Announcing the Inaugural Winner of the GCIPR Data Science and Modeling for Green Chemistry Award

ACSGCI
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By Jared Piper, Director of Process Chemistry at Pfizer

We're thrilled to announce that a team from Bristol Myers Squibb is being recognized with the ACS GCI Pharmaceutical Roundtable's first-ever Data Sciences and Modeling Award for Green Chemistry. Read about how their project demonstrated excellence in the research and development of computational tools that advance green chemistry.

Contributed by Jared Piper, Director of Process Chemistry at Pfizer

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Process chemist Richard Fox and coworkers at Bristol Myers Squibb (BMS) are being recognized as the recipients of the first ever Data Sciences and Modeling Award for Green Chemistry. The award recognizes innovation or excellence in the research and development of computational tools that empower users to effectively design, implement, and evaluate green processes with reduced process mass intensity, waste, health and safety impact, and other aspirational improvements. The award was open to both academic and industrial applicants, and a number of excellent nominations were received for consideration. Fox and his team will be presented the award during a special awards session at this year’s 28th Annual ACS Green Chemistry and Engineering Conference (June 2-5, Atlanta, GA).

Richard Fox, Jun Li, Jacob Albrecht, Jason Stevens, Jose Tabora, Alina Borovika, and Benjamin Shields (BMS) partnered with Jose Garido (Princeton), Professor Ryan Adams (Princeton), and Professor Abigail Doyle (UCLA) to showcase two tools that are combined to arrive at greener processes during the design, development, and optimization of synthetic transformations. A process mass intensity (PMI) prediction app that utilizes predictive analytics and historical data of large-scale syntheses to help enable better decision-making during ideation and route design was coupled with a subsequent Experimental Design via Bayesian optimization application (i.e., EDBO/EDBO+) to accelerate the optimization of the subsequent individual chemical transformations was included. Taken together, these two tools enable process scientists to incorporate state-of-the-art open-access data science tools and algorithms into both defining their overall project strategies and conducting their daily laboratory experimentation to accelerate the advancement of “greener-by-design” outcomes.

Highlighting a real clinical candidate, the authors illustrated a quantitative method for the prediction of potential efficiencies centered around Process Mass Intensity (PMI) of proposed synthetic routes prior to their evaluation in a laboratory. This allows scientists to select the most efficient option prior to development and arrive at a holistically more sustainable chemical synthesis to a molecule moving into manufacturing. Following predictions for PMI across different synthetic sequences, the researchers rapidly identify the optimized conditions for a particular transformation using a machine learning Bayesian optimization (BO)n approach to explore chemical space and identify more sustainable reaction conditions with fewer experiments and resources. For the specific example included in their work, a process that yielded 70% yield and 91%ee through traditional one factor at a time (OFAT) using 500 experiments, was surpassed by the EDBO+ platform, providing 80% yield and 91%ee in only 24 experiments. Both technology platforms are open source and available for researchers at no charge, increasing the potential for adoption and use by others.

 

About the ACS GCI Pharmaceutical Roundtable

The ACS GCI Pharmaceutical Roundtable (GCIPR) is the leading organization dedicated to catalyzing the integration of green chemistry and engineering in the pharmaceutical industry. Established in 2005 by the American Chemical Society’s Green Chemistry Institute, the Roundtable’s activities are driven by the shared belief that green chemistry and engineering is imperative for business and environmental sustainability. Learn more on the ACS GCIPR website.