A framework for selecting analytical techniques in profiling authentic and counterfeit Viagra and Cialis.
Several analytical techniques aimed at profiling drugs are deemed costly and time consuming, and may not be promptly available for analysis when required. This paper proposes a method for identifying the analytical techniques providing the most relevant data for classification of drug samples into authentic and unauthentic categories. For that matter, we integrate principal components analysis (PCA) to k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classification tools. PCA is first applied to data from five techniques, i.e., physical profile, X-ray fluorescence (XRF), direct infusion electrospray ionization mass spectrometry (ESI-MS), active pharmacological ingredients profile (ultra performance liquid chromatography, UPLC-MS), and infrared spectroscopic profile (ATR-FTIR). Subsets of PCA scores are then combined with a "leave one subset out at a time" approach, and the classification accuracy using KNN and SVM evaluated after each subset is omitted. Subsets yielding the maximum accuracy indicate the techniques to be prioritized in profiling applications. When applied to data from Viagra and Cialis, the proposed method recommended using the data from UPLC-MS, physical profile and ATR-FTIR techniques, which increased the categorization accuracy. In addition, the SVM classification tool is suggested as more accurate when compared to the KNN.
Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99 - 5° andar, Porto Alegre, RS, Brazil. Electronic address: michel.anzanello@gmail.com.
Michel J
MJ
Anzanello
Rio Grande do Sul Technical and Scientifical Division, Brazilian Federal Police, Avenida Ipiranga 1365, 90160-093 Porto Alegre, RS, Brazil. Electronic address: rafaelortiz.rso@dpf.gov.br.
Rafael S
RS
Ortiz
Department of Pharmacy, Universidade Federal do Rio Grande do Sul, Av. Ipiranga, 2752, 90610-000 Porto Alegre, RS, Brazil. Electronic address: renata@ufrgs.br.
Renata
R
Limberger
Department of Pharmacy, Universidade Federal do Rio Grande do Sul, Av. Ipiranga, 2752, 90610-000 Porto Alegre, RS, Brazil. Electronic address: krismariotti@gmail.com.
Kristiane
K
Mariotti
Analytical technique; Counterfeit drugs; PCA; Support Vector Machine; k-Nearest Neighbor