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.

What the analysis covers

Differential response

Differential expression, protein abundance and metabolite levels per condition against matched controls, with QC and exclusions recorded. Targeted confirmation of selected transcripts and proteins by qPCR, dPCR or Olink.

Dose and time

Responses modelled across exposure and time to separate progressive, threshold and plateau behaviour, and early events from later consequences. The design makes dose and time analysable dimensions rather than nuisance variables.

Pathway and target analysis

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

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 cell lines, primary cells, organoids or in-vivo models to identify conserved responses, model-specific responses, and the biological differences likely to limit translation.

Compound comparison

Candidates 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.

Reporting

Design, groups, controls and endpoints. Molecular results per condition with QC and exclusions. Interpretation written against the question the study started from, stating what the data support and what they do not. Methods, software versions, parameters, processed matrices and versioned pipelines, delivered for internal review or independent reproduction.

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 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 in patient-derived material or clinical cohorts.

From compound to readout

Where a programme needs the compound applied before the readout, CTI Biotech provides the model and runs the treatment, and the treated material comes to us for extraction, QC and the omics layers. The model is chosen for the question: primary cells, reconstructed tissue or an established line, depending on what you need to demonstrate.

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.

Data generated elsewhere often arrives without the metadata the analysis needs. We tell you what can be concluded from it before any work starts, rather than after.

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

Not whether a compound changes a biological system, but what changes, how, and whether it will hold.

Book drug discovery platforms on OMICS4

Case studies for this industry are in preparation: get in touch to discuss a project directly.

Drug Discovery & Biotechnology