Computational Toxicology

ILS staff utilize their computational toxicology, data mining, bioinformatics and cheminformatics expertise to provide a mechanistic understanding of potential adverse effects, anchoring the data science to the toxicology.

The computational toxicology group at ILS offers a wide range of expertise across informatics areas, allowing us to take data from your ILS assays, in-house resources, and literature to deliver decision-ready products.

Faced with limited toxicological data for a variety of chemicals present in the environment, quantitative structure activity relationship (QSAR) programs have become more important in prioritizing chemicals and studies. ILS has significant experience with a variety of QSAR platforms, using both commercially available and open source software to identify the presence of structural alerts in a chemical under evaluation that are positively or negatively associated with an observed activity (e.g., genotoxicity).

Furthermore, we simplify the integration and interpretation of Tox21 and ToxCast data from quantitative high-throughput screening (qHTS) and high-content (HC) assays using a variety of existing tools and software as well as novel computational workflows developed and applied by ILS scientists. These workflows are created to account for unique client needs as well as the continual updating of source data, ensuring flexibility and relevance. Our expertise make this complex work approachable and understandable, enabling our clients to leverage such large data sources effectively and with confidence.

ILS staff can help maximize the information from in vitro data. Our toxicologists and information scientists leverage domain expertise including subject-specific ontologies to characterize an assay’s mechanism of action and possible adverse outcome pathway targets. Using this information, in vitro to in vivo extrapolation (IVIVE) models can provide equivalent administered dose estimates useful in dose selection and estimation of toxicity reference/screening values. ILS staff are experienced in conducting IVIVE including reverse toxicokinetic modeling and physiological based pharmacokinetic models.

  • Analysis and application of Tox21 and ToxCast data from quantitative high throughput screening (qHTS) and high content (HC) assays
  • Analysis of qPCR, microarray, NextGen sequencing data
  • In vitro to in vivo extrapolation including PBPK modeling
  • Development of integrated approaches to testing and assessment
  • Adverse outcome pathway (AOP)-based testing and data anchoring

  • ReadAcross
  • Quantitative structure property relationship (QSPR)
  • Quantitative structure activity relationship (QSAR)

  • Literature review and background document preparation
  • Expert-led data extraction and curation
  • Database development
  • Data mining and visualization to support safety assessments