Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

Abstract

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both <i>in vivo</i> and <i>in vitro</i> assays. For <i>in vivo</i> TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but <i>in vitro</i> assays, as alternatives, usually demonstrate poor correlation with real <i>in vivo</i> assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from <i>in vivo</i> assays based on gene expression levels and to further utilize <i>in vitro</i> data to better simulate <i>in vivo</i> data is needed. We proposed a strategy based on post-modified non-negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate <i>in vivo</i> data by correcting <i>in vitro</i> data. The similarities between real <i>in vivo</i> data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real <i>in vivo</i> data with <i>in vitro</i> data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using <i>in vitro</i> data to provide potential substitutions for <i>in vivo</i> TGx and to reduce the launch of live-animal tests.