五个可预防的临床设计陷阱以及如何避免

IAN FISHER, Head of Development Analytics, Regulatory Affairs and Drug Development SolutionsKEITH MCDONALD, FRCPharm, FFPM(Hon.) Regulatory Strategy AdvisorPARVIN FARDIPOUR, PhD, Decision Sciences Table of contents Introduction1Pitfall #1 — Lack of a cohesive development plan2Pitfall #2 — Designing a trial that cannot demonstrate clinically meaningful effect2Pitfall #3 — Designing a trial that is not operationallyfeasible4Pitfall #4 — Misalignment withregulators4Pitfall #5 — Failure to generate evidence from representative patient populations5Planning ahead = Planning for success6About IQVIA6About the authors7 Introduction The outcome of a therapeutic development program is strongly influenced byclinical trial design, coherence, and execution. While late-stage attrition maybe attributed to inadequate demonstration of safety or efficacy, often, the truefailure is insufficient upstream planning, including lack of early engagement withregulators and alignment between clinical objectives and commercializationstrategy. Such gaps lead to operational risks, protocol amendments, budgetoverruns, timeline failures, regulatory non-compliance, and extensive programrework, which, in concert, may result in program failure. To increase the likelihoodof trial success, sponsors should proactively address the five critical pitfalls inclinical design and strategically implement corresponding solutions. Pitfall #2 — Designing a trialthat cannot demonstrateclinically meaningful effect Pitfall #1 — Lack of a cohesivedevelopment plan A clinical program may fail when it is designed totest a scientific hypothesis rather than meet explicitregulatory decision criteria, resulting in endpoints,doses, or populations that cannot support substantialevidence or an acceptable risk-benefit profile. Whilesponsors often prioritize rapid initiation of First-In-Human (FIH) trials to establish safety and tolerability,advancing without a comprehensive development planthat integrates nonclinical toxicology, pharmacokineticand pharmacodynamic (PK/PD) characterization, anddose-response assessment introduces substantialdownstream risk. While such trials are often perceived as failures ofscience, their underlying cause is often driven by poordesign choices. One such shortcoming is inadequatestatistical power due to insufficient sample sizesor overly optimistic assumptions about effect size.Underpowered studies can miss true treatmenteffects and produce false negative results, leading topremature program termination. Another is inadequatedose selection or failure to establish a dose-responserelationship. As a result, trials proceed with poorlyjustified doses, rendering difficult-to-interpret safety andefficacy outcomes. The solution First and foremost, a sponsor should develop arobust clinical development plan that articulates clearscientific rationale, context of use, clinical benefit,target indication, and evidence generation strategy tosupport regulatory and payer decision-making alongsidedownstream development activities. For regulators,an effective development plan defines the intendedtreatment population, clinically meaningful endpoints,biomarkers, population enrichment strategies, controland comparator selection, dose optimization, andpotential labeling claims. Sponsors should establishclear linkages between preclinical data, mechanism ofaction, PK/PD findings, proposed clinical outcomes,and explicitly define estimands, including handling ofintercurrent events to ensure alignment between trialobjectives, analysis, and interpretation. Other risks included inappropriate endpoint selection,i.e., surrogate or biomarker endpoints whoserelationship is not sufficiently validated in relation topatient-relevant outcomes, as well as poorly justifiednoninferiority margins, unreliable active controls, andheterogenous or mismatched patient populations. Trialswith insufficient duration or follow-up may also fail tocapture the true benefits of slow-acting or disease-modifying therapies. In therapeutic areas with strong placebo responses,failure to account for placebo effects in design canfurther obscure true treatment effects. Trials may yieldstatistically valid results with wide confidence intervalsthat encompass both clinically meaningful and minimaleffects; leaving decision makers unable to conclude thatthe therapy provides real patient benefit. An effective development plan is strengthened bystructured, objective decision-making frameworks thatsupport portfolio and program development. Examplesinclude AstraZeneca’s 5R Framework or Pfizer’s DecisionIntegrity & Continuous Evaluation (DICE). Theseframeworks are designed to systematically evaluatedevelopment risk, scientific rationale, and differentiationusing predefined criteria, and therefore enable go/no godecisions and indication prioritization. Ultimately, these programs fail not because the assetlacks activity, but because the trial design cannotreliably demonstrate a clinically interpretabl