Current Neuropharmacology - Volume 16, Issue 6, 2018

Background: As the number of elderly persons increases, neurodegenerative diseases are becoming ubiquitous. There is currently a great need for knowledge concerning management of oldage neurodegenerative diseases; the most important of which are: Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis, and Huntington's disease. Objective: To summarize the potential of computationally predicted molecules and targets against neurodegenerative diseases. Method: Review of literature published since 1997 against neurodegenerative diseases, utilizing as keywords: in silico, Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis ALS, and Huntington's disease was conducted. Results and Conclusion: Due to the costs associated with experimentation and current ethical law, performing experiments directly on living organisms has become much more difficult. In this scenario, in silico techniques have been successful and have become powerful tools in the search to cure disease. Researchers use the Computer Aided Drug Design pipeline which: 1) generates 3- dimensional structures of target proteins through homology modeling 2) achieves stabilization through molecular dynamics simulation, and 3) exploits molecular docking through large compound libraries. Next generation sequencing is continually producing enormous amounts of raw sequence data while neuroimaging is producing a multitude of raw image data. To solve such pressing problems, these new tools and algorithms are required. This review elaborates precise in silico tools and techniques for drug targets, active molecules, and molecular docking studies, together with future prospects and challenges concerning possible breakthroughs in Alzheimer's, Parkinson's, Amyotrophic Lateral Sclerosis, and Huntington's disease.

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. Computeraided 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.

Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis does not provide complete solution of AD due to multifactorial nature of the disease and one target one drug fails to provide better treatment against AD. Moreover, currently available treatments are limited and most of the upcoming treatments under clinical trials are based on modulating single target. So, the current AD drug discovery research is shifting towards a new approach for a better solution that simultaneously modulates more than one targets in the neurodegenerative cascade. This can be achieved by network pharmacology, multi-modal therapies, multifaceted, and/or the more recently proposed term “multi-targeted designed drugs”. Drug discovery project is a tedious, costly and long-term project. Moreover, multi-target AD drug discovery added extra challenges such as the good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off-target side effect and crossing of the blood-brain barrier. These hurdles may be addressed by insilico methods for an efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here, we are summarizing some of the most prominent and computationally explored single targets against AD and further, we discussed a successful example of dual or multiple inhibitors for same targets. Moreover, we focused on ligand and structure-based computational approach to design MTDL against AD. However, it is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy is useful in future MTDLs drug discovery alone or in combination with a fragment-based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery.

Background: Neurodegenerative disorders (NDs) are diverse group of disorders characterized by escalating loss of neurons (structural and functional). The development of potential therapeutics for NDs presents an important challenge, as traditional treatments are inefficient and usually are unable to stop or retard the process of neurodegeneration. Computer-Aided Drug Design (CADD) has emerged as an efficient means of developing candidate drugs for the treatment of many disease types. Applications of CADD approach to drug discovery are progressing day by day. The recent tendency in drug design is to rationally design potent therapeutics with multi-targeting effects, higher efficacies, and fewer side effects, especially in terms of toxicity. Methods: A wide literature search was performed for writing this review. An updated view on different types of NDs, their effect on human population and a brief introduction to CADD, various approaches involved in this technique, ranging from structural-based to ligand-based drug design has been discussed. The successful application of CADD approaches for the treatment of neurodegenerative disorders is also included in this review. Results: In this review, we have briefly described about CADD and its use in the development of the therapeutic drug candidates against NDs. The successful applications, limitations and future prospects of this approach have also been discussed. Conclusion: CADD can assist researchers studying interactions between drugs and receptors. We believe this review will be helpful for better understanding of CADD and its applications towards the discovery of new drug candidates against various fatal NDs.

Background: Histamine H3 receptor (H3R) is associated with several neuropsychological diseases, and thus it is an important target involved in several CNS disorders, such as narcolepsy, attention deficit hyperactivity disorder and schizophrenia. Since QSAR modeling is a feasible approach to explain the role of the molecular substituents in the biological activity, it can help in improving the design of better H3R ligands for these conditions. Methods: This article reviews papers previously published in literature to show the current status of the contribution from QSAR modeling to reach H3R antagonists/inverse agonists. Results: Classical and 3D-QSAR models were retrieved, showing that the steric and hydrophobic properties of the H3R ligands are most important to reach good affinity. Conclusion: Although QSAR methods are valuable to design better H3R antagonists/inverse agonists, pharmacokinetics should also be considered in future models to ensure good CNS penetration.

