Molecular Modeling Techniques Research Articles

Release Characteristics of an Essential Oil Component Encapsulated with Cyclodextrin Shell Matrices.

Essential oils are poor aqueous solubility and high volatility compounds. The encapsulation of essential oils with Cyclodextrins (CDs) can protect them from adverse environmental conditions and improve their stability. Therefore, increasing the functional capabilities of essential oils when they were used as additives in pharmaceutical and food systems. Additionally, the release of active compounds is an important issue. However, there were few studies about the effect of different CDs on the release of drugs after encapsulation. Therefore, the information on the study of release models is considerably limited. This study aimed to (i) characterize the physico-chemical properties and release behavior of myrcene encapsulated in the four different shell matrices of α-CD, β-CD, γ-CD and 2-hydroxypropyl-β- cyclodextrin (HP-β-CD), which were selected from the perspective of stability, and (ii) determine the release mechanism of myrcene in Inclusion Complexes (ICs). ICs of myrcene and four CDs were prepared by freeze-drying. The physico-chemical properties of ICs were fully characterized by laser diffraction particle size analyzer, Scanning Electron Microscope (SEM), Fourier-Transform Infrared spectroscopy (FT-IR) and Differential Scanning Calorimeter (DSC). The release behaviors of ICs at 50, 60, 70 and 80 °C were determined and described by zeroorder or first-order kinetics with the Henderson-Pabis, Peppas, Avrami and Page mathematical models. Moreover, the possible binding modes of ICs were identified with molecular modelling technique. Firstly, the structure of Particle Size Distribution (PSD), FT-IR, DSC and SEM showed that (i) CDs could effectively encapsulate the myrcene molecules, and (ii) the release kinetics were well simulated by Avrami and Page models. Secondly, the release rates of the ICs experienced an unsteady state in the early stage, and gradually became almost constants period after 20 hours. Except that the release of myrcene in γ-CD/myrcene belonged to the first-order kinetic, the release models of the remaining three ICs belonged to diffusion mode. Thirdly, the calculated binding energies of the optimized structures for α-CD/myrcene, β-CD/myrcene, γ-CD/myrcene, and HP-β-CD/myrcene ICs were -4.28, -3.82, -4.04, and -3.72 kcal/mol, respectively. Finally, the encapsulation of myrcene with α-CD and β-CD was preferable according to the stability and release characteristics. The encapsulation of myrcene was profoundly affected by the type of CDs, and the stability could be improved by complexation with suitable CDs. The binding behavior between guest and CD molecules, and the release profile of the guest molecules could be effectively explained by the kinetics parameters and molecular modelling. This study can provide an effective basis and guide for screening suitable shell matrices.

Kinetic interactions of nanocomplexes between astaxanthin esters with different molecular structures and β-lactoglobulin

The influence of different fatty acid carbon chains on the kinetic interactions of nanocomplexes between esterified astaxanthin (E-Asta) and β-lactoglobulin (β-Lg) were investigated by multi-spectroscopy and molecular modeling techniques. We synthesized ten different E-Asta bound to β-Lg and formed nanocomplexes (< 300 nm). Fluorescence spectroscopy showed moderate affinities (binding constants Ka = 103–104 M−1). Docosahexaenoic acid astaxanthin monoester (Asta-C22:6) had the strongest binding affinity towards β-Lg (Ka = 3.77 × 104 M−1). The fluorescence quenching of β-Lg upon binding of E-Asta displayed a static mechanism, with binding sites (n) equal to 1. Fourier transform infrared spectroscopy and ultraviolet–visible absorption spectroscopy revealed that E-Asta might enter the β-Lg hydrophobic cavity, leading to unfolding of the peptide chain skeleton. In summary, β-Lg and E-Asta can form stable nanocomplex emulsions to achieve an effective delivery process for E-Asta.

An overview of biomass conversion: exploring new opportunities.

