Applying knowledge-driven mechanistic inference totoxicogenomics

Abstract

Government regulators and others concerned about toxic chemicals in the environment hold that a mechanistic, causal explanation of toxicity is strongly preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. We present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually-curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, we experimentally validated some of our predictions for other chemicals without an established mechanism of toxicity. This framework can be modified to include additional mechanisms of toxicity, and is generalizable to other types of mechanisms of human biology.\n\nAuthor summarySeveral recent computational methods have displayed excellent performance in predicting toxicity outcomes [1-3] of chemicals. Yet, to our knowledge, there is to date no computational approach to generate mechanistic hypotheses to answer why these chemicals elicit a toxic response. There is great value in understanding the mechanism of toxicity for a chemical that appears to elicit an adverse response. Novel small molecule development is one example, where a chemical that failed initial toxicological screenings could be assessed to evaluate the actual mechanism of toxicity, greatly reducing research time and expenses on subsequent ones. The value of a mechanistic awareness of toxicity also applies to pharmacovigilance, when researching rare adverse effects of a drug in subsets of the population. The development of oncological chemotherapeutics is another example, where certain mechanisms of cytotoxicity can actually be desirable to eliminate different types of tumor cells. More importantly, the costs, time expenditure, and ethical concerns of toxicity animal models, make in vitro and in silico approaches an enticing alternative. We present a solution that uses a combination of gene expression assays and biomedical knowledge to address the gap of answering the why question.