Effects of IL‐1β–Blocking Therapies in Type 2 Diabetes Mellitus: A Quantitative Systems Pharmacology Modeling Approach to Explore Underlying Mechanisms

Abstract

Recent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)–blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin–insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β–blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

“…Palmér et al [17] used the developed QSP model to simulate a 13-week treatment with anakinra versus placebo plus a 39-week follow up period, and compared the simulation results with observed clinical effects [57,58]. The developed model predicted an acute increased beta-cell function with anakinra treatment, and a slower increase in beta-cell function [17]. This work provides a new perspective on disease mechanisms and possible mechanisms of drug action within a QSP approach, where short and long timescales are integrated.…”

“…The data from isolated human beta cells contained information on both disease mechanisms and drug intervention effects. Palmér et al [17] used the developed QSP model to simulate a 13-week treatment with anakinra versus placebo plus a 39-week follow up period, and compared the simulation results with observed clinical effects [57,58]. The developed model predicted an acute increased beta-cell function with anakinra treatment, and a slower increase in beta-cell function [17].…”

“…Nevertheless, there are a few examples where preclinical and clinical data have been integrated into translational systems pharmacology models. An outstanding example is the model developed by Palmér et al [17], whereby a long-term T2D disease progression model [54] was extended with short-term cellular data (figure 3). The data from isolated human beta cells contained information on both disease mechanisms and drug intervention effects.…”

“…The De Gaetano T2D progression model [54] has been used to simulate observations from the diabetes prevention programme [56]. In another approach, Palmér et al [17] used the De Gaetano model to simulate the long-term effects of the drug anakinra, an insulin secretion-stimulating agent, using short-term observations of the drug effect on isolated human beta cells. Simulation results were compared with observed clinical effects of the drug anakinra [57,58] (figure 3).…”

“…A systems pharmacology model translates mechanistic relationships to disease progression and treatment. A featured example of how to integrate different timescales in model development is the model developed in [17]. Therein, short-term cellular data are used to establish relations between (i) the concentration of the inflammatory substance interleukin-1b (IL-1b), (ii) the anti-diabetic drug anakinra which acts as an antagonist at the IL-1 receptor and (iii) replication and apoptosis of beta cells.…”