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Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.

ABSTRACT: Hepatotoxicity is a leading cause of attrition in the drug development process. Traditional preclinical and clinical studies to evaluate hepatotoxicity liabilities are expensive and time consuming. With the advent of critical advancements in high-throughput screening, there has been a rapid accumulation of in vitro toxicity data available to inform the risk assessment of new pharmaceuticals and chemicals. To this end, we curated and merged all available in vivo hepatotoxicity data obtained from the literature and public resources, which yielded a comprehensive database of 4089 compounds that includes hepatotoxicity classifications. After dividing the original database of chemicals into modeling and test sets, PubChem assay data were automatically extracted using an in-house data mining tool and clustered based on relationships between structural fragments and cellular responses in in vitro assays. The resultant PubChem assay clusters were further investigated. During the cross-validation procedure, the biological data obtained from several assay clusters exhibited high predictivity of hepatotoxicity and these assays were selected to evaluate the test set compounds. The read-across results indicated that if a new compound contained specific identified chemical fragments (ie, Molecular Initiating Event) and showed active responses in the relevant selected PubChem assays, there was potential for the chemical to be hepatotoxic in vivo. Furthermore, several mechanisms that might contribute to toxicity were derived from the modeling results including alterations in nuclear receptor signaling and inhibition of DNA repair. This modeling strategy can be further applied to the investigation of other complex chemical toxicity phenomena (eg, developmental and reproductive toxicities) as well as drug efficacy.

SUBMITTER: Zhao L

PROVIDER: S-EPMC7098374 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Read-across has become a primary approach to fill data gaps for chemical safety assessments. Chemical similarity based on structure, reactivity, and physic-chemical property information is a traditional approach applied for read-across toxicity studies. However, toxicity mechanisms are usually complicated in a biological system, so only using chemical similarity to perform the read-across for new compounds was not satisfactory for most toxicity endpoints, especially when the chemically similar compounds show dissimilar toxicities. This study aims to develop an enhanced read-across method for chemical toxicity predictions. To this end, we used two large toxicity datasets for read-across purposes. One consists of 3979 compounds with Ames mutagenicity data, and the other contains 7332 compounds with rat acute oral toxicity data. First, biological data for all compounds in these two datasets were obtained by querying thousands of PubChem bioassays. The PubChem bioassays with at least five compounds from either of these two datasets showing active responses were selected to generate comprehensive bioprofiles. The read-across studies were performed by using chemical similarity search only and also by using a hybrid similarity search based on both chemical descriptors and bioprofiles. Compared to traditional read-across based on chemical similarity, the hybrid read-across approach showed improved accuracy of predictions for both Ames mutagenicity and acute oral toxicity. Furthermore, we could illustrate potential toxicity mechanisms by analyzing the bioprofiles used for this hybrid read-across study. The results of this study indicate that the new hybrid read-across approach could be an applicable computational tool for chemical toxicity predictions. In this way, the bottleneck of traditional read-across studies can be overcome by introducing public biological data into the traditional process. The incorporation of bioprofiles generated from the additional biological data for compounds can partially solve the "activity cliff" issue and reveal their potential toxicity mechanisms. This study leads to a promising direction to utilize data-driven approaches for computational toxicology studies in the big data era.

Dataset Information

Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.

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