(448b) Simulation and Optimization Approaches for the Solid-Phase Peptide Synthesis (SPPS) Process

Synthetic peptides are becoming increasingly popular in the pharmaceutical industry to treat illnesses ranging from diabetes to osteoporosis to cancers [1, 2]. The primary method used in the industry to make synthetic peptides is the Solid-Phase Peptide Synthesis (SPPS) process, in which amino acids are added in cycles to build the peptide chain on a solid resin piece [3, 4]. The three main steps of the SPPS process include 1) deprotection, where the FMOC protecting group is removed to expose the amine, 2) activation, where the incoming amino acid is activated, and 3) coupling, where the activated amino acid couples to the deprotected end of the amino acid [2]. These steps continue in a cycle until the desired peptide chain is built.

The main drawback of the SPPS process is that the resulting peptide chains are rich in impurities, mainly due to “deletions” and isomeric amino acids [1, 5]. Deletion impurities occur after incomplete conversion of the active sites during a cycle, resulting in an amino acid being left out of the desired peptide chain sequence [2]. To further complicate the process, amino acids can racemize and form their stereoisomer [5, 6]. The undesired stereoisomer can then couple to the existing peptide chains, causing an isomer impurity. Deletion and isomer impurities, even in small amounts, can significantly impact the biological function of the peptide chain [2, 5]. In order to achieve the strict purity requirements needed for pharmaceutical products, time intensive and costly separation processes are required downstream. For additional time and cost, an intentional capping step can be applied at the end of a cycle to cap unreacted sites, ensuring the chain with a deletion impurity does not continue to react in future cycles while also allowing for ease of separation downstream. Therefore, to better understand how to most effectively make synthetic peptides while meeting their strict purity requirements, an optimal decision-making framework for the SPPS process is proposed to aid peptide chemists during the design of manufacturing protocols for these synthetic peptide chains.

The workflow to generate an optimal design of an SPPS process includes a detailed multi-cycle simulation followed by optimization. First, the detailed multi-cycle simulation is generated based on reaction mechanisms for the SPPS chemistry. To accurately describe the real system, each cycle of the SPPS process is broken into phases to capture the species’ charging, reacting, and discharging occurring in batch reactors. After generating the species’ balances throughout each phase of a single cycle, the differential equations are numerically integrated using established Python routines [7] . The results from each cycle are then used as the initialization for the next cycle until the peptide build is fully simulated. The evolution of the solid-phase species is also tracked from cycle to cycle to allow for purity and yield calculations after completing the peptide build.

After confirming the multi-cycle simulation results accurately align with experimental evidence, the work explores the optimization of the multi-cycle process, using both derivative-free and deterministic optimization techniques based on a discretized counterpart of the dynamic simulation model [8, 9, 10]. Here, degrees of freedom pertaining to the amount of raw materials consumed in each cycle, the timing of the reaction steps involved, and the decision to apply intentional capping after certain stages, are considered in the context of a bi-objective optimization problem targeting the maximization of throughput and the minimization of overall costs. Our results thus explicitly quantify the tradeoff between these two objectives and provide the decision-maker with a tool to design SPPS processes that are more effective and improve the yield compared to current practices.

References

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(3) Dalcol, I.; Rabanal, F.; Ludevid, M.-D.; Albericio, F.; Giralt, E. Convergent Solid Phase Peptide Synthesis: An Efficient Approach to the Synthesis of Highly Repetitive Protein Domains. J. Org. Chem. 1995, 60 (23), 7575–7581. https://doi.org/10.1021/jo00128a033.

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(5) Genchi, G. An Overview on D-Amino Acids. Amino Acids 2017, 49 (9), 1521–1533. https://doi.org/10.1007/s00726-017-2459-5.

(6) Bada, J. L. Racemization of Amino Acids. In Chemistry and Biochemistry of the Amino Acids; Barrett, G. C., Ed.; Springer Netherlands: Dordrecht, 1985; pp 399–414. https://doi.org/10.1007/978-94-009-4832-7_13.

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(8) Le Digabel, S. NOMAD: Nonlinear Optimization with the MADS Algorithm. ACM Trans Math Softw 2011, 37, 44.

(9) Bynum, M. L.; Hackebeil, G. A.; Hart, W. E.; Laird, C. D.; Nicholson, B. L.; Siirola, J. D.; Watson, J.-P.; Woodruff, D. L. Pyomo — Optimization Modeling in Python; Springer Optimization and Its Applications; Springer International Publishing: Cham, 2021; Vol. 67. https://doi.org/10.1007/978-3-030-68928-5.

(10) Nicholson, B.; Siirola, J. D.; Watson, J.-P.; Zavala, V. M.; Biegler, L. T. Pyomo.Dae: A Modeling and Automatic Discretization Framework for Optimization with Differential and Algebraic Equations. Math. Program. Comput. 2018, 10 (2), 187–223. https://doi.org/10.1007/s12532-017-0127-0.