From mechanism to phenotype: What fits in a basket trial

Developmental and epileptic encephalopathies (DEEs) are etiologically heterogeneous, pharmacoresistant, early life epilepsies and neurodevelopmental plateau, stagnation, or regression.1 Approximately 50% of affected individuals have identifiable genetic causes, with more than 1000 implicated genes.2 Conventional epilepsy trials have historically been anchored in three dominant paradigms: syndrome-specific (e.g., Dravet syndrome3), etiology-specific (e.g., tuberous sclerosis complex and mTOR inhibitors4), and seizure-type-specific designs (e.g., focal onset seizures5). Although scientifically rigorous, these paradigms exclude most individuals with rare DEEs whose phenotypes do not map neatly onto categorical trial structures, leaving treatment decisions reliant on off-label or compassionate-use therapies with limited supporting evidence.6 Master protocols—basket, umbrella, and platform trials—were originally developed in oncology to overcome analogous constraints enabling multiquestion trials under one infrastructure.7 Umbrella trials evaluate multiple targeted therapies within a single disease defined by shared clinical context; platform trials allow continuous addition or discontinuation of arms using adaptive rules (Figure 1). Their innovative designs provide a framework to overcome methodological and regulatory barriers that constrain evidence generation for rare disorders. They tailor interventions to patient-specific factors, including genetic variants, biomarkers, and other treatment-relevant characteristics.7 Monogenic epilepsies with mechanistic convergence (e.g., sodium channelopathies, GATORopathy-spectrum disorders) could support cross-syndrome or cross-etiology stratification with potential utility for these designs. After a successful phase 2 randomized clinical trial,8 the US Food and Drug Administration (FDA) authorized a phase 3 multisyndrome trial for multiple DEEs.9 It marks a regulatory milestone that deserves recognition but also a critical analysis regarding its potential, limitations, and the evolving methodology of basket analog trials for DEEs, particularly for enabling phenotype-based approaches to accelerate ethical and efficient treatment discovery. Basket trials or bucket trials emerged in oncology as histology- or tumor-agnostic trials evaluating the efficacy of a molecular or a biomarker-driven targeted therapy across multiple cancer types, despite location. In single-arm trials, all participants receive the same targeted therapy, and efficacy—typically measured by overall response rate or progression-free survival—is evaluated within biomarker-defined subgroups. In randomized trials, patients are assigned to the targeted therapy or a control arm, allowing direct comparison of efficacy outcomes and providing stronger evidence of treatment benefit.10 Therefore, baskets are not “mixed bags,”11 they represent rigorously designed studies conducted under a single master protocol, within which each basket functions as a predefined, hypothesis-driven, and statistically independent substudy that may share information. The “basket” denotes a highly planned and systematic grouping based on shared biological rationale, not a random aggregation.11 Basket trial methodology has been slowly and asymmetrically incorporated into neurology, with the earliest and most robust applications arising in neuro-oncology. This trajectory mirrors that of medical oncology more broadly; tumors provide clear molecular targets, well-established biomarkers, and quantifiable radiological endpoints, features that facilitate the operationalization of master protocols. For this reason, the field of neuro-oncology currently offers the clearest and most mature examples of basket designs applicable to neurological disorders. The multicenter abemaciclib basket trial represents one of the strongest demonstrations of a biomarker-driven neurological basket protocol. Several central nervous system tumor groups harboring CDK4/6 pathway dysregulation were prospectively evaluated within a unified master protocol. The meningioma subgroup achieved 68.2% 6-month progression-free survival, markedly surpassing historical controls and prompting expansion of that cohort. In contrast, other subgroups—including gliomas and additional CDK4/6-altered tumors—did not meet prespecified activity thresholds and were not advanced to expansion stages.12 This divergence illustrates the main principle of basket methodology; each subgroup constitutes an independent, hypothesis-driven experiment designed to test a specific mechanistic prediction, rather than to guarantee uniform benefit across all biomarker-linked diseases. This is precisely why the abemaciclib trial serves as the most instructive exemplar for neurology; it demonstrates the feasibility, the biological rationale, and the necessary selectivity that define successful basket architectures. Beyond neuro-oncology, early foundational work is emerging across other neurological domains. In rare neurological diseases, a