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Record W2766888414 · doi:10.1021/acs.jmedchem.7b01102

Noncovalent Protein Arginine Deiminase (PAD) Inhibitors Are Efficacious in Animal Models of Multiple Sclerosis

2017· article· en· W2766888414 on OpenAlexafffund
Elizabeth J. Curiel Tejeda, Angélica M. Bello, Ewa Wasilewski, Adam Koebel, Shannon E. Dunn, Lakshmi P. Kotra

Bibliographic record

VenueJournal of Medicinal Chemistry · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsToronto General HospitalCanada Research ChairsUniversity Health NetworkUniversity of Toronto
FundersInstitute of Infection and ImmunityCanadian Institutes of Health ResearchCanada Foundation for Innovation
KeywordsChemistryPharmacologyHydantoinMultiple sclerosisIn vivoMoietyPharmacokineticsPotencyArginineBiochemistryStereochemistryAmino acidIn vitroImmunology

Abstract

fetched live from OpenAlex

Peptidyl arginine deiminases have been shown to be hyperactive in neurodegenerative diseases including multiple sclerosis. An α-amino acid-based core structure, derived from a hydantoin core, with unique heterocycles on the side chains were synthesized as potential noncovalent inhibitors of PAD enzymes. Among the various heterocycles investigated, compound 23, carrying an imidazole moiety, exhibited the highest potency in this series with some selectivity for PAD2, and was further investigated in vivo. Pharmacokinetics in mice suggested the C max to be 12.0 ± 2.5 μg/mL and 170 ± 10 ng/mL in the serum and brain, respectively, when compound 23 was administered at 50 mg/kg via single dose ip. At the same dose, compound 23 also reversed physical disability and cleared the brain of T-cell infiltration in an EAE mouse model of multiple sclerosis (MS). This novel series of compounds show promise for further development as disease modifying agents for the potential treatment of MS.

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 score0.002
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.035
GPT teacher head0.258
Teacher spread0.223 · 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

Citations24
Published2017
Admission routes2
Has abstractyes

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