Generable
Generable provides a Bayesian scientific computing platform that helps pharmaceutical and biotech companies design clinical trials and connect low-level measurements like drug concentrations and tumor size to clinically meaningful outcomes such as progression-free survival and adverse events. The platform, built by core developers of the Stan probabilistic programming language, emphasizes calibrated predictions and robust performance on small datasets common in early-phase trials. Its models are explicitly defined through code, making them transparent and explainable.
- Biotechnology
- Data & Analytics
- Healthcare Technology
- Drug Discovery & Therapeutics
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Pharmaceutical and biotech companies face significant uncertainty when designing clinical trials and making go/no-go decisions. Traditional statistical methods often struggle to integrate diverse data sources, handle small sample sizes, and provide calibrated predictions about patient outcomes, leading to costly trial failures and delayed treatments.
Solution
Generable provides a Bayesian scientific computing platform for model development, optimization, and training in clinical trials. The software connects low-level measurements such as drug concentrations, tumor size, and circulating tumor DNA to clinically meaningful endpoints including progression-free survival, overall survival, and adverse events. By leveraging Bayesian inference, the platform enables companies to answer questions about trial design, biomarker predictiveness, patient response differentiation, dosing optimization, and treatment comparisons against standard of care. The platform emphasizes calibration, predictive accuracy, and robust performance on small datasets, making it suitable for the data-constrained environment of drug development. Models are defined explicitly through code rather than learned from data, ensuring transparency and explainability in predictions.
Target Audience
Leading pharmaceutical and biotech companies engaged in clinical drug development, including those designing Bayesian trials and seeking regulatory alignment with recent FDA guidance on Bayesian methods.
Features
- Bayesian modeling tools developed by core contributors to the Stan probabilistic programming language
- Support for connecting pharmacokinetic and pharmacodynamic measurements to clinical outcomes such as PFS, OS, and adverse events
- Capabilities for Bayesian trial design, including dose optimization and treatment comparison against competitors or standard of care
- Analytical tools for assessing biomarker predictiveness and distinguishing responder from non-responder patient populations
- Use of advanced computational methods including dynamic Hamiltonian Monte Carlo with the No-U-Turn Sampler and Pathfinder Variational Inference to fit models with hundreds of thousands of parameters
- Focus on calibrated predictions and accuracy, particularly in small-data settings common in early-phase trials and rare diseases