MétaCan
Menu
Back to cohort

Synergistic drug combination effectively blocks Ebola virus infection

2016· article· en· W2556090342 on OpenAlexaff
Wei Sun, Shihua He, Carles Martínez‐Romero, Jennifer Kouznetsova, Gregory J. Tawa, Miao Xu, Paul Shinn, Ethan G. Fisher, Yan Long, Omid Motabar, Shu Yang, Philip E.J. Sanderson, Peter R. Williamson, Adolfo García‐Sastre, Xiangguo Qiu, Wei Zheng

Bibliographic record

VenueAntiviral Research · 2016
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersNational Institute of Allergy and Infectious DiseasesNational Center for GM CropsNational Institutes of Health
KeywordsEbola virusDrugPosaconazoleEbolavirusVirologyPharmacologyToremifeneMefloquineMedicineAntiviral drugBiologyVirusImmunologyMicrobiologyMalariaInternal medicineChloroquineAmphotericin B

Abstract

fetched live from OpenAlex

Although a group of FDA-approved drugs were previously identified with activity against Ebola virus (EBOV), most of them are not clinically useful because their human blood concentrations are not high enough to inhibit EBOV infection. We screened 795 unique three-drug combinations in an EBOV entry assay. Two sets of three-drug combinations, toremifene-mefloquine-posaconazole and toremifene-clarithromycin-posaconazole, were identified that effectively blocked EBOV entry and were further validated for inhibition of live EBOV infection. The individual drug concentrations in the combinations were reduced to clinically relevant levels. We identified mechanisms of action of these drugs: functional inhibitions of Niemann-Pick C1, acid sphingomyelinase, and lysosomal calcium release. Our findings identify the drug combinations with potential to treat EBOV infection.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.406
Teacher spread0.348 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations100
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueAntiviral ResearchSame topicViral Infections and Outbreaks ResearchFrench-language works237,207