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Record W2426571582 · doi:10.1124/jpet.116.232249

Robust Translation of γ-Secretase Modulator Pharmacology across Preclinical Species and Human Subjects

2016· article· en· W2426571582 on OpenAlexfundno aff
Jeremy H. Toyn, Kenneth M. Boy, Joseph Raybon, Jere E. Meredith, AS Robertson, Valerie Guss, Kimberly H. Snow, Rex Denton, Dmitry Zuev, Lorin A. Thompson, J. S. Morrison, James E. Grace, F. Berisha, Ramesh Padmanabha, Lynda Cook, Cong Wei, Dieter M. Drexler, John E. Macor, Maciej Ga̧sior, Richard E. Olson, Q. Hong, Holly Soares, Malaz Abutarif, Michael K. Ahlijanian

Bibliographic record

VenueJournal of Pharmacology and Experimental Therapeutics · 2016
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesSchool of Medicine, New York UniversityYork UniversityPfizer
KeywordsIn vivoAmyloid precursor protein secretaseIn vitroChemistryPeptideGamma secretasePyrimidineTranslation (biology)PharmacologyAmyloid (mycology)Amyloid precursor proteinBiochemistryAlzheimer's diseaseMedicineBiologyDiseaseMessenger RNAInternal medicineGene

Abstract

fetched live from OpenAlex

The amyloid-β peptide (Aβ)-in particular, the 42-amino acid form, Aβ1-42-is thought to play a key role in the pathogenesis of Alzheimer's disease (AD). Thus, several therapeutic modalities aiming to inhibit Aβ synthesis or increase the clearance of Aβ have entered clinical trials, including γ-secretase inhibitors, anti-Aβ antibodies, and amyloid-β precursor protein cleaving enzyme inhibitors. A unique class of small molecules, γ-secretase modulators (GSMs), selectively reduce Aβ1-42 production, and may also decrease Aβ1-40 while simultaneously increasing one or more shorter Aβ peptides, such as Aβ1-38 and Aβ1-37. GSMs are particularly attractive because they do not alter the total amount of Aβ peptides produced by γ-secretase activity; they spare the processing of other γ-secretase substrates, such as Notch; and they do not cause accumulation of the potentially toxic processing intermediate, β-C-terminal fragment. This report describes the translation of pharmacological activity across species for two novel GSMs, (S)-7-(4-fluorophenyl)-N2-(3-methoxy-4-(3-methyl-1H-1,2,4-triazol-1-yl)phenyl)-N4-methyl-6,7-dihydro-5H-cyclopenta[d]pyrimidine-2,4-diamine (BMS-932481) and (S,Z)-17-(4-chloro-2-fluorophenyl)-34-(3-methyl-1H-1,2,4-triazol-1-yl)-16,17-dihydro-15H-4-oxa-2,9-diaza-1(2,4)-cyclopenta[d]pyrimidina-3(1,3)-benzenacyclononaphan-6-ene (BMS-986133). These GSMs are highly potent in vitro, exhibit dose- and time-dependent activity in vivo, and have consistent levels of pharmacological effect across rats, dogs, monkeys, and human subjects. In rats, the two GSMs exhibit similar pharmacokinetics/pharmacodynamics between the brain and cerebrospinal fluid. In all species, GSM treatment decreased Aβ1-42 and Aβ1-40 levels while increasing Aβ1-38 and Aβ1-37 by a corresponding amount. Thus, the GSM mechanism and central activity translate across preclinical species and humans, thereby validating this therapeutic modality for potential utility in AD.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.132
GPT teacher head0.447
Teacher spread0.315 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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