Effects of Some Antihypertensive Drugs on the Metabolism and Pharmacokinetics of Indapamide in Rats
Bibliographic record
Abstract
PURPOSE: Indapamide, a non-thiazide antihypertensive diuretic agent, has been widely coadministered with other classes of antihypertensive agents to reach target systolic blood pressure. Indapamide is extensively metabolized by cytochromes P450. Interaction of indapamide and other antihypertensive drugs are unknown. We investigated the effects of other antihypertensive drugs on the metabolism and pharmacokinetics of indapamide in vitro and in vivo. METHODS: Indapamide metabolism was studies in vitro using human liver microsomes pretreated with or without different concentrations of CYP-selective inhibitors and seven major antihypertensive drugs, felodipine, nifedipine, nitrendipine, telmisartan, irbesartan, valsartan and puerarin. Furthermore, the pharmacokinetics of indapamide was determined by HPLC-MS/MS to evaluate the effects of felodipine coadministered on the bioavailability of indapamide in rats in vivo. RESULTS: The Km and Vmax of indapamide metabolism were 114.35 ± 3.47 μM and 23.13 ± 6.61 μmol/g/min. The metabolites of indapamide, hydroxyl-indapamide and dehydrogen-indapamide, were followed. CYP3A4 and CYP2C19 were involved in indapamide metabolism in human live microsomes. In addition, felodipine, nifedipine and nitrendipine significantly inhibited indapamide metabolism with the maximum inhibitory rates of 82.6%, 72% and 95%, respectively. Felodipine significantly elevated indapamide plasma concentration and prolonged its half-life. CONCLUSIONS: Combination therapy of indapamide and felodipine might lead to the alteration of indapamide metabolism and pharmacokinetics. The consequence of such an interaction that may include increased effectiveness and side effect needs to be tudeis in human.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".