The Large Action of Chlorpromazine: Translational and Transdisciplinary Considerations in the Face of COVID-19
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a severe acute respiratory syndrome (SARS) in humans that is caused by SARS-associated coronavirus type 2 (SARS-CoV-2). In the context of COVID-19, several aspects of the relations between psychiatry and the pandemic due to the coronavirus have been described. Some drugs used as antiviral medication have neuropsychiatric side effects, and conversely some psychotropic drugs have antiviral properties. Chlorpromazine (CPZ, Largactil ® ) is a well-established antipsychotic medication that has recently been proposed to have antiviral activity against SARS-CoV-2. This review aims to 1) inform health care professionals and scientists about the history of CPZ use in psychiatry and its potential anti- SARS-CoV-2 activities 2) inform psychiatrists about its potential anti-SARS-CoV-2 activities, and 3) propose a research protocol for investigating the use of CPZ in the treatment of COVID-19 during the potential second wave. The history of CPZ’s discovery and development is described in addition to the review of literature from published studies within the discipline of virology related to CPZ. The early stages of infection with coronavirus are critical events in the course of the viral cycle. In particular, viral entry is the first step in the interaction between the virus and the cell that can initiate, maintain, and spread the infection. The possible mechanism of action of CPZ is related to virus cell entry via clathrin-mediated endocytosis. Therefore, CPZ could be useful to treat COVID-19 patients provided that its efficacy is evaluated in adequate and well-conducted clinical trials. Interestingly, clinical trials of very good quality are in progress. However, more information is still needed about the appropriate dosage regimen. In short, CPZ repositioning is defined as a new use beyond the field of psychiatry.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".