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Record W4307713717 · doi:10.1208/s12248-022-00762-6

Anti-drug Antibody Sample Testing and Reporting Harmonization

2022· article· en· W4307713717 on OpenAlex
Darshana Jani, Robin Marsden, Michele Gunsior, Laura Schild Hay, Bethany Ward, Kyra J. Cowan, Mitra Azadeh, Breann Barker, Liching Cao, Kristin R. Closson, Kelly Coble, Sanjay L. Dholakiya, Julie Dusseault, Amanda Hays, Carina Herl, Michael E. Hodsdon, Susan C. Irvin, Susan Kirshner, Gerry Kolaitis, Nadia Kulagina, Seema Kumar, Ching Ha Lai, Francesco Lipari, Susana Liu, Keith D. Merdek, Ioana R. Moldovan, Reza Mozaffari, Luying Pan, Corina Place, Veerle Snoeck, Marta Starcevic Manning, Dennis Stocker, Magdalena Tary‐Lehmann, Amy S. Turner, Inna Vainshtein, Daniela Verthelyi, William T. Williams, Haoheng Yan, Weili Yan, Lili Yang, Lin Yang, Jennifer Zemo, Zhandong Don Zhong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe AAPS Journal · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsPfizer (Canada)Canadian Nuclear Laboratories
FundersBill and Melinda Gates Foundation
KeywordsImmunogenicityHarmonizationComputer scienceScope (computer science)BioanalysisSample (material)MedicineChemistryAntibodyChromatographyImmunology

Abstract

fetched live from OpenAlex

A clear scientific and operational need exists for harmonized bioanalytical immunogenicity study reporting to facilitate communication of immunogenicity findings and expedient review by industry and health authorities. To address these key bioanalytical reporting gaps and provide a report structure for documenting immunogenicity results, this cross-industry group was formed to establish harmonized recommendations and a develop a submission template to facilitate agency filings. Provided here are recommendations for reporting clinical anti-drug antibody (ADA) assay results using ligand-binding assay technologies. This publication describes the essential bioanalytical report (BAR) elements such as the method, critical reagents and equipment, study samples, results, and data analysis, and provides a template for a suggested structure for the ADA BAR. This publication focuses on the content and presentation of the bioanalytical ADA sample analysis report. The interpretation of immunogenicity data, including the evaluation of the impact of ADA on safety, exposure, and efficacy, is out of scope of this publication.

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.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.075
GPT teacher head0.339
Teacher spread0.264 · 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