Twintafo
Twintafo provides AI-powered digital twin simulations for oncology clinical trials, enabling biotech, pharma, CROs, and biostatistics teams to stress-test design decisions and evaluate external controls before enrolling real patients. The platform generates synthetic longitudinal patient cohorts to compare against observed data, surfacing recruitment bottlenecks, control-arm challenges, and site variability early. It follows a three-step loop—define simulations, generate digital twins, and stress-test outcomes—to quantify risk and improve trial robustness.
- Artificial Intelligence
- Healthcare Technology
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Oncology trial planning carries high uncertainty, with slow recruitment, difficult-to-enroll control arms, and heterogeneous patient populations across sites creating unpredictable variability. Minor protocol design choices can introduce bias and derail interpretation, adding quarters to a program and compounding risk across the pipeline. These challenges delay timelines and inflate trial costs before teams commit real patients and budgets.
Solution
Twintafo provides AI-powered digital twin simulations that let oncology trial teams model outcomes, stress-test design decisions, and evaluate external controls before investing in real-world studies. The platform creates realistic synthetic patient journeys—calibrated digital twins—that can be compared against observed cohorts to highlight recruitment bottlenecks, control-arm challenges, and site variability early. A structured three-step loop guides users from defining simulations and aligning on endpoints and population profiles, to generating synthetic longitudinal patient cohorts, to stress-testing power, bias, timelines, and robustness. The platform surfaces pressure points before enrollment, helping teams reduce trial delays and cost overruns while maintaining scientific rigor.
Target Audience
Primary users are biotech and pharma companies de-risking oncology programs before pivotal investment, CROs strengthening proposals with simulation-backed evidence, and biostatistics teams pressure-testing design decisions with transparent assumptions.
Features
- Digital twin simulation engine that generates synthetic longitudinal patient cohorts calibrated to trial assumptions
- Live comparison of observed versus simulated cohorts with confidence bands and forecasts to stress-test outcomes before enrollment
- Sensitivity analyses and structured decision summaries to evaluate power, bias, timelines, and robustness
- Reproducible simulation configs for transparent, auditable trial design decisions
- Privacy-first architecture producing synthetic outputs only, designed for trial workflows
- Tailored deliverables including trial simulation plans, synthetic longitudinal datasets, and structured decision summaries