Role of OATP4C1 in Renal Handling of Remdesivir and its Nucleoside Analog GS-441524: The First Approved Drug for Patients with COVID-19
Bibliographic record
Abstract
PURPOSE: Remdesivir and its active metabolite are predominantly eliminated via renal route; however, information regarding renal uptake transporters is limited. In the present study, the interaction of remdesivir and its nucleoside analog GS-441524 with OATP4C1 was evaluated to provide the detailed information about its renal handling. METHODS: We used HK-2 cells, a proximal tubular cell line derived from normal kidney, to confirm the transport of remdesivir and GS-441524. To assess the involvement of OATP4C1 in handling remdesivir and GS-441524, the uptake study of remdesivir and GS-441524 was performed by using OATP4C1-overexpressing Madin-Darby canine kidney II (MDCKII) cells. Moreover, we also evaluated the IC50 and Ki value of remdesivir. RESULTS: The time-dependent remdesivir uptake in HK-2 cells was observed. The results of inhibition study using OATs and OCT2 inhibitors and OATP4C1 knockdown suggested the involvement of renal drug transporter OATP4C1. Remdesivir was taken up by OATP4C1/MDCKII cells. OATP4C1-mediated uptake of remdesivir increased linearly up to 10 min and reached a steady state at 30 min. Remdesivir inhibited OATP4C1-mediated transport in a concentration-dependent manner with the IC50 and apparent Ki values of 42 ± 7.8 μM and 37 ± 6.9 μM, respectively. CONCLUSIONS: We have provided novel information about renal handling of remdesivir. Furthermore, we evaluated the potential drug interaction via OATP4C1 by calculating the Ki value of remdesivir. OATP4C1 may play a pivotal role in remdesivir therapy for COVID-19, particularly in patients with kidney injury.
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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) | 0.002 | 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".