Computational Modeling Of Drugs Against Alzheimer S Disease

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.

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 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.

This volume describes different computational methods encompassing ligand-based approaches (QSAR, pharmcophore), structure-based approaches (homology modeling, docking, molecular dynamics simulation), and combined approaches (virtual screening) with applications in anti-Alzheimer drug design. Different background topics like molecular etiologies of Alzheimer’s disease, targets for new drug development, and different cheminformatic modeling strategies are covered for completeness. Special topics like multi-target drug development, natural products, protein misfolding, and nanomaterials are also included in connection with computational modeling of anti-Alzheimer drug development. In Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Cutting-edge and authoritative, Computational Modeling of Drugs Against Alzheimer’s Disease is a valuable resource for learning about the latest computational techniques used to study this disease.

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.

Alzheimer's disease (AD) is the leading cause of dementia and, unfortunately, remains incurable. The social, emotional and financial implications of AD are immeasurable, and about 47 million people worldwide are affected by AD or other forms of dementia. As lifespans are improved by healthcare systems worldwide, age-associated neurodegenerative diseases are imposing an increasing challenge to science. It is becoming imperative for us to understand the causes of these diseases, AD in particular, at molecular and cellular levels. Starting with the broader picture from a biological perspective, this book takes the reader through fascinating dynamics within and outside of neurons in the brain.Alzheimer's Disease: Biology, Biophysics and Computational Models helps the reader to understand AD from mechanistic and biochemical perspectives at intra- and inter-cellular levels. It focuses on biochemical pathways and modeling associated with AD. Some of the recent research on biophysics and computational models related to AD are explained using context-driven computational and mathematical modeling and essential biology is discussed to understand the modeling research.

This reference book compiles the recent advances in computational and experimental modeling to screen and manage Alzheimer's disease. The book discusses how computer-aided drug design approaches reduce costs and increase biological test efficiency.

This book illustrates the importance of natural products as the source for the development of novel drugs for the treatment of neurodegenerative disorders, including Alzheimer's disease. It also highlights the role of reactive oxygen species and altered metal homeostasis in the progression of Alzheimer’s disease and examines the potential of antioxidants and anti-chelating agents in the clinical intervention of neurodegenerative diseases. The book also discusses the role of neuroinflammation in the pathogenesis of Alzheimer’s disease. The chapters provide information about the drug targets, progress in the development of natural product-based therapeutics, biomarkers, fluorescent diagnostic tools, and theranostic for Alzheimer's disease. The book also provides information about the design and synthesis of natural product-based derivatives against the various targets of Alzheimer's disease including epigenetic targets and the metal dyshomeostasis hypothesis. Cutting across different disciplines, this book is a valuable source for neuroscientists, chemical biologists, pharmaceutical researchers, and synthetic biologists.