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Record W4412983127 · doi:10.1016/j.toxrep.2025.102104

Selective modulation of orexinergic receptors by neem-derived phytochemicals: Computational analysis of structure-activity relationships

2025· article· en· W4412983127 on OpenAlexaff
Oluwaseun E. Agboola, Samuel S. Agboola, Precious Eseose Agboinghale, Zainab A. Ayinla, Abel Kolawole Oyebamiji, Oluranti E. Olaiya, Omotola M Fajana, Oluwatoyin Mary Oyinloye, Adetola Ibukunoluwa Adewale, Olajumoke Tolulope Idowu, Foluso Oluwagbemiga Osunsanmi, Basiru Olaitan Ajiboye, Babatunji Emmanuel Oyinloye

Bibliographic record

VenueToxicology Reports · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry and biological activity of medicinal plants
Canadian institutionsUniversity of WaterlooUniversity of Ottawa
FundersUniversity of Zululand
KeywordsQuantitative structure–activity relationshipReceptorChemistryComputational biologyBiologyStereochemistryBiochemistry

Abstract

fetched live from OpenAlex

Orexinergic system dysfunction is the fundamental basis for several neurological illnesses like narcolepsy, insomnia, and drug dependency, yet none of the existing medications are subtype receptor specific. This study examines 124 chemicals from neem to determine if they can be utilised as specific orexinergic receptor modulators using advanced computational methods. The methodology includes detailed clustering, pharmacophoric interaction, pharmacokinetic, statistical, and clustering analyses. Molecular property profiling indicated the majority of the compounds exhibit excellent drug-like qualities (MW 350-450 Da, LogP 0-2), while principal component analysis captured 100% structural variability between two components (92.5% and 7.5%, respectively). Molecular docking simulations indicated selective binding to the 6V9S receptor (-11.3 to -4 kcal/mol) over 4S0V (-9.7 to -4 kcal/mol). Lead compounds Neem_PDB_10257 (Tirucallol) (-11.3 kcal/mol) and Neem_PDB_12072821 ([(5 R,7 R,8 R,9 R,10 R,13S,17 R) -17-(2-methoxy-5-oxo-4,4,8,10,13-pentamethyl-3-oxo-5,6,7,9,11,12,16,17-octahydrocyclopenta[a]phenanthren-7-yl] acetate) were particularly 6V9S selective (>2 kcal/mol difference), whereas Neem_PDB_10160319 ((4S,4aS,5S,10S,13S,14S,17-4,4,10,13,14-pentam -1, 2, 3, 5, 6, 7, 11, 12, 15, 17-decahydrocyclopenta[a]phenanthren-16-one) was most sensitive towards 4S0V. Two top-ranked compound families were discovered by hierarchical cluster analysis with a distance requirement of 35 units, and receptor-specific dendrograms revealed distinctive subcluster branching patterns (4S0V: 5.5 and 6.7 unit subclusters; 6V9S: 7.1 and 7.2 unit subclusters). Interaction pattern (heatmap analysis) identified major interaction hotspots, including TYR348, TRP120, PHE227, and HIS350. Neem_PDB_163184214 (Meliatetraolenone) specifically targeted ASN318 in 6V9S, while Neem_PDB_54580354 (7-Benzoylnimbocinol) favored interaction with GLN134 in 4S0V (>90 interactions). These findings dispute the "one-pharmacophore" theory for orexinergic modulators, showing that intentional functionalization of NEM templates can deliver subtype-selective treatments with maximal sleep-wake modulation and low off-target effects.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.250
Teacher spread0.233 · 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 designSimulation or modeling
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

Citations7
Published2025
Admission routes1
Has abstractyes

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