Organic Chemistry

Driving Aspirational Process Mass Intensity Using SMART-PMI and Innovative Chemistry

Authors

Abstract

An important metric for gauging the impact a synthetic route has on chemical resources, cost, and sustainability is process mass intensity (PMI). Calculating the overall PMI or step PMI for a given synthesis from a process description is more and more common across the pharmaceutical industry, especially in process chemistry departments. Our company has established a strong track record of delivering on our Corporate Sustainability goals, being recognized with eight EPA Green Chemistry Challenge Awards in the last 15 years and we show how these routes help define aspirational PMI tar-gets. While green chemistry principles help in optimizing PMI and developing more sustainable processes, a key challenge for the field is defining what a ‘good’ PMI for a molecule looks like given its structure alone. An existing tool chemists have at their disposal to predict PMI requires the synthetic route be provided or proposed (e.g., via retrosynthetic analysis) which then enables practitioners to compare predicted PMIs between routes. We have developed SMART-PMI (in-Silico MSD Aspirational Research Tool) to fill the gap in predicting PMI from molecular structure alone. Using only a 2D chemical structure, we can generate a predicted SMART-PMI from a measure of molecular complexity. We show how these predictions correlate with historical PMI data from our company’s clinical and commercial portfolio of processes. From this SMART-PMI prediction, we have established target ranges which we termed “Successful”, “World Class”, and “Aspirational” PMI. The goal of this range is to set the floor for what is a “good” PMI for a given molecule and provide ambitious targets to drive innovative green chemistry. Using this model, chemists can develop synthetic strategies that make the biggest impact on PMI. As innovation in chemistry and processes lead to better and better PMIs , in turn, this data can drive ever more aggressive targets for the model. The potential of SMART-PMI to set industry-wide aspirational PMI tar-gets is discussed.

Version notes

This is an updated version of the manuscript prior to submission to the final journal.

Content

Thumbnail image of Merck_PMI_paper.pdf

Supplementary material

Thumbnail image of Merck_PMI_paper_SI.pdf
Merck PMI paper supporting information
Supporting Information. Table of PMI values. Table of descriptors. Structures of compounds used to build the model. This material is available free of charge via the Internet at http://pubs.acs.org. The SMART-PMI model can be accessed at https://github.com/Merck/compoundcomplexity.

Supplementary weblinks

Merck PMI model
Compound Complexity Calculator USAGE: bin/compoundcomplexity.sh [ -sdf <sdfile> | -smi <smilefile> ] NOTES: This is an implementation of Compound Complexity for use in the SMART-PMI as described by Sherer et al. It contains derived training data as required by the described Random Forest Model in order to replicate data presented in paper as well as applying to novel data.