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      Application of Metabolomics to Quality Control of Natural Product Derived Medicines

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          Abstract

          Metabolomics has been used as a powerful tool for the analysis and quality assessment of the natural product (NP)-derived medicines. It is increasingly being used in the quality control and standardization of NP-derived medicines because they are composed of hundreds of natural compounds. The most common techniques that are used in metabolomics consist of NMR, GC-MS, and LC-MS in combination with multivariate statistical analyses including principal components analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Currently, the quality control of the NP-derived medicines is usually conducted using HPLC and is specified by one or two indicators. To create a superior quality control framework and avoid adulterated drugs, it is necessary to be able to determine and establish standards based on multiple ingredients using metabolic profiling and fingerprinting. Therefore, the application of various analytical tools in the quality control of NP-derived medicines forms the major part of this review. Veregen ® (Medigene AG, Planegg/Martinsried, Germany), which is the first botanical prescription drug approved by US Food and Drug Administration, is reviewed as an example that will hopefully provide future directions and perspectives on metabolomics technologies available for the quality control of NP-derived medicines.

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          Antioxidant Activity of Various Tea Extracts in Relation to Their Antimutagenicity

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            A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding.

            The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.
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              Metabolomics: Current analytical platforms and methodologies

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                Author and article information

                Journal
                Biomol Ther (Seoul)
                Biomol Ther (Seoul)
                Biomol Ther (Seoul)
                ksp
                Biomolecules & Therapeutics
                The Korean Society of Applied Pharmacology
                1976-9148
                2005-4483
                November 2017
                14 June 2017
                : 25
                : 6
                : 559-568
                Affiliations
                College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea
                Author notes
                [* ]Corresponding Author: E-mail: hykychoi@ 123456cau.ac.kr , Tel: +82-2-820-5605, Fax: +82-2-812-3921
                Article
                bt-25-559
                10.4062/biomolther.2016.249
                5685424
                28605829
                5463b135-8acd-4463-b2fb-5ffd4a5543da
                Copyright © 2017 The Korean Society of Applied Pharmacology

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 03 November 2016
                : 21 March 2017
                : 30 March 2017
                Categories
                Review

                metabolomics,natural product-derived medicines,quality control,veregen®

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