(667a) Greenness By Design for Pharmaceutical Synthetic Processes

Designing efficient and green approaches to complex molecules is a challenge faced by any organization seeking to deliver modern pharmaceutical compounds to patients in a prompt manner. To measure process greenness, Process Mass Intensity (PMI) is a basic metric of the environmental efficiency for a synthetic step or sequence of steps. The PMI for synthetic sequence generally decreases as the process becomes optimized and environmentally friendly, however in selecting the synthetic route to a drug substance, the environmental impact of the final optimized process is uncertain. Therefore, choosing between different routes based on environmental impact becomes challenging and can be difficult for project teams to justify.

In this presentation we incorporate a data science approach to classify historical processes according to the sequence of chemical transformations to support a predictive analytics framework capable of quantifying the probable efficiency of a proposed synthesis. This method leverages real-world data to predict PMI green chemistry scores for proposed synthesis options, acting as a decision tool during the route selection process, predicting greenness metrics for any proposed, potential, or any unoptimized synthetic route, and enabling the direct comparison of the greenness score of an optimized synthesis to all comparable chemistry. Monte Carlo simulation provides teams with a realistic expectation of typical and best-case process performance, and a calculator interface facilitates prediction and comparison of PMI metrics. We envision that this rational approach for route selection will deliver significant impact to the chemistry community enabling greener decisions to be made during the route selection and process development.