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Technology
A predictive platform for translational oncology decisions
PreComb supports biopharma translational decisions with our platform which captures in vivo-like tumor responses through longitudinal and clinically relevant drug testing A scientific discussion with the CSO, no sales pitch.
Technology at a glance
Patient-derived tumor models capture in vivo-like treatment dynamics
Functional outcomes measured through dynamic and longitudinal outcome analysis
Translational predictivity provided by testing at physiologically relevant drug concentrations
From patient biology
to decision-ready insight
From patient biology to decision-ready insight
Application-driven 3D cancer models capture clinically relevant treatment dynamics
Dynamic, longitudinal testing delivers clinically relevant functional outcomes in vitro
Translational predictivity anchored by benchmarking against standard-of-care drugs at clinically relevant concentrations
Why oncology decisions are still made without clinically interpretable data
Many discovery assays are optimised for throughput and rank-order potency. As a result, readouts like IC50/Emax often summarise response at a single timepoint and can miss dynamic effects (e.g. adaptation, delayed response, or regrowth after drug pressure).
Animal studies provide more clinically familiar endpoints, but they are typically lower-throughput, resource intensive, and influenced by species-specific pharmacology. This makes it difficult to explore the breadth of conditions required for modern translational decisions (indication breadth, schedules, combinations)
PreComb’s 3DTwin platform closes this gap. Longitudinal, dynamic profiling of 3D cancer models, from cell lines to patient-derived tumors, generates in vivo-like functional outcomes that can be interpreted in a translational context. Predictive power increases with model fidelity, enabling earlier, more confident decisions without relying on slow, costly animal studies.
How the platform operates (technical overview)
Diverse tumor material input
3D cancer models are generated from standardized cell lines, patient-derived cells, and fresh tumour tissue, providing a biologically and clinically relevant foundation for functional assessment across oncology indications.
3D microtumour production
3D cancer models are produced in a standardized 384-well plate format, providing flexibility in the number of drugs, treatment conditions, timepoints, and downstream analyses that can be applied, without compromising biological relevance or reproducibility
Drug response profiling
Therapeutic candidates are evaluated across models under standardized conditions, with responses benchmarked against standard-of-care therapies tested at physiologically relevant concentrations to anchor functional outcomes in a clinical context
Data-driven interpretation
Functional responses are analysed to reveal meaningful patterns across treatments and conditions.
Translational Decision support
Longitudinal microtumor growth data is analyzed to generate functional outcome metrics adapted from clinical response criteria (e.g., tumor control and RECIST-like assessment).
Cross-model learning enables benchmarking across different tumor types, drug classes, and experimental conditions, producing predictive insights that inform early pipeline prioritization, combination strategies, and therapy decisions.
Built for translational relevance
and actionable decisions
- Longitudinal, functional profiling across diverse 3D models
Captures treatment response over time, preserving clinically relevant dynamics rather than relying on single endpoints.
- Standardized, automated, and scalable production
384-well 3D microtumor platform enables reproducible assays, flexible experimental design, and high-throughput data generation.
- Close clinical and tissue collaborations
Partnerships with hospitals and tissue providers ensure access to patient-derived material and the ability to integrate longitudinal clinical outcomes.
- Decentralized screening with centralized analytics
Enables multi-site engagement while preserving comparability through centralized data processing.
- Decision-driven outputs for translational impact
Cross-model learning and benchmarking produce predictive insights that directly inform compound prioritization, combination strategies, and therapy decisions.
Applications supported
Candidate prioritization
Identify compounds with the most robust and durable functional responses across 3D cancer models to inform early pipeline investment and prioritization decisions.
Indication prioritization
Evaluate activity across different cancer types to guide optimal indication selection for each drug candidate.
Combination strategy development
Test and rank rational combination hypotheses using longitudinal functional data, reducing risk before costly downstream studies.
Drug repurposing
Systematically assess existing compounds across diverse cancer models to uncover novel therapeutic opportunities and indications.
Additional readouts (e.g. transcriptomics) can be offered to support further analysis.
Scientific validation and resources
Access publications, posters, and scientific materials that demonstrate
real-world use of the platform in translational oncology.
3DTwin® Pharma 2026 (PDF)
Download a concise overview of the 3DTwin® platform and its role in translational oncology
decision-making.
3DTwin® Methodology & Use Cases
Technical overview of functional drug response profiling and translational oncology
applications.
Identification of novel treatments
Identification of novel treatments
for childhood brain cancers
Demonstration of immunotherapy-induced
Demonstration of immunotherapy-induced alterations in
cytokine
A 3D Heterotypic Multicellular Tumor Spheroid Assay Platform to Discriminate Drug Effects on Stroma versus Cancer Cells.
(SLAS Discov. 2020 Mar;25(3):265-276) Weydert Z, Lal-Nag M, Mathews-Greiner L, Thiel C, Cordes H, Küpfer L, Guye P, Kelm JM, Ferrer M.
Translational in vitro research: integrating 3D drug discovery and development processes into the drug development pipeline.
(Drug Discovery Today 2019 Jan;24(1):26-30) Kelm JM, Lal-Nag M, Sittampalam GS, Ferrer M.
A novel three-dimensional heterotypic spheroid model for the assessment of the activity of cancer immunotherapy agents.
(Cancer Immunol Immunother. 2017 Jan;66(1):129-140) Herter S, Morra L, Schlenker R, Sulcova J, Fahrni L, Waldhauer I, Lehmann S, Reisländer T, Agarkova I, Kelm JM, Klein C, Umana P, Bacac M.
PreComb launch Collaborative Research in Precision Oncology
PreComb Therapeutics AG, the University Children’s Hospital Zurich (KISPI), and the Inselspital, Bern University Hospital are pleased to announce a new research collaboration aimed at advancing precision oncology.
PreComb completes Series A financing round to drive commercialization
PreComb Therapeutics announced the successful completion of the Series financing round led by Koodos, a prominent investment fund specializing in transformative biotech ventures.
PreComb announces collaboration with Numab
PreComb is pleased to announce a commercial collaboration with Numab Therapeutics to evaluate one of their exploratory T-Cell engagers with PreComb’s 3DTwin platform in one of the major cancer indications.
Applications supported
Explore the individual components that power the 3DTwin® platform.