Impact of Intracellular Concentrations on Metabolic Drug-Drug Interaction Studies

Abstract

Accurate prediction of drug-drug interactions (DDI) is a challenging task in drug discovery and development. It requires determination of enzyme inhibition in vitro which is highly system-dependent for many compounds. The aim of this study was to investigate whether the determination of intracellular unbound concentrations in primary human hepatocytes can be used to bridge discrepancies between results obtained using human liver microsomes and hepatocytes. Specifically, we investigated if Kp_uu could reconcile differences in CYP enzyme inhibition values (K_i or IC₅₀). Firstly, our methodology for determination of Kp_uu was optimized for human hepatocytes, using four well-studied reference compounds. Secondly, the methodology was applied to a series of structurally related CYP2C9 inhibitors from a Roche discovery project. Lastly, the Kp_uu values of three commonly used CYP3A4 inhibitors-ketoconazole, itraconazole, and posaconazole-were determined and compared to compound-specific hepatic enrichment factors obtained from physiologically based modeling of clinical DDI studies with these three compounds. Kp_uu obtained in suspended human hepatocytes gave good predictions of system-dependent differences in vitro. The Kp_uu was also in fair agreement with the compound-specific hepatic enrichment factors in DDI models and can therefore be used to improve estimations of enrichment factors in physiologically based pharmacokinetic modeling.

Keywords: drug-drug interaction; intracellular bioavailability; physiologically based pharmacokinetic modeling; scaling factor; unbound drug concentrations.

Fig. 1

a Comparison of f_u,mic in human vs. rat liver microsomes. Rat values are derived from Brown et al. (20). b Comparison of Kp_uu in human vs. rat hepatocytes. c Log fold difference of apparent and corrected (unbound) IC₅₀ determined in human liver microsomes and human hepatocytes or K_i determined in rat microsomes and rat hepatocytes. The dotted lines represent a 2-fold difference. Error bars represent standard deviations. Apparent and corrected K_i or IC₅₀ values are presented in Tables S1 and S2. Enox, E: enoxacin; Clar, C: clarithromycin; Saq, S: saquinavir; Nel, N: nelfinavir

Fig. 2

Kp_uu as correction factor of IC₅₀ in the RO discovery series. a Structures of RO discovery compound. b–e Comparison of IC₅₀ values measured in HLM and HH. Hepatocellular IC_50,app were corrected with Kp_uu (= Kp · f_u,cell) in suspended HH and microsomal IC_50,app values were corrected with f_u,mic to obtain IC_50,corr. The dotted line indicates the line of unity. The labels of the data points correspond to the structure numbers in panel (a)

Fig. 3

Correlation of IVIVE correction factors with Kp_uu. a Overview of correction factors used for the azole antifungal compounds in in vivo DDI studies. b Illustration of hepatic uptake scalars in rat and human DDI models (error bars represent range) vs. in vitro Kp_uu in suspended HH (error bars represent SD)

Fig. 4

Summary of use and interpretation of Kp_uu in the context of CYP-mediated DDI