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Record W2939235761 · doi:10.1080/2162402x.2019.1599636

Daratumumab in combination with urelumab to potentiate anti-myeloma activity in lymphocyte-deficient mice reconstituted with human NK cells

2019· article· en· W2939235761 on OpenAlexaff
María C. Ochoa, Elísabeth Pérez-Ruiz, Luna Minute, Carmen Oñate, Guiomar Pérez, Inmaculada Rodríguez, Aintzane Zabaleta, Diego Alignani, Myriam Fernández-Sendin, Ascensión López, Aura Muntasell, Miguel F. Sanmamed, Bruno Paiva, Miguel López‐Botet, Pedro Berraondo, Ignacio Melero

Bibliographic record

VenueOncoImmunology · 2019
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsInstitute of Infection and Immunity
FundersH2020 European Institute of Innovation and TechnologyInstituto de Salud Carlos IIIWorldwide Cancer Research
KeywordsDaratumumabAntibody-dependent cell-mediated cytotoxicityCD137Cancer researchCD38MedicineMonoclonal antibodyImmunologyCD8Multiple myelomaImmunotherapyAntibodyImmune systemBiologyCell biologyCD34

Abstract

fetched live from OpenAlex

Daratumumab is an anti-CD38 fully human IgG1 mAb approved for multiple myeloma treatment. One of the proposed mechanisms of action is the induction of antibody-dependent cellular cytotoxicity (ADCC) mediated by NK cells. NK cells acquire surface CD137 expression in the presence of solid-phase-attached daratumumab and when encountering a daratumumab-coated CD38+ tumor cell line. In this setting, addition of the agonist anti-CD137 mAb urelumab enhances NK-cell activation increasing CD25 expression and IFNɣ production. However, in vitro ADCC is not increased by the addition of urelumab both in 4h or 24h lasting experiments. To study urelumab-increased daratumumab-mediated ADCC activity in vivo, we set up a mouse model based on the intravenous administration of a luciferase-transfected multiple myeloma cell line of human origin, human NK cells and daratumumab to immuno-deficient NSG mice. In this model, intravenous administration of urelumab 24h after daratumumab delayed tumor growth and prolonged mice survival.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.588
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.275
Teacher spread0.262 · 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 teacher head, 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

Citations28
Published2019
Admission routes1
Has abstractyes

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