Natural substances, particularly medicinal plants and their extracts, are still today intended as source for new Active Pharmaceutical Ingredients (APIs). Alternatively they can be validly employed to prepare medicines, food supplements or medical devices. The most adopted analytical approach used to verify quality of natural substances like medicinal plants is based still today on the traditional quantitative determination of marker compounds and/or active ingredients, besides the acquisition of a fingerprint by TLC, NIR, HPLC, GC.
Here a new analytical approach based on untargeted metabolomic fingerprinting by means of Mass Spectrometry (MS) to verify the quality of grinTuss adulti syrup, a complex products based on medicinal plants, is proposed. Recently, untargeted metabolomic has been successfully applied to assess quality of natural substances, plant extracts, as well as corresponding formulated products, being the complexity a resource but not necessarily a limit. The untargeted metabolomic fingerprinting includes the monitoring of the main constituents, giving weighted relevance to the most abundant ones, but also considering minor components, that might be notable in view of an integrated – often synergistic – effect on the biological system.
Two different years of production were investigated. The collected samples were analyzed by Flow Injection ElectroSpray Ionization Mass Spectrometry Analysis (FIA-ESI-MS) and a suitable data processing procedure was developed to transform the MS spectra into robust fingerprints. Multivariate Statistical Process Control (MSPC) was applied in order to obtain multivariate control charts that were validated to prove the effectiveness of the proposed method.
Medicinal plants; Metabolomic; Batch quality comparison
Citation: L. Mattoli, M. Burico, G. Fodaroni, S. Tamimi, S. Bedont, P. Traldi, M. Stocchero New frontiers in pharmaceutical analysis: A metabolomic approach to check batch compliance of complex products based on natural substances doi:10.1016/j.jpba.2016.04.010
Received: 31 December 2015 Revised: 8 April 2016 Accepted: 9 April 2016 Available online: 13 April 2016
Copyright: © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).