Clinical trials have become more scientifically sophisticated, yet many of the operational challenges behind them remain stubbornly unchanged. In this episode of Breaking Protocol, Ram Yalamanchili speaks with Ash Jayagopal, Chief Scientific and Development Officer at Opus Genetics, about the realities of running clinical trials in the era of gene therapy and ultra-rare diseases.
Ash brings a rare perspective from the front lines of ophthalmology drug development, where some programs target patient populations measured in the hundreds rather than the thousands. In these environments, traditional clinical trial infrastructure begins to break down. Finding patients becomes a global search problem. Published prevalence numbers often prove unreliable. Registries require constant maintenance. And clinical trial planning still depends on fragmented datasets that were never designed for modern drug development.
The conversation explores why patient identification remains one of the most persistent bottlenecks in clinical trials. Ash explains how inaccurate diagnostic coding, inconsistent genetic testing, and fragmented clinical data make it difficult to identify eligible patients even when they technically exist within healthcare systems. Registries and centers of excellence have helped improve visibility, but they still require significant manual effort to maintain and query.
Ram and Ash also discuss how automation, data infrastructure, and emerging AI tools could fundamentally change this landscape. If patient registries, clinical data, and eligibility criteria could be integrated and continuously updated, trial sponsors could move from a “needle in a haystack” search to a far more targeted model of recruitment. The potential for AI-assisted patient identification, registry management, and trial planning represents a major opportunity for modernizing clinical operations.
Beyond patient recruitment, the discussion turns to regulatory innovation. Ash outlines how agencies such as the FDA are beginning to adapt to the realities of rare disease drug development, including more flexible manufacturing requirements and adaptive trial designs such as Bayesian approaches. These changes acknowledge the practical reality that some gene therapies may require only a handful of manufacturing batches to treat an entire patient population.
Finally, the conversation examines why certain regions outside the United States sometimes move faster in early clinical development. Special regulatory pathways, investigator-initiated trials, and rapid proof-of-concept mechanisms can accelerate early studies, though Ash emphasizes that the fundamentals remain unchanged: successful trials still depend on strong clinical networks and centers of excellence that know where the patients are.
At its core, this episode explores a simple but important question: clinical science is advancing rapidly, so why does clinical trial execution still lag behind? The answer may lie in how the industry modernizes its operational infrastructure.
For leaders in biotech, clinical development, and clinical operations, this discussion offers a candid look at where the system works today, where it breaks down, and how emerging technology could reshape the future of clinical trials.
