When trials don’t use a traditional control — What the FDA guidance says
In the usual drug-development pathway, regulators expect a trial where patients are randomly assigned either to receive the investigational drug or a comparator (placebo or active treatment). This randomized-controlled-trial (RCT) design is the “gold standard” for showing that a drug works (i.e., that the benefit is due to the drug not to other factors). For NDA/BLA approvals, FDA issued two guidance documents:
- Demonstrating Substantial Evidence of Effectiveness for Human Drug and Biological Products
- Demonstrating Substantial Evidence of Effectiveness With One Adequate and Well-Controlled Clinical Investigation and Confirmatory Evidence
However, there are settings—rare diseases, rapidly progressive conditions, or severe unmet need—where recruiting a traditional randomized control arm is difficult or ethically questionable. In those settings, sponsors may propose to compare patients treated under an investigational protocol against a group of patients drawn from an outside dataset (e.g., prior trials, patient registries, real-world data) who did not receive the investigational drug. Such a comparison group is often called an external control (sometimes “synthetic control” or “historical control”).
In this design:
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The treatment arm is prospective, treated under a defined protocol.
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The control arm comes from a separate dataset (regardless of when or how collected) and did not receive the investigational product under the same protocol.
Because the groups were not randomized together, there are extra risks of bias and confounding. The key question is: can the FDA rely on such evidence to reach its statutory standard of “substantial evidence of effectiveness”?
In February 2023 the FDA issued a draft guidance for industry titled “Considerations for the Design and Conduct of Externally Controlled Trials for Drug and Biological Products”.
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The guidance explicitly recognizes that externally-controlled trials may, in some circumstances, serve as an adequate and well-controlled investigation to support approval of a drug or biologic.
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But the guidance is cautious: it states that in many cases “the likelihood of credibly demonstrating the effectiveness of a drug of interest with an external control is low.”
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The guidance puts heavy emphasis on using patient-level data (not just summary published numbers) for the external control.
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It emphasizes key threats: unmeasured confounding, bias, differences in assessment or follow-up, intercurrent events, immortal time bias, and temporal changes in standard of care or diagnostics.
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It recommends early and frequent communication between sponsor and FDA if an external control design is under consideration.
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It doesn’t endorse a single statistical adjustment method (propensity scores, Bayesian modelling, etc.). Instead, it says the analytic plan must be prespecified, transparent, and justified in the context of the specific trial.
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The guidance makes clear this is not the default approach—it remains a specialized tool rather than the standard route.
In short: the FDA is signaling openness to external controls in selected settings—but the bar remains high.
How this ties to the two recent cases
Here’s how the guidance connects to the two real-world situations.
Case 1: uniQure and AMT-130 in Huntington’s disease
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uniQure reported a treated cohort using AMT-130 (a one-time gene therapy) and compared outcomes to a natural-history/external cohort.
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The agency essentially pushed back: although the signals were strong, the design raised concerns (especially given the lack of randomization, external control issues) and the company reported that pre-BLA discussions did not confirm a clean path.
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Under the FDA external-control guidance, the concerns would include: are the treated and external groups sufficiently similar (baseline disease features, prior therapies, timing)? Was the index date (“time zero”) aligned? Are the outcome assessments the same, and is the follow-up and endpoint definition comparable? Are missing data or unmeasured confounders plausibly influencing the result? The guidance states that if any of these are weak, the design may not credibly distinguish drug effect from other influences.
The uniQure case illustrates: even with promising effect size, the agency may conclude that uncertainties tied to external control reduce confidence in the evidence.
Case 2: Biohaven Pharmaceuticals and Vyglxia (troriluzole) in Spinocerebellar ataxia
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Biohaven’s NDA included a large externally-controlled dataset (real-world evidence for the control arm) reporting disease-slowing.
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The FDA issued a Complete Response Letter (CRL) citing concerns with the external-control-based evidence (e.g., bias, data‐quality, missing/unmeasured information). According to Biohaven, the agency rejected its drug, called troriluzole, due to issues that can be "inherent to real-world evidence and external control studies, including potential bias, design flaws, law of pre-specification and unmeasured confounding factors."
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According to the guidance, when the anticipated treatment effect size is modest, an externally controlled trial is less likely to be acceptable unless the design is very strong.
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Also the guidance emphasizes that outcome ascertainment, timing, and data source differences between arms are key threats. The Biohaven case appears to reflect those issues.
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This case reinforces that external controls are not a way to bypass rigorous design—they still demand high‐quality data, pre-specification, and detailed justification.
What this means for patients, clinicians and developers
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For patients & clinicians: When you see a newly approved drug whose pivotal evidence is based on an external control rather than a randomized trial, ask: how comparable were the groups? How solid was the external data (patient-level, good follow-up, similar measurement)? Has a confirmatory trial been required or planned?
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For developers: If you plan to rely on an external control, start very early: identify and curate the external dataset, lock the eligibility and analytic plan, engage FDA in frequent meetings, anticipate the agency will probe data quality, comparability, missing data and bias. Treat the external control design as a rigorous undertaking—not a shortcut.
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For the field of rare diseases and high-unmet-need areas (for example your interest area of pulmonary arterial hypertension): External controls offer a promising option when randomization is infeasible—but you must make the case strongly. The FDA’s guidance gives you the checklist: patient-level data, alignment of arms, clear index date, consistent endpoint measurement, robust analytics. If you can’t satisfy those, a randomized or concurrent control may still be needed.
Final thoughts
The FDA’s external‐control guidance is a signal that regulators recognize the realities of these challenging settings—but it does not mean external controls are easy, or will automatically yield approval. The recent uniQure and Biohaven cases are a clear reminder: you may have impressive effect size or compelling unmet need—but the agency will still closely scrutinize design, data source, comparability, missing data, and bias.