Background: Alzheimer's disease (AD) as the most common cause of dementia among older people has aroused the universal concern of the whole world. However, until now there is still none effective treatments. Consequently, the development of new drugs targeting this complicated brain disorder is urgent and needs more efforts. In this review, we detailed the current state of knowledge about new candidate drugs targeting the pathological proteins especially the drugs which are employed using the combined methods of in vitro and in silico. Methods: We looked up and reviewed online papers related to the pathogenesis and new drugs development of AD. Then, articles up to the requirements were respectively analyzed and summaried to provide the latest knowledge about the pathogenic effect and the new candidate drugs targeting Aβ and Tau proteins. Results: New candidate drugs targeting the Aβ include decreasing the production, promoting the clearence and preventing aggregation. However these drugs have mostly failed in Phase III clinical trial stage due to the unsuccessful of reversing cognition symptoms. As to tau protein, the prevention of tau aggregation and propagation is a promising strategy to synthesize/design mechanismbased drugs against tauopathies. Some candidate drugs are under research. Moreover, because of the complex pathogenesis of AD, multi-target drugs have also shed light on the treatment of AD. Conclusion: Given to the consecutive failure of Aβ-directed drugs and the feasibilities of tautargeted therapy, more and more researchers suggested that the AD treatment should be moved from Aβ to tau or focused on considering the soluble form of Aβ and tau as a whole. Moreover, the novel in silico methods also have great potential in drug discovery, drug repositioning, virtual screening of chemical libraries. No matter how many difficulties and challenges in prevention and treatment of AD, we firmly believe that the effective and safe drugs will be found using the combined methods in the immediate future with the global effort.

Background: Blood brain barrier and Alzheimer's disease are interrelated. This interrelation is detected by physicochemical methods, pharmacological and electrophysiological analyses. Nature of the phenomenon is extremely complex. The description of this interrelation in mathematical terms is a very important task. Objective: The systematization of facts, which are described in the literature and related to interaction between processes, which influence Alzheimer's disease and blood brain barrier is the subject of this work. In addition, establishing of correlations between molecular features and endpoints, which are related to the treatment of Alzheimer's disease and blood brain barrier using the CORAL software are subjects of this work. Methods: The information on logically structured analysis is available in the literature and building up quantitative structure – activity relationships (QSARs) by the Monte Carlo method has been used to solve the task of systematization of facts related to the "treatment of Alzheimer's disease vs. blood brain barrier". Results: Comparison of agreements and disagreements of the available published papers together with the statistical quality of built up QSARs are results of this work. Conclusion: The facts from published papers and technical details of QSAR built up in this study give possibility to formulate the following rules: (i) there are molecular alerts, which are promoters to increase blood brain barrier and therapeutic activity of anti-Alzheimer disease agents; (ii) there are molecular alerts, which contradict each other.

Parkinson's Disease (PD) is a long-term neurodegenerative brain disorder that mainly affects the motor system. The causes are still unknown, and even though currently there is no cure, several therapeutic options are available to manage its symptoms. The development of novel antiparkinsonian agents and an understanding of their proper and optimal use are, indeed, highly demanding. For the last decades, L-3,4-DihydrOxyPhenylAlanine or levodopa (L-DOPA) has been the gold-standard therapy for the symptomatic treatment of motor dysfunctions associated to PD. However, the development of dyskinesias and motor fluctuations (wearing-off and on-off phenomena) associated with long-term L-DOPA replacement therapy have limited its antiparkinsonian efficacy. The investigation for non-dopaminergic therapies has been largely explored as an attempt to counteract the motor side effects associated with dopamine replacement therapy. Being one of the largest cell membrane protein families, G-Protein-Coupled Receptors (GPCRs) have become a relevant target for drug discovery focused on a wide range of therapeutic areas, including Central Nervous System (CNS) diseases. The modulation of specific GPCRs potentially implicated in PD, excluding dopamine receptors, may provide promising non-dopaminergic therapeutic alternatives for symptomatic treatment of PD. In this review, we focused on the impact of specific GPCR subclasses, including dopamine receptors, adenosine receptors, muscarinic acetylcholine receptors, metabotropic glutamate receptors, and 5-hydroxytryptamine receptors, on the pathophysiology of PD and the importance of structure- and ligand-based in silico approaches for the development of small molecules to target these receptors.

