MétaCan
Menu
Back to cohort
Record W1827791620 · doi:10.1080/19420862.2015.1073429

Consistency of quality attributes for the glycosylated monoclonal antibody Humira® (adalimumab)

2015· article· en· W1827791620 on OpenAlexaff
Paul W. Tebbey, Amy Varga, Michael Naill, Jerry Clewell, Jaap Venema

Bibliographic record

VenuemAbs · 2015
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsGlobal Affairs Canada
Fundersnot available
KeywordsAdalimumabMonoclonal antibodyGlycosylationGlycoproteinComputational biologyAntibodyTumor necrosis factor alphaChemistryComputer scienceBiochemistryBiologyImmunology

Abstract

fetched live from OpenAlex

Humira® (adalimumab) is a recombinant human IgG1 monoclonal antibody (mAb) glycoprotein consisting of 1330 amino acids that is specific for human tumor necrosis factor (TNF). The biological activity and clinical profile of mAb therapeutics, including adalimumab, is influenced by their protein structure and glycosylation patterns, which can be affected by the expression system, cell culture conditions and purification process methodology. While clinical outcome cannot yet be attributed to many of the individual structural features that constitute a mAb, it is evident that detailed structural attribute analysis is necessary if structural contributions to function are to be comprehensively defined. Adalimumab product quality data generated from over a decade of manufacturing across multiple production sites and through a series of manufacturing scale changes are presented here. These data reveal a consistent and tightly controlled profile for the product.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.180
GPT teacher head0.433
Teacher spread0.253 · 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 designObservational
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

Citations63
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuemAbsSame topicMonoclonal and Polyclonal Antibodies ResearchFrench-language works237,207