Skip to main navigation Skip to search Skip to main content

Physiologically Based Pharmacokinetic Modelling to Predict Pharmacokinetics of Enavogliflozin, a Sodium-Dependent Glucose Transporter 2 Inhibitor, in Humans

  • Min Soo Kim
  • , Yoo Kyung Song
  • , Ji Soo Choi
  • , Hye Young Ji
  • , Eunsuk Yang
  • , Joon Seok Park
  • , Hyung Sik Kim
  • , Min Joo Kim
  • , In Kyung Cho
  • , Suk Jae Chung
  • , Yoon Jee Chae
  • , Kyeong Ryoon Lee
  • Seoul National University
  • Korea Research Institute of Bioscience and Biotechnology
  • Daewoong Pharmaceutical Co., Ltd.
  • Sungkyunkwan University
  • Woosuk University
  • University of Science and Technology UST

Research output: Contribution to journalArticlepeer-review

Abstract

Enavogliflozin is a sodium-dependent glucose cotransporter 2 (SGLT2) inhibitor approved for clinical use in South Korea. As SGLT2 inhibitors are a treatment option for patients with diabetes, enavogliflozin is expected to be prescribed in various populations. Physiologically based pharmacokinetic (PBPK) modelling can rationally predict the concentration–time profiles under altered physiological conditions. In previous studies, one of the metabolites (M1) appeared to have a metabolic ratio between 0.20 and 0.25. In this study, PBPK models for enavogliflozin and M1 were developed using published clinical trial data. The PBPK model for enavogliflozin incorporated a non-linear urinary excretion in a mechanistically arranged kidney model and a non-linear formation of M1 in the liver. The PBPK model was evaluated, and the simulated pharmacokinetic characteristics were in a two-fold range from those of the observations. The pharmacokinetic parameters of enavogliflozin were predicted using the PBPK model under pathophysiological conditions. PBPK models for enavogliflozin and M1 were developed and validated, and they seemed useful for logical prediction.

Original languageEnglish
Article number942
JournalPharmaceutics
Volume15
Issue number3
DOIs
StatePublished - Mar 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • diabetes mellitus
  • DWP16001
  • enavogliflozin
  • GCC5694A
  • in vitro–in vivo extrapolation
  • mechanistic kidney model
  • pharmacokinetics
  • physiologically based pharmacokinetic modelling
  • sodium-glucose cotransporter 2 inhibitor

Fingerprint

Dive into the research topics of 'Physiologically Based Pharmacokinetic Modelling to Predict Pharmacokinetics of Enavogliflozin, a Sodium-Dependent Glucose Transporter 2 Inhibitor, in Humans'. Together they form a unique fingerprint.

Cite this