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

Peptides are essential in pharmaceuticals and function as therapeutic agents for a wide range of conditions. The chemical synthesis process of peptides often leaves impurities, such as inorganic salts and residual reagents, which can interfere with the analysis and contaminate the mass spectrometer’s source in in-process quality control (IPQC). Moreover, process monitoring using short liquid chromatography-high-resolution mass spectrometry (LC-HRMS) runtimes presents challenges due to the presence of multiple organic impurities. To address this, four distinct LC-HRMS templates were developed to accommodate a diverse range of peptides. A principal component analysis (PCA)-based methodology was then developed and validated to select the appropriate LC-HRMS method based on the peptides’ physicochemical properties, including sequence length, hydrophobicity, isoelectric point, molecular weight, and clog P. The PCA methodology efficiently classified peptides into distinct quadrants, guiding the selection of the appropriate short LC-HRMS method without the need for trial-and-error LC-HRMS method development. With a success rate exceeding 90%, the methodology accurately predicted the appropriate LC-HRMS method for the peptides. This systematic approach streamlines method selection and ensures the precise elution of peptides. Furthermore, by directing the initial LC flow to waste, the short methods minimize the risk of mass source contamination from inorganic impurities. This developed methodology is suitable for peptides with sequence lengths ranging from tetrapeptides to octapeptides, providing a robust tool for peptide analysis in IPQC workflows.

Authors

Institutions

Publication Details

Journal
Organic Process Research & Development
Published
2024-12-20
DOI
https://doi.org/10.1021/acs.oprd.4c00393
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.72
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Ravi P. Shah, Prashant Ingle, Bichismita Sahu, Cherukuri Venkata Apparao et al.
2 citations
Organic Process Research & Development
Computational Drug Discovery Methods
0.72
article

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

Ravi P. Shah, Prashant Ingle, Bichismita Sahu, Cherukuri Venkata Apparao, Sumit Kumar, Naveen Chandrasekar
article en
2 citations

Abstract

Peptides are essential in pharmaceuticals and function as therapeutic agents for a wide range of conditions. The chemical synthesis process of peptides often leaves impurities, such as inorganic salts and residual reagents, which can interfere with the analysis and contaminate the mass spectrometer’s source in in-process quality control (IPQC). Moreover, process monitoring using short liquid chromatography-high-resolution mass spectrometry (LC-HRMS) runtimes presents challenges due to the presence of multiple organic impurities. To address this, four distinct LC-HRMS templates were developed to accommodate a diverse range of peptides. A principal component analysis (PCA)-based methodology was then developed and validated to select the appropriate LC-HRMS method based on the peptides’ physicochemical properties, including sequence length, hydrophobicity, isoelectric point, molecular weight, and clog P. The PCA methodology efficiently classified peptides into distinct quadrants, guiding the selection of the appropriate short LC-HRMS method without the need for trial-and-error LC-HRMS method development. With a success rate exceeding 90%, the methodology accurately predicted the appropriate LC-HRMS method for the peptides. This systematic approach streamlines method selection and ensures the precise elution of peptides. Furthermore, by directing the initial LC flow to waste, the short methods minimize the risk of mass source contamination from inorganic impurities. This developed methodology is suitable for peptides with sequence lengths ranging from tetrapeptides to octapeptides, providing a robust tool for peptide analysis in IPQC workflows.

Organic Process Research & DevelopmentVol. 29(1)
National Institute of Pharmaceutical Education and Research - Ahmedabad (IN)
Openalex Percentile: Top 24%
Computational Drug Discovery Methods
0.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.