Evaluation of P-glycoprotein expression in pain relevant tissues: understanding translation of efflux from preclinical species to human
Bibliographic record
Abstract
Various efflux transporters, such as P-glycoprotein (P-gp) are now widely accepted to have profound influence on the disposition of substrates. Nevertheless, there is paucity of information about their expression and functionality in the pain relevant tissues (such as brain, spinal cord and dorsal root ganglia (DRG)) across various species. Therefore, our attempts were directed at evaluating P-gp expression in these tissues to understand its effect on the central nervous system (CNS) disposition. As a means of characterizing the normal tissue distribution of P-gp, immunohistochemistry was performed with two antibodies (C219 and H241) directed against different epitopes of MDR1 gene. Notable expression of P-gp was detected in the DRG of Sprague Dawley rat, Beagle Dog, Cynomolgous monkey as well as human. The expression of P-gp was observed in the CNS tissues with evident species differences, the expression of P-gp in human brain and spinal cord was lower than in rats and dogs but relatively comparable to that in monkeys. However, no species related differences were seen in the expression at the DRG level. Double-labelling using an antibody against a marker of endothelial cells confirmed that P-gp was exclusively localized in capillary endothelial cells. This study highlights the cross species similarities and differences in the expression of P-gp and thus serves as a vital step in understanding the translation of exposure of P-gp substrates to human.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".