Parkinson’s disease (PD) is caused by human physiological function and is ranked as the second most common neurodegenerative disorder. One of the prominent therapies currently available for PD is the use of dopamine agonists which mimic the natural action of dopamine in the brain and stimulate dopamine receptors directly. Currently, available pharmaceutical drugs provide only temporary relief of the disease. Phytocompounds have been identified as promising target of research in the quest for new pharmaceutical compounds as they can produce secondary metabolites with novel chemical structure. In this review the drug development of Parkinson disease has been analyzed using computational tools.
Parkinson disease; Phytocompounds; Computational methods; Drug development and design
Citation: S.Vijayakumar, S.Prabhu, S.Rajalakhsmi, P Manogar Review On Potential Phytocompounds In Drug Development For Parkinson Disease: A Pharmacoinformatic Approach http://dx.doi.org/10.1016/j.imu.2016.09.002
Received: 22 May 2016, Revised: 13 September 2016, Accepted: 13 September 2016, Available online: 15 September 2016
Copyright: © 2016 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V.
The conventional medicines and treatment, available today, have proven ineffective in curing the multifunctional pathological mechanisms of Parkinson’s disease. Use of bioactive compounds of medicinal plants, on the other hand, has shown to possess the potential to modify or slow down the progress of Parkinson disesase. Identification of new bioactive compounds becomes the need of the hour to develop new and effective drugs. Pharmacoinformatics approach has to be followed in the modern drug design to understand the drug-receptor interaction. On the basis of this review, it is concluded that modern computational methods can strongly support and facilitate the design of novel, effective and more potent inhibitors for the Parkinson disease with a better understanding of the drug and receptor interactions. The computational tools made the time save, avoid risk, low cost and easy to detect the efficient molecules with that selected target.
The authors are grateful to the DST-SERB Major Research Project, New Delhi, India [Project File No.SB/YS/LS-109/2014, vide Diary No. SERB/F/8339/2014] for funding this project. We especially express our thanks to the management of A.V.V.M. Sri Pushpam College (Autonomous), Poondi, India for providing necessary facilities and support to carry out this work.