A simplified approach to predict CYP3A-mediated drug–drug interactions at early drug discovery: validation with clinical data
Bibliographic record
Abstract
1. The present study evaluates which factors should be incorporated into a simplified approach to reasonably predict CYP3A-mediated drug-drug interaction (DDI) at an early drug discovery stage. 2. CYP3A IC50 values were obtained using human liver microsomes (HLM) and hepatocytes. Plasma and microsomal protein binding and in vitro hepatocyte partition coefficient (Kp) were also determined for 10 drugs. Therapeutic human maximum plasma concentrations (Cmax) were retrieved from the literature. DDI predictions were performed using an equation incorporating the fraction of the substrate metabolized by CYP3A with the total or free plasma Cmax, with or without correction for hepatocyte Kp. 3. Based on the Ki data from HLM, the use of total Cmax provided a prediction of DDI within 2-fold of the observed clinical values for 9 out of 10 drugs. 4. In comparison, free drug corrections for both Cmax and Ki values from HLM led to an underprediction of DDI (>3-fold error for five drugs). 5. Data from hepatocytes showed, in general, lower prediction accuracy than data from HLM. 6. CYP3A-mediated DDIs can be predicted with a high level of accuracy based on Ki estimates from HLM data and the total therapeutic plasma Cmax of the inhibitors. This approach should be widely applicable to the assessment of clinically significant DDIs risk in early drug discovery programs.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".