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Record W4318484507 · doi:10.1039/d2bm00819j

Subcutaneous delivery of an antibody against SARS-CoV-2 from a supramolecular hydrogel depot

2023· article· en· W4318484507 on OpenAlexfundno aff
Catherine M. Kasse, Anthony C. Yu, Abigail E. Powell, Gillie A. Roth, Celine S. Liong, Carolyn K. Jons, Awua Buahin, Caitlin L. Maikawa, Xueting Zhou, Sawsan Youssef, Jacob E. Glanville, Eric A. Appel

Bibliographic record

VenueBiomaterials Science · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsnot available
FundersChemistry, Engineering and Medicine for Human Health, Stanford UniversityStanford Maternal and Child Health Research InstituteStanford Bio-XNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaBill and Melinda Gates FoundationNational Defense Science and Engineering GraduateU.S. Department of DefenseNational Institutes of HealthNational Science Foundation
KeywordsDepotAntibodySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ChemistryCoronavirus disease 2019 (COVID-19)Supramolecular chemistry2019-20 coronavirus outbreakSubcutaneous injectionVirologyNanotechnologyMedicineImmunologyBiologyMaterials scienceBioinformaticsPathologyMoleculeOrganic chemistry

Abstract

fetched live from OpenAlex

drug release assays and diffusion measurements indicate that the PNP hydrogels prevent burst release and slow the release of encapsulated antibodies. Delivery of bnAbs against SARS-CoV-2 from PNP hydrogels is compared to standard routes of administration in a preclinical mouse model. We develop a multi-compartment model to understand the ability of these subcutaneous depot materials to modulate the pharmacokinetics of released antibodies; the model is extrapolated to explore the requirements needed for novel materials to successfully deliver relevant antibody therapeutics with different pharmacokinetic characteristics.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.307
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 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

Citations34
Published2023
Admission routes1
Has abstractyes

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