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ChAcNLS, a Novel Modification to Antibody-Conjugates Permitting Target Cell-Specific Endosomal Escape, Localization to the Nucleus, and Enhanced Total Intracellular Accumulation

2016· article· en· W2344220366 on OpenAlexafffund
Simon Beaudoin, Andreanne Rondeau, Olivier Martel, Marc‐André Bonin, Johan E. van Lier, Jeffrey V. Leyton

Bibliographic record

VenueMolecular Pharmaceutics · 2016
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversité de Sherbrooke
FundersBanting Research FoundationFaculté de médecine et des sciences de la santé, Université de SherbrookeCancer Research Society
KeywordsIntracellularEndosomeCell biologyNuclear transportFlow cytometryChemistryCellBiophysicsBiochemistryMolecular biologyBiologyCell nucleusNucleus

Abstract

fetched live from OpenAlex

The design of antibody-conjugates (ACs) for delivering molecules for targeted applications in humans has sufficiently progressed to demonstrate clinical efficacy in certain malignancies and reduced systemic toxicity that occurs with standard nontargeted therapies. One area that can advance clinical success for ACs will be to increase their intracellular accumulation. However, entrapment and degradation in the endosomal-lysosomal pathway, on which ACs are reliant for the depositing of their molecular payload inside target cells, leads to reduced intracellular accumulation. Innovative approaches that can manipulate this pathway may provide a strategy for increasing accumulation. We hypothesized that escape from entrapment inside the endosomal-lysosomal pathway and redirected trafficking to the nucleus could be an effective approach to increase intracellular AC accumulation in target cells. Cholic acid (ChAc) was coupled to the peptide CGYGPKKKRKVGG containing the nuclear localization sequence (NLS) from SV-40 large T-antigen, which is termed ChAcNLS. ChAcNLS was conjugated to the mAb 7G3 (7G3-ChAcNLS), which has nanomolar affinity for the cell-surface leukemic antigen interleukin-3 receptor-α (IL-3Rα). Our aim was to determine whether 7G3-ChAcNLS increased intracellular accumulation while retaining nanomolar affinity and IL-3Rα-positive cell selectivity. Competition ELISA and cell treatment assays were performed. Cell fractionation, confocal microscopy, flow cytometry, and Western blot techniques were used to determine the level of antibody accumulation inside cells and in corresponding nuclei. In addition, the radioisotope copper-64 ((64)Cu) was also utilized as a surrogate molecular cargo to evaluate nuclear and intracellular accumulation by radioactivity counting. 7G3-ChAcNLS effectively escaped endosome entrapment and degradation resulting in a unique intracellular distribution pattern. mAb modification with ChAcNLS maintained 7G3 nM affinity and produced high selectivity for IL-3Rα-positive cells. In contrast, 7G3 ACs with the ability to either escape endosome entrapment or traffic to the nucleus was not superior to 7G3-ChAcNLS for increasing intracellular accumulation. Transportation of (64)Cu when complexed to 7G3-ChAcNLS also resulted in increased nuclear and intracellular radioactivity accumulation. Thus, ChAcNLS is a novel mAb functionalizing technology that demonstrates its ability to increase AC intracellular accumulation in target cells through escaping endosome entrapment coupled to nuclear trafficking.

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 head0.051
GPT teacher head0.363
Teacher spread0.312 · 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".

Quick stats

Citations27
Published2016
Admission routes2
Has abstractyes

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