Background: The last decade was characterized by a growing awareness about the severity of dementia in the field of age-related and no age-related diseases and about the importance to invest resources in the research of new, effective treatments. Among the dementias, Alzheimer's plays a substantial role because of its extremely high incidence and fatality. Several pharmacological strategies have been tried but still now, Alzheimer keeps being an untreatable disease. In literature, the number of QSAR related drug design attempts about new treatments for Alzheimer is huge, but only few results can be considered noteworthy. Providing a detailed analysis of the actual situation and reporting the most notable results in the field of drug design and discovery, the current review focuses on the potential of molecular topology as a reliable tool in finding new anti-Alzheimer lead compounds. Methods: Published works on QSAR applied to the search of anti-Alzheimer's drugs during the last 10 years has been tracked. 2D and 3D-QSAR, HQSAR, topological indexes, etc. have been analyzed, as well as different mechanisms of action, such as MAO, AchE, etc. An example of topological indexes' application to the search of potential anti-Alzheimer drugs is reported. Results: Results show that QSAR methods during the last decade represented an excellent approach to the search of new effective drugs against Alzheimer's. In particular, QSAR based on molecular topology allows the establishment of a direct structure-property link that results in the identification of new hits and leads. Conclusion: Molecular topology is a powerful tool for the discovery of new anti-Alzheimer drugs covering simultaneously different mechanisms of action, what may help to find a definitive cure for the disease.

Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by debilitating motor deficits, as well as autonomic problems, cognitive declines, changes in affect and sleep disturbances. Although the scientific community has performed great efforts in the study of PD, and from the most diverse points of view, the disease remains incurable. The exact mechanism underlying its progression is unclear, but oxidative stress, mitochondrial dysfunction and inflammation are thought to play major roles in the etiology. Objective: Current pharmacological therapies for the treatment of Parkinson's disease are mostly inadequate, and new therapeutic agents are much needed. Methods: In this review, recent advances in computer-aided drug design for the rational design of new compounds against Parkinson disease; using methods such as Quantitative Structure-Activity Relationships (QSAR), molecular docking, molecular dynamics and pharmacophore modeling are discussed. Results: In this review, four targets were selected: the enzyme monoamine oxidase, dopamine agonists, acetylcholine receptors, and adenosine receptors. Conclusion: Computer aided-drug design enables the creation of theoretical models that can be used in a large database to virtually screen for and identify novel candidate molecules.

Background: The computational development of human monoamine oxidase (MAO) inhibitors led to advancement in drug design and the treatment of many neurodegenerative diseases and neuropsychiatric disorders. The computational development of human monoamine oxidase (MAO) inhibitors led to advancement in drug design and the treatment of many neurodegenerative diseases and neuropsychiatric disorders. Different natural heterocyclic structures are reported to display selective MAO inhibitory activity by preclinical and in-silico modeling. Objective: Currently, the major interest is devoted to the study of natural based therapeutic agents from the different categories. Therefore, we presenting the review to critically discuss and outline the recent advances in our knowledge on the importance of natural and natural based ligand-MAO insilico methods for novel MAO inhibitors. Discussion: Several natural and related synthetic heterocyclic compounds such as coumarins, β- carboline, piperine, naphthoquinone, morpholine, caffeine, amphetamine moreover flavonoids, chalcones, xanthones, curcumin are discussed for their MAO inhibitory profile along with molecular docking and quantitative structure-activity relationship studies. Conclusion: It is clear that, by this computational drug design approach, more particular, reversible and potent compounds can be proposed as MAO inhibitors by exact changes on the fundamental framework.