A data-driven strategy for predicting greenness scores, rationally comparing synthetic routes and benchmarking PMI outcomes for the synthesis of molecules in the pharmaceutical industry. Issue 1 (1st November 2016)

• Record Type: Journal Article
• Authors: Li, Jun; Simmons, Eric M.; Eastgate, Martin D.
• Is Part Of: Green chemistry. Volume 19:Issue 1(2017)
• Page Start: 127
• Page End: 139
• Publication Date: 2016-11-01
• Subjects: Environmental chemistry -- Industrial applications -- Periodicals; Environmental management -- Periodicals; 660
• DOI: 10.1039/c6gc02359b
• Languages: English
• ISSNs: 1463-9262

Abstract

Abstract : A predictive analytics approach to understanding process mass intensity (PMI) is described. This method leverages real-world data to predict probable PMI outcomes for a potential synthetic route and to compare PMI outcomes to the summation of prior experience. Abstract : Cumulative Process Mass Intensity (PMI) is one of the most popular greenness metrics tracked during the lifecycle of a pharmaceutical compound. Its use is wide-spread, having come to represent the foundation of many assessments of efficiency. These metrics are critical during the development of a compound as analysis of efficiency data (such as PMI outcomes) can help minimize the environmental impact of pharmaceutical manufacturing, highlight areas for potential improvement and thus drive sustainability. However, there are several issues with many of the current metrics, one of the most pressing being the absence of such information when key synthetic strategy decisions are made in early development; many metrics articulate the impact of strategy decisions made in the absence of efficiency data. In this article, we develop a predictive analytics framework, coupled to Monte Carlo simulation, to address this issue and enable a rich understanding of potential PMI outcomes during both the decision making process (prediction) and the outcome review process (comparison). This method leverages real-world data to predict probable PMI ranges for a potential synthesis being considered, utilizing accumulated data which spans a range of molecules and phases of development. The approach can serve two critical functions lacking in current methods: (1) it can act as a decision-aiding tool during the route discovery process, predicting probable PMI outcomes for proposed, potential or unoptimized synthetic routes; (2) it can enable the direct comparison of the PMI outcome of a synthesis to all comparable chemistry, thus providing a benchmarking methodology capable of comparing PMIs across molecules. We envision that this approach will deliver significant impact to the green chemistry community by enabling greener decisions to be made at critical phases of invention, namely the ideation, route selection and development processes (designing green), along with providing a rational method to compare a specific outcome to prior art (benchmarking).