The Reciprocal Interaction of Small Molecule Protein Kinase Inhibitors and ATP-Binding Cassette Transporters in Targeted Cancer Therapy
Bibliographic record
Abstract
Protein kinaseshave become the second most important group of drug targets, after G-protein-coupled receptors. Currently,15 small molecule protein kinase inhibitors (PKIs) have received food and drug administrator (FDA) approval to be used as cancer treatments. However, in the course of clinical use of these small molecule PKIs, drug resistance has become a recurring problem. Their therapeutic potential depends on access to their intracellular targets, which significantly affected by certain membrane ATP-binding cassette(ABC) transporters. ABC transporters were major causesof clinical multiple drug resistance (MDR) and might be resulting in the development of resistance to PKIs in cancer patients. Some PKIs could modulate the activity of ABC transporters and affect the metabolism of themselves and other chemically unrelated drugs. Moreover, it has been recently reported that some PKIs could regulate the expression of ABC transporters in tumor cells, thereby affect their intracellular accumulation and antitumor efficacy. In this review,the reciprocal interaction of clinically important PKIs with the MDR-related ABC transporters, in particular ABCB1 and ABCG2,wassummarized. Keyword:Protein Kinase Inhibitors, ABC Transporters, P-gp/ABCB1, BCRP/ABCG2, Targeted Cancer Therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".