(Q)SAR: A Tool for the Toxicologist. (July 2015)

• Record Type: Journal Article
• Authors: Steinbach, Thomas; Gad-McDonald, Samantha; Kruhlak, Naomi; Powley, Mark; Greene, Nigel
• Is Part Of: International journal of toxicology. Volume 34:Number 4(2015)
• Page Start: 352
• Page End: 354
• Publication Date: 2015-07
• Subjects: (Q(SAR -- computational modeling -- risk assessment; Toxicology -- Periodicals; 615.9005
• DOI: 10.1177/1091581815584914
• Languages: English
• ISSNs: 1091-5818

Abstract

A continuing education (CE) course at the 2014 American College of Toxicology annual meeting covered the topic of (Quantitative) Structure–Activity Relationships [(Q)SAR]. The (Q)SAR methodologies use predictive computer modeling based on predefined rules to describe the relationship between chemical structure and a chemical's associated biological activity or statistical tools to find correlations between biologic activity and the molecular structure or properties of a compound. The (Q)SAR has applications in risk assessment, drug discovery, and regulatory decision making. Pressure within industry to reduce the cost of drug development and societal pressure for government regulatory agencies to produce more accurate and timely risk assessment of drugs and chemicals have necessitated the use of (Q)SAR. Producing a high-quality (Q)SAR model depends on many factors including the choice of statistical methods and descriptors, but first and foremost the quality of the data input into the model. Understanding how a (Q)SAR model is developed and applied is critical to the successful use of such a tool. The CE session covered the basic principles of (Q)SAR, practical applications of these computational models in toxicology, how regulatory agencies use and interpret (Q)SAR models, and potential pitfalls of using them.