Nuclear Receptors and the Regulation of Drug‐Metabolizing Enzymes and Drug Transporters: Implications for Interindividual Variability in Response to Drugs
Bibliographic record
Abstract
Erratic or unpredictable response to drugs remains a challenge of modern drug therapy. An important determinant of such interindividual differences in drug response is variability in the expression of drug-metabolizing enzymes and/or transporters at sites of absorption and/or tissue distribution. Variable drug-metabolizing enzyme and transporter expression can result in unpredictable exposure and tissue distribution of drugs and may manifest as adverse effects or therapeutic failure. In the past decade, important new insights have been made relating to the regulatory mechanisms governing the expression of drug-metabolizing enzymes and transporters by ligand-activated nuclear receptors. Specifically, there is compelling evidence to demonstrate that PXR, CAR, FXR, LXR, VDR, HNF4alpha, and AhR form a battery of nuclear receptors that regulate the expression of many important drug-metabolizing enzyme and transporters. In this review, the authors focus on clinically important drug-metabolizing enzymes such as CYP3A4, CYP2B6, CYP2C9, CYP2C19, UGT1A1, SULT2A1, and glutathione S-transferases and their regulation by nuclear receptors. They also review the nuclear receptor-mediated regulation of drug transporters such as MDR1, MRP2, MRP4, BSEP, BCRP, NTCP, OATP1B3, and OATP1A2. Finally, they outline how the drug development process has been affected by the current understanding of the involvement of nuclear receptors in the regulation of drug disposition genes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".