Bioequivalence metrics for absorption rates: linearity, specificity, sensitivity
Bibliographic record
Abstract
Aims:In order to ensure the therapeutic equivalence of generic products, it would be important to contrast measures additional to Cmax in order to assess differences in absorption rates. Our aim was to compare partial AUC (PAUC), Swing, and PTF to Cmax in terms of sensitivity, specificity and linearity under identical kinetic conditions. Methods:Single-dose and multiple-dose concentration curves were generated assuming one-compartment models. Kinetic sensitivity curves were obtained by gradually changing the absorption rate constant and keeping all other parameters fixed. Results:A metric should reflect specifically the investigated kinetic feature (e.g., the rate of absorption), be linearly related to it, and should exhibit high kinetic sensitivity. Cmax is related nonlinearly to the rate of absorption, is nonspecific to it (reflects also the extent of absorption as well as the rates of disposition processes), lacks kinetic sensitivity even following a single administration. Compared to Cmax, PAUC was always more sensitive under every investigated condition. Swing and PTF showed high kinetic sensitivity but, in contrast to PAUC, they could be evaluated only in multiple-dose studies. Conclusion:Under identical conditions, different metrics provide widely differing point estimates. Differences in kinetic sensitivity among bioequivalence metrics should be accounted for when results of different metrics are compared.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.127 | 0.282 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".