Recycling biomass is indispensable these days not only because fossil energy sources are gradually depleted, but also because pollution of the environment, caused by the increasing use of energy, must be reduced. This article intends to overview the results of plant biomass processing methods that are currently in use. Our aim was also to review published methods that are not currently in use. It is intended to explore the possibilities of new methods and enzymes to be used in biomass recycling. The results of this overview are perplexing in almost every area. Advances have been made in the pre-treatment of biomass and in the diversity and applications of the enzymes utilized. Based on molecular modeling, very little progress has been made in the modification of existing enzymes for altered function and adaptation for the environmental conditions during the processing of biomass. There are hardly any publications in which molecular modeling techniques are used to improve enzyme function and to adapt enzymes to various environmental conditions. Our view is that using modern computational, biochemical, and biotechnological methods would enable the purposeful design of enzymes that are more efficient and suitable for biomass processing.

Molecular dynamics simulation, free energy landscape and binding free energy computations in exploration the anti-invasive activity of amygdalin against metastasis

Background and objectiveHistorically, amygdalin has been used as alternative medicine or in vitro and in vivo studies, but no single study exists which discusses the structural mechanism of amygdalin at a molecular level. This paper inquiries into the inhibitory actions of amygdalin on the selected targets: AKT1, FAK, and ILK, which are regulators for various mediated signaling pathways, and are associated with cell adhesion, migration, and differentiation. In order to get details at the molecular level of amygdalin's inhibitory activities against chosen proteins, molecular modeling and simulation techniques including double docking, molecular dynamics simulation, free energy landscape analysis, and binding free energy calculation were exerted. MethodsTo get molecular level details of amygdalin inhibitory effects against the relevant proteins; here the utilized tools are the following: the double docking, molecular dynamics simulation, free energy landscape analysis, g_mmpbsa, and interaction entropy were used to evaluate the inhibitory activity against targeted proteins. ResultsThe computational calculations revealed that amygdalin inhibits the selected targets via block the ATP-binding pocket of AKT1, FAK, and ILK by forming stable hydrogen bonds. Moreover, free energy landscape, FEL exposed that amygdalin stabilized the global conformations of both FAK and ILK proteins to the minimum global energy besides it reduced the essential dynamics of FAK and ILK proteins. MMPBSA computations provided further evidence for amygdalin's stability inside the ATP-binding pocket of AKT1, FAK, and ILK with a binding free energy of 45.067, −13.033, 13.109 kJ/mol, respectively. The binding free energies are lastly consistent with the hydrogen bonding and pairs within 0.35 nm results. The decomposition of binding energy shows the pivotal amino acid residues responsible for the stability of amygdalin's interactions inside the ATP-binding sites by forming hydrogen bonds. ConclusionsBefore this work, it was enigmatic to make predictions about how amygdalin inhibits metastasis of cancer. But the computational results contribute in several ways to our understanding of amygdalin activity and provide a basic insight into the activity of amygdalin as a multi-target drug in the metastasis and invasion of cancer.

Characterization and Prediction of the Non-Bonded Molecular Interactions between Racemic Ibuprofen and α-Lactose Monohydrate Crystals Produced from Melt Granulation and Slow Evaporation Crystallization

Granulation of racemic ibuprofen (±IBP) and α-lactose monohydrate (ALM) at a slightly lower (±IBP) melting point is an efficient method of binding the active pharmaceutical ingredients (API) and excipient in a binderless condition. However, the co-crystals may be formed from recrystallization of ±IBP on ALM. The objective of this study is to evaluate the tendency of co-crystal formation of granules (3:7 w/w ratio of ±IBP:ALM) by melt granulation process. Second, investigate the recovery of crystals from polyethylene glycol (PEG) 300 solutions containing ±IBP-ALM mixtures. Characterizations of the samples were performed using Fourier Transform Infrared (FTIR) spectroscopy, Differential Scanning Calorimetry (DSC) and Powder X-Ray Diffraction (PXRD) system of the ±IBP-ALM granules produced from melt crystallization and harvested crystals from PEG 300 solution which is produced using slow evaporation crystallization. Crystal analysis of solution containing ±IBP-ALM mixtures revealed that the crystals formed were not co-crystals. Molecular interactions assessment through binding prediction between ±IBP and ALM terminating surfaces was conducted using molecular modelling technique. The result showed that the favorable binding sites of ±IBP molecules were on the surfaces of (0-20), (1-10), (001) and (011) ALM crystals. Successful binding prediction by the attachment energy method has proven that the co-crystal formation between these molecules is theoretically possible.

