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Record W2576982875

ChAcNLS-A14, a novel antibody-conjugate PET tracer for targeting human IL-5Rα-positive muscle invasive bladder cancer

2016· article· en· W2576982875 on OpenAlexaff
Michel Paquette, Simon Beaudoin, Laurent Fafard-Couture, Luis-Guillermo Vilera-Perez, Nadia Ekindi‐Ndongo, Angel F. López, Roger Lecomte, Robert Sabbagh, Brigitte Guérin, Jeff Leyton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBiodistributionMonoclonal antibodyCancer researchPositron emission tomographyChemistryIn vivoIntracellularAntibodyEndosomeImaging agentMedicineIn vitroNuclear medicineBiologyImmunologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

52 Objectives Recently, it has been reported that Interleukin-5-receptor (IL-5R) is implicated in tumor invasiveness in muscle invasive bladder cancer (MIBC). A cholic acid coupled to a Nuclear Localisation Signal peptide (ChAcNLS) is a novel technology developed for increased intracellular accumulation of targeted chemotherapies with antibody-drug conjugates (ADCs). ChAcNLS-conjugated antibodies use an alternative strategy, contrary to the standard intracellular delivery approach which relies on ADC degradation via the endosomal-lysosomal pathway and drug release within the target cell. ChAcNLS enables ADCs to evade lysosome degradation by escaping endosome entrapment and redirecting their trafficking to the nucleus. This results in increased intracellular accumulation. The aim of this study was to evaluate if the functionalization of the monoclonal antibody (mAb) A14 with ChAcNLS could improve tumor accumulation of the positron emitter 64Cu in xenografts of IL-5R-positive MIBC and to assess its potential as a PET tracer for MIBC imaging. The in vivo pharmacokinetic and tumor-targeting properties of [64Cu]-A14-ChAcNLS was compared to [64Cu]-A14 in MIBC-tumor bearing mice by positron emission tomography (PET) imaging and biodistribution. Methods A14 was conjugated with the copper chelator NOTA, then further conjugated to ChAcNLS moieties. Labeling yielded a specific activity of 250 MBq/mg. NOD/SCID mice were implanted subcutaneously with 5×106 HT1376 (high IL-5R expression) and HTB9 (low IL-5R expression) human MIBC cells on separate flanks. When tumors reached >4 mm diameter, mice were injected with 25 µg (≍6 MBq) of [64Cu]-A14 or [64Cu]-A14-ChAcNLS followed by daily PET imaging, up to 72 h post-injection. Biodistribution was performed at 48 h and 96 h post-injection. Specific binding to the tumors was assessed by injection of 25 mg/kg unlabeled A14 24 h prior to tracer injection, followed by imaging and dissection at 48 h post-injection. Dissection and PET data were used to compare the in vivo uptake (%ID/g) of MIBC tumors relative to healthy organs. Results Increased radioactive uptake in HT1376 tumors relative to HTB9 tumors was observed at all time points using both ACs, which was consistent with their relative IL-5R expression levels. There was a significantly lower blood pool, along with lower muscle uptake when [64Cu]-ChAcNLS-A14 was used compared to 64Cu-A14. Higher kidney uptake was observed using [64Cu]-ChAcNLS-A14 compared to [64Cu]-A14. No bladder uptake could be detected in PET images. Tumors showed similar uptake for both tracers, and pre-injection of a blocking dose of A14 resulted in a ≍50% drop of uptake, assessed by both PET and biodistribution. Thanks to decreased background, tumor-to-muscle and tumor-to-blood ratios were improved with [64Cu]-A14-ChAcNLS, resulting in better specific targeting of MIBC tumors than with [64Cu]-A14. Conclusions A14-ChAcNLS exhibited improved pharmacokinetics resulting in enhanced tumor-specific targeting. A14-ChAcNLS displays suitable in vivo properties to proceed to IL-5Rα PET imaging of human MIBC.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.029
GPT teacher head0.332
Teacher spread0.303 · 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 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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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