Author Archives: Vera

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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Uncertainty-Aware Site-of-Metabolism Prediction from Ambiguous LC–MS Metabolite Identification Data

June 2026, ASMS Conference

Ramon Adàlia^{1, 2}; Ismael Zamora³

¹Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain; ²Lead Molecular Design, SL, Sant Cugat del Vallès, Spain; ³Mass Analytica, Sant Cugat del Valles, Spain

 

Abstract

Introduction

Liquid chromatography–mass spectrometry (LC–MS) is the dominant technology for metabolite identification in early drug discovery, yet it frequently produces structurally ambiguous metabolites with multiple plausible sites of metabolism (SoMs). Although this ambiguity is well understood in LC–MS workflows, most computational SoM models require unambiguous, binary annotations and therefore cannot directly exploit discovery-stage data. As a result, a large fraction of routinely generated LC–MS metabolite information is excluded from predictive modeling. We present an uncertainty-aware modeling strategy that preserves LC–MS-derived structural ambiguity by encoding relative SoM plausibility, enabling direct use of metabolite identification data without requiring definitive structure elucidation.

Methods

Human liver microsome LC–MS metabolite identification data were processed with software to generate candidate metabolite structures consistent with observed mass shifts and fragmentation patterns. For each metabolite peak, atom-level soft labels were constructed by averaging SoM assignments across equally scoring structural hypotheses, yielding relative plausibility scores rather than binary labels. Metabolite peaks assigning nonzero labels to an excessively large fraction of atoms were filtered during label construction to control noise. Atom rankings were learned using a graph attention neural network trained with a pairwise ranking objective. Molecular graph features were augmented with atom-level reactivity scores from MetaSite7. Model performance was evaluated using ranking-based metrics on the soft-labeled dataset and top-2 accuracy on an independent benchmark with experimentally confirmed SoMs.

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