The most common protocol problem we see on pre-pivotal IVD validation work is over-engineering. A sponsor with a candidate assay writes a validation protocol that reads like a pivotal trial — universal specialist assessments, central adjudication, exhaustive demographic stratification, conservative inclusion rules — and asks the CRO to execute it. The intent is regulatory rigor. The actual effect, on close to every study we've seen written this way, is recruitment failure.
This is not a fringe pattern. It is the modal pattern. We have reviewed several pre-pivotal tenders in the last twelve months alone where the protocol as written would cost the sponsor an additional $0.5M to $1M against a tighter design — and where the additional spend buys procedural rigor the FDA never asked for, while shrinking the eligible patient pool to a fraction of what the validation can actually convert.
The cost is not abstract. A validation study that recruits late is a pivotal trial that starts late. A validation study that exhausts the budget on adjudication infrastructure is a pivotal trial that starts under-resourced. Conservative validation protocols are sold to sponsors as the safe option. They are not. The four principles below describe how we scope validation work — at any stage between bench and pivotal — to actually serve the pivotal it is supposed to inform.
A validation study informs the pivotal. It does not replace it.
Conservative design at the validation stage answers questions FDA has not asked, while leaving the actual pivotal under-resourced and behind schedule.
The first question to ask of any pre-pivotal validation protocol is what it is for. The right answer is narrow. A validation is meant to do three things: confirm the assay performs against intended-use samples at scale, surface the operational issues that will appear in the pivotal, and produce a clean enough dataset to anchor a pre-submission to FDA. That is the entire job.
What it is not meant to do is pre-emptively answer every question the agency might raise. Sponsors who try to do this end up writing validation protocols with central adjudication panels, controlled ambient international shipping, multi-physician review boards, and universal application of specialist procedures — every one of which is appropriate for a pivotal, none of which is required to validate the underlying claim. The validation gets designed against an imagined pivotal, not the actual one. When the actual pivotal is later designed, the team has already burned half the budget on infrastructure the validation didn't need.
A sponsor recently asked us to revise a quote for a pre-clinical biomarker qualification study where the protocol had grown from a single-visit collection in pre-diagnosed pediatric subjects into a multi-step clinical workflow with re-diagnosis, central adjudication, and ambient international shipping. Our analysis put the cost difference at $1.9M–$2.25M against the original simpler design, with no additional regulatory benefit at the validation stage. The added complexity belonged in the pivotal, where FDA will ask for it explicitly. At the validation stage, it would have shrunk the enrollable pool and delayed the start of the pivotal that the validation was meant to enable.
For each requirement in a validation protocol, ask whether its absence would invalidate the assay's performance claim. If the answer is no, it belongs in the pivotal, not the validation.
Anchor every procedure to standard-of-care utilization.
If a required procedure is rare in routine clinical practice, requiring it universally collapses the recruitment funnel. The eligibility envelope is downstream of utilization.
The second question is whether the protocol's required procedures match the clinical workflows of the sites the study will recruit at. A protocol that requires a procedure most physicians don't routinely perform — even one that is well-validated as a tool — converts into a study that requires the site to add a non-standard step to every visit. That step has to be trained, scheduled, documented, and reimbursed. Sites that bill payers for clinical work do not absorb that cost willingly. Even if they nominally agree, the procedure becomes a deprioritized item in the visit flow, and the study stalls at first contact.
The fix is not to lower the rigor of the validation. The fix is to confirm that whatever the protocol mandates universally is something that would actually have happened anyway. If the procedure is genuinely required to validate the claim, it can usually be replicated through targeted adjudication or central review without mandating it on every subject. If the procedure is an inherited convention from a prior pivotal study at a different sponsor, it should be removed and the validation rewritten without it.
On the same revised tender, the sponsor had added universal administration of a specialist diagnostic assessment that, in U.S. claims data, applies to only a small share of patients with the relevant diagnosis. We surfaced the utilization rate against precedent submissions. Both prior cleared submissions in the category had used clinical best-estimate with central review as the reference standard, with the specialist procedure used selectively as a supporting measure rather than universally as an inclusion gate. The protocol could be restructured around adjudication of the existing diagnoses rather than re-assessment of every subject, preserving regulatory defensibility while restoring an enrollable pool.
