Addressing sustainability challenges in peptide synthesis with flow chemistry and machine learning

In the era of peptide therapeutics, solid phase peptide synthesis is becoming increasingly important in the pharmaceutical industry and related research. However, the high cost and the large amount of toxic waste generated during production overshadow the current technology, requiring the reduction of excess reagents and the replacement of the solvents used. Advances have been made to replace N,N-dimethylformamide with moderate success. Here, we report a recyclable anisole/dimethyl sulfoxide based and carefully tuned solvent system that is compatible with flow chemistry and outperforms DMF. By exploring the solvent parameter space, we have selected several mixtures, tested their swelling ability, amino acid solubility, coupling efficiency, and Fmoc-cleaving capacity, and found the Anisole/DMSO (17:3) mixture to be ideal for coupling. By adjusting the flow parameters, racemization was reduced to <2% in the case of His, and <1% for Cys. Several mixtures were screened for optimal Fmoc-cleavage, selected to cover the solvent parameter space uniformly. To test the selected solvent mixtures for aspartimide formation, and Fmoc-cleavage efficiency, both scorpion toxin II (VKDGYI) and JR10-mer (WFTTLISTIM) challenging sequences were synthesized and new correlations between reaction rates and solvent parameters were found. Further parameter optimizations were performed using a machine learning algorithm (Bayesian optimization) to reduce aspartimide formation and maximize Fmoc-deprotection. With the final parameters obtained, the Aib-ACP (10-mer), the glucagon like peptide 1 (GLP-1, 30-mer) and bovine pancreatic trypsin inhibitor (BPTI, 58-mer) polypeptides were synthesized with high efficiency and synthetic speed (12 min/cycle). The method is ideal for high temperature synthetic approaches. Based on sustainability metrics, the applied synthetic flow chemistry protocol, with the greener solvent mixture (Anisole/DMSO) performs outstandingly well compared to traditional methods and to state-of-the-art synthesizers.