Custers D, Canfyn M, Courselle P, De Beer J O, Apers S, Deconinck E
Division of Food, Medicines and Consumer Safety, Section Medicinal Products, Scientific Institute of Public Health (WIV-ISP), J. Wytsmanstraat 14, B-1050 Brussels, Belgium; Laboratory of Pharmacognosy and Pharmaceutical Analysis, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk, Belgium.
Division of Food, Medicines and Consumer Safety, Section Medicinal Products, Scientific Institute of Public Health (WIV-ISP), J. Wytsmanstraat 14, B-1050 Brussels, Belgium.
Talanta. 2014 Jun;123:78-88. doi: 10.1016/j.talanta.2014.01.020. Epub 2014 Jan 31.
Counterfeit medicines are a global threat to public health. These pharmaceuticals are not subjected to quality control and therefore their safety, quality and efficacy cannot be guaranteed. Today, the safety evaluation of counterfeit medicines is mainly based on the identification and quantification of the active substances present. However, the analysis of potential toxic secondary components, like residual solvents, becomes more important. Assessment of residual solvent content and chemometric analysis of fingerprints might be useful in the discrimination between genuine and counterfeit pharmaceuticals. Moreover, the fingerprint approach might also contribute in the evaluation of the health risks different types of counterfeit medicines pose. In this study a number of genuine and counterfeit Viagra(®) and Cialis(®) samples were analyzed for residual solvent content using headspace-GC-MS. The obtained chromatograms were used as fingerprints and analyzed using different chemometric techniques: Principal Component Analysis, Projection Pursuit, Classification and Regression Trees and Soft Independent Modelling of Class Analogy. It was tested whether these techniques can distinguish genuine pharmaceuticals from counterfeit ones and if distinct types of counterfeits could be differentiated based on health risks. This chemometric analysis showed that for both data sets PCA clearly discriminated between genuine and counterfeit drugs, and SIMCA generated the best predictive models. This technique not only resulted in a 100% correct classification rate for the discrimination between genuine and counterfeit medicines, the classification of the counterfeit samples was also superior compared to CART. This study shows that chemometric analysis of headspace-GC impurity fingerprints allows to distinguish between genuine and counterfeit medicines and to differentiate between groups of counterfeit products based on the public health risks they pose.
假药是对公众健康的全球性威胁。这些药品未经过质量控制，因此其安全性、质量和疗效无法得到保证。如今，假药的安全性评估主要基于对其中活性物质的鉴定和定量。然而，对潜在有毒的次要成分（如残留溶剂）的分析变得更为重要。评估残留溶剂含量以及对指纹图谱进行化学计量学分析，可能有助于鉴别真药和假药。此外，指纹图谱方法还可能有助于评估不同类型假药所带来的健康风险。在本研究中，使用顶空-气相色谱-质谱联用仪对多个真的和假的万艾可（伟哥）及希爱力样品的残留溶剂含量进行了分析。所得色谱图用作指纹图谱，并使用不同的化学计量学技术进行分析：主成分分析、投影寻踪、分类与回归树以及类类比软独立建模。测试了这些技术能否区分真药和假药，以及是否能根据健康风险区分不同类型的假药。这种化学计量学分析表明，对于这两个数据集，主成分分析都能清晰地区分真药和假药，类类比软独立建模生成了最佳预测模型。该技术不仅在区分真药和假药时的正确分类率达到了100%，而且在假药样品的分类方面也优于分类与回归树。本研究表明，对顶空气相色谱杂质指纹图谱进行化学计量学分析能够区分真药和假药，并能根据不同类型假药所带来的公共健康风险对其进行区分。