Before a procedure is mandated universally in a validation protocol, pull the U.S. claims utilization rate for that procedure within the indicated population. If it's not approximately at or above the target enrollment rate, the protocol is unenrollable as written.
The pre-submission goes first, not last.
Pre-subs run before the validation cost less and produce stronger pivotals than pre-subs run after the validation is complete.
The conventional sequencing is: design the validation, execute it, take the results to FDA in a pre-submission, then design the pivotal against agency feedback. This sequence is wrong twice. It is wrong because it assumes the validation should be designed without FDA input — meaning that whatever was over-engineered at the validation stage stays over-engineered. And it is wrong because by the time the pre-sub feedback arrives, the budget for any meaningful protocol redesign has already been spent.
The right sequence puts the pre-sub first. A short pre-submission with a clear set of design questions, run before the validation locks, produces actionable agency feedback at the moment when the protocol can still be reshaped against it. The validation then executes against an FDA-aligned design rather than against the sponsor's worst-case imagination of one. This sequencing also reduces the number of pre-subs the program needs over its lifecycle, because the questions don't recycle.
This is the sequence we are currently executing for another diagnostics client, where we are running a pared-back validation in parallel with a pre-submission and using the agency feedback to inform the pivotal design. The pattern recurs: where sponsors run the pre-sub first, the validation costs less and the pivotal lands faster. Where they run it last, the validation produces data that has to be re-litigated against agency feedback.
Before locking the validation protocol, draft the three to five design questions you want FDA to weigh in on. If you can articulate them, you can pre-sub them. If you can't, the protocol is not yet ready to execute.
Every requirement should pass a claims-data check.
U.S. claims data tells you what is real. Feasibility forms tell you what sites wish was real. Validation protocols should be sized against the former.
The fourth principle generalizes the first three. Every requirement in a validation protocol — every procedure, every demographic stratum, every sample type, every collection cadence — has a real-world utilization rate that can be looked up in U.S. claims data. That rate is the ceiling on what the protocol can recruit against. Sites can be motivated, recruitment vendors can be deployed, but no operational lift will sustainably exceed the underlying rate at which the requirement is actually performed in routine care.
This is the most undervalued check in pre-pivotal study design. The conventional alternative — sending feasibility forms to candidate sites and asking how many eligible patients they see per month — is systematically aspirational. Feasibility forms are sales documents. Sites overstate eligible counts because they want the work, and especially overstate counts for cohorts they don't routinely see. The corrective is not to interrogate the forms harder; it is to check the protocol against the data that already exists.
Across our rescue engagements we observe the same pattern: the prior CRO trusted feasibility-form responses, the protocol was scoped accordingly, and the actual eligible pool turned out to be a fraction of what the forms had predicted. Claims-data validation reverses the cost of this error. It is far cheaper to discover an unenrollable requirement in week two of design than in month nine of execution.
Before a validation protocol locks, every required procedure, demographic constraint, and sample type should be checked against U.S. claims utilization within the target indication. Each one whose utilization rate is materially below the target enrollment pace is a requirement that needs to be removed, narrowed, or replaced with adjudication.
What this framework rules out.
The four principles above describe how to scope validation work that actually serves the pivotal it is meant to inform. They also rule out a few things sponsors often defend.
They rule out protocol conservatism as a regulatory strategy. Conservative validation protocols do not produce stronger regulatory profiles. They produce delayed pivotals, exhausted budgets, and recruitment failures that get re-litigated against the agency anyway. The right way to manage regulatory risk at the validation stage is to engage the agency early, not to write a protocol that imagines every objection in advance.
They rule out feasibility forms as primary evidence for site capability. Forms are useful for confirming logistics and PI engagement. They are not useful for predicting the eligible patient pool against any procedure that diverges from standard of care. Claims data and EHR analytics have to do that work.
They rule out pivotal-grade infrastructure at the validation stage. Central adjudication panels, controlled ambient international shipping, universal specialist procedures — these are pivotal-stage requirements. Building them prematurely doesn't accelerate the pivotal; it raids its budget.
The framework is not closed. When the study outcome matters, you call RDI. Right-sizing the validation is how the pivotal gets to start on time.