novel pharmacometrics-informed trial simulation framework has been proposed to address settings where patient numbers are extremely limited and natural history data are sparse.13 Using autosomal recessive spastic ataxia of Charlevoix–Saguenay as a model, this approach integrates nonlinear mixed-effects modeling and likelihood-based testing to enhance statistical power and reduce bias in small, mechanistically similar cohorts. Such methodology could support multidisease basket trials across rare ataxias, leukodystrophies, and mitochondrial disorders, where mechanistic clustering is biologically plausible.13 In Alzheimer disease, trial eligibility increasingly mandates biomarker-confirmed pathology such as amyloid positron emission tomography (PET) or tau PET imaging.14 This shift is effectively creating the conceptual foundation for future cross-disease tauopathy trials, in which diverse neurodegenerative disorders linked by tau accumulation—progressive supranuclear palsy, corticobasal degeneration, frontotemporal lobar degeneration—could theoretically be evaluated under a single biomarker-anchored therapeutic protocol.14 Collectively, these examples illustrate that although true basket trials are not yet widely implemented in neurology, the methodological tools, biomarker frameworks, and mechanistic rationales are gradually evolving. Neuro-oncology provides the clearest proof of concept, and adjacent fields are now generating the biological and methodological prerequisites required to translate basket principles into broader neurological disease categories. Although the phase 3 bexicaserin trial (NCT06719141)9 has been mislabeled as a basket trial, it employs a cross-syndromic inclusion strategy within a unified trial framework translating some of these methodological principles into epilepsy. The design enables broad generalizability while retaining regulatory robustness. Bexicaserin was granted the FDA Breakthrough Therapy Designation, underscoring unmet needs and regulatory receptivity.9 Unlike basket trials that cluster by molecular biomarkers, the DEE trial clusters by shared clinical phenotypes—severe, early onset, and drug-resistant epilepsy with modification of neurodevelopment trajectory—representing network dysfunction rather than isolated etiologies or syndromes. These phenotypic anchors serve as unifying endpoints across heterogeneous DEEs. The phase 3 bexicaserin trial (NCT06719141)9 is not a basket trial in the strict regulatory or methodological sense. Instead, it has a unified, cross-syndrome phase 3 design that enrolls multiple DEEs under a common clinical phenotype: severe, early onset, pharmacoresistant epilepsy with developmental impairment. This structure retains classical phase 3 trial rigor (fixed intervention, randomization framework as applicable, prespecified endpoints) while incorporating features inspired by master protocols,15, 16 such as harmonized inclusion criteria across phenotypically similar but etiologically diverse DEEs. This design therefore represents a hybrid, cross-syndromic phase 3 framework, bridging conventional syndrome-specific trials and future mechanism- or phenotype-anchored master protocols. Although not a master protocol, it represents the evolving flexibility of regulatory agencies and trajectory of epilepsy trial methodology and provides a critical empirical anchor for the conceptual frameworks. This bold approach faces its challenges, as learnt from oncology. As summarized in Table 1, the experience from oncology shows that basket trials succeed primarily when the biological premise is robust, the biomarker exerts a consistent functional effect across tissues, and the statistical and operational frameworks are sufficiently rigorous to detect true signals.7, 10-12, 15, 16 Conversely, failures tend to arise when the biomarker is not a true disease driver, when histology-specific resistance mechanisms dominate, or when methodological limitations—such as underpowered variant-specific cohorts or permissive inclusion of variants of uncertain significance—compromise interpretability. These examples illustrate how the strengths and weaknesses of basket trial methodology emerge directly from the interaction between (1) biological validity, (2) diagnostic precision, (3) statistical design, and (4) feasibility constraints. Such insights provide a conceptual foundation for considering how mechanism-based trial structures might be adapted to genetically defined epilepsies, where similar issues of heterogeneity, developmental timing, and biomarker reliability will determine the feasibility of cross-syndrome approaches. From a methodological perspective, basket trials can be understood as quasiexperimental designs in which genetically defined subgroups share a common mechanism of disease and receive the same intervention. The analytical challenge is therefore the comparison of multiple groups under a unified biological framework, a problem that classical statistics has addressed for decades.7, 10-12, 15, 16 For numerical outcomes, models such as analysis of