1507P - Revolutionizing clinical trials: Harnessing artificial intelligence for optimized phase III studies in pancreatic cancer

Background

Pancreatic cancer (PC) constitutes over 3% of new cancer diagnoses in Europe, yet accounts for 7% of cancer deaths, ranking as the fourth leading cause of cancer mortality. PC remains lethal in over 80% of patients, with a 5-year relative survival rate of 13%. Although new therapies are explored, interventional clinical trials for PC represent merely 5.13% of global cancer trials. Addressing this high unmet need necessitates probing questions to enhance patient access to novel therapies and ensure robust trial design and execution.

Methods

Utilizing the Aurora Suite®, we generated data aggregation, visualization, and analysis. The Aurora Suite® is an advanced web application that leverages machine learning and AI to support the design, planning, and execution of clinical trials. Our approach integrated public domain and primary data sources. The trial NCT02184195 (POLO)- served as a reference and was compared to Aurora Suite® results for developing trial optimization recommendations.

Results

Using the Aurora Suite® and advanced AI capabilities in trial optimization, we compared the phase III PC reference trial 'POLO' with our results. We identified that POLO's country distribution includes a mix of favorable and non-favorable countries: Tier 1 (most favorable): Belgium, Canada, and Germany. Tier 2 (moderately favorable): Australia, Israel, Italy, and Great Britain. Tier 3 (minimally favorable): South Korea, The Netherland, and France. The Aurora Suite® identified additional countries and sites that could have been used to optimize POLO’s trial strategy and enrollment. POLO enrolled 154 participants across 103 sites over 49 months, achieving a 0.03 patients/site/month (p/s/m) enrollment rate. Our analysis indicates an industry median enrollment of 0.12 p/s/m. The Aurora Suite® could have enabled POLO to enroll more than 200% higher, whilst reducing sites required and enrollment time by 30%.

Conclusions

Innovative data-driven decision tools offer potential for expediting PC clinical trials, reducing trial duration, and providing enhanced efficiency leading to accelerated market access for new therapies and amplified patient benefits.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Aurora Analytica A.S.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.