Evaluating the Performance of a Non-Bonded Cu2+ Model Including Jahn−Teller Effect into the Binding of Tyrosinase Inhibitors

Tyrosinase (TYR) is a metalloenzyme classified as a type-3 copper protein, which is involved in the synthesis of melanin through a catalytic process beginning with the conversion of the amino acid l-Tyrosine (l-Tyr) to l-3,4-dihydroxyphenylalanine (l-DOPA). It plays an important role in the mechanism of melanogenesis in various organisms including mammals, plants, and fungi. Herein, we used a combination of computational molecular modeling techniques including molecular dynamic (MD) simulations and the linear interaction energy (LIE) model to evaluate the binding free energy of a set of analogs of kojic acid (KA) in complex with TYR. For the MD simulations, we used a dummy model including the description of the Jahn–Teller effect for Cu2+ ions in the active site of this enzyme. Our results show that the LIE model predicts the TYR binding affinities of the inhibitor in close agreement to experimental results. Overall, we demonstrate that the classical model provides a suitable description of the main interactions between analogs of KA and Cu2+ ions in the active site of TYR.

BSA-binding studies of 2- and 4-ferrocenylbenzonitrile: voltammetric, spectroscopic and molecular docking investigations

The binding affinity of 2-ferrocenylbenzonitrile (2FBN) and 4-ferrocenylbenzonitrile (4FBN) with bovine serum albumin (BSA) has been investigated by cyclic voltammetry, absorption spectroscopy and molecular modelling techniques. The results indicated that both of the two derivatives could bind to BSA and cause conformational changes with the order 2FBN &gt; 4FBN. The voltammetric behavior of 2FBN and 4FBN before and after the addition of the BSA suggests that the redox process is kinetically controlled by the diffusion step, and demonstrated that the diffusion coefficients of the 2FBN-BSA and 4-FBN-BSA adducts are lower than that of the free compounds. Furthermore, molecular docking suggested that the binding mode of the two compounds to BSA is of hydrophobic and hydrogen bond interactions, moreover the ligand 2FBN additionally show a π-cation interaction.

Computational investigation on the effects of pharmaceutical polymers on the structure and dynamics of interleukin2 in heat stress

Application of proteinous drugs can be associated with difficulties during both in storage/transportation and in the body when they are used. However, using pharmaceutical carbohydrates that are widely employed in drug delivery systems, besides the drug can be protected, these systems leading to gradually release the drug over time, or deliver it to the target cell. Using a combination of molecular modeling and simulation techniques, in this study the effects of five carbohydrate polymers of Chitosan, Alginate, Cyclodextrin, Hyaluronic acid and Pectin on structure and dynamics of interleukin2 protein at 298 K and 343 K, are investigated. Data achieved using molecular modeling methods showed that when the temperature rises, the protein stability decreases. Among different polymers, Chitosan and Cyclodextrin have shown to be able to protect protein against the negative effects of high temperatures in comparison with other polymers which suggests that the use of Cyclodextrin biopolymer for the preparation of pharmaceutical formulations of interleukin2 can be the best possible choice among other polymers investigated in this research. Communicated by Ramaswamy H. Sarma

Recent Trends in Drug Design and Discovery.

