Accelerate Drug Development with
3D Microtumors and Real-Time Profiling
Our advanced tumor modeling technology enables pharma to predict patient drug response in a clinically relevant setting, identifying effective drug candidates faster and with unmatched precision.
Why Pharma & Biotech Leaders Trust PreComb
Faster & More Accurate Screening
Identify promising molecules early with high-throughput 3D tumor screening.
✔ Fully-automated drug testing
Reduce Preclinical Failure Rates
Predict drug response with patient-derived tumor models beyond dose-response curves.
✔ In vivo-like outcome analysis
Improve Success in Clinical Trials
Minimize R&D spending by testing drugs in human-relevant conditions before trials.
✔ Decrease risk & save $$$
Recreating the Tumor
Microenvironment for
Unmatched Drug Evaluation
A patient-derived approach that replicates real tumor microenvironments, including immune cells, to provide clinically relevant drug screening. By preseving key biological Interactions, the 3DTwin® model enables more accurate drug response testing across chemotherapies, targeted therapies, immunotherapies, and combination treatments, helping researches predict real patient outocmes with greater confidence.

Why 95% of Cancer Drugs Fail in Clinical Trials
Developing effective cancer therapies is time-intensive, costly, and fraught with uncertainty. Traditional drug screening models offen fail to accurately predict real-world patient responses, resulting in costly failures and delays.
Precision Drug Testing with 3DTwin® Profiler
PreComb’s 3DTwin® Profiler is an advanced, AI-enhanced drug screening platform designed to automate and standardize precision medicine. By eliminating manual inconsistencies and assay variability, we ensure reliable, reproducible results across locations, drug types, and cancer models.
- Fully-automated, versatile testing platform
- AI-powered analysis & drug response prediction
- In vivo-like accurarcy


Strict Quality
Practices
New Advanced
Instruments
Customized Lab
Solutions
A New Era in Drug Screening & Validation
PreComb’s 3DTwin® Technology bridges the gap between lab models and clinical outcomes. Our next-generation screening method uses 3D tumor twins (cell-line based or patient-derived) to replicate the human tumor microenvironment with unprecedented accuracy.
How it works:
- 3D tumor model generation
- Drug screening automation with machine learning
- Data analysis & in vivo-like response prediction
- Decision making & validation of most promising compounds
Patient-Derived 3D Models Accelerate
Every Stage of Drug Development
Discovery
Phase
Identify promising moleculess early with faster, more accurate high-throughput screening.
✅ AI-powered, high-throughput 3D tumor models replicate patient-specific conditions.
✅Retains immune cells for accurate drug screening.
✅Provides superior dose-response analysis and early failure detection.
Book A Strategy Call!
Contact Us
Preclinical
Validation
Immune-retaining tumor models predict real patient response.
✅ Patient-derived 3D tumor models precit real human response better than animal testing.
✅Enables immune-oncology drug validation for checkpoint inhibitors.
✅Allows for combinatorial drug testing, reducing risk of clinical failure.
Book A Strategy Call!
Contact Us
Repurposing
Text existing drugs on new indications faster and with confidence.
✅Tests already-approved drugs on diferent cancers using real tumor conditions.
✅Provides strong preclinical validation to support clinical trial investments.
✅Reduces R&D costs by optimizing drug repurposing strategies.
Book A Strategy Call!
Contact Us
What Researches Say About PreComb:
“One of the biggest challenges in cancer research is bridging the gap between preclinical testing and real patient outcomes. PreComb’s approach addresses this challenge by providing patient-relevant, functional models that allow us to test therapies in a way that truly mimics human biology.“
Javad Nazarian
Profesor Univerity of Zurich, Head DMG Research Center at University Children’s Hospital Zurich.

See 3DTwin® in Action
Gain Deeper Insights into
AI-Powered Drug Testing












