Deep Learning Methods Applied to Drug Concentration Prediction of Olanzapine.

Pharmacometrics and the utilization of population pharmacokinetics play an integral role in model-informed drug discovery and development (MIDD). Recently, there has been a growth in the application of deep learning approaches to aid in areas within MIDD. In this study, a deep learning model, LSTM-ANN, was developed to predict olanzapine drug concentrations from the CATIE study.

Development of a Plasma Screening Panel for Pediatric Nonalcoholic Fatty Liver Disease Using Metabolomics.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in children, but diagnosis is challenging due to limited availability of noninvasive biomarkers. Machine learning applied to high-resolution metabolomics and clinical phenotype data offers a novel framework for developing a NAFLD screening panel in youth. Here, untargeted metabolomics by liquid chromatography-mass spectrometry was performed on plasma samples from a combined cross-sectional sample of children and adolescents ages 2-25 years old with NAFLD (n = 222) and without NAFLD (n = 337), confirmed by liver biopsy or magnetic resonance imaging.

The cannabinoid receptor 2 agonist, β-caryophyllene, improves working memory and reduces circulating levels of specific proinflammatory cytokines in aged male mice.

Age-related cognitive decline has been associated with proinflammatory cytokines, yet the precise relationship between cognitive decline and cytokine load remains to be elucidated. β-caryophyllene (BCP) is a cannabinoid receptor 2 (CB) agonist with established anti-inflammatory effects that is known to improve memory and increase lifespan. It is of interest to explore the potential of BCP to reduce age-related cognitive decline and proinflammatory cytokine load.