MDMA (Ecstasy) tablets collected in the UK between 2001 and 2018 show increasing MDMA content over time, with median free-base content exceeding 100 mg for the first time in 2018. Analysis of 412 tablets revealed dramatic within-batch content variability, with differences up to 136 mg. Dissolution testing on 247 tablets showed that tablets can be categorized as fast-, intermediate-, or slow-releasing, but no tablet characteristics predicted dissolution classification, meaning users cannot know a tablet's release profile beforehand. Within-batch variation in dissolution rate was also observed. Rapid assessment of MDMA content alone does not account for variability in remaining tablets in a batch or dissolution profiles. High-content, slow-releasing tablets may cause delayed or prolonged toxicity, increasing risk of re-dosing if absorption is delayed.
A low-cost, field-portable device using Hybridized Spectral Fingerprinting (HSF) and deep learning can rapidly screen illicit drug samples for MDMA dosage and cocaine/ketamine purity. A convolutional neural network trained on 62 GC-EI-MS quantified MDMA tablets classified samples into 1–170 mg or 170–300 mg dosage brackets, achieving 98% accuracy on 195 external test samples. Another model trained on 1H NMR quantified cocaine and ketamine samples provided presumptive identification and semi-quantitative purity estimation with 96% accuracy on 47 external samples. The approach supports harm reduction and police screening by alerting on potentially harmful MDMA tablets and estimating cocaine and ketamine purity.