Retrospective and Prospective Human Intravenous and Oral Pharmacokinetic Projection of Dipeptidyl peptidase-IV Inhibitors Using Simple Allometric Principles – Case Studies of ABT-279, ABT-341, Alogliptin, Carmegliptin, Sitagliptin and Vildagliptin
Bibliographic record
Abstract
PURPOSE: The purpose of this exercise was to explore the utility of allometric scaling approach for the prediction of intravenous and oral pharmacokinetics of six dipeptidy peptidase-IV (DPP-IV) inhibitors viz. ABT-279, ABT-341, alogliptin, carmegliptin, sitagliptin and vildagliptin. METHODS: The availability of intravenous and oral pharmacokinetic data in animals enabled the allometry scaling of 6 DPP-IV inhibitors. The relationship between the main pharmacokinetic parameters [viz. volume of distribution (Vd) and clearance (CL)] and body weight was studied across three or four mammalian species, using double logarithmic plots to predict the human pharmacokinetic parameters of CL and Vd using simple allometry. RESULTS: A simply allometry relationship: Y = aWb was found to be adequate for the prediction of intravenous and oral human clearance/volume of distribution for DPP-IV inhibitors. The allometric equations for alogliptin, carmegliptin, sitagliptin, vildagliptin, ABT-279 and ABT-341 were 1.867W0.780, 1.170W0.756, 2.020W0.529, 1.959 W0.847, 0.672 W1.016, 1.077W 0.649, respectively, to predict intravenous clearance (CL) and the corresponding equations to predict intravenous volume of distribution (Vd) were: 3.313W0.987, 6.096W0.992, 7.140W0.805, 2.742W0.941, 1.299W0.695 and 5.370W0.803. With the exception of a few discordant values the exponent rule appeared to hold for CL (0.75) and Vd (1.0) for the predictions of various DPP-IV inhibitors. Regardless of the routes, the predicted values were within 2-3 fold of observed values and intravenous allometry was better than oral allometry. CONCLUSION: Simple allometry retrospectively predicted with reasonable accuracy the human reported values of gliptins and could be used as a prospective tool for this class of drugs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".