Predictive Methodology\nfor Selecting the “Fit-for-Purpose”\nLC-HRMS Method for Quality Assessment in Peptide Synthesis

Peptides are essential in pharmaceuticals and function\nas therapeutic\nagents for a wide range of conditions. The chemical synthesis process\nof peptides often leaves impurities, such as inorganic salts and residual\nreagents, which can interfere with the analysis and contaminate the\nmass spectrometer’s source in in-process quality control (IPQC).\nMoreover, process monitoring using short liquid chromatography-high-resolution\nmass spectrometry (LC-HRMS) runtimes presents challenges due to the\npresence of multiple organic impurities. To address this, four distinct\nLC-HRMS templates were developed to accommodate a diverse range of\npeptides. A principal component analysis (PCA)-based methodology was\nthen developed and validated to select the appropriate LC-HRMS method\nbased on the peptides’ physicochemical properties, including\nsequence length, hydrophobicity, isoelectric point, molecular weight,\nand clog <i>P</i>. The PCA methodology efficiently classified\npeptides into distinct quadrants, guiding the selection of the appropriate\nshort LC-HRMS method without the need for trial-and-error LC-HRMS\nmethod development. With a success rate exceeding 90%, the methodology\naccurately predicted the appropriate LC-HRMS method for the peptides.\nThis systematic approach streamlines method selection and ensures\nthe precise elution of peptides. Furthermore, by directing the initial\nLC flow to waste, the short methods minimize the risk of mass source\ncontamination from inorganic impurities. This developed methodology\nis suitable for peptides with sequence lengths ranging from tetrapeptides\nto octapeptides, providing a robust tool for peptide analysis in IPQC\nworkflows.