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An Integrative Computational Approach for the Identification of C-Abl Kinase Inhibitors from Anti-Parkinson Plant-Derived Bioactive

2025· article· en· W4406400543 on OpenAlex
Haruna Isiyaku Umar, Zainab Ashimiyu‐Abdusalam, Neeraj Kumar, Najwa Ahmad Kuthi, Omoboyede Victor, Zainab Naeem Abdulsalam, E. O. Aribo, Ridwan Opeyemi Bello, Yousef A. Bin Jardan, Hiba‐Allah Nafidi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedicinal Chemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNilotinibDasatinibKinaseDocking (animal)KinomePharmacologyParkinson's diseaseChemistryIn silicoTyrosine kinaseBiochemistryBiologyMedicineSignal transductionDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Oxidative stress is strongly linked to neurodegeneration through the activation of c-Abl kinase, which arrests α-synuclein proteolysis by interacting with parkin interacting substrate (PARIS) and aminoacyl tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2). This activation, triggered by ataxia-telangiectasia mutated (ATM) kinase, leads to dopaminergic neuron loss and α -synuclein aggregation, a critical pathophysiological aspect of Parkinson's disease (PD). To halt PD progression, pharmacological inhibition of c-Abl kinase is essential. Despite three generations of tyrosine kinase inhibitors (TKIs) being explored for PD treatment, they present significant concerns including poor blood-brain barrier penetration, off-target effects, and severe side effects. Notably, there are currently no FDA-approved c-Abl kinase inhibitors in clinical usage for PD treatment, highlighting the urgent need for potent, safe, and cost-effective alternatives. OBJECTIVE: This study aims to identify potential c-Abl kinase inhibitors from plant-derived compounds with reported anti-Parkinson's potential and their derivatives using molecular docking, molecular dynamics simulations (MDS), and in silico pharmacokinetics and toxicity profiling. METHODS: Seventy-eight compounds sourced from literature were docked against c-Abl kinase using Maestro 12.5. The top three hit compounds, along with nilotinib (control drug), were subjected to drug-likeness, ADMET profiling using the AI Drug Lab server and 100 ns MDS using Desmond. RESULTS: Amburoside A, diarylheptanoid MS13, and dimethylaminomethyl-substituted-curcumin showed binding affinities close to nilotinib, with values of -12.615, -12.556, and -11.895 kcal/mol respectively, compared to nilotinib's -16.826 kcal/mol. The three plant-derived compounds exhibited excellent structural stability and favorable ADMET profiles, including optimal blood-brain barrier permeation. CONCLUSION: experiments are necessary to validate these findings.

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.

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 score0.060
Threshold uncertainty score0.498

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 head0.013
GPT teacher head0.280
Teacher spread0.268 · 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