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Record W2761229854 · doi:10.1021/jacs.7b09790

Perfluoroarene–Based Peptide Macrocycles to Enhance Penetration Across the Blood–Brain Barrier

2017· article· en· W2761229854 on OpenAlexfundno aff
Colin M. Fadzen, Justin M. Wolfe, Choi‐Fong Cho, E. Antonio Chiocca, Sean Lawler, Bradley L. Pentelute

Bibliographic record

VenueJournal of the American Chemical Society · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchDivision of Graduate EducationMassachusetts Institute of TechnologySontag Foundation
KeywordsChemistryPeptideBlood–brain barrierPenetration (warfare)BiophysicsEx vivoCell-penetrating peptideIn vivoParenchymaOligopeptideCysteineBiochemistryIn vitroNeuroscienceCentral nervous systemPathologyEnzyme

Abstract

fetched live from OpenAlex

Ar chemistry to improve the ability of peptides to cross the blood-brain barrier. Multiple macrocyclic analogues of the peptide transportan-10 were investigated that displayed increased uptake in two different cell lines and improved proteolytic stability. One of these analogues (M13) exhibited substantially increased delivery across a cellular spheroid model of the blood-brain barrier. Through ex vivo imaging of mouse brains, we demonstrated that this perfluoroarene-based macrocycle of TP10 exhibits increased penetration of the brain parenchyma following intravenous administration in mice. Finally, we evaluated macrocyclic analogues of the BH3 domain of the BIM protein to assess if our approach would be applicable to a peptide of therapeutic interest. We identified a BIM BH3 analogue that showed increased penetration of the brain tissue in mice.

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 head0.006
GPT teacher head0.290
Teacher spread0.284 · 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

Citations87
Published2017
Admission routes1
Has abstractyes

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