Remdesivir-induced conduction abnormalities: A molecular model-based explanation
Bibliographic record
Abstract
Purpose: Remdesivir use in COVID-19 is associated with cardiac conduction abnormalities from unclear mechanisms. A proposed mechanism is the bioaccumulation of the intermediate metabolite GS-441524 resulting in exogenous activation of cardiac adenosine A1 due to the structural similarity between adenosine and GS-441524. The prolonged half-life of GS-441524 can result in sustained activation of adenosine A1 receptors. In this study, we used molecular modeling of adenosine, GS-441524 and the adenosine A1 receptor to assess the potential mechanistic association of the proposed mechanism. Methods: Adenosine and GS-441524 structures were acquired from the PubChem database. Ligand docking was carried out using UCSF Chimera. Models were chosen based on greatest binding affinity and minimum root mean square deviation. Figures of resulting structural models were prepared using UCSF Chimera or PyMOL 2.3.5. Results: By modeling the interaction between the A1 G protein complex and both adenosine and GS-441524, we found that the proposed mechanism of exogenous A1 receptor activation is feasible based on docking compatibility. Conclusion: The proposed mechanism of exogenous cardiac A1 receptor activation from bioaccumulation of GS-441524 as a cause of observed cardiac conduction abnormalities with the use of remdesivir in COVID-19 is viable. Further studies are needed to assess causality.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".