Computational Toxicology

ToxStrategies professionals can assist clients in the application of computational toxicology to more efficiently predict chemical toxicity and integrate high-throughput exposure and toxicity data into the overall risk assessment process. There is a growing availability of toxicological response information accumulating in data repositories. These resources, coupled with the increased availability of high-throughput screening assays, can be integrated using computational toxicology approaches to inform chemical mode of action, adverse outcome pathways, dose-response relationships, inter-individual differences in susceptibility, uncertainty, and risk. Such approaches offer time and cost-efficient strategies that can be used to inform earlier decisions during drug discovery, product development, and chemical regulatory assessments.

The incorporation of computational toxicology into risk assessment practices is an evolving area of high interest, but is also associated with a number of challenges, particularly related to the knowledge needed to appropriately integrate and interpret such large volumes of data. ToxStrategies is uniquely suited to assist clients in these assessments, as incorporation of high content and high throughput datasets is increasingly becoming a standard part of our toxicological and risk assessment practices. ToxStrategies remains at the forefront of this field through primary research, participation in government agency workshops, professional society conferences, and use of these data in a variety of client applications. Areas of expertise in this field include the following:

 

Assessment of high-throughput exposure and toxicity predictions

  • Chemical Exposure Forecasting data and prediction models (ExpoCast)
  • Endocrine Disruption Screening Program for the 21st Century (EDSP21) Dashboard
  • Estimation Program Interface Suite (EpiSuite)
  • Interactive Chemical Safety for Sustainability (iCSS) Dashboard
  • Toxicity Forecaster data (ToxCast)
  • Toxicity Testing in the 21st century data (Tox21)

 

Integration with large repositories to characterize toxicological responses

  • Catalogue of Somatic Mutations in Cancer (COSMIC)
  • Chemical Effects in Biological Systems (CEBS)
  • Comparative Toxicogenomics Database (CTD)
  • Gene Expression Omnibus (GEO)
  • Ingenuity Pathway Analysis (IPA) Knowledge Base
  • Kyoto Encyclopedia of Genes and Genomes (KEGG)
  • National Center for Biotechnology Information (NCBI) databases (e.g., Genome, PubChem BioAssay, SNP)
  • The Cancer Genome Atlas (TCGA)
  • The Connectivity Map (CMAP)
  • Toxicity Reference Database (ToxRefDB)
  • Toxin and Toxin-Target Database (T3DB)

 

Expertise with advanced tools to evaluate computational toxicology data

  • Benchmark Dose Express (BMDExpress)
  • Database for Annotation, Visualization, and Integrated Discovery (DAVID)
  • Gene Set Enrichment Analysis (GSEA)
  • Ingenuity Pathway Analysis (IPA)
  • Kyoto Encyclopedia of Genes and Genomes (KEGG)
  • Partek Genomics Suite Software
  • Toxicological Priority Index (ToxPi)
  • Flexible, reproducible customized analysis using R and Python

 

Integration with large repositories to infer or simulate exposures

  • Centers for Disease Control (CDC) National Health and Nutrition Examination Survey (NHANES)
  • U.S. Department of Agriculture (USDA) Food and Nutrient Database for Dietary Studies (FNDDS)
  • U.S. EPA Chemical and Product Categories database (CPCat)
  • U.S. EPA Consolidated Human Activity Database (CHAD)
  • U.S. EPA Toxics Release Inventory (TRI)
  • U.S. EPA National Emissions Inventory (NEI)
  • U.S. EPA Safe Drinking Water Act Contaminant Occurrence Databases
  • U.S. Geological Survey (USGS) National Water Information System (NWIS)

 

Implementation of advanced computational and statistical methods to inform risk assessment

  • Dose-response modeling
  • Bayesian inference methods to quantify uncertainty and variability
  • Machine learning methods
  • Non-parametric statistical methods
  • Physiologically-based pharmacokinetic/toxicokinetic (PBPK/PBTK) modeling
  • Quantitative risk assessment
  • In vitro to in vivo extrapolation
  • Inter-individual variability modeling
  • Probabilistic risk assessment
  • Sensitivity analysis
  • Uncertainty quantification