Tag Archives: 2026

Software-Aided Prediction of Key Peptide Properties Using LC–MS Data

June 2026, ASMS Conference

Paula Cifuentes^{1, 2, 3}; Ramon Adàlia^{2, 3, 4}; Lisa A.Vasicek⁵; Richard Gundersdorf⁵; Abigail Wheeler⁵; Paul Harradine⁵; Ismael Zamora³

¹Universitat Pompeu Fabra, Barcelona, Spain; ²Lead Molecular Design, SL, Sant Cugat del Vallès, Spain; ³Mass Analytica, S.L., Sant Cugat del Vallés, Spain; ⁴Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain; ⁵Merck & Co., Inc., West Point, PA

 

Abstract

Introduction

Peptides have emerged as promising therapeutic agents due to their high specificity, favorable safety profiles, and cost-effective synthesis. However, their clinical development is limited by low oral bioavailability and short half-lives. These challenges arise from high clearance rates, poor solubility, limited membrane permeability, and reduced metabolic stability caused by peptidase activity and modulated by post-translational modifications. Deficiencies in any of these properties can significantly impact peptide’s therapeutic efficacy. Consequently, in silico prediction tools have become increasingly important in the pharmaceutical industry, enabling early identification and elimination of unsuitable peptide drug candidates. Despite recent advances, existing tools are often limited to natural amino acids, cannot process cyclic peptides, and lack customization to user-specific experimental data, highlighting the need for further development.

Methods

The methodology defines a new workflow that integrates LC-MS data from peptide metabolism studies with a Graphormer-based machine learning model to predict five key peptide properties: potential cleavage sites, half-life, permeability, solvent accessibility, and post-translational modifications. The methodology operates without structural constraints, allowing cyclic peptides, and modified amino acids. The models employ transformer architecture with added mechanisms to encode graph structural information. Users can train models with their own LC-MS experimental data for improved alignment with specific peptides and continuously update them via a self-learning approach. The five selected end points predictive models have been compared to the state-of-the art tools. Additionally, the site of cleavage model and half-life models were validated using experimental MetID data from a pharmaceutical company.

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AI Parent-to-Metabolite Pathway Predictor

June 2026, ASMS Conference

Savannah M Mason¹; Paula Cifuentes^{1, 2, 3}; Tommaso Palomba^{1, 4}; Ismael Zamora¹

¹Mass Analytica, S.L., Sant Cugat del Vallés, Spain; ²Universitat Pompeu Fabra, Barcelona, Spain; ³Lead Molecular Design, SL, Sant Cugat del Vallès, Spain; ⁴Molecular Discovery, Borehamwood, United Kingdom

 

Abstract

Introduction

Most drugs undergo chemical transformations in the body, known as biotransformations, to produce metabolites that are more readily eliminated. These reactions are largely mediated by metabolic enzymes, primarily in the liver, and exhibit high specificity, with each enzyme favoring particular substrates. Understanding the enzymes responsible for metabolite formation is critical for elucidating the metabolic pathways, predicting metabolic behavior, and anticipating potential toxicity. Metabolite Identification (MetID) studies, performed in vitro or in vivo, rely heavily on LC-MS/MS for the detection and structural identification of metabolites. However, most discovery studies provide limited information about the enzymes involved. Consequently, experimental approaches to reaction phenotyping, including recombinant enzymes incubations or chemical inhibition, are time- and resource-intensive, making comprehensive pathway characterization challenging.

Methods

This workflow integrates LC-MS MetID experiments from in vitro incubations. Users may apply a model to an experimentally identified metabolite to predict the possible enzymatic pathways responsible for its formation, including Phase I and Phase II reactions. The computational algorithm evaluates the exposure of reactive atoms of xenobiotic compounds to catalytic residues of human metabolic enzymes by simulating interactions between the two, using the enzyme’s 3D structure. Multiple docking poses are generated and scored based on energy contributions. The best pose is normalized to rank the probability, which is provided in the output. MetID experiments were analyzed using MassMetaSite in the ONIRO server with LC-MS data from Sciex and Thermo instruments. The predictions were performed using MetaSite 7 inside Oniro.

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Development of Machine Learning assisted Fingermark Imaging Software (iFIS)

June 2026, ASMS Conference

Simona Francese¹; Elias Jensen²; Sara Tortorella³; Chloe Spencer^{1, 4}; Giuseppe Arturi⁵; Simon Cross⁶; Hassan Ugail²

¹Sheffield Hallam University, Sheffield, United Kingdom; ²University of Bradford, Bradford, United Kingdom; ³Mass Analytica, Sant Cugat del Valles, Spain; ⁴University of Nottingham, Nottingham, United Kingdom; ⁵Molecular Discovery, Borehamwood, United Kingdom; ⁶Mass Analytica, S.L., Borehamwood, United Kingdom

 

Abstract

Introduction

Molecular fingerprinting has been featured in the Fingermark Visualisation Manual edited by Dstl/Home Office and is being used in Police casework. It encompasses the application of Mass Spectrometry Imaging (MSI), particularly MALDI MSI, for the provision of biometric information, through generating multiple molecular images of crime scene fingermark evidence, alongside contextual (molecular) information. Whilst both advanced freeware and proprietary MSI exist, they are complex as mostly built to process biological tissue imaging data. We have developed a dedicated software, enabling auditable fingermark images “manipulation”, seamless separation of overlapping fingermarks and, crucially, integrating a machine learning algorithm capable of grading and finding fingermark images of the highest quality within a seconds, without manually having to inspect the entire mass range.

