DESIGNING SAFER CHEMICALS
Alternatives Assessment & De Novo Design- Oral
Symposium Organizer: Hans Plugge (3E Company, A Verisk Analytics Company); Longzhu Shen (Yale University)
There are two crucial elements in approaching the goal of sustainability in chemical products and processes: alternatives assessment and de novo molecular design. To date the focus of both areas has been hazard reduction i.e. minimizing biological consequences. In order to design chemical products with superior functionality and efficacy while minimizing their toxicity or to derive a proper alternatives assessment as per NAS 2014, it is necessary to incorporate factors like exposure, functionality and life cycle assessment into the overall design and assessment scheme. When considering functionality, alternatives assessments approach it posteriorly, whereas de novo design formulates it as a prior optimization problem. Presentations at the symposium should focus on integration of elements of molecular toxicology, computational chemistry, data science and exposure assessment aimed at ushering in a new era of holistic sustainability modeling for green chemical design and assessment.
Workshop on Data Uncertainty in Predictive Toxicology & Alternative Assessments- Oral
Symposium Organizer: Jakub Kostal (George Washington University)
Over the last two years we presented a course that outlined strategies for developing property- based design guidelines using various sources of in vitro and in vivo data; demonstrated how these models are relevant to comparative hazard assessment such as GreenScreen®; and presented future strategies for secure data-sharing to allow for design of more robust predictive models. We were encouraged by positive feedback from the past two years to use the workshop format as a way of introducing timely topics on chemical design that expand the horizons of both current and future developers and users. Thus, in addition to expanding upon computational approaches used in safer chemical design and predictive modeling, this year’s workshop will focus on the concept of data uncertainty and its importance in decision-making strategies and development of predictive tools. The synthesis of overlapping data sources with varying uncertainty and reliability is critical in developing robust predictive methods, yet it is one of the aspects of model development that is often undervalued or completely disregarded. We will discuss this issue from the point of view of experimentalists, modelers and practitioners involved in safer chemical design.