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Record W4319164392 · doi:10.1016/j.trecan.2023.01.003

Antibody–drug conjugates: in search of partners of choice

2023· review· en· W4319164392 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTrends in cancer · 2023
Typereview
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersUniversity of TorontoConquer Cancer Foundation
KeywordsDrugAntibody-drug conjugateComputational biologyPharmacologyMedicineAntibodyCancer researchMonoclonal antibodyImmunologyBiology

Abstract

fetched live from OpenAlex

Antibody-drug conjugates (ADCs) have become a credentialled class of anticancer drugs for both solid and hematological malignancies, with regulatory approvals mainly as single agents. Despite extensive preclinical and clinical efforts to develop rational ADC-based combinations, to date only a limited number have demonstrated survival improvements over standard of care. The most appealing partners for ADCs are those that offer additive or synergistic effects on tumor cells or their microenvironment without unacceptable overlapping toxicities. Coadministration with antiangiogenic compounds, HER2-targeting drugs, DNA-damage response agents and immune checkpoint inhibitors (ICIs) represent active forerunners. Through the identification of targets with tumor-specific expression, improved conjugation technologies, and novel linkers and payloads offering superior therapeutic indices, the next generation of ADCs brings optimism to combinatorial approaches.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.915
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.369
GPT teacher head0.608
Teacher spread0.239 · 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