Modeling in vitro inhibition of butyrylcholinesterase using molecular docking, multi-linear regression and artificial neural network approaches

Persulfate-Promoted Carbamoylation/Cyclization of Alkenes: Synthesis of Amide-Containing Quinazolinones

The incorporation of amide groups into biologically active molecules has been proven to be an efficient strategy for drug design and discovery. In this study, we present a simple and practical method for the synthesis of amide-containing quinazolin-4(3H)-ones under transition-metal-free conditions. This is achieved through a carbamoyl-radical-triggered cascade cyclization of N3-alkenyl-tethered quinazolinones. Notably, the carbamoyl radical is generated in situ from the oxidative decarboxylative process of oxamic acids in the presence of (NH4)2S2O8.

Human Butyrylcholinesterase Mutants for (-)-Cocaine Hydrolysis: A Correlation Relationship between Catalytic Efficiency and Total Hydrogen Bonding Energy with an Oxyanion Hole

A combined computational and experimental study has been carried out to explore and test a quantitative correlation relationship between the relative catalytic efficiency (RCE) of human butyrylcholinesrase (BChE) mutant-catalyzed hydrolysis of substrate (-)-cocaine and the total hydrogen bonding energy (tHBE) of the carbonyl oxygen of the substrate with the oxyanion hole of the enzyme in the modeled transition-state structure (TS1), demonstrating a satisfactory linear correlation relationship between ln(RCE) and tHBE. The satisfactory correlation relationship has led us to computationally predict and experimentally confirm new human BChE mutants that have a further improved catalytic activity against (-)-cocaine, including the most active one (the A199S/F227S/S287G/A328W/Y332G mutant) with a 2790-fold improved catalytic efficiency (kcat/KM = 2.5 × 109 min-1 M-1) compared to the wild-type human BChE. Compared to the reference mutant (the A199S/S287G/A328W/Y332G mutant) tested in the reported clinical development of an enzyme therapy for cocaine dependence treatment, this new mutant (with a newly predicted additional F227S mutation) has an improved catalytic efficiency against (-)-cocaine by ∼2.6-fold. The good agreement between the computational and experimental ln(RCE) values suggests that the obtained correlation relationship is robust for computational prediction. A similar correlation relationship could also be explored in studying BChE or other serine hydrolases/esterases with an oxyanion hole stabilizing the carbonyl oxygen in the rate-determining reaction step of the enzymatic hydrolysis of other substrates.

Photocatalytic Charge-Transfer Complex Enables Hydroarylation of Alkenes for Heterocycle Synthesis

Here, we report a photocatalytic charge-transfer complex (CTC) strategy for one electron reduction of alkenes using thiolate as a catalytic electron donor. This catalytic CTC system could engage hydroarylation of both activated and unactivated alkenes for the synthesis of various heterocycles. The reactions do not require any photocatalysts or acids and are easy to perform. Mechanistic studies revealed the formation of a CTC between catalytic thiolate and alkene.

Metal-Free Photoinduced Hydrocyclization of Unactivated Alkenes toward Ring-Fused Quinazolin-4(3H)-ones via Intermolecular Hydrogen Atom Transfer

A visible-light-induced hydrocyclization of unactivated alkenes was developed using 3CzClIPN as the photocatalyst to generate substituted α-methyldeoxyvasicinones and α-methylmackinazolinones in moderate to good yields. An intermolecular hydrogen atom transfer with THF as the hydrogen source was involved. Mechanism studies indicated that the intramolecular addition of the in situ formed aminal radical to the unactivated alkene generated the polycyclic quinazolinone.

Synthesis of Quinazolinone-Fused Tetrahydroisoquinolines and Related Polycyclic Scaffolds by Iodine-Mediated sp3 C-H Amination

An iodine-mediated intramolecular sp³ C-H amination reaction producing quinazolinone-fused polycyclic skeletons from 2-aminobenzamide precursors is reported. This reaction does not use transition metals, has a broad substrate scope, and can be used on a gram scale. Under the optimal reaction conditions, a variety of quinazolinone-fused tetrahydroisoquinolines and derivatives of Rutaecarpine were synthesized from readily accessible compounds. The reaction proceeds well with crude 2-aminobenzamide derivatives, allowing for the synthesis of the products from simple 2-aminobenzoic acids and tetrahydroisoquinolines without purification of the 2-aminobenzamide intermediates. Preliminary biological experiments have identified Cereblon (CRBN) inhibitory activity and relevant anti-myeloma medicinal properties in some of these polycyclic products.