Structure-based drug design is a wide area of identification of selective inhibitors of a target of interest. From the time of the availability of three dimensional structure of the drug targets, mostly the proteins, many computational methods had emerged to address the challenges associated with drug design process. Particularly, drug-likeness, druggability of the target protein, specificity, off-target binding, etc., are the important factors to determine the efficacy of new chemical inhibitors. The aim of the present research was to improve the drug design strategies in field of design of novel inhibitors with respect to specific target protein in disease pathology. Recent statistical machine learning methods applied for structural and chemical data analysis had been elaborated in current drug design field. As the size of the biological data shows a continuous growth, new computational algorithms and analytical methods are being developed with different objectives. It covers a wide area, from protein structure prediction to drug toxicity prediction. Moreover, the computational methods are available to analyze the structural data of varying types and sizes of which, most of the semi-empirical force field and quantum mechanics based molecular modeling methods showed a proven accuracy towards analysing small structural data sets while statistics based methods such as machine learning, QSAR and other specific data analytics methods are robust for large scale data analysis. In this present study, the background has been reviewed for new drug lead development with respect specific drug targets of interest. Overall approach of both the extreme methods were also used to demonstrate with the plausible outcome. In this chapter, we focus on the recent developments in the structure-based drug design using advanced molecular modeling techniques in conjunction with machine learning and other data analytics methods. Natural products based drug discovery is also discussed.

Development of a New Borax-Based Formulation for the Removal of Pyrite Scales.

In the oil and gas industry, pyrite forms one of the most hardened scales in reservoirs, which hinders the flow of fluids. Consequently, this leads to blockage of the downhole tubular, formation damage, and complete shutdown of production and operational processes. Herein, a new green formulation based on borax (K2B4O7) is proposed for pyrite scale removal. The temperature effect, disk rotational speed, and borax concentration have been investigated using a rotating disk apparatus. Also, XPS and SEM–EDX analyses were conducted on the pyrite disk surface before and after the treatment with the green formulation. The new formulation showed the potential ability to dissolve pyrite without generating the toxic hydrogen sulfide (H2S). The dissolution rate of the scale in the new formulation is increased by 16% compared to that in a previous green formulation composed of 20 wt %DTPA+9 wt % K2CO3. Molecular modeling technique using DFT was used to study the solvation energies of Fe2+ and Fe3+. The latter had a higher solvation energy than the former, which confirmed that upon using the borax-based formulation to oxidize Fe2+ to Fe3+. It will aid the dissolution of pyrite scales. The new formulation achieved a corrosion rate that is 25 times lower than that of 15 wt % HCl, which is commercially used in treating scales. Finally, the proposed new formulation does not require the use of corrosion inhibitors; hence, it is expected to result in a more economical scale treatment method.

Repurposing approved drugs as inhibitors of SARS-CoV-2 S-protein from molecular modeling and virtual screening

Herein, molecular modeling techniques were used with the main goal to obtain candidates from a drug database as potential targets to be used against SARS-CoV-2. This novel coronavirus, responsible by the COVID-19 outbreak since the end of 2019, became a challenge since there is not vaccine for this disease. The first step in this investigation was to solvate the isolated S-protein in water for molecular dynamics (MD) simulation, being observed a transition from “up” to “down” conformation of receptor-binding domain (RBD) of the S-protein with angle of 54.3 and 43.0 degrees, respectively. The RBD region was more exposed to the solvent and to the possible drugs due to its enhanced surface area. From the equilibrated MD structure, virtual screening by docking calculations were performed using a library contained 9091 FDA approved drugs. Among them, 24 best-scored ligands (14 traditional herbal isolate and 10 approved drugs) with the binding energy below –8.1 kcal/mol were selected as potential candidates to inhibit the SARS-CoV-2 S-protein, preventing the human cell infection and their replication. For instance, the ivermectin drug (present in our list of promise candidates) was recently used successful to control viral replication in vitro. MD simulations were performed for the three best ligands@S-protein complexes and the binding energies were calculated using the MM/PBSA approach. Overall, it is highlighted an important strategy, some key residues, and chemical groups which may be considered on clinical trials for COVID-19 outbreak. Communicated by Ramaswamy H. Sarma

Auto QSAR‐ A Fast Approach for Creation and Application of QSAR Models through Automation

