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Record W2901732892 · doi:10.1016/j.celrep.2018.10.062

In Vivo Delivery of Synthetic Human DNA-Encoded Monoclonal Antibodies Protect against Ebolavirus Infection in a Mouse Model

2018· article· en· W2901732892 on OpenAlexaff
Ami Patel, Daniel H. Park, Carl W. Davis, Trevor R.F. Smith, Anders Leung, Kevin Tierney, Aubrey L. Bryan, Edgar Davidson, Xiaoying Yu, Trina Racine, Charles C. Reed, Marguerite E. Gorman, Megan C. Wise, Sarah T. C. Elliott, Rianne Esquivel, Jian Yan, Jing Chen, Kar Muthumani, Benjamin J. Doranz, Erica Ollmann Saphire, James E. Crowe, Kate E. Broderick, Gary Kobinger, Shihua He, Xiangguo Qiu, Darwyn Kobasa, Laurent Humeau, Niranjan Y. Sardesai, Rafi Ahmed, David B. Weiner

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversité LavalUniversity of ManitobaPublic Health Agency of Canada
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthDefense Advanced Research Projects AgencyCenter for Outcomes Research and Evaluation, Yale School of Medicine
KeywordsVirologyMonoclonal antibodyEbolavirusIn vivoBiologyAntibodyDNAImmunologyGeneticsOutbreakEbola virus

Abstract

fetched live from OpenAlex

Synthetically engineered DNA-encoded monoclonal antibodies (DMAbs) are an in vivo platform for evaluation and delivery of human mAb to control against infectious disease. Here, we engineer DMAbs encoding potent anti-Zaire ebolavirus (EBOV) glycoprotein (GP) mAbs isolated from Ebola virus disease survivors. We demonstrate the development of a human IgG1 DMAb platform for in vivo EBOV-GP mAb delivery and evaluation in a mouse model. Using this approach, we show that DMAb-11 and DMAb-34 exhibit functional and molecular profiles comparable to recombinant mAb, have a wide window of expression, and provide rapid protection against lethal mouse-adapted EBOV challenge. The DMAb platform represents a simple, rapid, and reproducible approach for evaluating the activity of mAb during clinical development. DMAbs have the potential to be a mAb delivery system, which may be advantageous for protection against highly pathogenic infectious diseases, like EBOV, in resource-limited and other challenging settings.

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 score0.047
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.031
GPT teacher head0.318
Teacher spread0.286 · 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

Citations51
Published2018
Admission routes1
Has abstractyes

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