An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation.

Drug toxicity evaluation is an essential process of drug development as it is reportedly responsible for the attrition of approximately 30% of drug candidates. The rapid increase in the number and types of large toxicology data sets together with the advances in computational methods may be used to improve many steps in drug safety evaluation. The development of in silico models to screen and understand mechanisms of drug toxicity may be particularly beneficial in the early stages of drug development where early toxicity assessment can most reduce expenses and labor time. To facilitate this, machine learning methods have been employed to evaluate drug toxicity but are often limited by small and less diverse data sets. Recent advances in machine learning methods together with the rapid increase in big toxicity data such as molecular descriptors, toxicogenomics, and high-throughput bioactivity data may help alleviate some of the current challenges. In this article, the most common machine learning methods used in toxicity assessment are reviewed together with examples of toxicity studies that have used machine learning methodology. Furthermore, a comprehensive overview of the different types of toxicity tools and data sets available to build in silico toxicity prediction models has been provided to give an overview of the current big toxicity data landscape and highlight opportunities and challenges related to them.

Novel Computational Approach to Predict Off-Target Interactions for Small Molecules.

Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 2,46,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of >1 million compounds (tracking >20 million compound-structure activity relationship/SAR data points) with known in vitro activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 diverse small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for >70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed in vivo effects. About 51.5% (2,025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW<200 for both categories. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of in vitro confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets. In conclusion, the OTSA process demonstrates good predictive performance characteristics and represents an additional tool with utility during the lead optimization stage of the drug discovery process. Additionally, the computed physiochemical properties such as clogP (i.e., lipophilicity), molecular weight, pKa and logS (i.e., solubility) were found to be statistically different between the approved and discontinued drugs, but the internal compounds were close to the approved drugs space in most part.

Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies.

Gene expression profiling is a useful tool to predict and interrogate mechanisms of toxicity. RNA-Seq technology has emerged as an attractive alternative to traditional microarray platforms for conducting transcriptional profiling. The objective of this work was to compare both transcriptomic platforms to determine whether RNA-Seq offered significant advantages over microarrays for toxicogenomic studies. RNA samples from the livers of rats treated for 5 days with five tool hepatotoxicants (α-naphthylisothiocyanate/ANIT, carbon tetrachloride/CCl4, methylenedianiline/MDA, acetaminophen/APAP, and diclofenac/DCLF) were analyzed with both gene expression platforms (RNA-Seq and microarray). Data were compared to determine any potential added scientific (i.e., better biological or toxicological insight) value offered by RNA-Seq compared to microarrays. RNA-Seq identified more differentially expressed protein-coding genes and provided a wider quantitative range of expression level changes when compared to microarrays. Both platforms identified a larger number of differentially expressed genes (DEGs) in livers of rats treated with ANIT, MDA, and CCl4 compared to APAP and DCLF, in agreement with the severity of histopathological findings. Approximately 78% of DEGs identified with microarrays overlapped with RNA-Seq data, with a Spearman's correlation of 0.7 to 0.83. Consistent with the mechanisms of toxicity of ANIT, APAP, MDA and CCl4, both platforms identified dysregulation of liver relevant pathways such as Nrf2, cholesterol biosynthesis, eiF2, hepatic cholestasis, glutathione and LPS/IL-1 mediated RXR inhibition. RNA-Seq data showed additional DEGs that not only significantly enriched these pathways, but also suggested modulation of additional liver relevant pathways. In addition, RNA-Seq enabled the identification of non-coding DEGs that offer a potential for improved mechanistic clarity. Overall, these results indicate that RNA-Seq is an acceptable alternative platform to microarrays for rat toxicogenomic studies with several advantages. Because of its wider dynamic range as well as its ability to identify a larger number of DEGs, RNA-Seq may generate more insight into mechanisms of toxicity. However, more extensive reference data will be necessary to fully leverage these additional RNA-Seq data, especially for non-coding sequences.