Methods

Following Ethical approval (ER52762288), fingermarks were matrix spray-coated using the HTX M3+™ Sprayer (HTX Technologies, USA) and imaged on a SELECT SERIES MRT MALDI mass spectrometer (Waters Corporation, UK) in positive mode. Lipostar MSI (Molecular Discovery, UK) – used as the skeleton to build iFIS –  generated 663 images which were graded according to the 5-point scale Scotland Yard system machine learning model. The ML algorithm utilised a range of deep features based on the Resnet50 architecture corresponding to the visual characteristics of the fingermark (minutiae and texture features). These were then fed to a Support Vector Machine-based classification algorithm for categorizing fingermarks into five distinct categories.

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From Molecular Structure to Pharmacokinetic Parameters: Autonomizing Quantitative Bioanalysis Across Modalities

June 2026, ASMS Conference

Ismael Zamora^{1, 2}; Luca Morettoni³; Fabien Fontaine²; Kevin Bateman⁴

¹Mass Spec Analytica, Sant Cugat del Valles, Spain; ²Lead Molecular Design, SL, Sant Cugat del Vallès, Spain; ³Mass Analytica, Sant Cugat del Valles, Spain; ⁴2KDAM Consulting, Halifax, NS

 

Abstract

Introduction

Mass spectrometry based quantitative analysis, typically built on triple quadrupole (QQQ) instruments, requires a disproportionate number of manual procedures for such an advanced analytical technique. The use of high-resolution mass spectrometry (HRMS) with data independent acquisition can obviate the need for compound specific methods but this approach lacks the required data processing capabilities to fully leverage the data content. We describe the development of a platform that automates all aspects of data processing through data reporting of pharmacokinetic parameters. The user provides the structure and assay acceptance criteria, and the platform holistically finds a quantitative model that best represents the data. We demonstrate this platform for drug discovery pharmacokinetic studies across multiple modalities, including small molecules, peptides and antisense oligonucleotides.

Methods

Plasma samples from rat PK studies of Sitagliptin, Liraglutide and  Mipomersen were processed for LC-MS analysis.  A Sciex ZenoTOF 7600 quadrupole time of flight mass spectrometer was used for data collection. A SWATH acquisition method with a TOF MS scan followed by MSMS scans collected using 25 Da windows was used for data acquisition. Data processing was done using AI-Quant (described below).

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End-to-End High-Throughput Biotransformation Workflow: Automated Data Acquisition and Processing of Sub-Second UHPLC Peaks Using Multi-Reflecting Time-of-Flight Mass Spectrometry and Data-Mining

June 2026, ASMS Conference

Ismael Zamora¹; Hania Khoury-Hollins²; Richard Lock²; David Pickles²; Robert S Plumb²; Ian Wilson³

¹Mass Spec Analytica, Sant Cugat del Valles, Spain; ²Waters Corporation, Wilmslow, United Kingdom; ³Imperial College London, London, United Kingdom

 

Abstract

Introduction

Traditional ultra-high performance liquid chromatography (UHPLC™) paired with high-resolution mass spectrometry (HRMS) has long been the standard for drug metabolite characterization, but its throughput is limited by peak dispersion the need to balance mass resolution with scan speed, and data processing and rationalisation. These factors, along with labour-intensive manual data review, slow down the delivery of actionable results.

To overcome these bottlenecks, we introduce a fully integrated high-throughput data dependent workflow combining a next generation UHPLC with multi reflecting time-of-flight technology (Xevo™ MRT MS) and dedicated data-mining software. This platform enables rapid, automated acquisition and processing of sub-second UHPLC peaks, achieving part-per-billion mass accuracy (≤500 ppb RMS), scan speeds up to 100 Hz, and high mass resolution (100K FWHM).

Methods

Male beagle dogs received a single intravenous (IV) dose, and whole blood was collected from the jugular vein both before and after dosing. Plasma was obtained by centrifugation and analyzed using reversed-phase UHPLC coupled to multi reflecting time of flight mass spectrometer.

Data dependent acquisition mode was used, and metabolites were detected and quantified in both positive and negative electrospray ionization modes, utilizing Xevo MRT MS and a dedicated MassMetaSite software.

Workflow improvements were assessed by comparing results obtained with shorter runtimes to those from conventional UHPLC method used by the contract research organization (CRO) that prepared the samples. This comparison focused on data quality, sample analysis times, and confidence in structural characterization.

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