Repurposing food molecules as a potential BACE1 inhibitor for Alzheimer's disease.

Alzheimer's disease (AD) is a severe neurodegenerative disorder of the brain that manifests as dementia, disorientation, difficulty in speech, and progressive cognitive and behavioral impairment. The emerging therapeutic approach to AD management is the inhibition of ß-site APP cleaving enzyme-1 (BACE1), known to be one of the two aspartyl proteases that cleave ß-amyloid precursor protein (APP). Studies confirmed the association of high BACE1 activity with the proficiency in the formation of ß-amyloid-containing neurotic plaques, the characteristics of AD. Only a few FDA-approved BACE1 inhibitors are available in the market, but their adverse off-target effects limit their usage. In this paper, we have used both ligand-based and target-based approaches for drug design. The QSAR study entails creating a multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model using 552 molecules with acceptable statistical performance (R 2 = 0.82, Q 2 loo = 0.81). According to the QSAR study, the activity has a strong link with various atoms such as aromatic carbons and ring Sulfur, acceptor atoms, sp2-hybridized oxygen, etc. Following that, a database of 26,467 food compounds was primarily used for QSAR-based virtual screening accompanied by the application of the Lipinski rule of five; the elimination of duplicates, salts, and metal derivatives resulted in a truncated dataset of 8,453 molecules. The molecular descriptor was calculated and a well-validated 6-parametric version of the QSAR model was used to predict the bioactivity of the 8,453 food compounds. Following this, the food compounds whose predicted activity (pKi) was observed above 7.0 M were further docked into the BACE1 receptor which gave rise to the Identification of 4-(3,4-Dihydroxyphenyl)-2-hydroxy-1H-phenalen-1-one (PubChem I.D: 4468; Food I.D: FDB017657) as a hit molecule (Binding Affinity = -8.9 kcal/mol, pKi = 7.97 nM, Ki = 10.715 M). Furthermore, molecular dynamics simulation for 150 ns and molecular mechanics generalized born and surface area (MMGBSA) study aided in identifying structural motifs involved in interactions with the BACE1 enzyme. Molecular docking and QSAR yielded complementary and congruent results. The validated analyses can be used to improve a drug/lead candidate's inhibitory efficacy against the BACE1. Thus, our approach is expected to widen the field of study of repurposing nutraceuticals into neuroprotective as well as anti-cancer and anti-viral therapeutic interventions.

QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads.

ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.

Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual Screening, molecular Docking, MD simulation and MMGBSA approaches.

The aldose reductase (AR) enzyme is an important target enzyme in the development of therapeutics against hyperglycaemia induced health complications such as retinopathy, etc. In the present study, a quantitative structure activity relationship (QSAR) evaluation of a dataset of 226 reported AR inhibitor (ARi) molecules is performed using a genetic algorithm - multi linear regression (GA-MLR) technique. Multi-criteria decision making (MCDM) analysis furnished two five variables based QSAR models with acceptably high performance reflected in various statistical parameters such as, R2 = 0.79-0.80, Q2 LOO = 0.78-0.79, Q2 LMO = 0.78-0.79. The QSAR model analysis revealed some of the molecular features that play crucial role in deciding inhibitory potency of the molecule against AR such as; hydrophobic Nitrogen within 2 Å of the center of mass of the molecule, non-ring Carbon separated by three and four bonds from hydrogen bond donor atoms, number of sp2 hybridized Oxygen separated by four bonds from sp2 hybridized Carbon atoms, etc. 14 in silico generated hits, using a compound 18 (a most potent ARi from present dataset with pIC50 = 8.04 M) as a template, on QSAR based virtual screening (QSAR-VS) furnished a scaffold 5 with better ARi activity (pIC50 = 8.05 M) than template compound 18. Furthermore, molecular docking of compound 18 (Docking Score = -7.91 kcal/mol) and scaffold 5 (Docking Score = -8.08 kcal/mol) against AR, divulged that they both occupy the specific pocket(s) in AR receptor binding sites through hydrogen bonding and hydrophobic interactions. Molecular dynamic simulation (MDS) and MMGBSA studies right back the docking results by revealing the fact that binding site residues interact with scaffold 5 and compound 18 to produce a stable complex similar to co-crystallized ligand's conformation. The QSAR analysis, molecular docking, and MDS results are all in agreement and complementary. QSAR-VS successfully identified a more potent novel ARi and can be used in the development of therapeutic agents to treat diabetes.

QSAR Evaluations to Unravel the Structural Features in Lysine-Specific Histone Demethylase 1A Inhibitors for Novel Anticancer Lead Development Supported by Molecular Docking, MD Simulation and MMGBSA.

