Science-based Ethnic Bridging in Drug Development; Review of Recent Precedence and Suggested Steps Forward
Bibliographic record
Abstract
BACKGROUND: Exposure, safety and/or efficacy of drugs are subject to potential differences between human races or ethnicities, as acknowledged by regulatory guidance and by label texts of various, but not all approved drugs. OBJECTIVE: The objective of the present review was to assess recent regulatory precedence on drug use and race or ethnicity, with the goal of identifying opportunities for increasing the informative value of clinical ethnic or racial bridging in drug development. METHODS: Recently, (January 2014-July 2018) FDA approved drug product label texts and approval packages were reviewed for claims, comments and underlying data on use of the product in specific ethnic or racial groups. RESULTS: Among the 266 FDA-approved products, no product with unambiguous race- or ethnicity specific dosing instructions was retrieved. A small majority (55%) was approved with a claim or comment on race or ethnicity, and of these, a large majority (87%) was based on population pharmacokinetic data analysis. Statements were often related to incidence of a genotype for drug metabolizing enzyme or for other risk factors, or were related to body weight. Absence of clinically relevant exposure differences were often justified in terms of exposure ratios that notably exceeded the typical 0.80-1.25 no-effect boundary. CONCLUSIONS: Recent precedence reflected a pragmatic, descriptive approach of racial or ethnic bridging, apparently meeting current regulatory expectations, whilst not resulting in strict guidance to prescribers. We recommend further work on defining the objectives of bridging studies, as well as criteria for their design and data analysis. Regarding the latter, we recommend investigating the value of prospectively defined tests for similarity with appropriate follow-up analysis in the case where the test has failed.
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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".