Illicit PDE-5 inhibitors are frequently encountered by regulatory agencies. Self-medicating with substandard and falsified (SF) PDE-5 inhibitors could be dangerous as they are likely taken without any medical supervision and might be of poor quality which could result in adverse reactions. In order to provide an overview of the quality deficiencies present in recently seized illicit PDE-5 samples that may pose health risks, we set out to identify the products’ different chemical and/or biological risks. Our results indicate that 38% of the samples harbored a chemical risk including the significant exceedance of the maximum recommended dosage, a large heterogeneity in API content between the different tablets in the same package or blister and the presence of only 40% of the claimed dosage. Moreover, our results also demonstrate that 16 of the 32 samples were not compliant with the internationally set microbiological quality standards. Startlingly, two samples were severely contaminated with potentially pathogenic bacteria, which could result in a gastrointestinal illness upon oral intake.

The analysis of heroin samples, before use in the protected environment of user centra, could be a supplementary service in the context of harm reduction. Infrared spectroscopy hyphenated with multivariate calibration could be a valuable asset in this context, and therefore 125 heroin samples were collected directly from users and analysed with classical chromatographic techniques. Further, Mid-Infrared spectra were collected for all samples, to be used in Partial Least Squares (PLS) modelling, in order to obtain qualitative and quantitative models based on real live samples. The approach showed that it was possible to identify and quantify heroin in the samples based on the collected spectral data and PLS modelling. These models were able to identify heroin correctly for 96% of the samples of the external test set with precision, specificity and sensitivity values of 100.0, 75.0 and 95.5%, respectively. For regression, a root mean squared error of prediction (RMSEP) of 0.04 was obtained, pointing at good predictive properties. Furthermore, during mass spectrometric screening, 10 different adulterants and impurities were encountered. Using the spectral data to model the presence of each of these resulted in performant models for seven of them. All models showed promising correct-classification rates (between 92 and 96%) and good values for sensitivity, specificity and precision. For codeine and morphine, the models were not satisfactory, probably due to the low concentration of these impurities as a consequence of acetylation. For methacetin, the approach failed.