QUANTITATIVE STRUCTURE PHARMACOKINETIC RELATIONSHIP USING ARTIFICIAL NEURAL NETWORK: A REVIEW
Abstract
Quantitative structure activity relationship (QSAR) has become a tool for designing in various areas like drugs, food additive, Pesticides, biochemical reactant, environmental pollutant and toxic products. In QSAR biological activity can be related with physicochemical properties and in QSPkR (Quantitative Structure Pharmacokinetic Relationship), pharmacokinetic properties can be related with physicochemical properties, relation found in terms of quantity. A number of literature and review article have been published on Quantitative structure pharmacokinetic relationship. But prediction of human pharmacokinetic properties of known and unknown is much difficult job in pharmaceutical industry. Pharmacokinetic data of animal cannot be put straightforward. Artificial neural network (ANN) is used to predict the pharmacokinetic properties. Artificial neural network has basic structure like biological brain and compose of neurons which are interconnected to each other. The present review not only compiles the literature of QSPkR using ANN, but gives detail about the physicochemical properties and artificial neural network.
Keywords:
Artificial neural network, Quantitative Structure Pharmacokinetic Relationship, Statistics methods, Pharmacokinetic parametersDOI
https://doi.org/10.25004/IJPSDR.2009.010303References
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