Investigation into the predominate mechanism of OAT1 inhibition (1064.15)
Bibliographic record
Abstract
Renal tubular drug secretion mediated by the organic anion transporter 1 (OAT1) is a potential site of drug‐drug interactions. A number of marketed drugs inhibit OAT1, but the mechanism by which they do so is not typically known. The purpose was to investigate the predominant mechanism by which therapeutic drugs inhibit OAT1. OAT1 was expressed in Chinese hamster ovary cells and 3 H‐ para ‐aminohippurate was used as the substrate. The Kreb’s cycle intermediate alpha‐ketoglutarate (αKG) competitively inhibited OAT1 with a Ki value (5.4 μM) approximating its unbound plasma concentration. In the presence of αKG (5 μM) the potency of inhibition caused by probenecid, furosemide and ibuprofen decreased ~2‐fold (~2‐fold increase in IC 50 values), whereas the potency of inhibition caused by telmisartan was unaffected. This led us to speculate that probenecid, furosemide and ibuprofen interact competitively with αKG and 3 H‐ para ‐aminohippurate while telmisartan is a non‐competitive inhibitor. Probenecid, furosemide and ibuprofen all competitively inhibited OAT1 with Ki values of 10.5 μM, 17.8 μM and 3.1 μM, respectively. In contrast, telmisartan inhibited OAT1 by reducing the maximal transport rate without affecting the Michaelis constant, indicating a non‐competitive type inhibition (IC 50 of value of 0.33 μM). Interestingly, wash‐out experiments showed that the inhibitory effect of telmisartan is quasi‐irreversible. These data show that OAT1 inhibition can occur through different kinetic mechanisms. The mechanism by which inhibitors reduce OAT1 activity could influence the magnitude of drug‐drug interactions at OAT1‐mediated renal tubular drug secretion. Supported by NSHRF. Grant Funding Source : Supported by Nova Scotia Health Research Foundation
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".