Artificial Neural Network in Drug Delivery and Pharmaceutical Research



Vijaykumar Sutariyaa, *, Anastasia Grosheva, Prabodh Sadanab, Deepak Bhatiab, Yashwant Pathaka
a Department of Pharmaceutical Sciences, USF College of Pharmacy, University of South Florida, Tampa, FL 33612
b Department of Pharmaceutical Sciences, College of Pharmacy, Northeast Ohio Medical University, Rootstown, OH 44272


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© 2013 Pathak et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Pharmaceutical Sciences, University of South Florida College of Pharmacy, Tampa, Florida, 33612-4749, Tel: 813-974-1401; Fax: 813-974-9890; E-mail: vsutariy@health.usf.edu


Abstract

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

Keywords: Artificial neural networks, ANNs, drug discovery, non-linear systems, pharmacokinetics, pharmacodynamics.