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Record W2080183440 · doi:10.4155/bio.13.38

Bioanalysis of Antibody–Drug Conjugates: American Association of Pharmaceutical Scientists Antibody–Drug Conjugate Working Group Position Paper

2013· article· en· W2080183440 on OpenAlex

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

VenueBioanalysis · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsBioanalysisAntibody-drug conjugateConjugateDrugLinkerPharmacologyPharmaceutical industryComputational biologyChemistryComputer scienceCombinatorial chemistryAntibodyMedicineMonoclonal antibodyBiologyChromatographyMathematicsImmunology

Abstract

fetched live from OpenAlex

Antibody-drug conjugates (ADCs) typically consist of a cytotoxic drug covalently bound to an antibody by a linker. These conjugates have the potential to substantially improve efficacy and reduce toxicity compared with cytotoxic small-molecule drugs. Since ADCs are generally complex heterogeneous mixtures of multiple species, these novel therapeutic products present unique bioanalytical challenges. The growing number of ADCs being developed across the industry suggests the need for alignment of the bioanalytical methods or approaches used to assess the multiple species and facilitate consistent interpretation of the bioanalytical data. With limited clinical data, the current strategies that can be used to provide insight into the relationship between the multiple species and the observed clinical safety and efficacy are still evolving. Considerations of the bioanalytical strategies for ADCs based on the current industry practices that take into account the complexity and heterogeneity of ADCs are discussed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.132
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.309
Teacher spread0.296 · 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