From farm to pharma: Investigation of the therapeutic potential of the dietary plants Apium graveolens L., Coriandrum sativum, and Mentha longifolia, as AhR modulators for Immunotherapy.

Autoimmune diseases represent a complex array of conditions where the body's immune system mistakenly attacks its own tissues. These disorders, affecting millions worldwide, encompass a broad spectrum of conditions ranging from rheumatoid arthritis and multiple sclerosis to lupus and type 1 diabetes. The Aryl hydrocarbon receptor (AhR) translocator, expressed across immune and other cell types, plays crucial roles in immune disorders and inflammatory diseases. With a realm towards natural remedies in modern medicine for disease prevention, this study investigates the electronic properties and behaviors of bioactive compounds from dietary sources, including Apium graveolens L. (Celery), Coriandrum sativum seeds (Coriander), and Mentha longifolia, as AhR modulators. Through comprehensive analysis (HOMO-LUMO, ESP, LOL, and ELF), electron-rich and -poor regions, electron localization, and delocalization are identified, contrasting these compounds with the toxic AhR ligand, TCDD. Evaluation of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties reveals favorable pharmacokinetics without blood-brain barrier penetration, indicating drug-like characteristics. Molecular docking demonstrates stronger interactions of dietary flavonoid ligands with AhR transcription compared to TCDD. Molecular dynamics simulations confirm the stability of complexes and the sustainability of interactions formed. This research underscores the potential of natural compounds as effective AhR modulators for therapeutic interventions in immune-related disorders.

Inhibition of the Janus kinase protein (JAK1) by the A. Pyrethrum Root Extract for the treatment of Vitiligo pathology. Design, Molecular Docking, ADME-Tox, MD Simulation, and in-silico investigation.

This study delves into the therapeutic efficacy of A. pyrethrum in addressing vitiligo, a chronic inflammatory disorder known for inducing psychological distress and elevating susceptibility to autoimmune diseases. Notably, JAK inhibitors have emerged as promising candidates for treating immune dermatoses, including vitiligo. Our investigation primarily focuses on the anti-vitiligo potential of A. pyrethrum root extract, specifically targeting N-alkyl-amides, utilizing computational methodologies. Density Functional Theory (DFT) is deployed to meticulously scrutinize molecular properties, while comprehensive evaluations of ADME-Tox properties for each molecule contribute to a nuanced understanding of their therapeutic viability, showcasing remarkable drug-like characteristics. Molecular docking analysis probes ligand interactions with pivotal site JAK1, with all compounds demonstrating significant interactions; notably, molecule 6 exhibits the most interactions with crucial inhibition residues. Molecular dynamics simulations over 500ns further validate the importance and sustainability of these interactions observed in molecular docking, favoring energetically both molecules 6 and 1; however, in terms of stability, the complex with molecule 6 outperforms others. DFT analyses elucidate the distribution of electron-rich oxygen atoms and electron-poor regions within heteroatoms-linked hydrogens. Remarkably, N-alkyl-amides extracted from A. pyrethrum roots exhibit similar compositions, yielding comparable DFT and Electrostatic Potential (ESP) results with subtle distinctions. These findings underscore the considerable potential of A. pyrethrum root extracts as a natural remedy for vitiligo.

Design of New Aromatic Tertiary Amine-based as Butyrylcholinesterase Inhibitors Relying on Molecular Docking, ADME-Tox and Molecular Dynamics.

INTRODUCTION: Butyrylcholinesterase (BChE) plays a pivotal role in the progression of Alzheimer's disease. Empirical research demonstrated a fundamental alteration in the role of BChE concerning the reduction of cholinergic neurotransmission within the brains of individuals at advanced stages of Alzheimer's. METHOD: This study focuses on developing potent inhibitors for Butyrylcholinesterase (BChE) in the context of Alzheimer's disease (AD) treatment. Building upon previous research, a series of 44 aromatic tertiary amine-based compounds was investigated. Starting with ADME-Tox studies, the pharmacokinetic and pharmacodynamic properties of the compounds were analyzed to select promising candidates for BChE inhibition, which is a crucial factor in AD pathology. RESULTS: Molecular docking analyses identified compound M18 as the most promising candidate, and further compounds (X9 and X10) were proposed based on M18's chemical structure. These compounds displayed superior properties in terms of binding energies and hydrogen bonds in comparison to M18. CONCLUSION: The Molecular Dynamics (MD) simulations, which are over a 500 ns timeframe, confirmed the conformational stability of compounds X9 and X10, compared to M18. Overall, the stated results suggest that the proposed compounds, including X9 and X10 specifically, have a significant potential as candidates for BChE inhibition. This presents a promising avenue for therapeutic intervention in Alzheimer's disease.

Molecular toxicity of nitrobenzene derivatives to tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, docking, and MD simulations.

It is challenging to model the toxicity of nitroaromatic compounds due to limited experimental data. Nitrobenzene derivatives are commonly used in industry and can lead to environmental contamination. Extensive research, including several QSPR studies, has been conducted to understand their toxicity. Predictive QSPR models can help improve chemical safety, but their limitations must be considered, and the molecular factors affecting toxicity should be carefully investigated. The latest QSPR methods, molecular modeling techniques, machine learning algorithms, and computational chemistry tools are essential for developing accurate and robust models. In this work, we used these methods to study a series of fifty compounds derived from nitrobenzene. The Monte Carlo approach was used for QSPR modeling by applying the SMILES molecular structure representation and optimal molecular descriptors. The correlation ideality index (CII) and correlation contradiction index (CCI) were further introduced as validation parameters to estimate the developed models' predictive ability. The statistical quality of the CII models was better than those without CII. The best QSPR model with the following statistical parameters (Split-3): (R2 = 0.968, CCC = 0.984, IIC = 0.861, CII = 0.979, Q2 = 0.954, QF12 = 0.946, QF22 = 0.938, QF32 = 0.947, Rm2 = 0.878, RMSE = 0.187, MAE = 0.151, FTraining = 390, FInvisible = 218, FCalibration = 240, RTest2 = 0.905) was selected to generate the studied promoters with increasing and decreasing activity.