Downregulation of <i>mdr1a</i> expression in the brain and liver during CNS inflammation alters the <i>in vivo</i> disposition of digoxin
Bibliographic record
Abstract
1. Inflammation is a pathophysiological event that has relevance for altered drug disposition in humans. Two functions of P-glycoprotein (P-gp) are hepatic drug elimination and prevention of drug entry into the central nervous system (CNS). Our objective was to investigate if localized CNS inflammation induced by Escherichia coli lipopolysaccharide (LPS) would modify mdr1a/P-gp expression and function in the brain and liver. 2. Our major finding was that the CNS inflammation in male rats produced a loss in the expression of mdr1a mRNA in the brain and liver that was maximal 6 h after intracranial ventricle (i.c.v.) administration of LPS. When (3)H-digoxin was used at discrete time points, as a probe for P-gp function in vivo, an increase in brain and liver (3)H-radioactivity and plasma level of parent digoxin was produced 6 and 24 h following LPS treatment compared to the saline controls. Digoxin disposition was similarly altered in mdr1a(+/+) mice but not in mdr1a(-/-) mice 24 h after administering LPS i.c.v. 3. In male rats, the biliary elimination of parent digoxin was reduced at 24 h (60%) and 48 h (40%) after LPS treatment and was blocked by the P-gp substrate cyclosporin A. An observed loss in CYP3A1/2 protein and organic anion transporting polypeptide 2 mRNA in the liver may make a minor contribution to digoxin elimination in male rats after LPS treatment. 4. Conditions which impose inflammation in the CNS produce dynamic changes in mdr1a/P-gp expression/function that may alter hepatic drug elimination and the movement of drugs between the brain and the periphery. The use of experimental models of brain inflammation may provide novel insight into the regulation of P-gp function in that organ.
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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) | 0.000 | 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 | 0.001 |
| 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".