A compound can produce a measurable effect without revealing why it works, which pathways it touches, or whether the response will hold outside the model it was seen in. GenXMap integrates transcriptomic, proteomic and metabolomic data across dose, time and experimental system to connect molecular response to mechanism, identify biomarkers of response, and generate evidence that supports the next development decision.

The questions we take on

What a compound does at the molecular level, and which of those changes are primary effects rather than downstream consequences or stress responses
How the response develops across dose and time, and where it plateaus or switches on
Whether two candidates with the same phenotype act through the same mechanism
Whether a response seen in one model is conserved in another, or in a more relevant biological system

Mechanism of Action Programme

A designed programme rather than an analysis order: the strategy is set by the biological question and the development stage, not by a predefined omics workflow.

What the programme draws on

Transcriptomicsbulk RNA-seq for differential expression, pathway-level responses and expression signatures across conditions
Proteomicsmass-spectrometry-based protein profiling, including TIMS-TOF MS, to test whether transcriptional responses are carried through to the protein level
Metabolomicsmetabolic pathway changes where the compound acts on metabolism, signalling or cellular physiology
Targeted validationqPCR, RT-qPCR, dPCR and Olink panels to confirm selected transcripts and proteins
Dose-response and time-course designexperiments planned so that dose and time are analysable dimensions, not nuisance variables
Model comparisonresponses compared across cell lines, primary cells, organoids or in-vivo models
Biomarker discoverymolecular features associated with response, resistance or sensitivity
Multi-omics integrationconverging evidence across layers, with batch correction across experiments and platforms

How the analysis runs

Differential response

Differential expression, protein abundance and metabolite levels per condition against matched controls, with QC and exclusions recorded.

Dose and time

Responses modelled across exposure and time to separate progressive, threshold and plateau behaviour, and early events from later consequences.

Pathway and target analysis

Molecular changes mapped to pathways and processes; candidate targets, treatment-specific programmes and broader or off-target effects distinguished from isolated hits.

Layer convergence

Transcriptomic, proteomic and metabolomic results tested against each other: a pathway supported at two levels is reported as mechanism, one supported at a single level as a lead.

Model comparison

Signatures compared across experimental systems to identify conserved responses, model-specific responses and the biological differences likely to limit translation.

Compound comparison

Candidates within a programme compared for shared or distinct mechanism, shared unwanted effects, and differences in potency or response kinetics.

Biomarker definition

Candidate signatures defined against the study's endpoint and, where independent data exist, tested on data not used to derive them.

Modelling

Dose-response, kinetic and classification models with parameters, thresholds and versions recorded per study.

Where the analysis fits in the programme

Early discovery

characterise the molecular response to candidate compounds and the pathways involved.

Hit characterisation

compare hits against controls, concentrations, models and replicates to separate compound-driven biology from experimental variability, and decide which hits warrant follow-up.

Lead optimisation

determine whether chemical or formulation changes preserve the intended response while altering other effects.

Preclinical development

characterise mechanism, biomarkers and consistency across the relevant models.

Translational studies

test whether signatures from experimental models are detectable and relevant in patient-derived material or clinical cohorts. Stratification and validation in patient cohorts continue on the Precision Medicine page.

Working with what you have

GenXMap does not need to generate every dataset. Existing DNA/RNA-seq, proteomic, metabolomic and screening data, from earlier experiments, other providers or collaborative sources, can be analysed on their own or integrated with new data. What determines the analysis is the question, the design and the metadata, not who generated the data.

To scope a programme we need the compound or intervention, the model system, the endpoint, and the decision the result will support.

What the report contains

Experimental design

Groups, controls, doses, timepoints and endpoints, with the analytical strategy chosen.

Molecular characterisation

Transcriptomic, proteomic and metabolomic results per condition, with QC and exclusions.

Mechanism analysis

Dose- and time-resolved responses, pathway mapping and the integrated cross-layer interpretation.

Biomarker analysis

Candidate signatures, statistical results and association with the predefined endpoint.

Translational assessment

Comparison across models or independent datasets where available.

Interpretation

Written against the question the study started from, stating what the data support and what they do not.

Data package

Methods, software versions, parameters, thresholds, processed matrices and versioned pipelines, delivered for internal review or independent reproduction.

Drug Discovery & Biotechnology