Have Your Say on China’s Draft Regulation on Chemical Environmental Risk Assessment and Control

Learn from our 3 experts about emission sources originating from binder systems in large serial casting productions. Get to know potentials to reduce or even avoid emissions along the process chain by both chemical and technical product solutions.

Industry is currently facing a substantial challenge. There is an increasing need to develop chemical hazard data for both new and existing chemicals, while at the same time there are current and proposed regulatory mandates as well as ethical drivers to reduce, refine, and replace (the “3 R’s”) the use of animal test methods with alternative non-animal methodologies. This creates a challenge for innovators, formulators, and companies who strive to introduce a new chemical or fill data gaps on existing chemicals while adhering to the principles of the 3 R’s. In silico, or computational toxicology software offers an alternative method for predicting chemical hazards. Traditional Quantitative Structure Activity Relationship (QSAR) methodology employs simple analog identification and predicting hazard data from chemical analogs with known hazard data. This webinar will cover the UL Cheminformatics Tool Kit, a novel in silico approach combining big data with advanced machine learning incorporating data fusion. The approach integrates novel models called RASARs (Read-across Structure Activity Relationship) with data fusion techniques that extend this concept by considering all available property data on both the target and analogs rather than only the modeled hazard.

Within chemical process development, program timing often dictates making informed decisions with limited process understanding, especially in the early clinical delivery space. To facilitate this, the Merck Process Development Intensification Laboratory is tasked with developing tools and methodologies aimed towards acquisition of deeper, more fundamental process understanding in a resource sparing manner. These chemical process development techniques are centrally focused around data intensification, or more specifically, more efficient data capture, data reduction and data analysis enabled.

Chemical management is challenging; it requires coordination between multiple stakeholders including product stewardship, industrial hygiene, environmental and safety teams. Establishing an efficient and scalable chemical program is critical to maintain visibility of on-site inventory at any given time. Establishing visibility allows industry professionals to begin by mitigating the largest chemical-related risks in their businesses and enables a continuous improvement approach going forward.