Disulfiram Metabolites Permanently Inactivate the Human Multidrug Resistance P-Glycoprotein
Bibliographic record
Abstract
The human multidrug resistance P-glycoprotein (P-gp) uses ATP to transport a wide variety of structurally unrelated cytotoxic compounds out of the cell. The relatively high expression of P-gp in organs such as the intestine, kidney, blood-brain/testes barrier and in some tumor cells can compromise chemotherapy treatments for patients with cancer or AIDS/HIV. It has been difficult to inhibit P-gp during chemotherapy with noncovalent inhibitors because the relatively high levels of inhibitors have severe side effects. An alternative approach to inhibit P-gp would be to covalently modify cysteine residues within the NBDs. In this study, we tested whether metabolites of disulfiram, a drug currently used to treat chronic alcoholism, could inhibit P-gp. We show that the disulfiram metabolites, S-methyl N,N-diethylthiocarbamate sulfoxide and S-methyl N,N-diethylthiocarbamate sulfone inhibited the verapamil-stimulated ATPase activity of P-gp with IC50 values (concentrations that result in 50% inhibition of activity) of 9 and 4.8 microM, respectively. Similarly, S-methyl N,N-diethylthiocarbamate sulfoxide and S-methyl N,N-diethylthiocarbamate sulfone inhibited the activity of aldehyde dehydrogenase with IC50 values of 3.2 and 1.7 microM, respectively. Inhibition of P-gp by the metabolites was not reversed by addition of the reducing compound, dithiothreitol. We then determined which endogenous cysteine residue was responsible for inhibiting P-gp activity after exposure to the disulfiram metabolites. Treatment of P-gp mutants containing a single cysteine residue showed that inactivation was primarily due to modification of Cys1074 in NBD2. These results indicate that metabolites of disulfiram can covalently inactivate P-gp. Covalent modification of drug transporters could be a useful approach for inhibiting their activities during chemotherapy.
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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 | 0.000 | 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) | 0.001 | 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".