Related Experiment Video

Updated: Sep 19, 2025

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

10.3K

Development and Verification of a Physiologically Based Pharmacokinetic Model of Furmonertinib and Its Main

Yali Wu1,2, Helena Leonie Hanae Loer3, Yifan Zhang1

  • 1Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.

CPT: Pharmacometrics & Systems Pharmacology
|June 17, 2025
PubMed

Insights

Furmonertinib, a new EGFR inhibitor for lung cancer, undergoes auto-induction via CYP3A4 metabolism. A PBPK model accurately predicts its pharmacokinetics and drug-drug interactions, aiding safe co-administration.

Area of Science:

  • Pharmacology
  • Oncology
  • Drug Metabolism

Background:

  • Furmonertinib is an effective tyrosine kinase inhibitor for EGFR-mutated non-small cell lung cancer.
  • Furmonertinib is metabolized by CYP3A4 to an active metabolite, AST5902.
  • Both furmonertinib and AST5902 exhibit CYP3A4 induction properties, leading to nonlinear pharmacokinetics and auto-induction during chronic dosing.

Purpose of the Study:

  • To develop a physiologically based pharmacokinetic (PBPK) model for furmonertinib and its active metabolite AST5902.
  • To characterize the pharmacokinetic (PK) and drug-drug interaction (DDI) profile of furmonertinib.
  • To evaluate the predictive performance of the PBPK model for furmonertinib monotherapy and DDIs.

Main Methods:

  • Utilized in vitro data, clinical PK data, and DDI data from oncology patients and healthy subjects.
  • Constructed a PBPK model using PK-Sim Version 11, incorporating 44 concentration-time profiles.
  • Validated the model using goodness-of-fit plots and statistical evaluation of predicted versus observed values.

Main Results:

  • The PBPK model accurately predicted furmonertinib monotherapy concentration profiles, with 100% of AUC_last and C_max ratios within twofold of observed values.
  • The model successfully predicted DDIs where furmonertinib was a victim of CYP3A4 inhibition or induction, with 100% of AUC_last and C_max ratios within twofold of observed values.
  • Demonstrated the auto-induction phenomenon of furmonertinib and its metabolite AST5902.

Conclusions:

  • The developed PBPK model is suitable for predicting furmonertinib pharmacokinetics and DDIs.
  • The model can be used to predict DDIs with co-administered CYP3A4 perpetrators, accounting for auto-induction.
  • This tool aids in optimizing furmonertinib dosing strategies for improved patient outcomes in EGFR-mutated NSCLC treatment.

More Related Videos

Author Spotlight: Developing a Simple and Robust Hepatic Model for Pharmacological and Toxicological Applications07:23

Author Spotlight: Developing a Simple and Robust Hepatic Model for Pharmacological and Toxicological Applications

Published on: October 20, 2023

1.5K
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

2.2K

Related Concept Videos

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
88
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.2K
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
94
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
155
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
132
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
118