle dose of acetaminophen and nitrofurazone also cause glutathione depletion. The proposed algorithm has an additional benefit over the conventional method to discover new chemical entities toxicity. J. bio-sci. 25: 57-66, 2017