Building on a solid foundation: SAR and QSAR as a fundamental

Knowledge Source Identification
Knowledge source name Building on a solid foundation: SAR and QSAR as a fundamental
Owner/Developer Taylor & Francis Group
Country United States of America
Languages English
Description The development of more efficient, ethical, and effective means of assessing the effects of chemicals on human health and the environment was a lifetime goal of Gilman Veith. His work has provided the foundation for the use of chemical structure for informing toxicological assessment by regulatory agencies the world over. Veith’s scientific work influenced the early development of the SAR models in use today at the US Environmental Protection Agency. He was the driving force behind the Organisation for Economic Co-operation and Development QSAR Toolbox. Veith was one of a few early pioneers whose vision led to the linkage of chemical structure and biological activity as a means of predicting adverse apical outcomes (known as a mode of action, or an adverse outcome pathway approach), and he understood at an early stage the power that could be harnessed when combining computational and mechanistic biological approaches as a means of avoiding animal testing. Through the International QSAR Foundation he organized like-minded experts to develop non-animal methods and frameworks for the assessment of chemical hazard and risk for the benefit of public and environmental health. Avoiding animal testing was Gil’s passion, and his work helped to initiate the paradigm shift in toxicology that is now rendering this feasible.
Knowledge Source Category
Category Publication
Sub categories Review / Research article
Knowledge Dissemination and Sharing
Dissemination channel Website, Printed
Targeted audience (specified/objective analysis) Students, Scientists, Industry, Researchers
Users access Open access
Knowledge Characterization
3Rs relevance Replacement, Reduction
Purpose Toxicological and safety evaluation, Documentation and information
Technology/Tools Non-testing methods (in silico)