FDA Patient-Focused Drug Development Guidances
Bibliographic record
Abstract
Developmental and epileptic encephalopathies (DEE) are rare, often monogenic neurodevelopmental conditions. Most affected individuals have refractory seizures. All have multiple severe impairments which can be as life-limiting as or more limiting than the seizures themselves. Mechanism- and gene-targeted therapies for these individually rare, genetic conditions hold hope for treatment, amelioration of disease expression, and even cure. The near absence of fit-for-purpose (FFP) clinical outcome assessments (COA) to establish the benefits for nonseizure outcomes of these new therapies in clinical trials poses significant challenges to drug development. The Food and Drug Administration Patient-Focused Drug Development guidance series provides direction for how to overcome these challenges and to ensure FFP measures are available for trials. The goal is to have measures that address outcomes of importance to patients and caregivers, reliably and accurately measure the outcome in the spectrum of abilities for the target disease, and are sensitive to meaningful change over time. The guidances identify 3 primary strategies: (1) directly adopting and implementing available outcome measures; (2) creating measures de novo; and (3) a middle path of adapting or modifying existing measures. Emphasized throughout the guidances is the indispensable and extensive role of the patient or caregiver to assuring the goal of having fit measures is achieved. This review specifically considers the difficulties of adopting available COAs in severely impaired patient groups and ways to adapt or modify existing COAs to be FFP as encouraged in the guidances. Adaptations include alternative scoring, use of assessments in out-of-intended age ranges, and modifications for individuals with sensory or motor impairments. Some additional considerations that may facilitate achieving adequate clinical outcome measures, especially for rare diseases, include use of personalized endpoints, merging of existing COAs, and developing a consortium of rare DEE advocates and researchers to ensure fitness of adapted COAs across multiple rare disease groups. The FDA guidances help ensure that clinical trials targeting nonseizure outcomes, especially in severely impaired populations, will have adequately valid and sensitive outcome measures. This in turn will strengthen the ability of trials to provide informative tests of whether treatments provide meaningful therapeutic efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".