In vitro–in vivo extrapolation of bexarotene metabolism in the presence of chronic kidney disease and acute kidney injury in rat using physiologically based pharmacokinetic modeling and extrapolation to human

Abstract

Renal impairment can affect the elimination of hepatically metabolized drugs. Bexarotene (BXT) used for cutaneous T-cell lymphoma is highly bound in plasma and metabolized by CYP3A4. The BXT European Medicine Agency and Food and Drug Administration packages recommended the evaluation of renal impairment on BXT metabolism. The plasma protein binding of BXT can be changed in patients with renal dysfunction due to hypoalbuminemia and accumulation of uremic toxins. In vitro, microsomal stability and plasma protein binding studies were pursued. A preclinical pharmacokinetic study was pursued in control, chronic kidney disease (CKD), and acute kidney injury (AKI) rats. A BXT physiologically based pharmacokinetic (PBPK) model that utilized in vitro-in vivo extrapolation of metabolism was established and verified in healthy rats, customized to CKD and AKI rats, and extrapolated to healthy human subjects and those with CKD stages 3, 4, and 5. In vitro studies showed that AKI and CKD significantly increased the BXT fraction unbound in plasma (from 0.011 to 0.018 and 0.022, respectively) and decreased intrinsic clearance (from 4.1 to 2.5, and 2.2 mL/min/g liver, respectively). This could explain the reduced in vivo clearance observed in CKD rats (from 0.4 to 0.28 L/h/kg) and the 1.3-fold increase in BXT exposure. Changes in BXT disposition in AKI rats were not straightforward due to simultaneous changes in BXT distribution. The human PBPK model predicted an increased BXT exposure by 2-fold in CKD patients, suggesting the need for dose reduction and drug monitoring. The reduced BXT metabolism due to renal impairment is especially relevant in cancer patients with CKD. K E Y W O R D S acute kidney injury, bexarotene metabolism, chronic kidney disease, physiologically based pharmacokinetics, uremic toxins 1 | INTRODUCTION Cutaneous T-cell lymphoma (CTCL) is a skin cancer known for the growth of malignant T lymphocytes in the skin that has gained an increased incidence in the past 5 decades (Zhang et al., 2002).Bexarotene (BXT) sold as Targretin® in capsules (Eisai Inc.) shows an auspicious inhibitory effect against CTCL in patients whose cancer is resistant to other CTCL therapy (Wong, 2001;Zhang et al., 2002) in addition to activity against other cancer types ( Shen et al., 2018).

Targeting SEZ6L2 in Colon Cancer: Efficacy of Bexarotene and Implications for Survival

“…The effectiveness of the Bexarotene in reducing drug resistance has been observed through its regulation of RFX1 in embryonic carcinoma cells[51]. Applying physiologically based pharmacokinetic modeling, a previous study extrapolate the metabolism of bexarotene in the context of chronic kidney disease and acute kidney injury in rats[52]. However, the specific mechanisms of Bexarotene in relation to colon cancer are not well-defined, especially concerning whether Bexarotene directly influences colon cancer cells.…”

Physiologically Based Pharmacokinetic (PBPK) Modeling of small molecules: How Much Progress Have We Made?

“…These studies used a variety of simulation platforms/programs. For the renal impairment population three of the studies were conducted using PK-Sim platform (C Wu et al, 2022;Dubinsky et al, 2022;Alsmadi and Alzughoul, 2023), three were conducted using Simcyp population simulator (Miao et al, 2022;Wang and Chan, 2022b;a) and one using Gastroplus . For hepatic impairment populations three of the studies were conducted using Simcyp (Watanabe et al, 2022;X Wu et al, 2022;Ladumor et al, 2023) and one using PK-Sim (L Xu et al, 2022).…”

Linking in vitro–in vivo extrapolations with physiologically based modeling to inform drug and formulation development

“…The separation between system and drug parameters is an important feature of PBPK modeling with respect to extrapolating results between species and populations. In this special issue Alsmadi and Alzughoul integrate preclinical data and a IVIVE‐PBPK model to extrapolate drug elimination in renally impaired patients (Alsmadi & Alzughoul, 2023). In turn, Liang and co‐workers successfully applied PBPK modeling to optimize azithromycin dosing to pediatrics (Liang et al., 2023) and Zhang and co‐workers developed a PBPK‐based dose decision framework to inform clinical trials with a novel anesthetic agent in special populations (Zhang et al., 2023).…”