Visible-Light Photosynthesis of CHF2 /CClF2 /CBrF2 -Substituted Ring-fused Quinazolinones in Dimethyl Carbonate

With eco-friendly and sustainable CO₂ -derived dimethyl carbonate as the sole solvent, the visible-light-induced cascade radical reactions have been established as a green and efficient tool for constructing various CHF₂ /CClF₂ /CBrF₂ -substituted ring-fused quinazolinones.

Cocaine hydrolase blocks cocaine-induced dopamine transporter trafficking to the plasma membrane

Cocaine blocks dopamine uptake via dopamine transporter (DAT) on plasma membrane of neuron cells and, as a result, produces the high and induces DAT trafficking to plasma membrane which contributes to the drug seeking or craving. In this study, we first examined the dose dependence of cocaine-induced DAT trafficking and hyperactivity in rats, demonstrating that cocaine at an intraperitoneal dose of 10 mg/kg or higher led to redistribution of most DAT to the plasma membrane while inducing significant hyperactivity in rats. However, administration of 5-mg/kg cocaine (ip) did not significantly induce DAT trafficking or hyperactivity in rats. So the threshold (intraperitoneal) dose of cocaine that can significantly induce DAT trafficking or hyperactivity should be between 5 and 10 mg/kg. These data suggest that when a cocaine dose is high enough to induce significant hyperactivity, it can also significantly induce DAT trafficking to the plasma membrane. Further, the threshold brain cocaine concentration required to induce significant hyperactivity and DAT trafficking was estimated to be ~2.0 ± 0.8 μg/g. Particularly, for treatment of cocaine abuse, previous studies demonstrated that an exogenous cocaine-metabolizing enzyme, for example, CocH3-Fc(M3), can effectively block cocaine-induced hyperactivity. However, it was unknown whether an enzyme could also effectively block cocaine-induced DAT trafficking to the plasma membrane. This study demonstrates, for the first time, that the enzyme is also capable of effectively blocking cocaine from reaching the brain even with a lethal dose of 60-mg/kg cocaine (ip) and, thus, powerfully preventing cocaine-induced physiological effects such as the hyperactivity and DAT trafficking.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors

Background:

Alzheimer’s disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer’s disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites.

Methods:

In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria.

Results:

The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme.

Conclusion:

Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates

Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.

Molecular Modeling on Berberine Derivatives toward BuChE: An Integrated Study with Quantitative Structure-Activity Relationships Models, Molecular Docking, and Molecular Dynamics Simulations

A dataset of 67 berberine derivatives for the inhibition of butyrylcholinesterase (BuChE) was studied based on the combination of quantitative structure-activity relationships models, molecular docking, and molecular dynamics methods. First, a series of berberine derivatives were reported, and their inhibitory activities toward butyrylcholinesterase (BuChE) were evaluated. By 2D- quantitative structure-activity relationships studies, the best model built by partial least-square had a conventional correlation coefficient of the training set (R(2)) of 0.883, a cross-validation correlation coefficient (Qcv2) of 0.777, and a conventional correlation coefficient of the test set (Rpred2) of 0.775. The model was also confirmed by Y-randomization examination. In addition, the molecular docking and molecular dynamics simulation were performed to better elucidate the inhibitory mechanism of three typical berberine derivatives (berberine, C2, and C55) toward BuChE. The predicted binding free energy results were consistent with the experimental data and showed that the van der Waals energy term (ΔEvdw) difference played the most important role in differentiating the activity among the three inhibitors (berberine, C2, and C55). The developed quantitative structure-activity relationships models provide details on the fine relationship linking structure and activity and offer clues for structural modifications, and the molecular simulation helps to understand the inhibitory mechanism of the three typical inhibitors. In conclusion, the results of this study provide useful clues for new drug design and discovery of BuChE inhibitors from berberine derivatives.