Multidrug Efflux Pumps Attenuate the Effect of MGMT Inhibitors
Bibliographic record
Abstract
Various mechanisms of drug resistance attenuate the effectiveness of cancer therapeutics, including drug transport and DNA repair. The DNA repair protein O(6)-methylguanine-DNA methyltransferase (MGMT) is a key factor determining the resistance against alkylating anticancer drugs inducing the genotoxic DNA lesions O(6)-methylguanine and O(6)-chloroethylguanine, and MGMT inactivation or depletion renders cells more susceptible to treatment with methylating and chloroethylating agents. Highly specific and efficient inhibitors of the repair protein MGMT were designed, including O(6)-benzylguanine (O(6)BG) and O(6)-(4-bromothenyl)guanine (O(6)BTG) that are nontoxic on their own. Unfortunately, these inhibitors do not select between MGMT in normal and cancer cells, causing nontarget effects in the healthy tissue. Therefore, a targeting strategy for MGMT inhibitors is required. Here, we used O(6)BG and O(6)BTG conjugated to β-d-glucose (O(6)BG-Glu and O(6)BTG-Glu, respectively) in order to selectively inhibit MGMT in tumors, harnessing their high demand for glucose. Both glucose conjugates efficiently inhibited MGMT in several cancer cell lines, but with different extents of sensitization to DNA alkylating agents, with lomustine being more effective than temozolomide. We further show that the glucose conjugates are subject to ATP-binding cassette (ABC) transporter mediated efflux, involving P-glycoprotein, MRP1, and BCRP, which impacts the efficiency of MGMT inhibition. Surprisingly, also O(6)BG and O(6)BTG were subject to an active transport out of the cell. We also show that pharmacological inhibition of efflux transporters increases the induction of cell death following treatment with these MGMT inhibitors and temozolomide. We conclude that strategies of attenuating the efflux by ABC transporters are required for achieving successful MGMT targeting.
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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) | 0.001 | 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 | 0.001 |
| 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".