Blockage of Drug Resistance In Vitro by Disulfiram, a Drug Used to Treat Alcoholism
Bibliographic record
Abstract
BACKGROUND: P-glycoprotein (P-gp) pumps a wide range of cytotoxic drugs out of cells. Inhibiting maturation of P-gp would be a novel method for circumventing P-gp-mediated multidrug resistance, which complicates cancer chemotherapy and treatment of patients infected with human immunodeficiency virus. We examined the effect of disulfiram (Antabuse(TM)) on the maturation and activity of P-gp. METHODS: Embryonic kidney cells were transfected with a complementary DNA for the P-pg gene, and the effects of disulfiram on the sensitivity of the transfected cells to cytotoxic agents were determined. Enzyme assays were used to determine the effects of disulfiram on the verapamil-stimulated adenosine triphosphatase (ATPase) activity of P-gp. Disulfiram modifies cysteine residues, and mutant forms of P-gp that lack individual cysteines were used to determine whether particular cysteine residues mediate disulfiram's effects on P-gp activity. Maturation of recombinant P-gp was followed on immunoblots. RESULTS: Disulfiram increased the sensitivity of P-gp-transfected cells to vinblastine and colchicine and inhibited P-gp's verapamil-stimulated ATPase activity. Half-maximal inhibition of ATPase activity occurred at 13.5 microM disulfiram. Disulfiram (at 100 microM) inhibited a P-gp mutant by 43% (95% confidence interval [CI] = 37%-48%) when cysteine was present at position 431 only and by 72% (95% CI = 66%-77%) when cysteine was present at position 1074 only. Treatment of P-gp-transfected cells with 50 nM disulfiram blocked maturation of recombinant P-gp. CONCLUSIONS: Disulfiram can potentially reduce P-gp-mediated drug resistance by inhibiting P-gp activity (possibly via cysteine modification) and/or by blocking its maturation. These results suggest that disulfiram has the potential to increase the efficacy of drug therapies for cancer and acquired immunodeficiency syndrome.
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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".