Confidence Assessment of an Absorption Model Using Limited Solubility and Permeability Data for 21 Drugs within a Dynamic Physiologically-Based Pharmacokinetic Simulator
Bibliographic record
Abstract
The objective of this study was to assess the accuracy and precision of the Simcyp ADAM model to predict, from a ‘bottom-up’ approach, the human absorption component within a physiologically-based pharmacokinetic profile. 21 literature compounds with respective in vitro Caco-2 permeability and aqueous solubility limits were inputted in ADAM along with clinical values for volume of distribution and clearance. In this fashion, we directly test the absorption component predicted by ADAM within the PBPK model. Simulated pharmacokinetic parameters (T max , C max and AUC 0-τ ) were compared to clinical parameters. With respect to T max predictions, 58% of the simulations had an error of less than 2-fold. For the compounds with error > 2-fold, 75% were over-predicted. Predictions of C max showed that 48% of the simulations had an error of less than 2-fold. For the compounds with error > 2-fold, the majority (90%) of the C max values were under-predicted. Similar to this, 43% of the AUC 0-τ predictions had an error of less than 2-fold. For the compounds with error > 2-fold, 83% were under-predicted. Taken together, caution must be exercised in the utilization of a ‘bottom-up’ PBPK model approach using limited in vitro permeability data and/or solubility limits to simulate the exposure in human.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".