Digital Twins
Digital Twin for clinical research and development
Nova's Jinkō platform builds a Digital Twin of each patient: their physiology, their disease progression, and their response to treatment. Run the trial before you run the trial.
How our Digital Twin technology works
Data integration
Combines heterogeneous data sources such as clinical trial data, real-world evidence, molecular profiles, biomarkers, and patient characteristics.
Digital patient creation
Generates either high-fidelity Digital Twin patients or synthetic patients, enriched with demographic, disease-specific, and biomarker attributes.
Causal model
Applies biophysical and pharmacological models grounded in the biology of disease and drug mechanisms.
Trial simulation
Runs large-scale simulations of digital patients through trial protocols in minutes.
How the two approaches work together
Two independent lenses on the same question, stronger when combined.
Data-driven models
Statistical and machine learning
Asks: What patterns do the data show?
- Learns from large historical and clinical datasets
- Strong at detecting associations at scale
- Quantifies statistical uncertainty and sensitivity
Mechanistic models
QSP and Digital Twins
Asks: Given the biology, is this outcome plausible?
- Encodes disease and drug mechanisms
- Simulates unobserved drivers and what-if scenarios
- Works with sparse, varied data
Stronger together
Confidence increases: the result is both statistically supported and biologically plausible.
The discrepancy is informative: it can reveal hidden confounding, a population mismatch, or a model to revisit.
Nova combines both lenses. Triangulating across statistical and mechanistic methods yields credible, decision-ready evidence rather than relying on a single model.
Digital Twins, real-world impact
Smarter trial design
Test unlimited trial scenarios to optimize protocols, identify best responders, and avoid costly late-stage failures.
Accelerated, cost-efficient trials
Reduce required sample sizes and trial duration by supplementing or substituting control arms with credible Digital Twins.
Patient-centric insights
Explore rare populations, improve selection, and reduce exposure to ineffective treatments, ethically and efficiently.
Scientists, engineers, biologists, clinicians, and medical doctors working together
Validated models across therapeutic areas, including 12+ trained and validated in-house
Across oncology, cardiology, hepatology, infectious diseases, and rare diseases