During enrollment planning, genetic eligibility is often treated as a therapy area question, settled once a program knows which organ system it targets. Genetic recruitment may also be seen as a late-phase concern, addressed when a study approaches the scale of Phase 2 or Phase 3. Today’s active trial pipeline supports neither assumption.
Sano Genetics recently published a whitepaper classifying 4,565 actively recruiting trials across the US, UK, EU, Japan, and South Korea using data from ClinicalTrials.gov. The data highlights a gap between how genetic recruitment is planned and where it concentrates. This is a major issue for clinical development and operations leaders who own enrollment timelines and screen-failure rates. Scoping patient-finding around the wrong variable, or scoping it too late, sets a program up to discover its hardest recruitment work after the budget and timeline are already fixed. The pipeline points to a more reliable way to anticipate that work, and to a reason for resolving it before a program commits to a late-phase design.
Across the pipeline, one in four trials involves genetics and 15% target a rare disease. That quarter is distributed unevenly, and it follows treatment modality far more closely than therapy area. Gene therapy trials are 94% genetic and 67% rare disease. RNA therapy trials are 59% genetic and 50% rare disease. Protein replacement sits at 45% and 63%. Approximately 28% of small molecule trials, the largest modality at 1,900 trials, involve genetics.
How a medicine works therefore predicts where patient identification may be challenging more accurately than the organ it treats. That signal has a forward-looking use, because the pipeline behind these trials leans toward modalities with high genetic content. As of Q4 2024, 2,117 gene therapies were at some stage of development globally, with 318 in Phase 1 and only 35 in Phase 3, according to the ASGCT and Citeline landscape report. A pipeline that early-stage and that concentrated in gene therapy is one where genetic-eligibility complexity is the baseline condition rather than the exception. Reading a portfolio by modality tells a feasibility team where narrower populations and harder matching will surface before a single site opens.
Timing is where the planning gap does the most damage, because genetic involvement is not spread evenly across a program's life. It spikes at combined Phase 1/2 trials, where 51% involve genetics across 553 studies, roughly double the rate of single-phase trials. The eligibility burden arrives while a program is still establishing dose and safety, well before the point where phase-by-phase plans typically budget for large-scale recruitment.
For programs that wait until later-stage development to build genetic recruitment infrastructure, that timing means the infrastructure arrives after the hardest identification work has already begun. Enrollment timelines reflect the strain. An analysis of industry-sponsored Phase 3 trials registered on ClinicalTrials.gov tracked this directly. Median recruitment duration grew from 13 months in 2008 to 2011 to 18 months in 2016 to 2019, a roughly 38% increase. Recruitment and retention remain among the hardest operational challenges study sites face, as a 2020 review in Perspectives in Clinical Research documents. A molecular qualifier introduced late compounds both pressures, since every added criterion shrinks the eligible pool and lengthens the path to a confirmed match.
The cost of discovering the recruitment burden late is quantifiable. The Tufts Center for the Study of Drug Development's 2024 analysis of 447 protocols put the direct cost of running a Phase 3 trial at $55,716 per day, with a single day of delayed launch worth approximately $800,000 in lost prescription sales. Those figures replace the "$4 to 5 million per day" estimate that circulated for decades, which Tufts explicitly corrects. Every week of enrollment overrun on a genetically defined study converts directly into those numbers.
Protocol changes compound the exposure. Each Phase 3 amendment introduces close to three months of delay and up to $1 million in unplanned direct costs, according to WCG's benchmarking analysis drawing on Tufts data. A program that under-scopes its genetically eligible population, then amends the protocol to widen criteria or add sites once recruitment stalls, absorbs both the delay and the cost of the change.
These losses accumulate between protocol and patient, particularly at the genetic identification step, where the route from a positive result to a dosed patient runs through consent, confirmatory testing, and logistics. Building the infrastructure to manage that route early is the cheaper position, because the alternative prices delay into every subsequent phase.
A more durable approach starts by screening the portfolio on modality and phase rather than therapy area alone. Programs built on gene therapy, RNA therapy, or protein replacement should assume a high genetic-eligibility burden from the outset and resource patient identification accordingly. Combined early-phase studies deserve the same scrutiny, since the pipeline shows they carry genetic complexity at nearly the rate of dedicated late-phase work. Rare disease density adds a further consideration, because it concentrates in mid-size specialists rather than the largest sponsors by volume, which shapes where genetically defined populations are already being actively recruited.
Scoping early also changes what recruitment infrastructure has to do. Rather than finding patients for one protocol and starting over for the next, a sponsor building across a multi-asset precision portfolio can treat patient identification as reusable capacity, where genetic testing done once can qualify a participant for future studies and engagement persists between programs. That reframing turns recruitment from a per-trial cost into a compounding asset. It also depends on integrating genetic testing into the recruitment pathway, so a patient who expresses interest can be genetically screened and confirmed without leaving the process.
The pipeline gives clinical development leaders a concrete way to anticipate genetic recruitment work: read modality and phase rather than therapy area, and assume the burden lands early. The trials already recruiting show where narrower populations and harder identification concentrate. The cost data shows what it takes to resolve that burden after a program is locked into a fixed timeline.
Sano Genetics builds for the position that pipeline describes. The platform unifies patient recruitment, genetic testing, and long-term engagement into one modular system, ISO 27001 certified and compliant with HIPAA and GDPR. That lets sponsors scope patient-finding by modality and stand it up before later-stage development, when it may already be too late.
Teams planning genetically stratified programs across a portfolio can get in touch to work through where their own pipeline concentrates that burden.