Our approach
AI drug development
Challenges
Clinical trials are slow and expensive
Our solution: Optimized Development Engine
Better trials mean better drugs, sooner
AI drug development optimization
Why Owkin?
Continued conversations with EMA and FDA including an EMA letter of support for our data-driven covariate adjustment approach and a CE-marked AI diagnostic tool: MSIntuit CRC.
Access to continuously enriched, up-to-date, multimodal data representing the latest standard of care and capturing disease heterogeneity and the tumor microenvironment.
Renowned AI expertise with # published papers in leading journals, two strategic alliances with leading biopharma companies, and AI digital pathology diagnostics.
Case studies
We respond to the rapidly changing regulatory landscape
New guidelines from regulators encourage use of real-word evidence. FDA (2021) and EMA (2015) published guidelines on the use of RWE in regulatory submissions. Both regulators outlined that covariate adjustment improves the efficiency of analysis and produces stronger and more precise evidence if the covariates are prognostic.
The European Medicines Agency (EMA) has issued Owkin with a letter of support for our proposed statistical adjustment on deep learning prognosis covariates obtained from histological slides. Our method uses the predictions of two deep-learning models, MesoNet and HCCnet, as prognostic biomarkers for the adjustment of efficacy analysis on the overall survival of life-prolonging drugs in randomized phase II and phase III clinical trials.
AI external control arms
De-risk early clinical trials
Owkin synthetic control arms increase the confidence in value of a single arm trial
Inclusion criteria models
Identify subgroups of patients
Inclusion criteria models to define patient subgroups to improve clinical trial recruitment
Data-driven covariate adjustment
Identify prognostic signal
Evidence-based covariate adjustment to improve trials