The Evolving Role of Translational Toxicology in the Era of Translational Research and Science

Dear Colleagues,

Based on definitions from the National Center for Advancing Translational Sciences, Translation is the process of turning observations in the laboratory, clinic and community into interventions that improve the health of individuals and the public—from diagnostics and therapeutics to medical procedures and behavioral changes. Translational science is the field of investigation focused on understanding the scientific and operational principles underlying each step of the translational process.

In the field of toxicology and drug safety, increasing efforts are underway to use artificial intelligence (AI) and machine learning (ML) to expand the resources available to researchers in defining both good and bad aspects of molecules while the structures are still in the computational phase prior to synthetic efforts. This overall process has ushered in the era of Translational Toxicology. These efforts are addressing a major hurdle in the success of Drug Research and Development, that is, the accuracy of toxicity or safety predictions of new molecules entering the pipeline or advancing through clinical trials.

The prediction of potential drug toxicity early in the drug research and development process continues to expand in relevance due to the availability of large data sets of classical study information gained through new data mining efforts, multiple available sources of chemical/-omics data, and data from new developing technologies featuring human-based systems, such as organ-on-a-chip and induced pluripotent stem cell-derived models. In addition, the identification of key chemical–cell–tissue fluxes via technologies, such as imaging mass spectrometry, has also enhanced the knowledge of drug toxicity target interactions.

The fusion of bioinformatics and computational sciences with molecular biology and chemistry have ushered in the era of Translational Toxicology, which promotes the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Computational systems toxicology enables the identification of important pathways and molecules from large datasets to allow hypotheses on potential links between drug entities and potential toxicity at both the research and later development stages of drug development.

These rapidly developing tools and models hold tremendous promise for advancing applied and basic science, streamlining drug efficacy and safety testing, and to increase the efficiency and effectiveness of predicting off-target toxicities. These approaches also offer the potential to improve toxicological experimental design, reduce the overall number of experimental toxicology studies needed, and reduce the number of animals used in experimentation. Translational Toxicology is multidisciplinary and includes research in medicinal chemistry, molecular biology including the identification of genes/proteins as desired and/or unwanted targets of chemicals, rapid advances in biomarker identification, chemical/protein docking to identify potential target interactions, and imaging pathology including from biobanked tissues. The field also includes the personalized prediction of potential toxicity for individual patients with certain genetic susceptibilities, toxicity from multiple drugs within a regimen (polypharmacy), and potential drug interactions with dietary supplements and/or herbal medicine formulations. Advances in all phases of Translational Toxicology are showing promise to provide better analyses of potential human toxicity from single and multiple drugs.

Prof. Dr. Dale Johnson
Guest Editor

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