Analysis of Non-Pivotal Bioequivalence Studies Submitted in Abbreviated New Drug Submissions for Delayed-Release Drug Products
Bibliographic record
Abstract
The US FDA's rule on "Requirements for Submission of Bioequivalence Data" requiring submission of all bioequivalence (BE) studies conducted on the same formulation of the drug product submitted for approval was published in Federal Register in January 2009. With the publication of this rule, we evaluated the impact of data from non-pivotal BE studies in assessing BE and identified the reasons for failed in vivo BE studies for generic oral delayed-release (DR) drug products only. We searched the Agency databases from January 2009 toDecember 2016 to identify Abbreviated New Drug Applications (ANDAs) submitted for DR drug products containing non-pivotal BE studies. Out of 202 ANDAs, 43 ANDAs contained 102 non-pivotal BE studies. Forty-nine non-pivotal BE studies were conducted on the to-be-marketed (TBM) formulation and 53 were conducted on formulations different from the TBM formulation. These experimental formulations primarily differed in the ratio of components of the enteric coating layer and/or amount (i.e., %w/w) of enteric coating layer. Of the 49 non-pivotal BE studies conducted on the TBM formulation, 41 failed to meet the BE acceptance criteria. The majority of failed non-pivotal BE studies on the TBM DR generic products had insufficient power, which was expected as these studies are exploratory in nature and not designed to have adequate power to pass the BE statistical criteria. In addition, among the failed non-pivotal BE studies on the TBM DR generic products, the most commonly failing pharmacokinetic parameter was Cmax. The data from these non-pivotal BE studies indicate that inadequate BE study design can lead to failure of the BE on the same formulation. Also, the non-pivotal BE studies on formulations different from the TBM formulation help us link the formulation design to the product performance in vivo. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.014 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".