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Record W4390192911 · doi:10.1002/alz.077090

Novel Pyrazinoindolones As Amyloid‐Beta Aggregation Inhibitors

2023· article· en· W4390192911 on OpenAlexaff
Praveen P. N. Rao, Leila Hejazi, Arash Shakeri

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIndole testThioflavinChemistryAmyloid betaSmall moleculeNeurotoxicityDrug discoveryPopulationCombinatorial chemistryRational designDrug designComputational biologyNanotechnologyBiochemistryAlzheimer's diseaseDiseaseMedicineMaterials scienceBiologyOrganic chemistryPeptide

Abstract

fetched live from OpenAlex

Abstract Background Alzheimer’s disease (AD) is a devastating neurodegenerative condition which primarily affects the elderly population. One of hallmarks of AD is the accumulation of protein aggregates known as amyloid‐beta (Aβ) that promotes neurotoxicity and cognitive decline. Recent discoveries in the field led to the accelerated market approval of anti‐Aβ antibodies aducanumab and lecanemab. These are exciting discoveries. However, considering the cost and patient compliance issues with these biological therapies, discovering novel anti‐Aβ therapies that are based on small molecules is highly desirable due to several advantages over biological therapies including the ease of oral administration, reduced cost involved in bulk manufacturing and better stability on storage. Method A library of novel pyrazino[1,2‐a]indole‐1(2H)‐one derivatives were designed using the computational modeling software Discovery Studio Structure‐Based‐Design (Biovia Inc, San Diego USA). Then synthetic chemistry protocols were optimized to prepare small molecule library and were characterized by 1H and 13C NMR and Liquid Chromatography Mass Spectrometry studies. The anti‐Aβ activity of these novel molecules toward Aβ40 aggregation was evaluated by fluorescence spectroscopy using the thioflavin‐T based aggregation kinetic studies. In addition, the online web tool SwissADME was used to calculate the physicochemical properties of pyrazino[1,2‐a]indole‐1(2H)‐one derivatives to assess their drug‐likeness, blood‐brain barrier permeability and pharmacokinetics. Result A novel library of pyrazino[1,2‐a]indole‐1(2H)‐one derivatives were prepared by optimizing a four step or two step synthesis protocol starting from either phenylhydrazine or indole‐2‐carboxylic acid to afford pyrazino[1,2‐a]indole‐1(2H)‐one derivatives 5a–f. In the Aβ40 (5 μM) aggregation kinetics assay, they exhibited 14‐59% inhibition when tested at a range of concentrations (1, 5, 10 and 25 μM), demonstrating their anti‐Aβ properties. Their physicochemical properties suggest their drug‐likeness. Conclusion Structure‐activity relationship studies demonstrate that the fused tricyclic pyrazino[1,2‐a]indole‐1(2H)‐one is a suitable template to i) develop novel chemical tools to study and understand the mechanisms of Aβ40 aggregation and ii) discover novel small molecules as potential therapies for 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)0.000
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 score0.003
Threshold uncertainty score0.009

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)0.0030.001

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 head0.042
GPT teacher head0.309
Teacher spread0.267 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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