The Race to Faster Drug Development: A Quicker On-Ramp Doesn’t Help If You’re Headed the Wrong Way 

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Clinical Trial Feasibility Assessment: Speed Without Direction

FDA’s recent effort to accelerate the path from drug identification to first-in-human studies is directionally important. The goal is to reduce avoidable delays, improve early regulatory interaction, and help promising programs reach Phase 1 sooner. Excellent and much needed regulatory modernization. But if the US is serious about improving competitiveness in drug development, regulatory speed cannot be the only lever. A quicker pathway into the clinic only creates value when the science, evidence, and trial strategy supporting it are equally strong. That is the premise of this 2-part blog, and I think it becomes even more relevant as the global competition for development speed intensifies. 

A significant amount of early development is still based on assumptions that eventually become expensive commitments. We estimate how many patients meet the eligibility criteria, where those patients are located, how quickly sites can enroll them, whether endpoints are realistic in the intended population, and how sensitive the study will be to dropout, protocol complexity, or changes in treatment effect. These are often treated as study-planning questions, but they are really development decisions because they influence capital allocation, timelines, and ultimately probability of success. 

Real-world data can help answer many of these questions, but simply possessing more data does not solve the problem. Healthcare information remains fragmented across electronic health records, laboratory systems, claims, registries, prior studies, and other sources. These data have to be standardized, curated, connected, and interpreted before they can reliably inform a clinical-development decision. This is where Datacise® can create a different kind of value. The objective is not simply to aggregate information, but to create a structured evidence environment that can support population characterization, feasibility, endpoint evaluation, country and site strategy, and other decisions before assumptions become embedded in the protocol. 

Turning Data Into Development Decisions

Once that evidence environment exists, simulation becomes substantially more useful. KerusCloud® can allow teams to test assumptions around patient populations, endpoints, recruitment, treatment effects, sample size, study duration, and other design choices before significant capital is committed. Rather than designing one protocol and hoping the underlying assumptions survive contact with reality, development teams can compare alternative scenarios and understand the consequences. They can ask whether narrower eligibility improves expected treatment effect enough to compensate for slower enrollment, whether a different geographic footprint changes recruitment feasibility, or whether alternative assumptions around dropout, effect size, or historical borrowing change the probability that the study will answer the intended question. 

That changes the role of real-world evidence from something predominantly retrospective into prospective development infrastructure. If the available patient population is smaller than anticipated, we should not discover that only after site activation and months of disappointing enrollment. We should model that possibility before finalizing the development strategy. Once a trial starts, actual screening, recruitment, dropout, and emerging study information can be compared against the assumptions used during planning, allowing the program to be re-baselined as new evidence arrives. The development model becomes a continuous learning cycle in which evidence informs simulation, simulation informs design, actual trial performance feeds back into the model, and subsequent decisions are made from a stronger evidence base. 

This is where the definition of acceleration needs to become broader. Moving through the same process more quickly has value, but avoiding a wrong turn can create substantially more value. This is particularly important for smaller biotechnology companies, where one poorly conceived study can consume a meaningful percentage of available capital and years of development time. Testing assumptions before they become commitments can improve both the scientific basis of the program and the quality of discussions with regulators. 

The opportunity therefore extends beyond FDA’s ability to accelerate the front end of development. Sponsors and their research partners need to improve the evidence and decision infrastructure surrounding that pathway. The next generation of CRO support cannot be defined solely by how efficiently we execute a protocol after it has been handed to us. It should increasingly help sponsors determine whether they are pursuing the right protocol for the right population, in the right countries and sites, with assumptions that have been challenged before large amounts of time and capital are committed. Regulatory science, real-world evidence, data engineering, simulation, and AI should increasingly operate as connected capabilities rather than separate services. 

The principle behind all of this is fairly straightforward. Speed is useful, but speed without direction can simply get us to the wrong destination sooner. If FDA can help create a faster on-ramp into first-in-human development, our responsibility as an industry is to make the decisions around that on-ramp equally intelligent. Better decisions made earlier reduce wasted capital, avoid unnecessary study amendments, improve feasibility, and create a stronger foundation for everything that follows. 

And that raises the next question. Once we believe we have selected the right route, why does the rest of the journey still take as long as it does?