Recent attempts at RNAi‐mediated P‐glycoprotein downregulation for reversal of multidrug resistance in cancer
Bibliographic record
Abstract
Multidrug resistance (MDR) is among the major mechanisms leading to failure in chemotherapy of cancer patients. The ATP-binding cassette proteins are major contributors to MDR, involved in the active efflux of xenobiotics out of cancer cells. Among them, P-glycoprotein (P-gp) is the most dominant protein involved in the efflux of drugs. For more than 30 years, scientists have searched for the ideal P-gp inhibitor to modulate drug resistance activity of P-gp. This inhibitor should be tissue and cell specific with side effects on other tissues, must not provoke immune responses from the host, should provide sustained inhibition, and must be synthesized readily with low cost. Chemical P-gp inhibitors tested to date, have shown nonspecific toxic effects limiting their clinical applications. Sequence-specific P-gp gene silencing by RNA interference (RNAi) may provide a more effective approach for downregulation of specific protein targets due to high specificity, limited toxicity and immunogenicity, and relative ease in synthesis. RNAi can be implemented by delivery of synthetic small interfering RNAs (siRNAs) or by gene expression of short hairpin RNAs using gene expressing vectors. Specific delivery systems and expression vectors have been designed for this purpose and many researchers have explored their effectiveness for P-gp downregulation. In this report, we review the efficiency of various methods for siRNA delivery and transfection for P-gp downregulation in cancer cells for MDR reversal. Novel ideas and observations by different research groups were discussed for future improvement in this essential field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".