An Open‐Source Framework for Virtual Bioequivalence Modeling and Clinical Trial Design
Bibliographic record
Abstract
To establish bioequivalence (BE) of a generic test formulation with respect to a reference listed drug, it is necessary to demonstrate a comparable rate and extent to which active ingredients reach the site of action. To decrease unnecessary human testing and simulate scenarios involving specific populations or challenges with recruitment or study design, industry and regulators are increasingly considering in silico virtual bioequivalence (VBE) approaches. This tutorial introduces the VBEToolbox R package: a toolbox within the Open Systems Pharmacology framework to streamline and standardize computational VBE workflows. The package integrates in vitro and in vivo data to train pharmacokinetic models through inference of inter-individual variability from clinical data and establishment of in vitro to in vivo extrapolations. A nonparametric approach is adopted to account for uncertainties from parameter non-identifiability. The trained model is then applied to determine the study size with statistical power needed to demonstrate BE virtually. The use of the VBE tool is illustrated with two case studies. The first evaluates the VBE of petrolatum and ethylene glycol dermal formulations of testosterone by integrating in vitro skin permeation tests, vehicle/skin partitioning data, testosterone solubility data, and in vivo absorption data in a mechanistic in vitro/in vivo dermal absorption model. The second assesses the VBE of two oral bupropion formulations by integrating in vitro dissolution data in a physiologically based pharmacokinetic model. These case studies highlight essential considerations for model development, training, and extrapolation toward application for VBE assessment.
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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 | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".