Connected DMPK.
Faster Decisions.
Support modern DMPK for any hybrid and conjugated therapeutic format.
Break down data silos between assay results and molecule registration. Connect mass spectrometry (MS) analytics, multi-assay management, and molecule-centric context, for AI-readiness across discovery and development.
Learn how Genedata can help.
Our Customers
One DMPK Platform. All Modalities and Assays.
As therapeutic formats become more complex, so do the analytical assays and the data they generate. DMPK teams must profile large numbers of analytes from in vitro and in vivo biotransformation studies while managing rapidly growing volumes of data.
The Genedata Biopharma Platform provides the enterprise end-to-end digital backbone for modern DMPK, transforming fragmented data into structured, traceable, AI-ready formats for predicting successful drug candidates.
How the Platform Supports your DMPK Workflows
Flexible Workflows. Automated.
- Support the analysis of any modality at scale through the flexible and transparent workflow design system.
- Automate MS-based processing of large, complex biotransformation libraries, containing hundreds of thousands of predicted metabolites.
- Transform bioanalysis data into reproducible, reviewable, and traceable results from in vivo and in vitro studies.
Heterogeneous Assays. Harmonized.
- Integrate and harmonize assays into structured, traceable sessions linked to molecule registration, sample batch, QC, and metadata.
- Eliminate fragmented tools and disconnected datasets by leveraging assay context in a single, shared digital environment.
- Enable assay performance monitoring, historical trend analysis, and cross-assay data visualization.
Molecule Traceability. End-to-End.
- Connect DMPK analytical results to registered therapeutic candidates across hybrid and conjugated therapeutics.
- Preserve molecule-, sample-, and project-level context for end-to-end traceability, and regulatory compliance.
- Support predictive modelling and AI by structuring all analytical assays into a central data backbone.
Genedata accelerates DMPK analytics by an order of magnitude. This allows stability assessment for downstream candidate selection before clinical phases.
We have been using Genedata across our early biologics discovery operations for many years and are very excited to onboard the US-DMPK group as this is a major step towards having complete end-to-end digital tracking of all BI’s therapeutic candidates.
The automated [Genedata] workflow dramatically accelerates data processing, eliminates tedious manual operations, and facilitates review, allowing us to focus on tasks that are key to business success, while reducing the time required to deliver high-quality results from weeks to days.
Customer Stories
FAQs
- What makes modern DMPK so complex?
- Why is biotransformation challenging for hybrid and conjugated modalities?
- Why are traditional DMPK workflows no longer sufficient?
- The data-to-decision loop. Why is it critical for modern DMPK?
Drug Metabolism and Pharmacokinetics (DMPK) organizations need to characterize increasingly diverse therapeutic formats, from biologics and oligonucleotides to hybrid and conjugated modalities. These molecules combine multiple components, each with distinct analytical behaviors.
In addition to the intact molecules, biotransformation studies must consider how each molecular component transforms; all fragments, metabolites, degradation products, and released payloads that may affect exposure, safety, or clearance. This is harder when analytes appear at low abundance in complex matrices such as plasma and tissues.
Individual assays, instruments, or data systems often provide only partial evidence and may be disconnected from molecule registration, format, sample batch, and assay context. DMPK teams need integrated workflows that connect multi-assay results with individual molecular components’ registration and experimental metadata to enable full traceability across the product lifecycle.
DMPK workflows need structured, annotated, and traceable data that preserves lineage from raw files to final reports. This digital foundation is critical for the lab of the future, supporting expert review, regulatory submissions, downstream analytics, and AI-based predictive modeling.