Prediction of the Pharmacokinetics, Pharmacodynamics, and Efficacy of a Monoclonal Antibody, Using a Physiologically Based Pharmacokinetic FcRn Model.

Manoranjenni Chetty,Krishna Machavaram,Amin Rostami-Hodjegan,Iain Gardner,Rachel Rose,Masoud Jamei,Linzhong Li

doi:10.3389/fimmu.2014.00670

Manoranjenni Chetty, Krishna Machavaram + Show 5 more

Open Access

https://doi.org/10.3389/fimmu.2014.00670

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Abstract

Although advantages of physiologically based pharmacokinetic models (PBPK) are now well established, PBPK models that are linked to pharmacodynamic (PD) models to predict pharmacokinetics (PK), PD, and efficacy of monoclonal antibodies (mAbs) in humans are uncommon. The aim of this study was to develop a PD model that could be linked to a physiologically based mechanistic FcRn model to predict PK, PD, and efficacy of efalizumab. The mechanistic FcRn model for mAbs with target-mediated drug disposition within the Simcyp population-based simulator was used to simulate the pharmacokinetic profiles for three different single doses and two multiple doses of efalizumab administered to virtual Caucasian healthy volunteers. The elimination of efalizumab was modeled with both a target-mediated component (specific) and catabolism in the endosome (non-specific). This model accounted for the binding between neonatal Fc receptor (FcRn) and efalizumab (protective against elimination) and for changes in CD11a target concentration. An integrated response model was then developed to predict the changes in mean Psoriasis Area and Severity Index (PASI) scores that were measured in a clinical study as an efficacy marker for efalizumab treatment. PASI scores were approximated as continuous and following a first-order asymptotic progression model. The reported steady state asymptote (Y ss) and baseline score [Y (0)] was applied and parameter estimation was used to determine the half-life of progression (Tp) of psoriasis. Results suggested that simulations using this model were able to recover the changes in PASI scores (indicating efficacy) observed during clinical studies. Simulations of both single dose and multiple doses of efalizumab concentration-time profiles as well as suppression of CD11a concentrations recovered clinical data reasonably well. It can be concluded that the developed PBPK FcRn model linked to a PD model adequately predicted PK, PD, and efficacy of efalizumab.

Highlights

Binding to the neonatal Fc receptor (FcRn) as well as therapeutic targets in vivo have a significant influence on the disposition of a monoclonal antibody
A Physiologically based pharmacokinetic (PBPK) model linked to a pharmacodynamics model (PD) will offer the advantage of predicting both the PK variability and the response to a monoclonal antibodies (mAbs)
PBPK LINKED PD MODEL A fitted the half-life of progression (T p) parameter value of 397 h was obtained using parameter estimation module of the Simulator and the clinical data by Gottlieb et al [15] Visual predictive checks suggested that the resulting PBPK/PD model was reasonably successful at recovering the changes in Psoriasis Area and Severity Index (PASI) scores over time as observed by Gottlieb et al [15] using escalating dosage (Figure 4)

Summary

Introduction

Binding to the neonatal Fc receptor (FcRn) as well as therapeutic targets in vivo have a significant influence on the disposition of a monoclonal antibody (mAb). Based pharmacokinetic (PBPK) models describing some of these processes in the disposition of mAbs in pre-clinical species and humans, have recently been published [1,2,3,4,5,6]. PBPK models are mechanistic and are regarded as a more realistic representation of drug disposition in vivo. The ultimate interest in mAbs is their therapeutic potential and the majority of current PBPK models have not advanced to include prediction of response to mAbs. A PBPK model linked to a pharmacodynamics model (PD) will offer the advantage of predicting both the PK variability and the response to a mAb. In addition, input to the PD model can be done from a tissue interstitial compartment and not just from plasma, which is important when modeling membrane bound receptors

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