Phesi's latest analysis of Phase III clinical trials indicates that overcollection of data, driven by increasingly complex protocol designs, is significantly delaying drug development timelines and increasing the burden on patients. The analysis, derived from Phesi's AI-driven Trial Accelerator platform, examined 2,401 industry-sponsored Phase III trials that have reached their primary endpoint since January 2020.
Impact of Excessive Outcome Measures
The study revealed a striking correlation between the number of outcome measures included in a trial protocol and the percentage of those outcomes reported in the results. On average, over a third (35%) of outcome measures went unreported. Trials with fewer than the median number of outcome measures reported 94% of those measures, while those collecting more than the median reported only 56%.
Dr. Gen Li, founder and CEO of Phesi, emphasized the importance of collecting the right data at the right time. "Many of the clinical trials we analysed had redundant outcome measures. For example, trials often use several different physical performance status measures on the same patients – which is a huge added burden for that patient even though each scale is measuring the same thing. This also puts undue pressure on investigator sites."
Enrollment and Trial Duration
The analysis also demonstrated that a lower number of outcome measures correlated with better site enrollment. A direct comparison of type 2 diabetes trials from two different sponsors showed that the sponsor using a median of 25 outcome measures had lower site enrollment performance (10.2 patients per site) and a lower enrollment rate (0.46 patients per site per month) compared to the sponsor with a median of only 10 outcome measures (11.2 patients per site and 0.53 patients per site per month, respectively).
In one specific T2DM trial, an outlier protocol with 39 outcome measures was initially planned to complete in 19 months but overran by seven months, ultimately taking 26 months.
Focus on Relevant Data
Phesi's findings suggest that investigators should avoid "data FOMO" and focus on collecting only the data they truly need. According to Dr. Li, being more precise with outcome measures can streamline site selection, accelerate patient recruitment, and improve the quality of collected data. These benefits collectively lead to a better return on investment, a critical consideration in the current economic climate for the pharmaceutical industry.
The top 5 diseases analyzed in the study were COVID-19, Type 2 diabetes, atopic dermatitis, non-small cell lung cancer, and cystic fibrosis.
