Overview of SLC22A and SLCO Families of Drug Uptake Transporters in the Context of Cancer Treatments
Bibliographic record
Abstract
The effectiveness of many anticancer agents is dependent on their disposition to the intracellular space of cancerous tissue. Accumulation of anticancer drugs at their sites of action can be altered by both uptake and efflux transport proteins, however the majority of research on the disposition of anticancer drugs has focused on drug efflux transporters and their ability to confer multidrug resistance. Here we review the roles of uptake transporters of the SLC22A and SLCO families in the context of cancer therapy. The many first-line anticancer drugs that are substrates of organic cation transporters (OCTs) organic cation/carnitine transporters (OCTNs) and organic anion- transporting polypeptides (OATPs) are summarized. In addition, where data is available a comparison of the localization of drug uptake transporters in healthy and cancerous tissues is provided. Expression of drug uptake transporters increases the sensitivity of cancer cell lines to anticancer substrates. Furthermore, early observational studies have suggested a causal link between drug uptake transporter expression and positive outcome in some cancers. Quantification of drug transporters by mass spectrometry will provide an essential technique for generation of expression data during future observational clinical studies. Screening of drug uptake transporter expression in primary tumors may help differentiate between susceptible and resistant cancers prior to 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".