Understanding Interindividual Variability in the Drug Interaction of a Highly Extracted CYP1A2 Substrate Tizanidine: Application of a Permeability-Limited Multicompartment Liver Model in a Population Based Physiologically Based Pharmacokinetic Framework

Abstract

Tizanidine, a centrally acting skeletal muscle relaxant, is predominantly metabolized by CYP1A2 and undergoes extensive hepatic first-pass metabolism after oral administration. As a highly extracted drug, the systemic exposure to tizanidine exhibits considerable interindividual variability and is altered substantially when coadministered with CYP1A2 inhibitors or inducers. The aim of the current study was to compare the performance of a permeabilitylimited multicompartment liver (PerMCL) model, which operates as an approximation of the dispersion model, and the well stirred model (WSM) for predicting tizanidine drug-drug interactions (DDIs). Physiologically based pharmacokinetic models were developed for tizanidine, incorporating the PerMCL model and the WSM, respectively, to simulate the interaction of tizanidine with a range of CYP1A2 inhibitors and inducers. Whereas the WSM showed a tendency to underpredict the fold change of tizanidine area under the plasma concentration-time curve (AUC ratio) in the presence of perpetrators, the use of PerMCL model increased precision (absolute average-fold error: 1.32-1.42 versus 1.58) and decreased bias (average-fold error: 0.97-1.25 versus 0.63) for the predictions of mean AUC ratios as compared with the WSM. The PerMCL model captured the observed range of individual AUC ratios of tizanidine as well as the correlation between individual AUC ratios and CYP1A2 activities without interactions, whereas the WSM was not able to capture these. The results demonstrate the advantage of using the PerMCL model over the WSM in predicting the magnitude and interindividual variability of DDIs for a highly extracted sensitive substrate tizanidine. SIGNIFICANCE STATEMENTThis study demonstrates the advantages of the PerMCL model, which operates as an approximation of the dispersion model, in mitigating the tendency of the WSM to underpredict the magnitude and variability of DDIs of a highly extracted CYP1A2 substrate tizanidine when it is administered with CYP1A2 inhibitors or inducers. The physiologically based pharmacokinetic modeling approach described herein is valuable to the understanding of drug interactions of highly extracted substrates and the source of its interindividual variability. ABBREVIATIONS: AAFE, absolute average-fold error; AFE, average-fold error; AUC, area under the plasma concentration-time curve; CL int , intrinsic clearance; CL int, H , hepatic intrinsic clearance; CL PD , passive diffusion clearance; CL PD, in vitro , passive diffusion clearance measured in vitro; CL PO , oral clearance; DDI, drug-drug interaction; DM, dispersion model; D N , dispersion number; E H , hepatic extraction ratio; EW, extracellular water; fm, fraction of the systemic clearance of a drug mediated by a given enzyme; IW, intracellular water; PBPK, physiologically based pharmacokinetic; PerMCL, permeability-limited multicompartment liver; PK, pharmacokinetics; WSM, well stirred model.

Utility of Minimal Physiologically Based Pharmacokinetic Models for Assessing Fractional Distribution, Oral Absorption, and Series-Compartment Models of Hepatic Clearance

“…The series-compartment model (SCM) that treats the liver as a cascade of identical well-stirred segments connected by hepatic blood flow (Q h ) reasonably approximates the DM (Roberts and Rowland, 1986a;Gray and Tam, 1987;Anissimov and Roberts, 2002). Subsequently, variants of the SCM were applied to mimic the DM for characterizing the hepatic disposition of transporter substrates (Watanabe et al, 2009;Jones et al, 2012;Li et al, 2014;Morse et al, 2017) and predicting the clinical drug-drug interactions (DDIs) involving hepatic transporters/enzymes (Asaumi et al, 2019;Zhang et al, 2022). The quantitative correlation of the SCM and DM was not elucidated until our recent work (Li and Jusko, 2023), where we demonstrated how the number of liver sub-compartments (n) in the SCM correlated with the dispersion number (D N ) of the DM that describes the relative spread of solute on passage through the liver.…”