Prediction of Drug-Drug Interactions From in Vitro Induction Data

Abstract

ABSTRACT:Cytochrome P450 induction-mediated drug-drug interaction (DDI) is one of the major concerns in clinical practice and for the pharmaceutical industry. Previously, a novel approach [the relative induction score (RIS)] was developed using the Fa2N-4 immortalized human hepatocyte line and proposed as a tool for predicting magnitude of clinical DDIs caused by induction of CYP3A. The approach is based on combining in vitro induction parameters (EC 50 and E max ) with the efficacious free plasma concentrations to calculate a relative induction score, which is correlated to the magnitude of clinical DDI for midazolam or ethinyl estradiol. To expand the applicability of the RIS model, we have measured induction caused by ten drugs in two different lots of human cryopreserved hepatocytes and correlated the data to clinical DDIs using the RIS. The results demonstrated that, as with Fa2N-4 hepatocytes, sigmoidal relationships can be derived between RIS and magnitude of induction of midazolam and ethinyl estradiol clearance in cryopreserved human hepatocytes. This study demonstrates the general applicability of the relative induction score approach using the human cryopreserved hepatocyte model to predict clinical DDI.The induction of cytochrome CYP3A4 can have important clinical significance. CYP3A4 gene expression is inducible by numerous xenobiotics, resulting in altered drug metabolism and drug-drug interactions in addition to enhanced metabolism of endogenous substrates including cortisol. CYP3A4 is predominantly expressed in the liver and the small intestine and is known to metabolize the majority of drugs whose biotransformation is known (Guengerich, 1996;Rendic and Di Carlo, 1997). Induction of CYP3A activity in the clinic can result in therapeutic failure such as tissue rejection in transplant patients caused by increased clearance of cyclosporine or unwanted pregnancy caused by increased clearance of oral contraceptive agents, among others (Mannel, 2004;Huwyler et al., 2006). Therefore, different models to study CYP3A4 induction have been developed to allow the selection and development of safer compounds and aid in the design of more efficient clinical trials.During the past several years, important advances have been made in our understanding of the mechanisms that regulate the expression of genes that determine drug clearance, including drug-metabolizing enzymes and drug transporters. Nuclear receptors, such as the pregnane X receptor (PXR) and constitutive androstane receptor (CAR), have been recognized as key mediators of drug-induced changes in the expression of CYP2B, CYP2C, and CYP3A, as well as of phase II enzymes and transporters. Moreover, it has been documented that hCAR and hPXR are both responsible for coordinating the regulation of a large number of drug metabolizing enzymes and drug transporter genes (Faucette et al., 2006;Konno et al., 2008).Considerable effort has been expended in an attempt to predict the magnitude of in vivo metabolic drug-drug interactions using in vitro data. A number...

An Automated Multiplexed Hepatotoxicity and CYP Induction Assay Using HepaRG Cells in 2D and 3D

“…Additional analysis could include assessing compound CYP induction potential, for example, the relative induction score (RIS) where EC 50 and E max values are utilized, although these methods often require clinical data (e.g., plasma concentration of inducer corrected for protein binding), which may not be available for all compounds. 31 Thus, we have shown that CYP-inducing compounds can be predicted using our 3D model in an automated multiplexed assay system, although more optimization and validation may be required to improve sensitivity.…”

Predictive Utility of In Vitro Rifampin Induction Data Generated in Fresh and Cryopreserved Human Hepatocytes, Fa2N-4, and HepaRG Cells

“…Recently, a number of in vitro systems have been used to predict the magnitude of rifampin induction in vivo (Fahmi et al, 2008b;Shou et al, 2008;Galetin et al, 2010). Whereas hepatocytes are considered the "gold standard," both HepaRG and Fa2N-4 have been proposed as appropriate surrogates for assessment of CYP3A4 induction (Fahmi et al, 2008a;Hariparsad et al, 2008;McGinnity et al, 2009). However, in vitro induction data vary greatly between in vitro systems ( Figs.…”

“…The concentration-dependent change in either CYP3A4 expression (mRNA or protein) or function (enzyme-specific substrate metabolism) can be modeled to estimate potency (EC 50 ) and magnitude (E max ) of induction in vitro. Primary and cryopreserved human hepatocytes and the immortalized liver cell lines Fa2N-4 and HepaRG are four in vitro systems commonly used to estimate EC 50 and E max (Fahmi et al, 2008a;Hariparsad et al, 2008;McGinnity et al, 2009). The data from these in vitro systems have been shown to be predictive of in vivo drug metabolism; however, each system has its own characteristics that may influence the induction parameter obtained (Vermeir et al, 2005).…”

“…Rifampin EC 50 has been assumed to be similar in vitro and in vivo; however, rifampin E max may be affected by mechanisms not present in vitro. Because in vivo estimates of this parameter are based on very limited data, attempts have been made to optimize in vitro E max to in vivo by fitting a prediction model to in vitro and in vivo induction data (Fahmi et al, 2008a). In practice, in vitro EC 50 and, in particular, E max estimates vary greatly between in vitro systems (Hariparsad et al, 2008;Martin et al, 2008;Shou et al, 2008), and this variability in rifampin in vitro parameters represents a major challenge to the utility of rifampin as a calibrator for novel inducers.…”

Evaluation of Normalization Methods To Predict CYP3A4 Induction in Six Fully Characterized Cryopreserved Human Hepatocyte Preparations and HepaRG Cells

“…Predictive mathematical models incorporating either induction alone or induction in combination with inhibition mechanisms have been applied by a number of authors (Fahmi et al, 2008b;Shou et al, 2008;Fahmi and Ripp, 2010;Kirby et al, 2011;Templeton et al 2011). Dynamic models based on an inducer concentration-time profile to account for the change in enzyme expression have also been proposed (Almond et al, 2009;Fahmi et al, 2009).…”