AI Article Synopsis

  • The interaction between the metabolic microbiome and humans is crucial for understanding human health and the development of diseases, with an emphasis on metabolite exchange.
  • The study aims to identify beneficial metabolites and disease modulators through targeted analysis of sulfated metabolites in plasma and urine samples.
  • Analysis of 27 individuals revealed a core sulfatome of 41 metabolites common in both plasma and urine, with age-related effects observed in 15 of these metabolites.

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A physiologically-based quantitative systems pharmacology model for mechanistic understanding of the response to alogliptin and its application in patients with renal impairment.

J Pharmacokinet Pharmacodyn

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Department of Clinical Pharmacy and Pharmacy Administration, West China school of Pharmacy, Sichuan University, Chengdu, 610064, China.

Alogliptin is a highly selective inhibitor of dipeptidyl peptidase-4 and primarily excreted as unchanged drug in the urine, and differences in clinical outcomes in renal impairment patients increase the risk of serious adverse reactions. In this study, we developed a comprehensive physiologically-based quantitative systematic pharmacology model of the alogliptin-glucose control system to predict plasma exposure and use glucose as a clinical endpoint to prospectively understand its therapeutic outcomes with varying renal function. Our model incorporates a PBPK model for alogliptin, DPP-4 activity described by receptor occupancy theory, and the crosstalk and feedback loops for GLP-1-GIP-glucagon, insulin, and glucose.

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Biomarker Panels for Discriminating Risk of CKD Progression in Children.

J Am Soc Nephrol

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Department of Pediatrics, Division of Nephrology, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.

Background: We have previously studied biomarkers of tubular health (EGF), injury (KIM-1), dysfunction (alpha-1 microglobulin), and inflammation (TNFR-1, TNFR-2, MCP-1, YKL-40, suPAR), and demonstrated that plasma KIM-1, TNFR-1, TNFR-2 and urine KIM-1, EGF, MCP-1, urine alpha-1 microglobulin are each independently associated with CKD progression in children. In this study, we used bootstrapped survival trees to identify a combination of biomarkers to predict CKD progression in children.

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