R&D Optimization and Cell Dynamics Prediction for Global Top 20 Pharmaceutical Firm

Published: April 10, 2024

Discover how Reveal helped a leading pharmaceutical company identify the right drug candidates to advance to late-stage clinical trials.

Drug Trials Require Money & Time

Clinical trials are expensive and take many years. Our client had developed multiple drug candidates to treat lupus, but clinical trial results for these compounds were mixed. Despite investing several years and substantial funding into drug development, our client lacked conclusive data to select individual candidates to advance to advanced clinical trial stages.

Using Machine Learning Models to Predict Clinical Outcomes Accurately

After analyzing our client’s challenges in detail, Reveal began training machine learning models. These models were trained to predict primary and secondary clinical outcomes (e.g. renal recovery) at the end of a clinical trial. A novel, neural ordinary differential equations (ODE) model was developed based on early time series data from the clinical trials to predict cell dynamics & chances for success.

Expected Impact on Clinical Trial

Currently, the Reveal-designed model is being validated in a Phase 3 clinical trial. Upon completion, it will be used by our client for selecting drugs from the pipeline to advance into later stages of clinical trials, thereby maximizing chances of approval success.

To learn how Reveal HealthTech can help your company, contact our team.

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