Sustainable chemicals: techniques for modeling hazard/risk assessment
Symposium Organizers: Hans Plugge, 3E Company; Longzhu Shen, Yale University; Alexandra Maertens, Johns Hopkins School of Public Health
Introduction of sustainable chemicals in the marketplace occurs through identification of “Greener Chemicals”, either from the existing library of chemicals or from de novo synthesized green chemicals. Often, assessing a new chemical for toxicological concerns comes after the R&D phase. A “green toxicology” seeks to change this by providing screening tools for a rapid, preliminary assessment during the design phase. Information derived from a combination of mechanistic and computational toxicology forms the nexus between toxicology and green chemistry. Emerging medium- and high-throughput in vitro methods as well as “big-data approaches” – the hallmarks of 21st century toxicology – will enhance our ability to rapidly assess the toxicity of candidate chemicals and enable further investment to be focused on chemicals with low toxicity.
Currently, Hazard and Risk assessment of all chemicals suffers from a paucity of real data, whether in vivo or in vitro. A paucity of “real” data implies that an immediate detailed (hazard) assessment is not likely to occur. Hazard screening techniques however can be applied and automated.
Various techniques have been deployed over the years to fill in these gaps in the in vivo and in vitro data: Read-Across, QSAR, computational toxicology, High Throughput Screening, and so on. All of these have deficiencies most notably with regard to accuracy/reliability: 70% is typical with an occasional 80%, other endpoints/techniques struggle to achieve 50%. .
A number of these alternative techniques do not result in classical toxicology endpoints, e.g. acute LC50’s, but rather act as flags. Interpreting these “flags” has proven difficult–especially in view of the often surprising results.
Data curation is a common strategy to reduce uncertainty and thus enhance data reliability. . Integrating data curation techniques with the data gap techniques should improve reliability while at the same time indicating where research is still needed to improve the accuracy/reliability.
Topic areas of interest include:
- Automated hazard assessment/screening techniques including mixtures/products
- In vitro tests for data gap filling
- Existing computational toxicology – approaches
- Estimation of data uncertainty and model accuracy/reliability
- Predictive modeling for de novo molecule design
- Integrated approaches to hazard/exposure/risk assessment
- Data curation techniques
Instructions to Authors:
For these sessions we are looking for novel approaches to hazard assessment, molecular /predictive toxicology (both health and ecological) and filling data gaps. Papers on both theoretical and practical approaches are encouraged. Presentations on integration of various data gap filling techniques are especially welcome. Presenters are expected to provide a draft presentation at least 10 days prior to the conference so as to allow the chairs to focus both introductory and summary reviews.