Prediction of Phase I single-dose pharmacokinetics using recombinant cytochromes P450 and physiologically based modelling
Bibliographic record
Abstract
1. Ten compounds from the Merck Research Laboratories pipeline were selected to evaluate the utility of using intrinsic clearance derived from recombinantly expressed cytochromes P450 (CYP) and physiologically based pharmacokinetic modelling to predict Phase I pharmacokinetics using simCYP. The compounds selected were anticipated to be eliminated predominantly by P450 metabolism. 2. There was a reasonable agreement between the predicted and actual clinical exposure with 80% of the predicted exposures being within three-fold of the observed values. Furthermore, prediction of C(t) (plasma concentration at a specified time point) and T(max) were acceptable with greater than or equal to 70% of the predicted data being within three-fold of the observed values. However, prediction of C(max) was unreliable and may have been due to error in predicting the time-dependent change in volume of distribution and/or error in estimating absorption rate. 3. Although it is acknowledged that research is needed to improve predictive performance, the data presented are supportive of using recombinant P450 intrinsic clearance and physiologically based pharmacokinetic modelling to predict Phase I pharmacokinetics for compounds eliminated by P450 metabolism.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".