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Record W3198359021 · doi:10.3390/biomedicines9091186

Safety and Tolerability of the Adeno-Associated Virus Vector, AAV6.2FF, Expressing a Monoclonal Antibody in Murine and Ovine Animal Models

2021· article· en· W3198359021 on OpenAlexafffund
Amira D. Rghei, Laura P. van Lieshout, Benjamin M. McLeod, Yanlong Pei, Jordyn A. Lopes, Nicole Zielinska, Enzo M. Baracuhy, Brenna A. Y. Stevens, Sylvia P. Thomas, Jacob G. E. Yates, Bryce M. Warner, Darwyn Kobasa, Hugues Fausther‐Bovendo, Gary Kobinger, Khalil Karimi, Brad Thompson, Byram W. Bridle, Leonardo Susta, Sarah K. Wootton

Bibliographic record

VenueBiomedicines · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversité LavalPublic Health Agency of CanadaUniversity of Guelph
FundersMitacs
KeywordsMonoclonal antibodyTolerabilityAntibodyVirologyVirusVector (molecular biology)VaccinationImmunologyBiologyImmunityMedicinePharmacologyAdverse effectImmune systemRecombinant DNA

Abstract

fetched live from OpenAlex

Adeno-associated virus (AAV) vector mediated expression of therapeutic monoclonal antibodies is an alternative strategy to traditional vaccination to generate immunity in immunosuppressed or immunosenescent individuals. In this study, we vectorized a human monoclonal antibody (31C2) directed against the spike protein of SARS-CoV-2 and determined the safety profile of this AAV vector in mice and sheep as a large animal model. In both studies, plasma biochemical parameters and hematology were comparable to untreated controls. Except for mild myositis at the site of injection, none of the major organs revealed any signs of toxicity. AAV-mediated human IgG expression increased steadily throughout the 28-day study in sheep, resulting in peak concentrations of 21.4-46.7 µg/ mL, demonstrating practical scale up from rodent to large animal models. This alternative approach to immunity is worth further exploration after this demonstration of safety, tolerability, and scalability in a large animal model.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.019
GPT teacher head0.304
Teacher spread0.285 · 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 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

Citations9
Published2021
Admission routes2
Has abstractyes

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