The use of a quantitative structure-activity relationship (QSAR) model to predict GABA-A receptor binding of newly emerging benzodiazepines.

The illicit market for new psychoactive substances is forever expanding. Benzodiazepines and their derivatives are one of a number of groups of these substances and thus far their number has grown year upon year. For both forensic and clinical purposes it is important to be able to rapidly understand these emerging substances. However as a consequence of the illicit nature of these compounds, there is a deficiency in the pharmacological data available for these 'new' benzodiazepines. In order to further understand the pharmacology of 'new' benzodiazepines we utilised a quantitative structure-activity relationship (QSAR) approach. A set of 69 benzodiazepine-based compounds was analysed to develop a QSAR training set with respect to published binding values to GABAA receptors. The QSAR model returned an R2 value of 0.90. The most influential factors were found to be the positioning of two H-bond acceptors, two aromatic rings and a hydrophobic group. A test set of nine random compounds was then selected for internal validation to determine the predictive ability of the model and gave an R2 value of 0.86 when comparing the binding values with their experimental data. The QSAR model was then used to predict the binding for 22 benzodiazepines that are classed as new psychoactive substances. This model will allow rapid prediction of the binding activity of emerging benzodiazepines in a rapid and economic way, compared with lengthy and expensive in vitro/in vivo analysis. This will enable forensic chemists and toxicologists to better understand both recently developed compounds and prediction of substances likely to emerge in the future.

Evaluation of apoptosis imaging biomarkers in a genetic model of cell death.

PURPOSE: We have previously developed the caspase-based radiotracer, 18F-ICMT-11, for PET imaging to monitor treatment response. We further validated 18F-ICMT-11 specificity in a murine melanoma death-switch tumour model with conditional activation of caspase-3 induced by doxycycline. METHODS: Caspase-3/7 activity and cellular uptake of 18F-ICMT-11, 18F-ML-10 and 18F-FDG were assessed in B16ova and B16ovaRevC3 cells after death-switch induction. Death-switch induction was confirmed in vivo in xenograft tumours, and 18F-ICMT-11 and 18F-ML-10 biodistribution was assessed by ex vivo gamma counting of select tissues. PET imaging was performed with 18F-ICMT-11, 18F-ML-10 and 18F-FDG. Caspase-3 activation was confirmed by immunohistochemistry. RESULTS: Significantly increased caspase-3/7 activity was observed only in B16ovaRevC3 cells after death-switch induction, accompanied by significantly increased 18F-ICMT-11 (p < 0.001) and 18F-ML-10 (p < 0.05) and decreased 18F-FDG (p < 0.001) uptake compared with controls. B16ova and B16ovaRevC3 tumours had similar growth in vivo; however, B16ovaRevC3 growth was significantly reduced with death-switch induction (p < 0.01). Biodistribution studies showed significantly increased 18F-ICMT-11 tumour uptake following death-switch induction (p < 0.01), but not for 18F-ML-10. Tumour uptake of 18F-ICMT-11 was higher than that of 18F-ML-10 after death-switch induction. PET imaging studies showed that 18F-ICMT-11 can be used to detect apoptosis after death-switch induction, which was accompanied by significantly increased expression of cleaved caspase-3. 18F-FDG signal decreased in tumours after death-switch induction. CONCLUSIONS: We demonstrate that 18F-ICMT-11 can be used to detect caspase-3 activation in a death-switch tumour model, independent of the confounding effects of cancer therapeutics, thus confirming its specificity and supporting the development of this radiotracer for clinical use to monitor tumour apoptosis and therapy response.