Multi-Omics Integration for Drug Discovery

Drug discovery programmes generate increasingly complex questions that no single omics technology can answer alone. Does a candidate engage its intended target at the protein level? What downstream metabolic pathways are perturbed? Are there off-target lipid or phosphorylation signatures that signal toxicity? These questions require an integrated view across multiple molecular layers.

Our MassTarget™ platform addresses this need by offering 16 complementary MS-based omics services, organized into four core categories. Each service is independently validated and can be deployed individually or combined into a custom multi-omics workflow tailored to your specific drug discovery question.

Proteomics provides the most direct readout of drug activity at the protein level. Changes in protein abundance, degradation, and post-translational modification reveal how a compound alters cellular signalling, engages its target, and triggers compensatory pathways.

Our proteomics services for drug discovery cover four complementary approaches. DIA/SWATH proteomics delivers deep, reproducible proteome coverage for drug-response profiling across dose and time. Shotgun proteomics provides unbiased discovery of drug-induced protein changes in any biological system. For signalling-focused studies, phosphoproteomics activation mapping captures kinase-driven phosphorylation events that mediate drug efficacy and resistance. And for targeted degradation programmes, ubiquitinomics offers direct evidence of PROTAC-mediated ubiquitination and degradation.

Each service is designed to integrate with other omics data — for example, correlating proteomic changes with metabolomic perturbations to build a complete mechanism-of-action picture.

Metabolites and lipids are the ultimate readouts of cellular phenotype. Drug-induced changes in metabolic flux, lipid composition, and small-molecule signalling provide critical insights into mechanism of action, off-target effects, and biomarker discovery.

Our metabolomics and lipidomics portfolio for drug discovery includes three core services. Untargeted metabolomics provides broad-spectrum profiling of thousands of metabolites to identify pathways perturbed by drug treatment, making it ideal for MoA elucidation and phenotypic screening follow-up. Targeted metabolomics panel screening offers quantitative, hypothesis-driven measurement of specific metabolic pathways — such as glycolysis, TCA cycle, or amino acid metabolism — with high sensitivity and reproducibility. Lipidomics drug profiling captures the full lipidome, including phospholipids, sphingolipids, and glycerolipids, to reveal lipid-mediated signalling and membrane remodelling induced by drug candidates.

These services can be deployed individually or combined with proteomics data to correlate protein-level changes with downstream metabolic consequences.

Confirming that a drug candidate engages its intended target — and understanding the conformational consequences of that engagement — is critical for progressing compounds through the discovery pipeline. Our structural proteomics services use mass spectrometry to probe protein conformation, stability, and ligand-induced structural changes at proteome-wide scale.

LiP-MS combined with metabolomics couples limited proteolysis mass spectrometry with parallel metabolomics to simultaneously detect target engagement and metabolic perturbations in a single experiment. LiP-MS + TPP integrates limited proteolysis with thermal proteome profiling for orthogonal conformational evidence. DIA + TPP leverages data-independent acquisition for deeper thermal profiling coverage. And thermal proteomics for MoA provides a standalone thermal shift readout to identify drug targets and off-targets in complex biological samples.

Together, these approaches provide the structural evidence needed to validate target engagement and guide medicinal chemistry optimisation.

Drugs do not act in isolation — they perturb complex interaction networks that span protein–protein contacts, metabolic pathways, and signalling cascades. Understanding these network-level effects is essential for predicting efficacy, identifying resistance mechanisms, and avoiding toxicity.

Our systems-level services include AP-MS interactomics for proximity-dependent protein interaction mapping, which reveals how drug treatment rewires the interactome. AI-driven multi-omics integration applies machine learning to cross-omics datasets (proteomics, metabolomics, lipidomics, phosphoproteomics) to identify predictive biomarkers and mechanistic signatures. 13C tracer fluxomics tracks carbon flow through central metabolic pathways, providing dynamic rather than static metabolic information. Systems pharmacology MS modeling builds quantitative models of drug–target–pathway interactions. And network pharmacology + MS integrates experimental MS data with network biology to predict polypharmacology and multi-target effects.

These services are particularly valuable for complex therapeutic programmes where single-target models are insufficient.

Successful multi-omics integration requires more than running multiple assays in parallel. It demands coordinated experimental design, consistent sample handling across omics workflows, and a unified data analysis framework that can correlate findings across molecular layers.

Our platform approach follows a structured workflow:

1

Consultation & experimental design

We work with your team to select the optimal combination of omics modalities based on your drug target, compound class, and biological question.

2

Coordinated sample processing

Samples are processed in parallel across selected omics workflows using standardised protocols to minimise batch effects and ensure data comparability.

3

High-resolution MS data acquisition

Each omics modality is analysed on optimised LC-MS/MS platforms (Orbitrap, QToF, QQQ) with appropriate acquisition strategies (DIA, DDA, PRM, MRM).

4

Individual omics data processing

Each dataset is processed independently using validated pipelines — peptide/protein identification, metabolite annotation, lipid identification, PTM site localisation.

5

Cross-omics correlation & integration

Multi-omics datasets are integrated using correlation analysis, pathway enrichment, and multivariate modelling to identify cross-layer relationships.

6

Biological interpretation & reporting

Integrated results are interpreted in the context of your drug discovery programme, with actionable insights and visualisations delivered in a comprehensive report.

Choosing a multi-omics partner for drug discovery requires evaluating more than just the number of assays offered. The value lies in how those assays are integrated, how the data is interpreted, and whether the partner understands the specific demands of drug development programmes.

Sample requirements vary depending on the combination of omics modalities selected. Below are general guidelines for common sample types across our multi-omics workflows. Our scientific team will provide detailed requirements during the consultation phase.