Design and Synthesis of Potential Alzheimer’s Disease (AD) Therapeutics and Modeling Studies on β3-Adrenergic Receptor (β3-AR) Agonists

Abstract

The drug discovery and development process is still a long process with low discovery rate of new therapeutics. The average total cost per drug development varies from US$ 800 million to 1.8 billion, and takes an average of 10–15 years. In general, the drug discovery process involves the identification of a target (e.g. protein) and the discovery of some suitable drug candidates that can block or activate the target. In the modern drug discovery and development paradigm, the rational approaches to drug design play a major role in the lead identification and optimization steps in the process of the drug discovery research. The identification of pharmacophore either empirically through wide literature search and/or by using advanced computational approaches like substructure analysis, core hopping, pattern recognition techniques etc help greatly in the design of new chemical entities (NCEs) as promising lead for the desired biological activity. It is followed by the optimization of the biological activity (lead optimization) by judicious modulation of the structure of the lead to affect its pharmacokinetic and pharmacodynamic properties without major alteration in its prototypic structure. The computer-aided drug design (CADD), which is a specialized discipline that uses computational methods to simulate drug-receptor interactions, in association with the bioinformatics offer significant benefits to drug discovery program by reducing the inputs in terms of manpower, money and time. Based on the above theme, the present thesis presents the results of my research work on the two broad topics: (1) Design and synthesis of potential Alzheimer’s disease (AD) therapeutics using rational drug design approaches, and (2) Modeling studies on β3- adrenergic receptor (β3-AR) agonists. The research work embodied in the present thesis has been divided in mainly three chapters. The first chapter is an introductory chapter covering a comprehensive review on current computer-aided drug design techniques. The second chapter and sub-chapters present the results of the research work carried on the topic “Design and synthesis of potential Alzheimer’s disease (AD) therapeutics”. It includes six sub-chapters. The chapter 2.1 deals with an in-depth review on the therapeutic approaches to Alzheimer’s disease (AD). The chapter 2.2 deals with the 3D QSAR studies carried out on structurally diverse carbamates as acetylcholinesterase (AChE) inhibition. The computational insights gained in this study have been used in the rational design of novel potent AChE inhibitors, as described in the chapter 2.3 which presents the rational design and synthesis of novel substituted 1,2,3,4-tetrahydroquinoline and tetralin-based carbamates as potent and orally bioavailable AChE inhibitors for AD therapy. The chapter 2.4 describes the rational design and synthesis of novel heterovalent hybrid analogues as AChE inhibitors considering medicinal chemical hybridization (MCH) approach. The chapter 2.5 entails the elucidation of the structural features of the donepezil essential for AChE inhibition through structural modification. The last chapter 2.6 deciphers the hierarchical virtual screening study for the identification of novel potential β-site APP cleaving enzyme 1 (BACE1) inhibitors with an integrated use of ligand-based and structure-based drug design techniques. The third chapter and sub-chapters present the results of the research work carried out on the topic “Modeling studies on β3-adrenergic receptor (β3-AR) agonists”. The chapter 3.1 gives a brief review on the role of β3-adrenergic receptors in obesity and type-II diabetes. The chapter 3.2 describes the elucidation of the structural basis for the -AR subtype selectivity of agonists and antagonist using detailed computational studies including homology modeling, docking, molecular dynamics simulation and binding free energy calculation in view of the prime requisite of 3-AR selectivity over 1- and 2-ARs for the development of successful therapeutics for obesity and type-II diabetes. The last chapter 3.3 describes a systematic hierarchical virtual screening study consisting of pharmacophore modeling, docking, and VS of the generated focused virtual library, carried out to identify novel potential high-affinity and selective β3-AR agonists.