variance with appropriate post hoc procedures (Tukey for homoscedasticity, Games–Howell for heteroscedasticity, or Steel–Dwass variants for nonnormal data) remain robust and transparent. For categorical outcomes, logistic or multinomial regression with modern multiplicity adjustments—preferably Holm rather than overly conservative Bonferroni corrections—achieves adequate control of type I error. These methods do not eliminate false-positive risk but use distributions that explicitly incorporate the number of comparisons, thereby limiting the inflation that arises when multiple hypotheses are evaluated simultaneously.7, 10-12, 15-17 Frequentist and Bayesian frameworks are applicable to basket trials.17 Frequentist inference treats population parameters as fixed and unknown, providing point estimates, confidence intervals, and p-values derived entirely from the likelihood. Bayesian inference incorporates prior knowledge through probability distributions, producing posterior estimates and credible intervals, but depends heavily on the justification and specification of these priors, an issue that becomes critical when sample sizes are small. Contrary to common belief, Bayesian models do not “solve” the small-sample problem. Bayesian methods reweight information toward the prior when data are sparse or weak, which can stabilize inference but does not necessarily increase precision. The resulting estimates depend strongly on how well the prior reflects the underlying biological or statistical context. In settings where genetically defined subgroups may share correlated responses, hierarchical or multilevel models—estimated either through mixed-effects frequentist methods or Bayesian hierarchical structures—allow partial pooling of information while acknowledging subgroup heterogeneity. Such models rely on the assumption of exchangeability among subgroups, which must be explicitly justified in light of biological plausibility and prior evidence. These approaches do not replace careful design or adequate sample size but provide coherent ways to model the biological relationships that motivate basket trials in the first place. The FDA and the European Medicines Agency (EMA) have recognized the potential of master protocols for the development of innovative cancer therapies; however, their regulatory frameworks diverge significantly. The FDA's Complex Innovative Trial Design initiative is part of its broader strategy to enhance the efficiency and speed of the drug approval process. The FDA aims to modernize protocols allowing for flexibility in evidence generation. This approach supports the integration of biomarker-driven patient selection, providing an opportunity for more personalized treatments to enter the market more quickly.18 Additionally, the FDA's Real-Time Oncology Review (RTOR) accelerates the review process, allowing clinical data assessment as it becomes available, rather than waiting for the completion. The success of RTOR is a paradigm shift in an era when timely patients' access to innovative cancer treatments is critical.19 The EMA, through its Adaptive Pathways and Priority Medicines (PRIME) program, allows for a stepwise licensing process, enabling earlier access to new drugs by granting conditional approvals based on preliminary data, while requiring ongoing studies to confirm benefit–risk balances postapproval. The PRIME program was launched to prioritize and provide guidance for drugs addressing unmet medical needs, underscoring the commitment to facilitating innovative therapeutic solutions while ensuring rigorous safety and efficacy evaluations. Evidence generated through studies within the PRIME framework is anticipated to be adapted based on real-world data, aligning closely with patient needs and outcomes.20 In summary, both the FDA and EMA are modernizing the drug approval landscape with their respective initiatives, although with distinctive methodologies and regulatory frameworks. Whereas the FDA focuses on accelerated timelines and flexible designs, the EMA emphasizes iterative evidence generation alongside stringent postmarketing obligations. This regulatory divergence holds particular importance for neurology; as basket trial frameworks evolve, parallel dialogue with agencies will be critical to harmonize endpoints and extrapolation strategies. A critical and underdiscussed regulatory divergence with direct implications for epilepsy drug development concerns the treatment of adult and pediatric populations as separate licensing pathways. Whereas oncology increasingly permits age-spanning, tissue-agnostic approvals when disease biology and treatment response are conserved, neurology remains largely bound to sequential adult-first authorization, followed by delayed pediatric evaluation. Both the FDA and EMA acknowledge the limitations of this approach for early onset, lifelong disorders such as DEEs and, consistent with the principles formalized in the ICH E11A Pediatric Extrapolation Guideline (2022)21 and the EMA's recent extrapolation frameworks,22, 23 have adopted structured