(695b) Identifying Operating Conditions for the Solid-Phase Peptide Synthesis Process Via Optimization

Synthetic peptides are an emerging product in the pharmaceutical industry being used to treat a range of illnesses due to their advantages over small and large molecule alternatives, including greater efficacy and lower toxicity¹. The primary method used in industry to make synthetic peptides is the Solid-Phase Peptide Synthesis (SPPS) process which is a cyclic process where the desired peptide is built on a solid resin support one amino acid at a time. Each cycle of the SPPS process consists of a series of steps, including 1) Deprotection, where the Fmoc protecting group is removed to expose the amine on the resin or existing peptide chain on the resin, 2) Activation, where the incoming amino acid is activated, and 3) Coupling, where the activated amino acid couples to the deprotected end of the growing peptide chain on the resin.

The drawback to the SPPS method, however, is the generation of several impurities during the process, such as deletions, isomers, insertions, and capping adducts, that contribute to decreases in the purity and yield of the final product¹. Given that many impurities are difficult to separate out in downstream processes, mitigating impurities during the build process becomes a main goal, but standard reagents and operating conditions to achieve this result in long build times and high process costs. Therefore, a decision-making framework is developed to provide users insight into the optimal operating conditions for a desired peptide build that will focus on impurity mitigation but also reduce long reaction times and high costs.

We have implemented an optimization-based decision-making framework in Python. In our previous work, we developed a detailed kinetic simulation in alignment with experimental results using the reaction mechanism for the SPPS process. This simulation was then numerically integrated to model the progression of all species in each cycle. The simulation allows the user to provide the operating conditions, such as the initial reagent charging, the reaction times for each cycle, and the intentional capping decision between cycles to help reduce impurity propagation throughout the build, for a desired peptide build and build metrics will be output, such as the peptide cost, throughput, purity, yield, etc. In this work, we tackle the inverse problem; given some objective, such as maximizing the throughput, minimizing the cost, or maximizing the purity of the desired build, the optimization framework solves for the optimal operating conditions to meet the objective while also adhering to strict purity requirements. Different methods of solving the optimization problem are explored, such as derivative-free optimization and surrogate-based approaches. Several benchmark cases are developed to show quantitative improvement over standard operation in reducing process time and costs while still ensuring the quality of the product². Overall, this SPPS decision-making framework quantifies improvements in cost and process time reduction while still mitigating impurity generation all while providing users with a quick way to determine operating conditions that can be further explored experimentally.

(1) Frederick, M. O.; Boyse, R. A.; Braden, T. M.; Calvin, J. R.; Campbell, B. M.; Changi, S. M.; Coffin, S. R.; Condon, C.; Gowran, O.; Groh, J. M.; Groskreutz, S. R.; Harms, Z. D.; Humenik, A. A.; Kallman, N. J.; Klitzing, N. D.; Kopach, M. E.; Kretsinger, J. K.; Lambertus, G. R.; Lampert, J. T.; Maguire, L. M.; Moynihan, H. A.; Mullane, N. S.; Murphy, J. D.; O’Mahony, M. E.; Richey, R. N.; Seibert, K. D.; Spencer, R. D.; Strege, M. A.; Tandogan, N.; Torres, F. L. T.; Tsukanov, S. V.; Xia, H. Kilogram-Scale GMP Manufacture of Tirzepatide Using a Hybrid SPPS/LPPS Approach with Continuous Manufacturing. Org. Process Res. Dev. 2021, 25 (7), 1628–1636. https://doi.org/10.1021/acs.oprd.1c00108.

(2) Walsh, M. E.; McClary Groh, J.; Berglund, M.; Wang, J.; Agrawal, P.; Smith, C.; Viswanath, S.; Laird, C. D.; Gounaris, C. E. Simulation-Based Optimization of the Solid-Phase Peptide Synthesis Process. 2024, Forthcoming.