AbstractA continuous and undefined malignant growth in cancer makes it an extremely heterogeneous complex disease. Different types of enzymes helps in detection of cancerous growth in the human body. In this work, different predictive Quantitative Structure‐Activity Relationship (QSAR) models by means of various molecular modeling techniques using 43 novel 6, 7‐disubstituted‐4‐phenoxyquinoline derivatives acting as Tyrosine‐protein kinase Met or hepatocyte growth factor receptor (HGFR) (c‐Met kinase) inhibitors were designed. Best QSAR models were generated through Auto QSAR. Predicted activity of these models was compared with the observed activity from literature, and it was observed that potent compound 18 b gave high docking results. Auto‐QSAR technique provided the perfect model for the designed derivatives. Binding affinity of compounds for c‐Met kinase enzyme was studied by molecular docking and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA dG) binding studies. Optimized compounds were subjected to in silico ADMET studies for predicting drug‐likeliness and toxicity properties. Reported work will assist to design, refine and construct the novel phenoxyquinoline derivatives as potent c‐Met kinase inhibitors in near future.

N1-Benzofused Modification of Fluoroquinolones Reduces Activity Against Gram-Negative Bacteria.

The fluoroquinolone class of antibiotics has a well-established structure–activity relationship (SAR) and a long history in the clinic, but the effect of electron-rich benzofused substituents at the N1 position remains poorly explored. Because groups at this position are part of the topoisomerase–DNA binding complex and form a hydrophobic interaction with the major groove of DNA, it was hypothesized that an electron-rich benzofused N1 substituent could enhance this interaction. Molecular modeling techniques were employed to evaluate the binding of certain N1-modified fluoroquinolones to DNA gyrase targets from both Staphylococcus aureus and Klebsiella pneumoniae species compared with ciprofloxacin and norfloxacin. Seven N1-modified fluoroquinolones were subsequently synthesized and tested against a panel of Gram-negative pathogens to determine minimum inhibitory concentration (MIC) values. Gram-negative outer membrane penetration was investigated using the membrane permeabilizer polymyxin B nonapeptide and compound efflux via resistance–nodulation–division-family efflux transporters was evaluated using the known efflux pump inhibitor phenylalanine–arginine β-naphthylamide. Additionally, the target inhibitory activity of representative compound 6e was determined in a cell-free environment. A correlation between N1 substituent hydrophobicity and activity was observed across the MIC panel, with compound activity decreasing with increased hydrophobicity. Those compounds with highest hydrophobicity were inactive because of poor solubility profiles whereas compounds with intermediate hydrophobicity were inactive because of impaired outer membrane penetration, and reduced inhibition of topoisomerase targets, the latter in contrast to modeling predictions. This study adds new information to the fluoroquinolone SAR and suggests limited utility of large hydrophobic substituents at the N1 position of fluoroquinolones.

Synthesis, Characterization and Morphological Study of Nicotinamide and &lt;i&gt;p&lt;/i&gt;-Coumaric Acid Cocrystal

Cocrystallization is one of the potent methods used to modify the physicochemical properties of drugs. Cocrystal of nicotinamide (NIC):p-coumaric acid (COU) was synthesized by a slow evaporation method using acetonitrile. The cocrystals with different feed molar ratios (NIC:COU : 1:1, 1:2, and 2:1) were characterized using DSC, PXRD, and FTIR, which revealed the formation of different polymorphs for each feed molar ratio. A single crystal of the NIC:COU (1:1) cocrystal was analyzed using single crystal X-ray diffraction (SCD), and 1H-NMR revealed a greater cocrystal structure stability compared to the previously published cocrystal. The intermolecular hydrogen bonds, N-H···O, and O-H···O interactions played a major role in stabilizing the cocrystal structure. A molecular modeling technique was used for prediction and surface chemistry assessment of the morphology showed an elongated (along y-axis) octagonal crystal shape which was in a reasonable agreement with the experimental crystal morphology. The reduction in values of the cocrystal solubility in ethanol was supported by the DSC data and simulation of crystal facets where most the crystal facets exposed to polar functional groups. At the concentration of 31.3 µM, NIC:COU (1:1) cocrystal showed more effective DPPH scavenging with 77.06% increased activity compared to NIC at the same concentration.

Effects of oligolignol sizes and binding modes on a GH11 xylanase inhibition revealed by molecular modeling techniques.

