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Lack of in Vivo Antibody Dependent Cellular Cytotoxicity with Antibody Containing Gold Nanoparticles

2015· article· en· W2049877674 on OpenAlexfundno aff
Marya Ahmed, Dorothy W. Pan, Mark E. Davis

Bibliographic record

VenueBioconjugate Chemistry · 2015
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsnot available
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of Canada
KeywordsAntibody-dependent cell-mediated cytotoxicityCetuximabPanitumumabIn vivoChemistryAntibodyCytotoxicityMonoclonal antibodyCancer researchIn vitroImmunologyBiochemistryBiology

Abstract

fetched live from OpenAlex

Antibody-dependent cellular cytotoxicity (ADCC) is a cytolytic mechanism that can elicit in vivo antitumor effects and can play a significant role in the efficacy of antibody treatments for cancer. Here, we prepared cetuximab, panitumumab, and rituximab containing gold nanoparticles and investigated their ability to produce an ADCC effect in vivo. Cetuximab treatment of EGFR-expressing H1975 tumor xenografts showed significant tumor regression due to the ADCC activity of the antibody in vivo, while the control antibody, panitumumab, did not. However, all three antibody containing nanoparticles are not able to suppress tumor growth in the same in vivo mouse model. The antibody containing nanoparticles localized in the tumors and did not suppress the immune function of the animals, so the lack of tumor growth suppression of the cetuximab containing nanoparticle suggests that immobilizing antibodies onto a nanoparticle significantly decreases the ability of the antibody to promote an ADCC response.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.056
GPT teacher head0.340
Teacher spread0.283 · 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 designBench or experimental
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

Citations30
Published2015
Admission routes1
Has abstractyes

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