P-GLYCOPROTEIN EFFECTS ON DRUGS PHARMACOKINETICS AND DRUG-DRUG- INTERACTIONS AND THEIR CLINICAL IMPLICATIONS
Bibliographic record
Abstract
During the last couple of decades, efflux transporters have received considerable attention due to their ability to alter, either beneficially or detrimentally the pharmacokinetic and pharmacodynamic for an administered drug. The expression of the energy dependent transporter, member of the ATP binding cassette (ABC) transporters superfamily, is not only limited to cancerous tissues, but is also expressed in different normal tissues and barriers such as the blood brain barrier and placenta. Furthermore, its unique distribution at the sites of absorption such as small intestine has been shown to greatly affect the bioavailability of the drug-substrates, and thus altering their effect. In addition, the striking overlap of substrates between P-glycoprotein (P-gp) and the phase I enzyme cytochrome P450 3A4 (CYP3A4) in addition to their coexistence at the same site has been shown to act synergistically to decrease oral drug bioavailability. Interestingly, the co-administration of a drug-substrate and an inhibitor of P-gp have been shown to increase the plasma concentration of the drug-substrates causing lethal toxicities that warrants critical evaluation of drugs as whether or not they could be substrates or inhibitors to P-gp. The availability of various in vitro cell culture models and in vivo knockout models of P-gp are currently serving the pharmaceutical sciences community to deliver safer drug use and lower risks of drug-drug interactions based on P-gp interactions. Therefore, the purpose of the current review is to summarize the current knowledge about the role of P-gp in determining drug ADME profile, and its role in drug-drug-interactions and their clinical implications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".