Decreased potency of fimasartan in liver cirrhosis was quantified using mixed-effects analysis

Kim, Choon Ok; Jeon, Sangil; Han, Seunghoon; Hong, Tae Kyung; Park, Min Soo; Yoon, Young‐Ran

doi:10.12793/tcp.2017.25.1.43

Cited by 1 publication

(1 citation statement)

References 17 publications

(31 reference statements)

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“…The PBPK model developed in this study can be used to extend dosing regimens and predict drug–drug interactions and population-related alterations in the pharmacokinetic profiles of enavogliflozin in humans. For example, some marketed drugs require their dosing regimen to be adjusted or are not recommended for patients with hepatic or renal impairment [ 12 , 41 , 42 , 43 ]. Additionally, researchers involved in new drug development may need to predict the expected exposure or concentration profiles of the agent before clinical observation.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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