The influence of expression of P-glycoprotein on the penetration of anticancer drugs through multicellular layers
Bibliographic record
Abstract
The success of chemotherapy in the treatment of solid tumours may be limited by cellular mechanisms leading to drug resistance and/or by the slow penetration of drugs through tissue, resulting in a steep concentration gradient from tumour blood vessels. One mechanism leading to the development of multidrug resistance is overexpression of the membrane-based export pump P-glycoprotein (P-gp). The relationship between expression of P-gp by constituent cells and the penetration of P-gp substrates through tissue was studied by comparing the penetration of P-gp substrates through multicellular layers derived from either wild-type or P-gp overexpressing cell lines. P-gp reversal agents were added to confirm the contribution of P-gp in influencing the penetration of its substrates. Our data indicate: 1) penetration of the P-gp substrates, 99mTc-sestaMIBI and 14C-doxorubicin, is greater through multicellular layers formed from P-gp overexpressing cell lines as compared with wild-type cells; 2) the addition of agents that inhibit the function of P-gp results in decreased penetration of these substrates through multicellular layers with P-gp expression. There was no effect of P-gp reversal agents on penetration of 14C-sucrose or of 3H-5-fluorouracil (non-substrate controls). Our data suggest that the administration of agents that inhibit the function of P-gp might have opposing effects on therapeutic index in solid tumours: increased sensitivity of perivascular tumour cells but decreased penetration of P-gp substrates to more distal cells. These effects may explain, in part, the limited therapeutic benefit for solid tumours that has accrued from use of agents that reverse the effects of P-gp.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".