Lignin and phenolic compounds have been shown as the main recalcitrance for biomass decomposition, as they inhibit a number of lignocellulose-degrading enzymes. Understanding the inhibition mechanisms and energetic competitions with the native substrate is essential for the development of lignin resistive enzymes. In this study, atomistic detail of the size-dependent effects and binding modes of monomeric coniferyl alcohol, dimeric oligolignol, and tetrameric oligolignol made from coniferyl alcohols on the GH11 xylanase from Bacillus firmus strain K-1 was investigated by using molecular docking and atomistic molecular dynamics (MD) simulations. From the MD simulation results on the docked conformation of oligolignol binding within the "Cleft" and the "N-terminal," changes were observed both for protein conformations and positional binding of ligands, as binding with "Thumb" regions was found for all oligolignin models. Moreover, the uniquely stable "N-terminal" binding of the coniferyl alcohol monomer had no effect on the highly fluctuated Thumb region, showing no sign of inhibitory effect, and was in good agreement with recent studies. However, the inhibitory effect of oligolignols was size dependent, as the estimated binding energy of the tetrameric oligolignol became stronger than that of the xylohexaose substrate, and the important binding residues were identified for future protein engineering attempts to enhance the lignin resistivity of GH11. Graphical Abstract Size-dependent binding modes of coniferyl alcohol monomers (upper panels) and the dimers (lower panels). Uniquely stable "N-terminal" binding of the monomer is shown to have no effect on the binding pocket, and hence no sign of inhibition, which was in good agreement with some recent studies.

Sleep-enhancing Effects of Phytoncide Via Behavioral, Electrophysiological, and Molecular Modeling Approaches.

Sleep is indispensable for living animals to live and maintain a normal life. Due to the growing number of people suffering from sleep disorders such as insomnia, there have been increasing interests in environmentally friendly therapeutic approaches for sleep disorders to avoid any side effects of pharmacological treatment using synthetic hypnotics. It has been widely accepted that the various beneficial effects of forest, such as relieving stress and anxiety and enhancing immune system function, are caused by plant-derived products, also known as phytoncide. Recently, it has been reported that the sleep-enhancing effects of phytoncide are derived from pine trees such as (-)-α-pinene and 3-carene. These are the major constituents of pine tree that potentiate the inhibitory synaptic responses by acting as a positive modulator for GABAA-BZD receptor. In this review, we discuss the effects of phytoncide on sleep and review the latest approaches of sleep-related behavioral assay, electrophysiological recording, and molecular modeling technique.

A series of molecular modeling techniques to reveal selective mechanisms of inhibitors to β-Site amyloid precursor protein cleaving enzyme 1 (BACE1) and β-site amyloid precursor protein cleaving enzyme 2 (BACE2)

Inhibition of β-Site amyloid precursor protein cleaving enzyme 1 (BACE1) has been shown to be an effective treatment for Alzheimer’s disease. A wealth of research has focused on finding highly selective small-molecule inhibitors targeting the BACE1 over its close homologue BACE2 to avoid potential side effects. However, given the highly structural similarities of BACE1 and BACE2, designing highly selective BACE1 inhibitors remains a huge challenge. Recently, it has been reported that a potential BACE1 inhibitor named C28 (∼52-fold selectivity) exhibited greater selectivity to BACE1 over BACE2 than the previously reported inhibitors AZD3293 and AZD3839 (∼1.5-fold and 14-fold selectivity). However, few computational studies have been performed to reveal its underlying mechanisms. In this study, a series of molecular modeling techniques were performed to reveal the selective mechanisms. Classical molecular dynamics (cMD) simulations indicated that the major variations appeared to be controlled by overall protein dynamics. Free energy calculations further suggested that the binding affinities of AZD3293 to BACE1 and BACE2 are similar, but the binding affinity of AZD3839 and C28 to BACE1 is much higher than to BACE2, and that the major variations are electrostatic interactions. The protein dynamics and energy differences were further observed in accelerated molecular dynamics (aMD) simulations. In addition, the umbrella sampling simulations revealed the inhibitors’ different patterns of dissociation from the binding pockets of BACE1 and BACE2, and that different energy barriers were responsible for the selectivity. The physical principles revealed by this study may facilitate the rational design of more potent BACE1 selective inhibitors. Communicated by Ramaswamy H. Sarma

