P-glycoprotein Inhibition as a Therapeutic Approach for Overcoming Multidrug Resistance in Cancer: Current Status and Future Perspectives
Bibliographic record
Abstract
One of the major causes of failure in cancer chemotherapy is multidrug resistance (MDR), where cancer cells simultaneously become resistant to different anticancer drugs. Over-expression of membrane efflux pumps like P-glycoprotein (P-gp) that recognizes different chemotherapeutic agents and transports them out of the cell, plays a major role in MDR. The shortcoming of P-gp inhibitors in clinic has been attributed to their non-specific action on P-gp and/or non-selective distribution to non-target organs that leads to intolerable side effects by the P-gp inhibitor at doses required for P-gp inhibition upon systemic administration. Another major issue is the reduced elimination of P-gp substrates (e.g. anticancer drugs) and intolerable toxicities by anticancer drugs when co-administered with P-gp inhibitors. To overcome these shortcomings, new generation of P-gp inhibitors with improved specificity for P-gp have been developed. More recently, attention has been paid to the use of drug delivery systems primarily to restrict P-gp inhibition to tumor and reduce the non-selective inhibition of P-gp in non-target organs. This review will provide an overview and update on the status of P-gp inhibition approaches and the role of drug delivery systems in overcoming P-gp mediated MDR.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".