Using 84 structurally diverse and experimentally validated LSD1/KDM1A inhibitors, quantitative structure-activity relationship (QSAR) models were built by OECD requirements. In the QSAR analysis, certainly significant and understated pharmacophoric features were identified as critical for LSD1 inhibition, such as a ring Carbon atom with exactly six bonds from a Nitrogen atom, partial charges of lipophilic atoms within eight bonds from a ring Sulphur atom, a non-ring Oxygen atom exactly nine bonds from the amide Nitrogen, etc. The genetic algorithm-multi-linear regression (GA-MLR) and double cross-validation criteria were used to create robust QSAR models with high predictability. In this study, two QSAR models were developed, with fitting parameters like R2 = 0.83-0.81, F = 61.22-67.96, internal validation parameters such as Q2LOO = 0.79-0.77, Q2LMO = 0.78-0.76, CCCcv = 0.89-0.88, and external validation parameters such as, R2ext = 0.82 and CCCex = 0.90. In terms of mechanistic interpretation and statistical analysis, both QSAR models are well-balanced. Furthermore, utilizing the pharmacophoric features revealed by QSAR modelling, molecular docking experiments corroborated with the most active compound's binding to the LSD1 receptor. The docking results are then refined using Molecular dynamic simulation and MMGBSA analysis. As a consequence, the findings of the study can be used to produce LSD1/KDM1A inhibitors as anticancer leads.

Ultrasound-Assisted Facile Synthesis of Nanostructured Hybrid Vesicle for the Nasal Delivery of Indomethacin: Response Surface Optimization, Microstructure, and Stability.

This work is devoted to design a novel nanostructured hybrid vesicle (NHV) made of lecithin and an acrylate/C10-C30 alkyl acrylate for the nasal delivery of a model active indomethacin (IND), and further to probe its microstructure, intermolecular interactions, drug release behavior, ex vivo permeation, and stability. NHVs were prepared by cavitation technology employing RSM-based central composite design (CCD). Amount of lecithin (X1), power of ultrasound (X2), and sonication time (X3) were selected as three independent variables while the studied response included Z-Avg (nm), polydispersity index (PDI), and zeta potential (mV). The designed system (NHV) was investigated through dynamic (DLS) and electrophoretic light scattering (ELS), attenuated total reflectance (ATR-FTIR), oscillatory measurement (stress and frequency sweep), and transmission electron microscopy (TEM). CCD was found useful in optimizing NHV. An optimized formulation (S6) had Z-Avg 80 nm, PDI 0.2, and zeta potential of - 43.26 mV. Morphology investigation revealed spherical vesicles with smaller TEM diameters (the largest particle being 52.26 nm). ATR analysis demonstrated significant intermolecular interactions among the drug (IND) and the components of vesicles. The designed vesicles had an elastic predominance and displayed supercase II (n > 1) type of drug release. Besides, the vesicles possessed potential to transport IND across the nasal mucosa with the steady-state flux (µg/cm2/h) and permeability coefficient (cm/h) of 26.61 and 13.30 × 10-3, respectively. NHV exhibited an exceptional stability involving a combination of electrostatic and steric interactions while the histopathology investigation confirmed their safety for nasal administration.

Development of a Single Ion Pair HPLC Method for Analysis of Terbinafine, Ofloxacin, Ornidazole, Clobetasol, and Two Preservatives in a Cream Formulation: Application to In Vitro Drug Release in Topical Simulated Media-Phosphate Buffer Through Rat Skin.

Present work reports an HPLC method with UV detection for quantification of terbinafine, ofloxacin, ornidazole, and clobetasol in a cream formulation along with two preservatives methyl and propyl paraben. The chromatographic separation and quantification was achieved by an octyl bonded column and a gradient elution program involving an ion-pairing reagent, hexanesulfonic acid (0.2%, pH modified to 2.7 using orthophosphoric acid) and acetonitrile. The method was simple and devoid of buffer salts and therefore advantageous for system and column life. The three step gradient program was initiated with 30% (v/v) acetonitrile for the first 5 min and ramped linearly to 60% in the next 7 min. The mobile phase remained constant for the next 11 min and then concluded at 30% (v/v) of acetonitrile. Flow rate throughout was 0.8 mL/min, and all the signals were monitored at 243 nm. The method was applied for assay of a cream formulation and its in vitro permeation studies to determine the penetration profile of the four drugs and two preservatives. A marketed cream formulation was selected for the permeation study, which was carried out using a diffusion cell consisting of topical simulated media, phosphate buffer (pH=6.8) solution containing 1% sodium lauryl sulfate as a receiver medium.