Fexuprazan is a new drug candidate in the potassium-competitive acid blocker (P-CAB) family. As proton pump inhibitors (PPIs), P-CABs inhibit gastric acid secretion and can be used to treat gastric acid-related disorders such as gastroesophageal reflux disease (GERD). Physiologically based pharmacokinetic (PBPK) models predict drug interactions as pharmacokinetic profiles in biological matrices can be mechanistically simulated. Here, we propose an optimized and validated PBPK model for fexuprazan by integrating in vitro, in vivo, and in silico data. The extent of fexuprazan tissue distribution in humans was predicted using tissue-to-plasma partition coefficients in rats and the allometric relationships of fexuprazan distribution volumes (V_SS) among preclinical species. Urinary fexuprazan excretion was minimal (0.29–2.02%), and this drug was eliminated primarily by the liver and metabolite formation. The fraction absorbed (Fa) of 0.761, estimated from the PBPK modeling, was consistent with the physicochemical properties of fexuprazan, including its in vitro solubility and permeability. The predicted oral bioavailability of fexuprazan (38.4–38.6%) was within the range of the preclinical datasets. The C_max, AUC_last, and time-concentration profiles predicted by the PBPK model established by the learning set were accurately predicted for the validation sets.

Olaparib is a first-in-class poly (ADP-ribose) polymerase oral inhibitor used to treat various tumors. In this study, we clarified the roles of ABCB1/Abcb1 and ABCG2/Abcg2 transporters in restricting olaparib distribution to the brain. Olaparib was efficiently transported by human ABCG2, human ABCB1, and mouse Abcg2 in vitro. In the in vivo disposition study of olaparib using single or combination knockout mice, the systemic exposure of olaparib did not differ significantly between the strains over an 8-h period. However, the brain-to-plasma unbound concentration ratio of olaparib increased 5.6- and 8.1-fold in Abcb1a/1b and Abcb1a/1b;Abcg2 knockout mice, respectively, compared with wild-type mice. The Abcg2 single knockout mice exhibited a similar brain-to-plasma unbound concentration ratio to wild-type mice. Moreover, the brain distribution of olaparib could be modulated by the ABCB1/ABCG2 dual inhibitor elacridar to reach a similar degree of inhibition to Abcb1a/1b^-/-. These findings suggest that olaparib is actively transported by both human and mouse ABCB1/Abcb1 and ABCG2/Abcg2; while Abcb1a/1b is a major determinant of olaparib brain penetration in mice, Abcg2 is likely to be a minor contributor. Concomitant treatment with temozolomide slightly increased the brain distribution of olaparib in mouse, but the clinical impact of the interaction was expected to be limited.

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.

Warfarin is well-recognized for its high-affinity and capacity-limited binding to the pharmacological target and undergoes target-mediated drug disposition (TMDD). Here, we developed a physiologically-based pharmacokinetic (PBPK) model that incorporated saturable target binding and other reported hepatic disposition components of warfarin. The PBPK model parameters were optimized by fitting to the reported blood PK profiles of warfarin with no stereoisomeric separation following oral dosing of racemic warfarin (0.1, 2, 5, or 10 mg) using the Cluster Gauss-Newton Method (CGNM). The CGNM-based analysis yielded multiple "accepted" sets for six optimized parameters, which were then used to simulate the warfarin blood PK and in vivo target occupancy (TO) profiles. When further analyses examined the impact of dose selection on uncertainty in parameter estimation by the PBPK modeling, the PK data from 0.1 mg dose (well below target saturation) was important in practically identifying the target binding-related parameters in vivo. When stereoselective differences were incorporated for both hepatic disposition and target interactions, our PBPK modeling predicted that R-warfarin (of slower clearance and lower target affinity than S-warfarin) contributes to TO prolongation following oral dosing of racemic warfarin. Our results extend the validity of the approach by which the PBPK-TO modeling of blood PK profiles can yield TO prediction in vivo (applicable to the drugs with targets of high affinity and abundance and limited distribution volume via non-target interactions). Our findings support that model-informed dose selection and PBPK-TO modeling may aid in TO and efficacy assessment in preclinical and clinical phase-1 studies. Significance Statement The current PBPK modeling incorporated the reported hepatic disposition components and target binding of warfarin and analyzed the blood PK profiles from varying warfarin doses, practically identifying target binding-related parameters in vivo. By implementing the stereoselective differences between R- and S-warfarin, our analysis predicted the role of R-warfarin in prolonging overall target occupancy. Our results extend the validity of analyzing blood PK profiles to predict target occupancy in vivo, which may guide efficacy assessment in preclinical and clinical phase-1 studies.