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

Min-Soo KimYoo-Kyung SongJi-Soo ChoiHye Young JiEunsuk YangJoon Seok ParkHyung-Sik KimMin-Joo KimIn-Kyung ChoSuk-Jae ChungYoon-Jee ChaeKyeong-Ryoon Lee
Published in: Pharmaceutics (2023)
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.
Keyphrases
  • clinical trial
  • ms ms
  • type diabetes
  • adipose tissue
  • metabolic syndrome
  • blood glucose
  • machine learning
  • big data
  • systematic review
  • randomized controlled trial
  • weight loss
  • double blind