Meta-Modeling of Methylprednisolone Effects on Glucose Regulation in Rats

Abstract

A retrospective meta-modeling analysis was performed to integrate previously reported data of glucocorticoid (GC) effects on glucose regulation following a single intramuscular dose (50 mg/kg), single intravenous doses (10, 50 mg/kg), and intravenous infusions (0.1, 0.2, 0.3 and 0.4 mg/kg/h) of methylprednisolone (MPL) in normal and adrenalectomized (ADX) male Wistar rats. A mechanistic pharmacodynamic (PD) model was developed based on the receptor/gene/protein-mediated GC effects on glucose regulation. Three major target organs (liver, white adipose tissue and skeletal muscle) together with some selected intermediate controlling factors were designated as important regulators involved in the pathogenesis of GC-induced glucose dysregulation. Assessed were dynamic changes of food intake and systemic factors (plasma glucose, insulin, free fatty acids (FFA) and leptin) and tissue-specific biomarkers (cAMP, phosphoenolpyruvate carboxykinase (PEPCK) mRNA and enzyme activity, leptin mRNA, interleukin 6 receptor type 1 (IL6R1) mRNA and Insulin receptor substrate-1 (IRS-1) mRNA) after acute and chronic dosing with MPL along with the GC receptor (GR) dynamics in each target organ. Upon binding to GR in liver, MPL dosing caused increased glucose production by stimulating hepatic cAMP and PEPCK activity. In adipose tissue, the rise in leptin mRNA and plasma leptin caused reduction of food intake, the exogenous source of glucose input. Down-regulation of IRS-1 mRNA expression in skeletal muscle inhibited the stimulatory effect of insulin on glucose utilization further contributing to hyperglycemia. The nuclear drug-receptor complex served as the driving force for stimulation or inhibition of downstream target gene expression within different tissues. Incorporating information such as receptor dynamics, as well as the gene and protein induction, allowed us to describe the receptor-mediated effects of MPL on glucose regulation in each important tissue. This advanced mechanistic model provides unique insights into the contributions of major tissues and quantitative hypotheses for the multi-factor control of a complex metabolic system.

Receptor/gene/protein-mediated signaling connects methylprednisolone exposure to metabolic and immune-related pharmacodynamic actions in liver

“…Blood glucose homeostasis is sustained mainly through the balance between hepatic glucose output (HGO) and glucose uptake primarily by brain, muscle and adipose tissue [67]. Liver is the major site for gluconeogenesis and also a primary metabolic target of CS.…”

“…The current model is limited in that the roles of skeletal muscle and adipose tissue in the development of insulin resistance, which also contribute to increases blood glucose were not accounted for. While a multi-organ model of glucose regulation by transcriptomic CS actions has been reported [67], our goal in this analysis was to link protein-level measures to PD outcomes. While the present model could adequately predict glucose and insulin dynamics from a different acute dosing study (Fig.…”

The Effect of Methylprednisolone, Interferon Beta and Glatiramer Acetate Treatment on the Levels of Leptin, Adiponectin and Resistin in Multiple Sclerosis Patients

“…Routine treatment of MS relapse with methylprednisolone induces an unfavorable effect leading to the increase of leptin concentration. Experimental studies showed similar effects of methylprednisolone infusion on plasma leptin concentration [32] with subsequent metabolic effects in adipose tissue and skeletal muscles leading to limitation of insulin-mediated glucose utilization. In another experimental study, the peak plasma leptin concentration appeared 12 hours after methylprednisolone infusion [33].…”

On the analysis of complex biological supply chains: From process systems engineering to quantitative systems pharmacology

“…As the complexity of a pharmacodynamic model increases and becomes mechanistically-based, many parallels can be drawn from the methodology and concepts associated with systems biology models (Danhof et al, 2008). In that context, the examples of indirect effect, transduction and transit models, tolerance models, and feedback mechanisms (Fang et al, 2013; Hazra et al, 2007; Jin et al, 2003; Jin & Jusko, 2009; Sukumaran et al, 2011) adapt basic elements of reaction kinetics towards revealing the exposure-response relationship of a drug.…”