Theoretical Designing of Acetylcholine Esterase (Ache) Inhibitors

Alzheimer’s disease still remains as an untreatable neurological disorder with no successful treatment strategy to prevent or cure or reverse the damage. Globally, AD is 5th major cause of death. Over last three decades, accumulating evidence for Alzheimer’s suggest multiple causes, predominantly the gene make up of affected individuals are altered. Affected individuals suffer from severe loss in brain weight and volume. AD is also characterized by increase in neuronal populations in some regions of brain than the other areas. The loss of neuron is sought to be one of the reason to cause loss of brain volume. However, previous studies have reported a strong correlation of acetylcholine deficiency and memory loss along with cognitive deficits of patients suffering from AD. Acetyl cholinesterase (AChE) is reported to be a key enzyme in AD. Over decades in Alzheimer’s drug research, inhibitors of AChE remain to be of prime focus. Despite substantial efforts in designing efficient drug that can either treat or halt the progression of AD, none of them appears to be fully effective until now. Of note, the drug market for managing AD in 2010 alone is 604 billion dollars [1]. The pharmaceutical industry continues to seek new molecule that could improve the treatment strategy for AD at lower cost. Herein, we attempt to identify potential lead molecule for AD treatment by virtual drug screening approach. We employ structure activity relationship (SAR) to develop a pharmacophore model [2]; from the basic phamacophore structure we have developed a database with potential lead molecules that could inhibit AChE [3-5]. This report discusses the binding behavior of lead molecule from the generated chemical database using suitable computational methods. In addition, we also report the experiment results of toxicity analysis, prediction of active spectra for biological activity that includes pharmacological effects, predicted mechanisms of action, interaction with metabolic enzymes influence on gene expression. Virtual design and analysis allows us to understand and perform preliminary screening for a complete set of novel compounds whose experimental data is not available in literature till date.