approaches to support pediatric extrapolation when scientific similarity across ages is sufficiently strong. In the FDA model, the extent of extrapolation ranges from full (requiring only pharmacokinetic and safety data) to partial (requiring limited pediatric efficacy data), depending on similarity in disease progression, pharmacology, and exposure–response relationships. The EMA applies the same scientific principles within the regulatory architecture of the Pediatric Investigation Plan, which operationalizes extrapolation strategies through model-informed pharmacokinetic–pharmacodynamic bridging, ontogeny-aware physiological modeling, natural history data, and iterative evidence generation. When biological continuity across development is well supported, harmonized age-range licensing becomes feasible, as illustrated by the recent bexicaserin authorization from 2 to 65 years.9 For master protocol approaches in epilepsy—whether mechanism-anchored or phenotype-defined—harmonized regulatory strategies are essential to avoid duplicative trials, ensure timely access for children, and align approval pathways with the inherent biological unity of early life DEEs. The basket conceptual framework is relevant to epilepsy, particularly to monogenic epilepsies, where heterogeneous clinical phenotypes may arise from convergent molecular mechanisms.24 In this context, the use of the basket concept is metaphorical; phenotype-anchored "baskets" are conceptually aligned with biomarker-defined designs but remain methodologically distinct, as their unit of stratification is clinical phenotype rather than molecular driver. Similar to oncology, success in neurology will depend on identifying alterations that function as true “disease drivers” rather than secondary or modifying influences.25 Molecularly defined groups may form the closest neurological analogue to “baskets.” However, the translation is not straightforward. Unlike cancer, where a single dominant driver variant controls tumor growth, genetic epilepsies involve complex neurodevelopmental trajectories shaped by function (gain vs. loss), timing, cellular context, and coexisting genetic modifiers.26 This increases the risk that a shared biomarker might not predict uniform drug response across diverse phenotypes, age groups, or developmental stages. The challenges observed in oncology—coalterations and context-dependent pathogenicity—appear in epilepsy as variable expression patterns, network reorganization, differences between cortical and subcortical circuits, and age-dependent phenotypic expression.26 These factors can undermine the tissue-agnostic assumption and must be addressed when designing mechanism-based neurological trials. Statistical considerations also map directly onto epilepsy. As in oncology, randomization becomes difficult when standards of care vary across epilepsies, ages, or countries.10, 15, 16 Most precision-medicine trials in epilepsy will be early phase, small, and nonrandomized, risking underpowered subgroup analyses and overinterpretation of heterogeneous responses.10, 15, 16 Bayesian hierarchical models—already used in rare-disease neurology—may support “borrowing of information” across genetically related cohorts (e.g., across sodium channel disorders), while maintaining control of false discoveries.17 Adaptive multistage designs are likely essential, allowing discontinuation of inactive genetic cohorts and concentrating statistical power on the most biologically plausible responders.10, 15, 16 Finally, the feasibility challenges are even more pronounced in epilepsy than in oncology. Rare variants, early childhood onset, developmental comorbidities, and the need for longitudinal electroencephalography-based outcomes complicate recruitment and retention. Harmonization of outcome measures, careful phenotypic stratification, and precise biomarker definitions (e.g., genotype–phenotype correlations, electrophysiologic endophenotypes) are necessary to avoid the pitfalls that have limited basket trials in oncology. In summary, basket trial principles are highly applicable to epilepsy, particularly in monogenic DEEs, but require greater attention to factors associated with phenotypic heterogeneity. If these factors are incorporated into design, mechanism-based cross-syndrome therapeutic studies may become a viable route for precision medicine in rare epilepsies. Kette D. Valente is the sole author and is responsible for the conception of the study; the acquisition, analysis, or interpretation of data for the work; drafting the work or revising it critically for important intellectual content; final approval of the to be and ensuring that related to the or of The author to to for and critical and guidance in statistical and on the design and analysis of master protocols the and the conceptual framework of this The for the of this was by the is the of a from do has been as a or has support from and has been the for trials by and These are not related to this I confirm that I have the on issues in ethical and that this is consistent with