Computational toxicology is a field of science that uses in silico (computer-based) methodologies to predict potential adverse toxicological features and properties of chemicals.

Computational toxicology is an emerging field that is gaining increasing scientific and regulatory acceptance. Computational toxicology provides methods and models for use in all disciplines of toxicology, including regulatory, academic, clinical, and industrial, and will play an increasingly important role in safety evaluation and risk assessment.

The next webinar of the Computational Toxicology Specialty Section (CTSS) of the Society of Toxicology (SOT), runs under the title “CTSS—The Predictive Toxicogenomics Space (PTGS) Modeling Tool Captures Diverse Cellular and Organ Toxicity Mechanisms and Serves for In Vitro Model-Driven Prediction of Drug-Induced Liver Injury”, on 27^th April at 17:00h CEST.

“Toxicogenomics” represents a steadily developing “Big Data” informatics analysis field. Increasing amounts of safety testing-derived gene expression data require interpretation related to existing knowledge for characterizing hazard and risks coupled to agents such as drugs, chemicals and nanomaterials. We have elucidated toxicity mechanisms from embracing the network character of systems biology as well as the complementary linear analysis scheme characteristic of the adverse outcome pathway (AOP) concept. An “artificial intelligence”-derived 14 gene component-based “predictive toxicogenomics space (PTGS)” tool generates toxicity estimates intrinsic to omics-data via broad coverage of toxicity reactions and mechanisms. The tool enables application of in vitro data to assess tissue injury in multiple organs of experimental animals subjected to repeated-dose toxicity bioassays, including the accurate prediction of human drug-induced liver injury.

PTGS/omics-driven Mode-of-Action and adverse outcome pathway analyses serve for grouping and hazard characterization of nanomaterial products. Although the lecture has its focus on drug molecules, it is conceptually relevant also for SABYDOMA work. Roland Grafström will among several matters review chemical space coverage, Big Data and explainable Artificial Intelligence. The lecture captures an important part of the cell line testing concepts by partner MISVIK. Also, PTGS serves as a successful example of utilizing Key Exploitable Results (KER) for generation of hazard-testing relevant IPR.

Professor Roland Grafström, from SABYDOMA partner Misvik Biology, is giving on April 27, 17:00h CEST an invited open lecture with the Society of Toxicology, Computational Toxicology Specialty Section (CTSS). The presentation is about the PTGS concept and its application to DILI prediction.