Know which arms will work, before enrolling your first patient.
cResponse™ tests your compounds directly on living human tumor tissue, with the full microenvironment intact, so pipeline decisions are grounded in a direct biological measurement rather than an animal model, a cell line, or a computational estimate alone.
CE marked,
ISO 15189:2022 Medical Laboratories
90%+ Accuracy
Prediction accuracy, PPV & NPV
against clinical outcomes
All Solid Tumors
Applicable to
all solid tumors
All Therapy Classes
Applicable to all therapy
classes and combinations
Roughly 90% of oncology drugs entering clinical trials fail, most from a lack of efficacy that preclinical models never predicted. The average cost to bring a single drug to market has climbed past $2.3 billion, with attrition concentrated late and expensively: oncology fails at the Phase II-to-III transition and at regulatory submission at nearly double the rate of non-oncology therapeutic areas.
The market's own workaround is telling. Contingent value rights, deal structures that let acquirers pay less upfront and more only if an asset later proves out, are being used more than ever in licensing deals. A CVR is a financial patch for scientific uncertainty that was never resolved before signing. cResponse™ exists to resolve that uncertainty earlier, on real human tissue, before the deal or the trial is designed.
Selecting the best treatment per indication
A few months of functional testing on human tumor tissue is enough to change the shape of a drug portfolio decision entirely. Instead of designing a sprawling, complex, multi-arm Phase I/II program to discover empirically which compound-indication pairings work, an earlier ex-vivo screen narrows the field first: a 12-arm, long, expensive study with a high failure rate becomes a smaller, focused, highly successful one, because the arms that go forward are the ones already known to have a realistic chance.
| 2D cell lines / animal models | Computational / AI simulation | PDX models | Organoid models | cResponse™ | |
|---|---|---|---|---|---|
| Biological reality | Low, no human TME | None, no biology tested | Partial, no human immune system | Partial, TME not preserved | Full, intact human TME |
| Turnaround | Weeks to months | Days | Months | Weeks | ~2 weeks, per tissue |
| Sourcing | Commercial cell lines / bred colonies | No tissue required | Surgical resection, engraftment-dependent | Biopsy or resection, culture-dependent | Frequency/incidence based |
| Validated vs. real outcomes | Poorly correlated | Population-level only | ~60–80% concordance | 65–85% accuracy reported | 90%+ PPV/NPV vs. clinical outcomes |
| Multi-compound, same tissue | Yes, but not comparative | Not applicable | Limited by engraftment | Limited by tissue supply | Up to 10 arms, one baseline |
Preclinical pipeline validation
Rank multiple candidates on the same human tumor tissue, go/no-go decisions on human-validated data rather than animal predictions.
Indication finding
Test a compound across a tissue panel to identify which indications show the strongest functional response, before trial design.
Mechanism of action & TME dynamics
Map immune activation, stromal remodeling, and resistance mechanisms in a living human tissue context.
Trial simulation, biologically grounded
A bridged ORR/mPFS range, built from a calibrated reference panel run alongside your compound, cross-checked against real-world and trial-level outcome data. The biological ground-truth layer a population-only simulator cannot generate for itself.
Biomarker discovery & stratification
Correlate response patterns with molecular or genomic features to stratify responders early, enriching trial cohort design.
Compound upgrading via combinations
Identify synergistic combination partners for underperforming compounds, particularly valuable near patent cliffs.
Human filtering for AI-discovery candidates
Provide the human biology anchor that filters AI-generated candidates against real tumor tissue before committing to development.
Pipeline audit & asset prioritization
Rank existing pipeline assets by functional potency and breadth of response, an objective priority order for R&D resource allocation.
Biological due diligence for in-licensing
Validate biological activity on human tumor tissue before in-licensing or acquiring early-phase oncology assets.
Cancer cell death
Quantitative tumor cell viability and apoptosis readouts, the primary efficacy endpoint for drug potency ranking.
Immune dynamics
Immune cell activation, infiltration, and exhaustion profiling within the intact TME, critical for IO and cell therapy assessment.
Transcriptomics
RNA-seq and gene expression profiling of drug-treated tissue, enabling pathway analysis and resistance mechanism mapping.
IHC / immunofluorescence
Protein expression and spatial distribution in tissue sections, target engagement and TME composition analysis.
ELISA / cytokine profiling
Secreted protein and cytokine quantification from conditioned media, immune activation and paracrine signaling dynamics.
Flow cytometry
Multi-parameter immune and tumor cell phenotyping from dissociated tissue, cell population dynamics post-treatment.
Proteomics
Global or targeted protein expression profiling, drug target engagement and resistance protein identification.
Genomics / biomarkers
Molecular correlates of response, matching functional outcomes to genomic or immune biomarkers for stratification strategies.
Custom endpoints
Project-specific analytical endpoints designed around your compound's mechanism and your scientific questions.