Insights into structures of imidazo oxazines as potent polyketide synthase XIII inhibitors using molecular modeling techniques

Tuberculosis, a major global health concern, and its drug development toward the disease are too devastating to meet the clinical demands. The present work emphasizes a detailed QSAR study using QSARINS which developed descriptors favoring an excellent model equation. The best model equation generated has four variables namely AlogP, ATSc4, mindssC, and MDEC23 with statistical values R2 = 0.7406, LOF = 0.1858, CCCtr = 0.8510, Q2LOO = 0.6569, Q2LMO = 0.6286, CCCcv = 0.8037, R2ext = 0.8600, and CCCext = 0.9252. The developed QSAR model justifies that the key structural fragments highly correlate with activity. Docking the designed compounds with PKS XIII, a novel target catalyzes the formation of mycolic acids and its results distinctly improve expected antitubercular activity showing all probable interactions. Compounds were further screened for ADME analysis and toxicity.

Physicochemical characterization and molecular modeling study of host–guest systems of aripiprazole and functionalized cyclodextrins

Aripiprazole (ARP), an innovative atypical antipsychotic drug, exhibits very low aqueous solubility, affecting its dissolution and absorption and high lipophilicity. Its complexation with cyclodextrins (CDs) was designed to improve drug solubility and consequently its bioavailability. The inclusion complexes of aripiprazole with two β-cyclodextrin derivative, namely random methyl-β-cyclodextrin (RAMEB) and heptakis(2,3,6-tri-O-methyl)-β-cyclodextrin, were obtained and investigated both in solution and in solid state. The kneading method was used to prepare the inclusion complexes, and they were characterized with different analytical techniques, including thermal analysis, powder X-ray diffractometry, universal attenuated total reflectance Fourier-transform IR spectroscopy and UV spectroscopy. The stoichiometry of both APR/CDs inclusion complexes was found as 1:2 by employing continuous variation method. Benesi–Hildebrand equation was used for the apparent constant stability determination. Structural studies of the inclusion complexes were carried out using molecular modeling techniques in order to explain the complexation mechanism. The results of the studies demonstrate that the physicochemical properties of the kneaded products are different from ARP, testifying the inclusion complexes formation between aripiprazole and CDs when the kneading method is used. The solubility of the drug substance was improved upon complexation with CDs; higher increase in ARP solubility was noticed in the presence of RAMEB.

Prediction of the Binding Affinities and Selectivity for CB1 and CB2 Ligands Using Homology Modeling, Molecular Docking, Molecular Dynamics Simulations, and MM-PBSA Binding Free Energy Calculations.

Cannabinoids are a group of chemical compounds that have been used for thousands of years due to their psychoactive function and systemic physiological effects. There are at least two types of cannabinoid receptors, CB1 and CB2, which belong to the G protein-coupled receptor superfamily and can trigger different signaling pathways to exert their physiological functions. In this study, several representative agonists and antagonists of both CB1 and CB2 were systematically studied to predict their binding affinities and selectivity against both cannabinoid receptors using a set of hierarchical molecular modeling and simulation techniques, including homology modeling, molecular docking, molecular dynamics (MD) simulations and end point binding free energy calculations using the molecular mechanics/Poisson-Boltzmann surface area-WSAS (MM-PBSA-WSAS) method, and molecular mechanics/generalized Born surface area (MM-GBSA) free energy decomposition. Encouragingly, the calculated binding free energies correlated very well with the experimental values and the correlation coefficient square (R2), 0.60, was much higher than that of an efficient but less accurate docking scoring function (R2 = 0.37). The hotspot residues for CB1 and CB2 in both active and inactive conformations were identified via MM-GBSA free energy decomposition analysis. The comparisons of binding free energies, ligand-receptor interaction patterns, and hotspot residues among the four systems, namely, agonist-bound CB1, agonist-bound CB2, antagonist-bound CB1, and antagonist-bound CB2, enabled us to investigate and identify distinct binding features of these four systems, with which one can rationally design potent, selective, and function-specific modulators for the cannabinoid receptors.