ToxicR

ToxicR provides flexible computational methods for dose–response and toxicogenomic analyses within the R programming environment, supporting high-throughput toxicogenomic and other omic platforms and enabling implementation of custom algorithms, and was developed by the Biostatistics and Computational Biology Branch of the National Institute of Environmental Health Sciences in collaboration with the National Toxicology Program and the US Environmental Protection Agency.

Key Features:

• Integration of Established Software: Built on the same codebase as the EPA's Benchmark Dose software and NTP's BMDExpress, providing direct access to those methodologies.
• Comprehensive Analytical Capabilities: Supports dose–response analyses for continuous and dichotomous data using Bayesian methods, maximum likelihood estimation, and model averaging, and implements statistical tests used in rodent toxicology and carcinogenicity studies such as poly-K and Jonckheere trend tests.
• Customizable Analysis Pipelines: Enables users to program new algorithms and construct custom analysis pipelines within the R programming environment.
• Modular Extendability: Allows addition of new computational modules to expand analytical functionality and adapt to novel research questions.
• Demonstration Workflows: Includes example custom workflows for analyzing toxicogenomic data to illustrate analytical capabilities.

Scientific Applications:

• High-throughput toxicogenomics and other omic studies: Analysis of complex biological responses to environmental chemical exposures across omic platforms.
• Dose–response modeling and benchmark dose analysis: Quantitative dose–response assessment for continuous and dichotomous endpoints used in rodent toxicology and carcinogenicity studies.
• Method development in computational toxicology: Implementation and testing of novel algorithms and statistical approaches within R.
• Regulatory and public health assessment: Generation of quantitative dose–response and trend results to inform regulatory evaluations and public health assessments.

Methodology:

Implements dose–response analyses for continuous and dichotomous data using Bayesian methods, maximum likelihood estimation, and model averaging, includes poly-K and Jonckheere trend tests, and is built on the codebase of the EPA Benchmark Dose software and NTP BMDExpress.