Augmentation strategies for clozapine refractory schizophrenia: A systematic review and meta-analysis.

BACKGROUND: Although clozapine is the most effective medication for treatment refractory schizophrenia, only 40% of people will meet response criteria. We therefore undertook a systematic review and meta-analysis of global literature on clozapine augmentation strategies. METHODS: We systematically reviewed PubMed, PsycInfo, Embase, Cochrane Database, Chinese Biomedical Literature Service System and China Knowledge Resource Integrated Database for randomised control trials of augmentation strategies for clozapine resistant schizophrenia. We undertook pairwise meta-analyses of within-class interventions and, where possible, frequentist mixed treatment comparisons to differentiate treatment effectiveness Results: We identified 46 studies of 25 interventions. On pairwise meta-analyses, the most effective augmentation agents for total psychosis symptoms were aripiprazole (standardised mean difference: 0.48; 95% confidence interval: -0.89 to -0.07) fluoxetine (standardised mean difference: 0.73; 95% confidence interval: -0.97 to -0.50) and, sodium valproate (standardised mean difference: 2.36 95% confidence interval: -3.96 to -0.75). Memantine was effective for negative symptoms (standardised mean difference: -0.56 95% confidence interval: -0.93 to -0.20). However, many of these results included poor-quality studies. Single studies of certain antipsychotics (penfluridol), antidepressants (paroxetine, duloxetine), lithium and Ginkgo biloba showed potential, while electroconvulsive therapy was highly promising. Mixed treatment comparisons were only possible for antipsychotics, and these gave similar results to the pairwise meta-analyses. CONCLUSIONS: On the basis of the limited data available, the best evidence is for the use of aripiprazole, fluoxetine and sodium valproate as augmentation agents for total psychosis symptoms and memantine for negative symptoms. However, these conclusions are tempered by generally short follow-up periods and poor study quality.

Plausible computational insights and new atomic-level perspective of epicathechin gallate from Crataegus oxycantha extract in preventing caspase 3 activation in conditions like post-myocardial infarction.

Cardiovascular disease (CVD) is the leading cause of mortality among the human species, however the non-existence of successful therapies to curtail the effect of Myocardial Infarction (MI) is a disquieting reality. Even though successful herbal formulations using Crataegus oxycantha (COC) is available, however, it is not recognized as an alternative medicine due to the lack of explanation on the molecular mechanism of COC extract on CVD conditions. In vivo studies revealed that COC extract significantly prevented caspase activation in conditions like post-MI; however, the role of a specific secondary metabolite that could be involved in this action is under quest. The present study, therefore, aims at predicting the plausible mechanism of action of key secondary metabolite in COC extract on apoptotic executioner caspase - caspase 3 during MI through in silico tools. The protein-protein interaction network, QikProp, and molecular docking studies were performed to identify the lead compound that revealed Epicatechin Gallate (ECG) of COC as an effective inhibitor against candidate MI/apoptosis mediator - caspase 3. The docked complex was further taken for molecular dynamics simulation, which was achieved through Desmond. Molecular dynamics further confirmed the stability of the binding interactions between the docked complex. The overall in silico results proved that ECG could prevent the dissociation of cleaved caspases, which is essential for their activation. Computational observations were strongly supported by experimental evidence obtained from in vivo studies in the MI-model system. From the above observations, it was concluded that computational analysis was in good agreement with the experimental analysis on ECG's potential to prevent caspase 3 activation during MI.