What?
Drug counterfeiters, beware. A new method has allowed scientists from China to analyze for adulteration in widely available health supplements – detecting small amounts of Viagra, as well as other phosphodiesterase type 5 enzyme (PDE-5) inhibitors.Why?
Adulterated medication and supplements can be extremely dangerous to human health. “Natural” aphrodisiacs are frequently adulterated with pharmaceutical drugs such as PDE-5 inhibitors. Drugs like Viagra can already cause side effects such as dizziness and a runny nose – not exactly conducive to an amorous encounter – but, more seriously, unmeasured or unapproved doses (which are impossible to judge in cases of adulteration) can cause cardiovascular problems and are dangerous for those with heart disease.Current techniques?
Common methods used for this type of analysis include HPLC-DAD (diode array detection), nuclear magnetic resonance spectroscopy, LC-MS and GC-MS – each of which, while effective, require the skills of highly-trained technical staff and can be time- and resource-intensive. The team from Tianjin University of Science and Technology, and Beijing Technology & Business University, both China, felt a more rapid solution was needed.How?
The researchers spiked supplements with six PDE-5 inhibitors: sildenafil, hydroxyhomosildenafil, thioaildenafil, acetildenafil, vardenafil dihydrochloride salt and pseudo vardenafil before attempting detection using a combination of thin-layer chromatography (TLC), surface-enhanced Raman spectroscopy (SERS) and a BP neural network.Findings?
Using this technique, a limit of detection of less than 5mg/kg was obtained.So what?
Its ability to cheaply and quickly achieve this level of sensitivity means TLC-SERS has scope in other areas vulnerable to adulteration, such as cosmetics, agriculture and food.Newsletters
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References
- N Sukenik et al., “Rapid detection of six phosphodiesterase type 5 enzyme inhibitorsin health care products using thin-layer chromatography and surface enhanced Raman spectroscopy combined with BP neural network”, PLOS 12 (2017).