Translational Modeling and Simulation in Supporting Early-Phase Clinical Development of New Drug: A Learn–Research–Confirm Process

Abstract

Background and Objective

Pharmacokinetic/pharmacodynamic modeling and simulation can aid clinical drug development by dynamically integrating key system- and drug-specific information into predictive profiles. In this study, we propose a methodology to predict pharmacokinetic/pharmacodynamic profiles of sinogliatin (HMS-5552, RO-5305552), a novel glucokinase activator to treat diabetes mellitus, for first-in-patient (FIP) studies.

Methods and Results

Initially, pharmacokinetic/pharmacodynamic profiles of sinogliatin and another glucokinase activator (US2) previously acquired from healthy subjects were fitted using Model A incorporating an indirect response mechanism. The pharmacokinetic/pharmacodynamic profiles of US2 in patients with type 2 diabetes mellitus (T2DM) were then fitted using Model B incorporating circadian rhythm and food effects after thoughtful research on the difference between healthy subjects and T2DM patients. The differences in results between the two US2 modeling populations were used to scale the values of the pharmacodynamic parameters and refine the pharmacodynamic model of sinogliatin, which was then utilized to project pharmacokinetic/pharmacodynamic profiles of sinogliatin in T2DM patients after an 8-day simulated treatment. Results showed that the projected pharmacokinetic/pharmacodynamic values of five parameters were within 70–130% of values fitted from observed clinical data while the other two remaining projected parameters were within a twofold error. Population pharmacokinetic/pharmacodynamic analysis conducted for sinogliatin also suggested that age and sex were significantly correlated to pharmacokinetic/pharmacodynamic characteristics. Additionally, Model B was combined with a glycosylated hemoglobin (HbA_1c) compartment to form Model C, which was then used to project serum HbA_1c levels in patients after a 1-month simulated treatment of sinogliatin. The predicted HbA_1c changes were nearly identical to observed clinical values (0.82 vs. 0.78%).

Conclusions

Model-based drug development methods utilizing a learn–research–confirm cycle may accurately project pharmacokinetic/pharmacodynamic profiles of new drugs in FIP studies.

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