An automated software-assisted approach for exploring metabolic susceptibility and degradation products in macromolecules using High-Resolution Mass Spectrometry
72nd ASMS Conference on Mass Spectrometry. June 2024
Paula Cifuentes1,3; Ismael Zamora2; Fabien Fontaine2; Albert Garriga2; Luca Morettoni2; Tatiana Radchenko1
1Lead Molecular Design, Sant Cugat del Vallès, Spain; 2Mass Analytica, Sant Cugat del Vallès, Spain; 3Universitat Pompeu Fabra, Barcelona, Spain
Abstract
Introduction
An essential aspect of the drug development process is the comprehensive identification and characterization of the major metabolites of the candidate drug and the enzymes responsible for its metabolic transformation, commonly known as drug metabolism. Recently, there has been a strong emphasis on developing more efficient systems and tools aimed for these studies. However, to achieve this goal, different challenges must be faced, including computational aspects such as high data processing times, others related to peak detection using monoisotopic mass or the most abundant isotope for mass calculation, and complications in compound visualization. Even though automating data analysis has simplified many design stages, the analysis of metabolic study samples, particularly for macromolecules, remains time-consuming, emphasizing the necessity for customized solutions.
Methods
The work employed a software tool automating data analysis stages for LC-MS (High Resolution) data. This included selecting chromatographic peaks related to the compound, retrieving mass spectral information, assigning potential structures through theoretical fragmentation comparison with experimental m/z values, and scoring solutions based on fragment analysis. Results from different experimental conditions are clustered into a unified experiment entity and stored in a database. For each compound two distinct algorithms have been employed for peak selection, allowing for outcome comparison. After data consolidation, manual interpretation is performed according to predefined criteria. Data from different acquisition modes has been processed, and two structure visualization methods are presented: an expanded form depicting all atoms and bonds, and a non-expanded form linking monomer acronyms.
Preliminary data
The aim of this study is to describe new algorithms/approaches for automated LC-MS (High Resolution) data analysis that addresses the mentioned challenges encountered in the processing of macromolecules. These challenges encompass optimizing the input and visualization of chemical structures and degradation products. Additionally, it has successfully optimized the reduction of processing memory and time consumption (from 2 hours to 25 minutes) in the execution of algorithms for potential structure generation and fragmentation. Furthermore, the proposed methods aim to provide a workflow capable of interpreting results across various data acquisition formats and modes.
Analysis was conducted on six datasets spanning a molecular range of 700 to 15,000 Da. These datasets consist of both linear and cyclic peptides, incorporating natural and unnatural amino acids, as well as an oligonucleotide. Specifically, dataset-1 comprises nine commercially available peptides, dataset-2 includes one commercially available peptide and four synthetic analogues, dataset-3 involves a natural peptide hormone and seven synthetic analogues, dataset-4 features an antisense oligonucleotide, dataset-5 contains 28 commercially available peptides, and dataset-6 is composed of a peptide hormone.
Comparisons of the results obtained for certain compounds with those of prior studies have enabled a comprehensive evaluation across various parameters. This evaluation encompasses aspects such as the number and structure of identified metabolites, along with a consideration of the time consumed during the data processing step.
The results obtained indicate that, in larger molecules, the most abundant mass algorithm demonstrated higher scores and a greater number of matches, and therefore greater confidence in the accurate prediction of metabolite structures. Furthermore, this study shows three visualization options for representing macromolecules during data analysis. This visualization algorithm allows the combination of monomer and atom/bond notation, facilitating a clear depiction of metabolic changes in the molecular structure.
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