Computational Modeling Approaches in Search of Anti-Alzheimer's Disease Agents: Case Studies of Phosphodiesterase Inhibitors

Alzheimer’s disease (AD) is one of the major public health concerns. Phosphodiesterases (PDEs) are a major class of enzymes which hydrolyze two second messengers: cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). Due to the high expression of various PDE subfamilies in the human brain, PDE inhibition has a substantial impact on neurodegenerative diseases by controlling the level of cAMP or cGMP. In this regard, several synthetic or natural compounds that inhibit specific PDE subtypes, for instance, rolipram and roflumilast (PDE4 inhibitors), vinpocetine (PDE1 inhibitor), cilostazol and milrinone (PDE3 inhibitors), sildenafil and tadalafil (PDE5 inhibitors), etc., have been stated as exhibiting excellent results for the treatment of AD. PDEs are currently believed to be a potential target for the treatment of AD since several PDE inhibitors have demonstrated significant cognitive improvement effects in preclinical investigations and more than 33 of them have been subjected to clinical trials. In the search for novel drugs, computational drug design methods are now essential. Computational approaches, whether structure-based (protein structure prediction, molecular docking, MD simulation, pharmacophore modeling, fragment-based de novo design, etc.) or ligand-based (QSAR, chemical read-across, pharmacophore modeling, similarity search), are used in almost every drug discovery project. To investigate new drugs, many drug targets have been researched employing computational techniques. Many researchers across the world have recently focused on the development of more advanced and selective phosphodiesterases as treatments for inflammatory illnesses, CNS disorders (including Alzheimer’s disease), and numerous other diseases. The majority of these groups have used computational tools for drug discovery and design at various stages of their research. The objective of the current chapter is to provide a concise summary of the most relevant and recent research on PDE inhibitors as anti-AD therapeutics with promising results utilizing various computational modeling techniques, which can assist in the further development and identification of new anti-AD agents. In this chapter, we will present relevant and recently published computational studies for the identification or design of potential PDE inhibitors using various computational approaches. Moreover, the chapter will give the audience a broad overview of effective computational drug discovery research in this particular field of applications.