ToxCompl completion of the DrugMatrix toxicogenomics database: an integrated resource for hypothesis generation.

The DrugMatrix database contains systematically generated toxicogenomics data from short-term in vivo studies for over 600 chemicals. However, most potential endpoints are missing due to a lack of experimental measurements. Therefore, we leveraged matrix factorization and machine learning methods to predict the missing values, which includes gene expression across 8 tissues on 2 expression platforms along with paired clinical chemistry, hematology, and histopathology. We propose a method, ToxCompl, that applies systematic hybrid sampling guided by Bayesian optimization in conjunction with low-rank matrix factorization to predict the missing values. In-depth validation of the ToxCompl predicted data from machine learning, biological, and toxicological perspectives shows that the predicted differential gene expression aligns well with what would be anticipated. This includes examining the connectivity pattern of predicted gene expression responses, characterizing molecular pathway-level responses from sets of differentially expressed genes, evaluating known transcriptional biomarkers of tissue toxicity, and characterizing predicted apical endpoints. For example, we identified kidney toxicants using the transcriptional biomarker Havcr1. All measured and predicted DrugMatrix data (i.e., gene expression, clinical chemistry, hematology, and histopathology) are available to the public (https://rstudio.niehs.nih.gov/toxcompl/). Notably, predicted clinical chemistry of subtle effects and histopathological prediction are 2 areas we will continue to improve. The main advantage of the ToxCompl approach is that it drastically extends the toxicogenomic landscape into many data-poor tissues in the absence of acquiring additional experimental data, thereby allowing researchers to formulate mechanistic hypotheses about effects in tissues that have been underrepresented in the literature.