Simulation-Based Optimization of the Solid-Phase Peptide Synthesis Process

Synthetic peptide drugs are becoming increasingly popular in a multitude of therapeutic areas. The primary method employed to manufacture synthetic peptides at an industrial scale is the Solid-Phase Peptide Synthesis (SPPS) process, where the desired sequence of amino acids are added in series to build the peptide on solid resin. Arguably, SPPS is a complex process that involves many steps and degrees of freedom. In this work, we propose a simulation-based optimization framework to determine optimal operating variables, such as raw material charges and batch times, for any given peptide build of interest. Through the use of Python-based tools, a detailed simulation is formulated based on the reaction network for the process and used to determine key performance metrics, such as cost, throughput, and purity, for a specific operating point. This simulator is then utilized as the basis for a derivative-free optimization approach that allows us to determine optimal raw material charges and reaction times for each cycle of the peptide build. A comprehensive computational study based on a set of benchmark multicycle peptide builds showcases our framework’s ability to extract improvements in cost, throughput, and purity over standard operating protocols and manage the trade-off among all of these metrics. In general, this computational tool helps practitioners in the pharmaceutical industry quickly identify promising operating conditions for novel peptide builds that can be further validated experimentally.