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Record W4311111624 · doi:10.3390/ddc1010003

Failed Repurposing of Lysosomotropic Drugs for COVID-19 Treatment or Prevention

2022· article· en· W4311111624 on OpenAlexafffund
François Marceau

Bibliographic record

VenueDrugs and Drug Candidates · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPhospholipidosisPharmacologyRepurposingDrug repositioningHydroxychloroquineDrugEndocytosisMicrovesiclesMedicineCoronavirus disease 2019 (COVID-19)ChemistryBiologyDiseaseCellPathologyBiochemistry

Abstract

fetched live from OpenAlex

The hope for the rapid discovery of an effective drug therapy for COVID-19 has led to several efforts to repurpose drugs approved for other indications. Lysosomotropic drugs, organic amines such as chloroquine, hydroxychloroquine, amiodarone and many others, were found to interfere with the viral life cycle in vitro but have failed in clinical trials. The properties of lysosomotropic drugs and the vacuolar cytopathology induced by them are briefly reviewed, including the critical role of lipophilicity, the central role of vacuolar (V)-ATPase for their concentration in acidic organelles, the altered function of these organelles including impaired endocytosis and secretion, macroautophagic accumulation and secondary phospholipidosis. The apparent preferential uptake of lysosomotropic drugs by phagocytic leukocytes (macrophages, neutrophils) and the high concentrations needed for a sustained disruption of vacuolar trafficking may have contributed to the failure of lysosomotropic drug repurposing for COVID-19.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.412
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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