Interaction of drugs of abuse and maintenance treatments with human P-glycoprotein (ABCB1) and breast cancer resistance protein (ABCG2)
Bibliographic record
Abstract
Drug interaction with P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) may influence its tissue disposition including blood-brain barrier transport and result in potent drug-drug interactions. The limited data obtained using in-vitro models indicate that methadone, buprenorphine, and cannabinoids may interact with human P-gp; but almost nothing is known about drugs of abuse and BCRP. We used in vitro P-gp and BCRP inhibition flow cytometric assays with hMDR1- and hBCRP-transfected HEK293 cells to test 14 compounds or metabolites frequently involved in addiction, including buprenorphine, norbuprenorphine, methadone, ibogaine, cocaine, cocaethylene, amphetamine, N-methyl-3,4-methylenedioxyamphetamine, 3,4-methylenedioxyamphetamine, nicotine, ketamine, Delta9-tetrahydrocannabinol (THC), naloxone, and morphine. Drugs that in vitro inhibited P-gp or BCRP were tested in hMDR1- and hBCRP-MDCKII bidirectional transport studies. Human P-gp was significantly inhibited in a concentration-dependent manner by norbuprenorphine>buprenorphine>methadone>ibogaine and THC. Similarly, BCRP was inhibited by buprenorphine>norbuprenorphine>ibogaine and THC. None of the other tested compounds inhibited either transporter, even at high concentration (100 microm). Norbuprenorphine (transport efflux ratio approoximately 11) and methadone (transport efflux ratio approoximately 1.9) transport was P-gp-mediated; however, with no significant stereo-selectivity regarding methadone enantiomers. BCRP did not transport any of the tested compounds. However, the clinical significance of the interaction of norbuprenorphine with P-gp remains to be evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".