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Record W4303685418 · doi:10.3390/biomedicines10102516

A Candidate Therapeutic Monoclonal Antibody Inhibits Both HRSV and HMPV Replication in Mice

2022· article· en· W4303685418 on OpenAlexaff
Hugues Fausther‐Bovendo, Marie‐Ève Hamelin, Julie Carbonneau, Marie-Christine Venable, Liva Checkmahomed, Pierre‐Olivier Lavoie, Marie-Ève Ouellet, Guy Boivin, Marc‐André D’Aoust, Gary Kobinger

Bibliographic record

VenueBiomedicines · 2022
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsMedicago (Canada)Université Laval
Fundersnot available
KeywordsHuman metapneumovirusVirologyMonoclonal antibodyBiologyVirusFlow cytometryPalivizumabViral replicationTiterAntibodyParamyxoviridaeMicrobiologyRespiratory tract infectionsImmunologyRespiratory systemViral disease

Abstract

fetched live from OpenAlex

Human metapneumovirus (HMPV) and human respiratory virus (HRSV) are two leading causes of acute respiratory tract infection in young children. While there is no licensed drug against HMPV, the monoclonal antibody (mAb) Palivizumab is approved against HRSV for prophylaxis use only. Novel therapeutics against both viruses are therefore needed. Here, we describe the identification of human mAbs targeting these viruses by using flow cytometry-based cell sorting. One hundred and two antibodies were initially identified from flow cytometry-based cell sorting as binding to the fusion protein from HRSV, HMPV or both. Of those, 95 were successfully produced in plants, purified and characterized for binding activity by ELISA and neutralization assays as well as by inhibition of virus replication in mice. Twenty-two highly reactive mAbs targeting either HRSV or HMPV were isolated. Of these, three mAbs inhibited replication in vivo of a single virus while one mAb could reduce both HRSV and HMPV titers in the lung. Overall, this study identifies several human mAbs with virus-specific therapeutic potential and a unique mAb with inhibitory activities against both HRSV and HMPV.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.388
Teacher spread0.345 · 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 designObservational
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

Citations14
Published2022
Admission routes1
Has abstractyes

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