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This second edition volume expands on the previous edition with updated descriptions on different computational methods encompassing ligand-based, structure-based, and combined approaches with their recent applications in anti-Alzheimer drug design. Different background topics like recent advancements in research on the development of novel therapies and their implications in the treatment of Alzheimer’s Disease (AD) have also been covered for completeness. Special topics like basic information science methods for insight into neurodegenerative pathogenesis, drug repositioning and network pharmacology, and online tools to predict ADMET behavior with reference to anti-Alzheimer drug development have also been included. In the Neuromethods series style, chapter include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Cutting-edge and thorough, Computational Modeling of Drugs Against Alzheimer’s Disease, Second Edition is a valuable resource for all researchers and scientists interested in learning more about this important and developing field.
This contributed volume offers a comprehensive discussion on how to design and discover pharmaceuticals using computational modeling techniques. The different chapters deal with the classical and most advanced techniques, theories, protocols, databases, and tools employed in computer-aided drug design (CADD) covering diverse therapeutic classes. Multiple components of Structure-Based Drug Discovery (SBDD) along with its workflow and associated challenges are presented while potential leads for Alzheimer’s disease (AD), antiviral agents, anti-human immunodeficiency virus (HIV) drugs, and leads for Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV) disease are discussed in detail. Computational toxicological aspects in drug design and discovery, screening adverse effects, and existing or future in silico tools are highlighted, while a novel in silico tool, RASAR, which can be a major technique for small to big datasets when not much experimental data are present, is presented. The book also introduces the reader to the major drug databases covering drug molecules, chemicals, therapeutic targets, metabolomics, and peptides, which are great resources for drug discovery employing drug repurposing, high throughput, and virtual screening. This volume is a great tool for graduates, researchers, academics, and industrial scientists working in the fields of cheminformatics, bioinformatics, computational biology, and chemistry.
Alzheimer’s disease is the most common form of dementia which is incurable. Although some kinds of memory loss are normal during aging, these are not severe enough to interfere with the level of function. ß-Secretase is an important protease in the pathogenesis of Alzheimer’s disease. Some statine-based peptidomimetics show inhibitory activities to the ß-secretase. To explore the inhibitory mechanism, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies on these analogues were performed. Quantitative structure-activity relationship (QSAR) modeling pertains to the construction of predictive models of biological activities as a function of structural and molecular information of a compound library. The concept of QSAR has typically been used for drug discovery and development and has gained wide applicability for correlating molecular information with not only biological activities but also with other physicochemical properties, which has therefore been termed quantitative structure-property relationship (QSPR). In this study, 3D QSAR and pharmacophore mapping studies were carried out using Accelrys Discovery Studio 2.1. The best nine drugs were selected from the 16 ligands and pharmacophore features were generated.
This reference book compiles the recent advances in computational and experimental modelling to screen and manage Alzheimer’s disease. It covers basic etiopathology and various in vitro and in vivo strategies of disease intervention. The book discusses how computer-aided drug design approaches reduce costs and increase biological test efficiency. It reviews the screening for anti-Alzheimer drugs and biomarker analysis of disease inhibitors. The book also explores mechanistic aspects of neurodegeneration and the use of natural products as therapeutics for Alzheimer’s disease. Key features: Elaborates on the computational modelling of protein target inhibitors as anti-Alzheimer’s agents Explains the role of phytomolecules and natural products in Alzheimer’s therapy Reviews preclinical ways to assess drugs focusing on Alzheimer’s disease Covers biomarker analysis for Alzheimer’s disease Discusses the onset and progression of Alzheimer’s disease The book is meant for professionals, researchers, and students of neuroscience, psychology, and computational neurosciences.
Alzheimer's disease (AD) represents a growing biomedical, social, and economical problem. Millions of people have suffered from the disease globally. Studies have shown that aggregated forms of amyloid [beta] peptide adversely affect neuronal function and may represent the causative agent in AD. It has been demonstrated that chronic treatment with ibuprofen and naproxen reduces the risk of AD and improves the behavioral impairment for patients with AD. This dissertation utilizes high performance parallel computing, all-atom molecular dynamics simulation, and protein-ligand docking to understand the mechanism of the anti-aggregation effect of ibuprofen and naproxen in Alzheimer's amyloidogenesis. The results reveal different mechanisms of ligand binding to the monomers and fibrils formed by A[beta] peptides implicated in AD. Binding to A[beta] monomers is mostly governed by ligand-amino acid interactions, whereas binding to the fibril is determined by the fibril surface geometry and interligand interactions. The antiaggregation effect of ibuprofen and naproxen is explained by direct competition between these ligands and incoming A[beta] peptides for binding to the fibril.
