Population pharmacokinetic modelling of febuxostat in healthy subjects and people with gout

Population pharmacokinetic analysis of febuxostat with high focus on absorption kinetics and food effect

Febuxostat is commonly used in clinic for the treatment of hyperuricemia. Multiple-peak phenomenon has been observed in human plasma concentration-time profiles of febuxostat, but has not been paid enough attention in previous research. This study takes a pivotal step forward by conducting a comprehensive population pharmacokinetic (PopPK) analysis of febuxostat in a healthy Chinese cohort, with a central focus on delineating its absorption profile under contrasting fasting and fed conditions, while concurrently assessing the influence of food alongside other potential covariates on febuxostat's PK profile. The plasma concentration data used for modeling was obtained from two bioequivalence (BE) studies. Subjects were administered febuxostat 20 mg or 80 mg under fasting or fed condition. Goodness-of-fit plots, visual predict check (VPC), and normalized prediction distribution error (NPDE) were used for model evaluation. Based on the established model, PK profiles in healthy Caucasian subjects were simulated with parameter adjustment for race difference on clearance and bioavailability. Data from 128 subjects were used in the PopPK analysis. Febuxostat concentration-time curves were described by a two-compartment model with two deposit absorption compartments and lag times (Tlag). Prandial states (Food) showed significant impact on absorption rate ka1 and ka2, as well as Tlag1, and body weight was identified as a significant covariate on the apparent distribution volume. The PopPK analysis of febuxostat in healthy Chinese volunteers, under both fasted and fed conditions, successfully characterized its PK profile and underscored the significant influence of food on absorption. The potential difference of absorption between Chinese population and Caucasian population indicated from the simulations needs further investigation.

Comprehensive Evaluation of OATP- and BCRP-Mediated Drug-Drug Interactions of Methotrexate Using Physiologically-Based Pharmacokinetic Modeling

Methotrexate (MTX) is an antifolate agent widely used for treating conditions such as rheumatoid arthritis and hematologic cancer. This study aimed to quantitatively interpret the drug-drug interactions (DDIs) of MTX mediated by drug transporters using physiologically-based pharmacokinetic (PBPK) modeling. An open-label, randomized, 4-treatment, 6-sequence, 4-period crossover study was conducted to investigate the effects of rifampicin (RFP), an inhibitor of organic anionic transporting peptides (OATP) 1B1/3, and febuxostat (FBX), an inhibitor of breast cancer resistance protein (BCRP), on the pharmacokinetics of MTX in healthy volunteers. PBPK models of MTX, RFP, and FBX were developed based on in vitro and in vivo data, and the performance of the simulation results for final PBPK models was validated in a clinical study. In the clinical study, when MTX was co-administered with RFP or FBX, systemic exposure of MTX increased by 33% and 17%, respectively, compared with that when MTX was administered alone. When MTX was co-administered with RFP and FBX, systemic exposure increased by 52% compared with that when MTX was administered alone. The final PBPK model showed a good prediction performance for the observed clinical data. The PBPK model of MTX was well developed in this study and can be used as a potential mechanistic model to predict and evaluate drug transporter-mediated DDIs of MTX with other drugs.

Quantitative modeling of tumor dynamics and development of drug resistance in non-small cell lung cancer patients treated with erlotinib

Insight into the development of treatment resistance can support the optimization of anticancer treatments. This study aims to characterize the tumor dynamics and development of drug resistance in patients with non-small cell lung cancer treated with erlotinib, and investigate the relationship between baseline circulating tumor DNA (ctDNA) data and tumor dynamics. Data obtained for the analysis included (1) intensively sampled erlotinib concentrations from 29 patients from two previous pharmacokinetic (PK) studies, and (2) tumor sizes, ctDNA measurements, and sparsely sampled erlotinib concentrations from 18 patients from the START-TKI study. A two-compartment population PK model was first developed which well-described the PK data. The PK model was subsequently applied to investigate the exposure-tumor dynamics relationship. To characterize the tumor dynamics, models accounting for intra-tumor heterogeneity and acquired resistance with or without primary resistance were investigated. Eventually, the model assumed acquired resistance only resulted in an adequate fit. Additionally, models with or without exposure-dependent treatment effect were explored, and no significant exposure-response relationship for erlotinib was identified within the observed exposure range. Subsequently, the correlation of baseline ctDNA data on EGFR and TP53 variants with tumor dynamics' parameters was explored. The analysis indicated that higher baseline plasma EGFR mutation levels correlated with increased tumor growth rates, and the inclusion of ctDNA measurements improved model fit. This result suggests that quantitative ctDNA measurements at baseline have the potential to be a predictor of anticancer treatment response. The developed model can potentially be applied to design optimal treatment regimens that better overcome resistance.