A Mechanism-based Disease Progression Model for Comparison of Long-term Effects of Pioglitazone, Metformin and Gliclazide on Disease Processes Underlying Type 2 Diabetes Mellitus

Abstract

Effective long-term treatment of Type 2 Diabetes Mellitus (T2DM) implies modification of the disease processes that cause this progressive disorder. This paper proposes a mechanism-based approach to disease progression modeling of T2DM that aims to provide the ability to describe and quantify the effects of treatment on the time-course of the progressive loss of beta-cell function and insulin-sensitivity underlying T2DM. It develops a population pharmacodynamic model that incorporates mechanism-based representations of the homeostatic feedback relationships between fasting levels of plasma glucose (FPG) and fasting serum insulin (FSI), and the physiological feed-forward relationship between FPG and glycosylated hemoglobin A1c (HbA1c). This model was developed on data from two parallel one-year studies comparing the effects of pioglitazone relative to metformin or sulfonylurea treatment in 2,408 treatment-naïve T2DM patients. It was found that the model provided accurate descriptions of the time-courses of FPG and HbA1c for different treatment arms. It allowed the identification of the long-term effects of different treatments on loss of beta-cell function and insulin-sensitivity, independently from their immediate anti-hyperglycemic effects modeled at their specific sites of action. Hence it avoided the confounding of these effects that is inherent in point estimates of beta-cell function and insulin-sensitivity such as the widely used HOMA-%B and HOMA-%S. It was also found that metformin therapy did not result in a reduction in FSI levels in conjunction with reduced FPG levels, as expected for an insulin-sensitizer, whereas pioglitazone therapy did. It is concluded that, although its current implementation leaves room for further improvement, the mechanism-based approach presented here constitutes a promising conceptual advance in the study of T2DM disease progression and disease modification.

Quantitative Model of the Relationship Between Dipeptidyl Peptidase‐4 (DPP‐4) Inhibition and Response: Meta‐Analysis of Alogliptin, Saxagliptin, Sitagliptin, and Vildagliptin Efficacy Results

“…The rate of disease progression has been described as an increase of 0.2% HbA1c per year. 33 Patients recently diagnosed with T2DM are more likely to be naïve to drug therapy and therefore subject to a greater benefit from diet and exercise compared to patients who have progressed in their disease. 34 Unfortunately, information on the average disease duration was not available to further evaluate this hypothesis.…”

Interaction of fast and slow dynamics in endocrine control systems with an application to β-cell dynamics

“…In formulating a mathematical model for the long-term dynamics of the β-cell mass, it is tempting, although incorrect, to represent the glycemic load as a constant that represents the average of the daily cycle (e.g., Topp et al, 2000;De Winter et al, 2006;Ribbing et al, 2010). However, the dynamics of x [1] 7 in slow time τ do in fact depend critically on the "temporal fine structure" of glucose uptake on the fast time scale t, through the integration in equation (12).…”

Nonlinear turnover models for systems with physiological limits

“…We also mention earlier work by Bolie, 1961, Ackerman et al, 1964 and recent work of de Winter et al, 2006, who, in a similar fashion, modify a feedback model in order to accommodate a lower physiological bound in their analysis of glucose-insulin dynamics (For a recent review see Landersdorfer and Jusko, 2008).…”

“…We recall the glucose-insulin system originally proposed by Bolie, 1961 andAckerman et al, 1964 and extended by numerous investigators and more recently applied by de Winter et al, 2006:…”