Psychotropic drug repurposing for COVID-19: a systematic review
Bibliographic record
Abstract
Introduction Recently, several antidepressants, mood stabilizers, and antipsychotics have been suggested to have favorable effects in the treatment of COVID-19. Objectives The aim of this systematic review was to collect evidence from preclinical and clinical studies concerning the scientific evidence for the repurposing of psychotropic drugs in COVID-19 treatment. Methods Two independent authors searched PubMed-MEDLINE, Scopus, PsycInfo, Clinical Trial Registration Site US (ClinicalTrials.gov) databases, and reviewed the reference lists of articles for eligible articles published up to May 31st, 2021. All preclinical and clinical studies on the effect of any psychotropic drug on Sars-CoV-2 or patients with COVID-19 were included. The Newcastle-Ottawa scale was used for the quality assessment of clinical studies. This systematic review adheres to the PRISMA guidelines. Results 22 studies were included in the synthesis: 9 clinical studies, 9 preclinical studies, and 4 computational studies. The use of antidepressants, both SSRI and non-SSRI, was associated with a reduced risk of severe complications of COVID-19. Several antipsychotics showed an increased risk for both Sars-CoV-2 infection and severe complications during COVID-19. Conclusions The current evidence supports a potential anti-SARS-CoV-2 role for several antidepressants, while the evidence on mood stabilizers or antipsychotics remains controversial. Drug repurposing proved highly successful in response to the current pandemic and psychotropic medications are widely used in clinical practice with well-known safety and tolerability profiles, showing antiviral, immunomodulatory, and anti-inflammatory properties, being perfect candidates for possible treatment of COVID-19. Further research will deliver optimized and specific therapeutic tools that will increase the preparedness of health systems for possible future epidemics. Disclosure No significant relationships.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".