Application of population physiologically based pharmacokinetic modelling to optimize target expression and clearance mechanisms of therapeutic monoclonal antibodies

Author:

Reig‐Lopez Javier12,Tang Weifeng3,Fernandez‐Teruel Carlos2,Merino‐Sanjuan Matilde14,Mangas‐Sanjuan Victor14ORCID,Boulton David W.3,Sharma Pradeep2ORCID

Affiliation:

1. Pharmacy and Pharmaceutical Technology and Parasitology Department, Faculty of Pharmacy University of Valencia Valencia Spain

2. Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca Cambridge UK

3. Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca Gaithersburg Maryland USA

4. Interuniversity Research Institute for Molecular Recognition and Technological Development Polytechnic University of Valencia‐University of Valencia Valencia Spain

Abstract

AimsTo use population physiologically based pharmacokinetic (PopPBPK) modelling to optimize target expression, kinetics and clearance of HER1/2 directed therapeutic monoclonal antibodies (mAbs). Thus, to propose a general workflow of PopPBPK modelling and its application in clinical pharmacology.MethodsFull PBPK model of pertuzumab (PTZ) was developed in patient population using Simcyp V21R1 incorporating mechanistic targeted‐mediated drug disposition process by fitting known clinical PK and sparse receptor proteomics data to optimize target expression and kinetics of HER2 receptor. Trastuzumab (TTZ) PBPK modelling was used to validate the optimized HER2 target. Additionally, the simulator was also used to develop a full PBPK model for the HER1‐directed mAb cetuximab (CTX) to assess the underlying targeted‐mediated drug disposition‐independent elimination mechanisms.ResultsHER2 final parameterisation coming from the PBPK modelling of PTZ was successfully cross validated through PBPK modelling of TTZ with average fold error (AFE), absolute AFE and percent prediction error values for area under the concentration–time curve (AUC) and maximum plasma concentration (Cmax) of 1.13, 1.16 and 16, and 1.01, 1.07 and 7, respectively. CTX PBPK model performance was validated after the incorporation of an additional systemic clearance of 0.033 L/h as AFE and absolute AFE showed an acceptable predictive power of AUC and Cmax with percent prediction error of 13% for AUC and 10% for Cmax.ConclusionsOptimisation of both system and drug related parameters were performed through PBPK modelling to improve model performance of therapeutic mAbs (PTZ, TTZ and CTX). General workflow was proposed to develop and apply PopPBPK to support clinical development of mAbs targeting same receptor.

Funder

AstraZeneca

Publisher

Wiley

Subject

Pharmacology (medical),Pharmacology

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