This new book takes an in-depth look at the emerging and prospective field of computational biology and bioinformatics, which possesses the ability to analyze large accumulated biological data collected from sequence analysis of proteins and genes and cell population with an aim to make new predictions pertaining to drug discovery and new biology. The book explains the basic methodology associated with a bioinformatics and computational approach in drug designing. It then goes on to cover the implementation of computational programming, bioinformatics, pharmacophore modeling, biotechnological techniques, and pharmaceutical chemistry in designing drugs. The major advantage of intervention of computer language or programming is to cut down the number of steps and costs in the field of drug designing, reducing the repeating steps and saving time in screening the potent component for drug or vaccine designing. The book describes algorithms used for drug designing and the use of machine learning and AI in drug delivery and disease diagnosis, which are valuable in clinical decision-making. The implementation of robotics in different diseases like stroke, cancer, COVID-19, etc. is also addressed. Topics include machine learning, AI, databases in drug design, molecular docking, bioinformatics tools, target-based drug design, and immunoinformatics, chemoinformatics, and nanoinformatics in drug design. Drug repurposing in drug design in general as well as for specific diseases, including cancer, Alzheimer’s disease, tuberculosis, COVID-19, etc., is also addressed in depth.
The mathematical model presented in this book, based on partial differential equations (PDEs) describing attractant-repellent chemotaxis, is offered for a quantitative analysis of neurodegenerative disease (ND), e.g., Alzheimer's disease (AD). The model is a representation of basic phenomena (mechanisms) for diffusive transport and biochemical kinetics that provides the spatiotemporal distribution of components which could explain the evolution of ND, and is offered with the intended purpose of providing a small step toward the understanding, and possible treatment of ND.The format and emphasis of the presentation is based on the following elements:In other words, a methodology for numerical PDE modeling is presented that is flexible, open ended and readily implemented on modest computers. If the reader is interested in an alternate model, it might possibly be implemented by: (1) modifying and/or extending the current model (for example, by adding terms to the PDEs or adding additional PDEs), or (2) using the reported routines as a prototype for the model of interest.These suggestions illustrate an important feature of computer-based modeling, that is, the readily available procedure of numerically experimenting with a model. The current model is offered as only a first step toward the resolution of this urgent medical problem.
Alzheimer’s disease is an increasingly common form of dementia and despite rising interest in discovery of novel treatments and investigation into aetiology, there are no currently approved treatments that directly tackle the causes of the condition. Due to its multifactorial pathogenesis, current treatments are directed against symptoms and even precise diagnosis remains difficult as the majority of cases are diagnosed symptomatically and usually confirmed only by autopsy. Alzheimer’s Disease: Recent Findings in Pathophysiology, Diagnostic and Therapeutic Modalities provides a comprehensive overview from aetiology and neurochemistry to diagnosis, evaluation and management of Alzheimer's disease, and latest therapeutic approaches. Intended to provide an introduction to all aspects of the disease and latest developments, this book is ideal for students, postgraduates and researchers in neurochemistry, neurological drug discovery and Alzheimer’s disease.
Alzheimer's Disease (AD) is a growing global public health challenge. The development of new therapies is urgently needed, and a complex ecosystem of organizations has grown to facilitate AD drug discovery and development. Masterfully collating information on the drug development ecosystem, this book emphasizes the contributions of each aspect in the pipeline with a uniform approach to chapters, enabling readers to access relevant information quickly. Topics covered include the use of non-clinical laboratory studies, biomarker development, artificial intelligence, design and management of clinical trials, and funding and financing models. Also discussed is the critical role of advocacy fundraising for drug development. With the approval of aducanumab, the function of the ecosystem has become apparent. This is a definitive overview of how the ecosystem works in transferring an AD drug from its discovery in the laboratory through clinical trial testing to regulatory review and eventual marketing.