Population Pharmacokinetics and Pharmacodynamics Modelling of Dilmapimod in Severe Trauma Subjects at Risk for Acute Respiratory Distress Syndrome

Abstract

Introduction

Dilmapimod is a potent p38 mitogen-activated protein kinase (MAPK) inhibitor and was investigated in a study (NCT00996840) for its anti-inflammatory effect in non-head injury trauma patients at risk for developing acute respiratory distress syndrome (ARDS). The purpose of this paper is to present the details of the development of a population pharmacokinetic (PK) model, an empirical population placebo response model, and the exploration of a PK/pharmacodynamic (PD) model of dilmapimod.

Methods

A population PK model was developed to characterise the PK profile of dilmapimod in this patient population; the potential effect of available covariates on the PK of dilmapimod was evaluated. An empirical population placebo response model was conducted, and a population PK/PD model was explored to evaluate the relationship between dilmapimod concentration and C-reactive protein (CRP) (a systemic biomarker of p38 inhibition). All analyses were performed using NONMEM software.

Results

Following intravenous dosing, dilmapimod was quickly distributed to peripheral compartments and then slowly eliminated. The plasma concentration of dilmapimod was adequately described by a three-compartment model. It increased approximately proportionally to the increase in dose. The population clearance (CL) parameter value was 35.87 L/h, and the steady-state volume of distribution (Vss) [sum of the volume of distribution of the central compartment (Vc) and of the peripheral compartments V2 and V3] was 160 L. The effect of body mass index (BMI) on CL and inter-compartment clearance (Q2) was found statistically significant, with an increase in BMI of 1 kg/m ² resulting in a 1.79 L/h and 0.52 L/h increase in CL and Q2, respectively. The CRP profile post injury was adequately described by an indirect response model, with a sharp increase in the CRP levels following injury, followed by them slowly diminishing. Data exploration indicated potential drug effects of dilmapimod on inhibiting the production of CRP levels; however, the current small dataset did not show a statistically significant improvement in the PK/PD modelling.

Conclusion

The population PK modelling adequately evaluated the dilmapimod plasma concentration–time profiles in severe trauma subjects at risk for ARDS, and BMI was found to be a significant covariate in the PK model. An indirect response model was adequate to describe the production and degradation of CRP levels in these subjects.