Toxicogenomics and Computational Toxicology

Toxicogenomics is a branch of pharmacology concerned with the gathering, interpreting, and archiving of data on gene and protein activity inside a specific cell or tissue of an organism in response to harmful chemicals. Toxicogenomics is a branch of molecular biology that combines toxicity with genomics or other high-throughput molecular profiling techniques including transcriptomics, proteomics, and metabolomics. Toxicogenomics aims to uncover the molecular mechanisms that have developed in the manifestation of toxicity, as well as to identify molecular expression patterns (i.e., molecular biomarkers) that can be used to predict toxicity or genetic predisposition to it. Toxicogenomics is the study of the structure and function of the genome as it responds to xenobiotic exposure in pharmacological research.

Computational toxicology is a burgeoning field that combines data and information from a number of sources to create mathematical and computer-based models to better understand and anticipate the negative health consequences of chemicals like environmental contaminants and medications. Computational toxicology explores the interactions between chemical agents and biological organisms at multiple scales, including biology, medicine, biochemistry, chemistry, mathematics, computer science, engineering, and other disciplines (e.g., population, individual, cellular, and molecular). By predicting a compound's toxicological potential early in the drug development process, computational toxicology helps to reduce late-stage attrition.

• Big Data in Toxicology
• Environmental Toxins
• Human Health and Environmental Toxicity
• Prediction of Toxicity
• Regulatory Acceptance of Models
• Toxicometabolomics
• Toxicoproteomics
• Transcriptional Toxicogenomics