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Record W4402003786 · doi:10.1002/advs.202405432

A Novel Antigen Design Strategy to Isolate Single‐Domain Antibodies that Target Human Nav1.7 and Reduce Pain in Animal Models

2024· article· en· W4402003786 on OpenAlexaff
Marzia Martina, Umberto Banderali, Álvaro Yogi, Mehdi Arbabi Ghahroudi, Hong Liu, Traian Sulea, Yves Durocher, Greg Hussack, Henk van Faassen, Balu Chakravarty, Qing Yan Liu, Umar Iqbal, Binbing Ling, Étienne Lessard, Joey Sheff, Anna Robotham, Debbie Callaghan, María Moreno, Tanya Comas, Dao Ly, Danica Stanimirovic

Bibliographic record

VenueAdvanced Science · 2024
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAntibodyAntigenComputer scienceComputational biologyMedicineBiologyImmunology

Abstract

fetched live from OpenAlex

Abstract Genetic studies have identified the voltage‐gated sodium channel 1.7 ( Na v 1.7 ) as pain target. Due to the ineffectiveness of small molecules and monoclonal antibodies as therapeutics for pain, single‐domain antibodies ( V H Hs ) are developed against the human Na v 1.7 ( hNa v 1.7 ) using a novel antigen presentation strategy. A 70 amino‐acid peptide from the hNa v 1.7 protein is identified as a target antigen. A recombinant version of this peptide is grafted into the complementarity determining region 3 ( CDR3 ) loop of an inert V H H in order to maintain the native 3D conformation of the peptide. This antigen is used to isolate one V H H able to i) bind hNa v 1.7, ii) slow the deactivation of hNa v 1.7, iii) reduce the ability of eliciting action potentials in nociceptors, and iv) reverse hyperalgesia in in vivo rat and mouse models. This V H H exhibits the potential to be developed as a therapeutic capable of suppressing pain. This novel antigen presentation strategy can be applied to develop biologics against other difficult targets such as ion channels, transporters and GPCRs.

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)0.002
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 score0.462
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.105
GPT teacher head0.374
Teacher spread0.270 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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