Tadalafil and Sildenafil illicit association: Stability- indicating HPLC method, photodegradation kinetic and toxicological studies

Abstract The illicit market of counterfeit medicines containing sildenafil and tadalafil has been causing serious public health problems. Thus, further studies on this illicit association are needed. A stability-indicating HPLC method was developed for simultaneous determination of tadalafil (TAD) and sildenafil (SIL) using a C18 column (250 x 4.6 mm, 5 µm). Detection was achieved at 284 nm, for TAD, and 292 nm, for SIL. The method was considered to be specific, linear, precise, accurate, robust, and sensitive. In the photodegradation kinetic studies, the drugs showed a first-order reaction rate when isolated, and zero-order when associated. Toxicological assays demonstrated that the photodegraded drugs decreased cell viability in compared to non- degraded drugs, suggesting cytotoxic activity. Additional, mutagenic activity was not observed under the tested conditions. Photodegraded drugs, in association, depicted DNA damage index, suggesting genotoxic effects. The obtained results will be able to support the forensic intelligence laboratories, as well as to alert the population about the risk inherent to consuming counterfeit products.

Identification of Chinese Caterpillar Medicinal Mushroom, Ophiocordyceps sinensis (Ascomycetes) from Counterfeit Species.

Ophiocordyceps sinensis is a valuable traditional Chinese medicine with a high market price. In this study, a polymerase chain reaction-restriction fragment-length polymorphism (PCR-RFLP) method based on 2 enzymes was developed to distinguish O. sinensis from 6 common counterfeit species. To verify the applicability of this method, we experimentally tested O. sinensis organisms, tablet preparations made from O. sinensis, and cultured mycelia isolated from O. sinensis. To validate the results from this PCR-RFLP method, all real samples were identified by internal transcribed spacer sequencing. This is, to our knowledge, the first time the PCR-RFLP method has been applied to identify O. sinensis. The selection of 2 restrictive enzymes for identification dramatically improved the accuracy and efficiency of this method. It is the great advantage of this method that sampling from either of 2 parts of O. sinensis-the fruiting body or the caterpillar body-would not cause any difference in the final experimental results. Therefore, this method is not only feasible for testing crude drugs of O. sinensis but it is also useful when the crude drugs are broken down into powder or made into tablets, demonstrating the promising prospect of application in quality control.

A genetic algorithm-based framework for wavelength selection on sample categorization.

In forensic and pharmaceutical scenarios, the application of chemometrics and optimization techniques has unveiled common and peculiar features of seized medicine and drug samples, helping investigative forces to track illegal operations. This paper proposes a novel framework aimed at identifying relevant subsets of attenuated total reflectance Fourier transform infrared (ATR-FTIR) wavelengths for classifying samples into two classes, for example authentic or forged categories in case of medicines, or salt or base form in cocaine analysis. In the first step of the framework, the ATR-FTIR spectra were partitioned into equidistant intervals and the k-nearest neighbour (KNN) classification technique was applied to each interval to insert samples into proper classes. In the next step, selected intervals were refined through the genetic algorithm (GA) by identifying a limited number of wavelengths from the intervals previously selected aimed at maximizing classification accuracy. When applied to Cialis®, Viagra®, and cocaine ATR-FTIR datasets, the proposed method substantially decreased the number of wavelengths needed to categorize, and increased the classification accuracy. From a practical perspective, the proposed method provides investigative forces with valuable information towards monitoring illegal production of drugs and medicines. In addition, focusing on a reduced subset of wavelengths allows the development of portable devices capable of testing the authenticity of samples during police checking events, avoiding the need for later laboratorial analyses and reducing equipment expenses. Theoretically, the proposed GA-based approach yields more refined solutions than the current methods relying on interval approaches, which tend to insert irrelevant wavelengths in the retained intervals. Copyright © 2016 John Wiley & Sons, Ltd.

Chemometrics and the identification of counterfeit medicines-A review.

This review article provides readers with a number of actual case studies dealing with verifying the authenticity of selected medicines supported by different chemometric approaches. In particular, a general data processing workflow is discussed with the major emphasis on the most frequently selected instrumental techniques to characterize drug samples and the chemometric methods being used to explore and/or model the analytical data. However, further discussion is limited to a situation in which the collected data describes two groups of drug samples - authentic ones and counterfeits.

Chemometrics and chromatographic fingerprints to discriminate and classify counterfeit medicines containing PDE-5 inhibitors.

Chromatographic fingerprints recorded for a set of genuine and counterfeit samples of Viagra(®) and Cialis(®) were evaluated for their use in the detection and classification of counterfeit samples of these groups of medicines. Therefore several exploratory chemometric techniques were applied to reveal structures in the data sets as well as differences among the samples. The focus was on the differentiation between genuine and counterfeit samples and on the differences between the samples of the different classes of counterfeits as defined by the Dutch National Institute for Public Health and the Environment (RIVM). In a second part the revealed differences between the samples were modelled to obtain a predictive model for both the differentiation between genuine and counterfeit samples as well as the classification of the counterfeit samples. The exploratory analysis clearly revealed differences in the data for the genuine and the counterfeit samples and with projection pursuit and hierarchical clustering differences among the different groups of counterfeits could be revealed, especially for the Viagra(®) data set. For both data sets predictive models were obtained with 100% correct classification rates for the differentiation between genuine and counterfeit medicines and high correct classification rates for the classification in the different classes of counterfeit medicines. For both data sets the best performing models were obtained with Least Square-Support Vector Machines (LS-SVM) and Soft Independent Modelling by Class Analogy (SIMCA).