Modulation of P-Glycoprotein (PGP) Mediated Multidrug Resistance (MDR) Using Chemosensitizers: Recent Advances in the Design of Selective MDR Modulators
Bibliographic record
Abstract
Over the past two decades, a number of chemical entities have been investigated in the continuing quest to reverse P-glycoprotein (PGP) mediated multidrug resistance (MDR) in cancer. The complexity of interactions between these agents and the proteins responsible for MDR in conjunction with the challenges associated with developing SAR/QSAR relationships for MDR modulators has hampered our ability to develop agents that modulate MDR with enhanced specificity of target, increased efficacy, and minimized toxicity when coadministered with anticancer drugs. With an increased understanding of the molecular interaction, target-mediated SAR and combinatorial chemistry approaches, newer more selective inhibitors have been recently reported. These agents have shown remarkable promise in preclinical trials; although their ultimate clinical therapeutic utility remains to be established. The emphasis of this review is placed on the current understanding of modulator-drug transport protein interactions and to review the advances in the structure-based design, synthetic efforts and the cellular pharmacology of MDR modulating activity of a number of known PGP inhibitors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".