Nudix hydrolase 18 catalyzes the hydrolysis of active triphosphate metabolites of the antivirals remdesivir, ribavirin, and molnupiravir
Bibliographic record
Abstract
Remdesivir and molnupiravir have gained considerable interest because of their demonstrated activity against SARS-CoV-2. These antivirals are converted intracellularly to their active triphosphate forms remdesivir-TP and molnupiravir-TP. Cellular hydrolysis of these active metabolites would consequently decrease the efficiency of these drugs; however, whether endogenous enzymes that can catalyze this hydrolysis exist is unknown. Here, we tested remdesivir-TP as a substrate against a panel of human hydrolases and found that only Nudix hydrolase (NUDT) 18 catalyzed the hydrolysis of remdesivir-TP with notable activity. The k cat /K m value of NUDT18 for remdesivir-TP was determined to be 17,700 s −1 M −1 , suggesting that NUDT18-catalyzed hydrolysis of remdesivir-TP may occur in cells. Moreover, we demonstrate that the triphosphates of the antivirals ribavirin and molnupiravir are also hydrolyzed by NUDT18, albeit with lower efficiency than Remdesivir-TP. Low activity was also observed with the triphosphate forms of sofosbuvir and aciclovir. This is the first report showing that NUDT18 hydrolyzes triphosphates of nucleoside analogs of exogenous origin, suggesting that NUDT18 can act as a cellular sanitizer of modified nucleotides and may influence the antiviral efficacy of remdesivir, molnupiravir, and ribavirin. As NUDT18 is expressed in respiratory epithelial cells, it may limit the antiviral efficacy of remdesivir and molnupiravir against SARS-CoV-2 replication by decreasing the intracellular concentration of their active metabolites at their intended site of action.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".