Special Issue (September) Online - 2455-3891 Print - 0974-2441 International Conference of Pharmacy (ICP-2017) at School of Pharmaceutical Sciences, Lovely Professional University, Punjab, India PREDICTION OF ACTIVITY SPECTRA OF SUBSTANCES ASSISTED PREDICTION OF BIOLOGICAL ACTIVITY SPECTRA OF POTENTIAL ANTI-ALZHEIMER’S PHYTOCONSTITUENTS ABHINAV ANAND, NEHA SHARMA, NAVNEET KHURANA* Department of Pharmacology, School of Pharmaceutical Sciences, Lovely Professional University, Punjab, India. Email: navi.pharmacist@gmail.com Results: Several phytoconstituents were predicted to have effects better than marketed drugs under some or the other out of the chosen six areas of pharmacological intervention. On the other hand, several new avenues were predicted in which the in vitro and in vivo evaluation of the phytoconstituents can be made on the basis of PASS predicted activities. Conclusion: PASS is an important tool for virtually screening the compounds of interest for the biological activities of interest. This helps the researchers to streamline the research. However, PASS has its own share of limitations amidst a multitude of merits. Keywords: Alzheimer’s disease, Prediction of activity spectra of substances, Phytoconstituents, Prediction. INTRODUCTION Alzheimer’s disease (AD) has been recognized as the most prevalent form of dementia among geriatric persons since the commencement of 21 st century. Over 47.5 million people globally were estimated to be living with dementia in 2016. By 2030, the figure is being speculated to rise to 75.6 million [1]. AD is a neurodegenerative disorder that generally appears in mid-to-late adulthood. It is associated with a progressive and rather irreversible decline in memory various other cognitive capabilities. In AD, there is neuronal destruction and deterioration of neural connections in the cerebral cortex region of the brain along with a substantial loss of brain mass [2]. AD is invariably progressive and lethal within 5-10 years of its onset [3]. Death usually ensues due to complications of the chronic illness. It is one of the top five most common causes of mortality in population of the United States [4]. In some rare cases, it appears in people in their 40 seconds and 50 seconds, but otherwise it is a disease of old age. Based on clinical, population-based studies, about 200,000 people under 65 years of age are suffering from AD. In contrast, around 5 million of those over 65 years of age have AD. As per speculations, a new case of AD is expected to be developed every 33 seconds, by 2050 [5]. AD is characterized by the presence of two neuropathological hallmarks, i.e., extracellular amyloid beta plaques (Aβ) and intracellular Tau neurofibrillary tangles (NFTs). The plaques constitute chiefly of the neurotoxic peptide amyloid, which forms after the sequential cleavage of a large precursor protein, i.e., amyloid precursor protein (APP) by two enzymes, namely, β-secretase and γ-secretase. However, Aβ is not formed if APP is first acted upon and cleaved by the enzyme α-secretase instead of β-secretase. NFTs comprise mainly of the protein tau. In the development of AD, Tau uncouples from microtubules and aggregates into tangles thereby inhibiting transport and resulting in microtubule disassembly. It also depends on the phosphorylation of Tau (Fig. 1) [6]. The current pharmacotherapeutic approaches for AD provide only symptomatic relief. There is an urgent need for discovery and development of new drugs that could halt or delay the progression of disease by treating the underlying causes [7,8]. The new drug development is a very tedious process and is associated with high probability of negative results in terms of pharmacological efficacy. In such a scenario, it becomes imperative that a tool should be available which could predict the pharmacological properties beforehand. It would enable the researchers to streamline the research more efficiently. Prediction of activity spectra of substances (PASS) is such a tool which can predict the pharmacological properties beforehand and would help in screening pharmacological potential leads for a particular condition [9]. The applicability of PASS to phytoconstituents has been exhibited in earlier investigations [12-14]. The current version of PASS is capable of predicting over 3750 biological effects, biochemical modes of action, specific toxicities, and metabolic terms based on 2D structures or canonical simplified molecular-input line-entry system (SMILES) with a mean accuracy of almost 95%. It predicts Full Proceeding Paper Received: 14 July 2017, Revised and Accepted: 25 July 2017 ABSTRACT Methods: Several phytoconstituents were selected on the basis of reported literature. The anti-AD activities of selected phytoconstituents were predicted by employing canonical simplified molecular-input line-entry system obtained from PubChem using PASS online. Objective: To predict the biological activity of certain phytoconstituents for their anti-AD effects. Objective: Alzheimer’s disease (AD) is a neurodegenerative disorder that is associated with loss of memory and cognition. It is responsible for 60-80% of dementia cases. The current pharmacotherapy provides only symptomatic relief. There is an urgent need for discovery and development of newer drugs that could delay or halt the progression of disease. Prediction of activity spectra of substances (PASS) is a valuable interface that should be adopted as a quintessential tool for predicting potential anti-AD capability of molecules. Plant sources have been an integral part of traditional medicine systems since ages, be it the Traditional Indian Medicine System or Traditional Chinese Medicine System. Around 70% of New Chemical Entities which later became drugs between the periods of 1981-2006 originated from plant sources [10]. Screening of molecules virtually is of specific importance to form basis of pharmacology and receptor interactions for phytoconstituents [11]. © 2017 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4. 0/) DOI: http://dx.doi.org/10.22159/ajpcr.2017.v10is4 .21330