Bibliographic record
Abstract
The multidrug resistance (MDR) phenotype exhibited by cancer cells is believed to be the major barriers to successful chemotherapy in cancer patients. The major form of MDR phenotype is contributed by a group of ATP-binding cassette (ABC) drug transporters which include P-glycoprotein, multidrug resistance-associated protein 1, and breast cancer resistance protein. There has been intense search for compounds which can act to reverse MDR phenotype in cultured cells, in animal models, and ultimately in patients. The ongoing search for MDR modulators, compounds that act directly on the ABC transporter proteins to block their activity, has led to three generations of drugs. Some of the third-generation MDR modulators have demonstrated encouraging results compared to earlier generation MDR modulators in clinical trials. These modulators are less toxic and they do not affect the pharmacokinetics of anti-cancer drugs. Significant numbers of natural products have also been identified for their effectiveness in reversing MDR in a manner similar to the MDR modulators. Other MDR reversing strategies that have been studied quite extensively are also reviewed and discussed in this chapter. These include strategies aimed at destroying mRNAs for ABC drug transporters, approaches in inhibiting transcription of ABC transporter genes, and blocking of ABC transporter activity using antibodies. This review summarizes the development of reversing agents for ABC drug transporters up to the end of 2008, and provides an optimistic view of what we have achieved and where we could go from here.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".