Description:

This webinar will feature two professional toxicologists who will share their experience working across the disciplines of chemistry and toxicology through predictive and computational toxicology tools. With the goal of enabling scientists to design products with reduced hazards, Dr. Spencer and Dr. Rowlands will provide modern-day examples of tools that bridge this divide.

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The Crossroads of Chemistry and Toxicology:  Advancing Greener, Safer Ingredients & Products
by Dr. Pamela Spencer, ANGUS Chemical

Abstract: Today, more than ever, chemicals and products deemed to have undesirable human health or environmental effects are being targeted for replacement with “greener” alternatives.  New greener candidates must be technologically feasible, deliver the same or better value in cost and performance while providing an improved profile for health and environmental safety.  For scientists developing new products, this means early detection of toxicological effects can be the difference in launching a more sustainable product or one targeted itself for future deselection.  Given the significant time, resources and money required to develop a new product, early identification of nonviable candidates can conserve finite business resources. ANGUS has developed and implemented a process to improve the screening of new chemical candidates or formulations using new “21^st century” safety assessment tools.  This presentation will highlight how toxicological assessments are integrated into the product development process and the critical role collaboration between chemists and toxicologists play in the introduction of new, more sustainable alternatives.

Developing Greener Chemicals through Big Data and Machine Learning Computational Toxicology
by Dr. Craig Rowlands, Underwriters Laboratory

Abstract: The development and preparation of new materials and chemicals must determine their safety to human health and the environmental throughout their lifecycles. Conventional approaches to such safety assessments can be costly, sacrifice a large number of animals for testing and are time intensive delaying introduction of new products into the market. Such considerations have underscored the need to develop new approaches to safety assessments by toxicologists and chemists working together at the earliest stages of new product development. These partnerships have resulted in innovative predictive safety strategies and approaches using in vitro and computational toxicology – or cheminformatic –tools. In collaboration with researchers from Johns Hopkins University, UL has developed a new machine learning (ML) cheminformatics software tool to predict chemical hazards. This ML tool called REACHAcross takes advantage of the increasing availability of big data in toxicology using a chemical similarity approach to predict highly accurate chemical hazards.  The results of the assessments can be used to design or select lower hazard chemicals for new chemicals or new formulations, respectively. REACHAcross predictions have additional uses including chemical regulatory registrations and hazard communications. REACHAcross is a versatile software application that can assist green chemistry practitioners in the development of lower hazed chemicals and producing the data needed for commercialization of new products.