Identifizierung und Charakterisierung von Arzneipflanzen mit »Metabolic Fingerprinting«

Zusammenfassung

Metabolomanalysen finden in der Systembiologie bereits breite Anwendung. Im Bereich der Phytotherapie ist es aufgrund der Komplexität des Pflanzenmetaboloms ebenfalls sinnvoll, Pflanzenextrakte in ihrer Gesamtheit zu erfassen, um die Qualität, Wirksamkeit und Unbedenklichkeit von Arzneipflanzen und auch Fertigarzneimitteln zu gewährleisten. Am Beispiel der Echten Kamille (Matricaria recutita L.), einigen ihrer Verfälschungen und der Römischen Kamille (Chamaemelum nobile (L.) All.) wird dargestellt, welche Möglichkeiten »Metabolic Fingerprinting« bietet, um Arzneipflanzen zu klassifizieren und sie von Verfälschungen abzugrenzen.

Summary

Metabolic fingerprinting for the identification and classification of medicinal plants

Metabolomic studies are commonly used in the field of systems biology. As the plant metabolome is enormously complex, it is also desirable to analyse plant extracts in a holistic approach when they are used for phytotherapy to ensure their quality, efficacy and safety. As a case study, chamomile flower (Matricaria recutita L.), examples of its adulterations and Chamaemelum nobile (L.) All. are used to examine the potential of the metabolic fingerprinting approach for classification of medicinal plants and identification of adulterations.

Schlüsselwörter

Qualitätskontrolle - Identifikation - pflanzliche Arzneimittel - Metabolom - Metabolic Fingerprinting - Analytik - Hauptkomponenten

Key words

Quality control - identification - herbal drugs - herbal medicinal products - metabolomics - metabolic fingerprinting - principal component analysis

Literatur

• 1 But PP, Tomlinson B, Cheung KO, et al.. Adulterants of herbal products can cause poisoning.  BMJ. 1996;  313 117
• 2 Carles M, Cheung MK, Moganti S, et al.. A DNA microarray for the authentication of toxic traditional Chinese medicinal plants.  Planta Med. 2005;  71 580-584
• 3 Cho IH, Kim YS, Choi HK. Metabolomic discrimination of different grades of pinemushroom (Tricholoma matsutake Sing.) using (1)H NMR spectrometry and multivariate data analysis.  J Pharm Biomed Anal. 2007;  43 900-904
• 4 Choi YH, Sertic S, Kim HK, et al.. Classification of Ilex species based on metabolomic fingerprinting using nuclear magnetic resonance and multivariate data analysis.  J Agric Food Chem. 2005;  53 1237-1245
• 5 Dunn WB, Bailey NJ, Johnson HE. Measuring the metabolome: current analytical technologies.  Analyst. 2005;  130 606-625
• 6 Fiehn O. Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks.  Comp Funct Genomics. 2001;  2 155-168
• 7 Förster J, Famili I, Fu P, et al.. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network.  Genome Res. 2003;  13 244-253
• 8 Gan F, Ye R. New approach on similarity analysis of chromatographic fingerprint of herbal medicine.  J Chromatogr A. 2006;  1104 100-105
• 9 Hill LR, Silvestri LG, Ihm P, et al.. Automatic classification of Staphylococci by principalcomponent analysis and a gradient method.  J Bacteriol. 1965;  89 1393-1401
• 10 Idle JR, Gonzalez FJ. Metabolomics.  Cell Metab. 2007;  6 348-351
• 11 Ihm P, Liebau A. Testing homogeneity of multidimensional medical data by means of principal component analysis.  Methods Inf Med. 1965;  4 107-111
• 12 Joshi K, Chavan P, Warude D, Patwardhan B. Molecular markers in herbal drug technology.  Current Science. 2004;  87 159-165
• 13 Kersten T, Daniel C, König GM, Knöß W. Das Potenzial PCR-basierter Markermethoden zur Identifizierung von Arzneipflanzen.  Z Phytotherapie. 2008;  29 122-128
• 14 Kessler W. Multivariate Datenanalyse für die Pharma-, Bio- und Prozessanalytik.  Weinheim: Wiley-VCH. 2007; 
• 15 Kim HK, Choi YH, Erkelens C, et al.. Metabolic fingerprinting of Ephedra species using 1HNMR spectroscopy and principal component analysis.  Chem Pharm Bull (Tokyo). 2005;  53 105-109
• 16 Ko RJ. A U.S. perspective on the adverse reactions from traditional Chinese medicines.  J Chin Med Assoc. 2004;  67 109-116
• 17 Krishnan P, Kruger NJ, Ratcliffe RG. Metabolite fingerprinting and profiling in plants using NMR.  J Exp Bot. 2005;  56 255-265
• 18 Lahner B, G ong J, Mahmoudian M, et al.. Genomic scale profiling of nutrient and trace elements in Arabidopsis thaliana .  Nat Biotechnol. 2003;  21 1215-1221
• 19 Mattoli L, Cangi F, Maidecchi A, et al.. Metabolomic fingerprinting of plant extracts.  J Mass Spectrom. 2006;  41 1534-1545
• 20 Nelson T, Gandotra N, Tausta SL. Plant cell types: reporting and sampling with new technologies.  Curr Opin Plant Biol. 2008;  11 567-573
• 21 Ott KH, Araníbar N, Singh B, Stockton GW. Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts.  Phytochemistry. 2003;  62 971-985
• 22 Pearson K. On lines and planes of closest fit to systems of points in space.  The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. 1901;  6 559-572
• 23 Shaw PC, Ngan FN, But PH, Wang J. Authentication of Chinese medicinal materials by DNA technology.  J Food Drug Anal. 1997;  5 273-284
• 24 Tarachiwin L, Ute K, Kobayashi A, Fukusaki E. 1H NMR based metabolic profiling in the evaluation of Japanese green tea quality.  J Agric Food Chem. 2007;  55 9330-9336
• 25 Wang Y, Tang H, Nicholson JK, et al.. Metabolomic strategy for the classification and quality control of phytomedicine: a case study of chamomile flower (Matricaria recutita L.)  Planta Med. 2004;  70 250-255