Risk assessment of drug–drug interactions using hepatocytes suspended in serum during the drug discovery process

Prediction of metabolizing enzyme-mediated clinical drug interactions using in vitro information

Evaluation of drug interactions is an essential step in the new drug development process. Regulatory agencies, including U.S. Food and Drug Administrations and European Medicines Agency, have been published documents containing guidelines to evaluate potential drug interactions. Here, we have streamlined in vitro experiments to assess metabolizing enzyme-mediated drug interactions and provided an overview of the overall process to evaluate potential clinical drug interactions using in vitro data. An experimental approach is presented when an investigational drug (ID) is either a victim or a perpetrator, respectively, and the general procedure to obtain in vitro drug interaction parameters is also described. With the in vitro inhibitory and/or inductive parameters of the ID, basic, static, and/or dynamic models were used to evaluate potential clinical drug interactions. In addition to basic and static models which assume the most conservative conditions, such as the concentration of perpetrators as C_max, dynamic models including physiologically-based pharmacokinetic models take into account changes in in vivo concentrations and metabolizing enzyme levels over time.

Quantitative prediction of the extent of drug-drug interaction using a physiologically based pharmacokinetic model that includes inhibition of drug metabolism determined in cryopreserved hepatocytes

1. A physiologically based pharmacokinetic (PBPK) model that includes inhibition constant evaluated in cryopreserved hepatocytes was used to predict drug-drug interactions (DDIs) between orally administered nifedipine, a CYP substrate, and fluconazole or ketoconazole, CYP inhibitors, in rats. 2. The Kp,uu, ratio of unbound inhibitor concentration in liver ([I]liver,u) to that in plasma ([I]sys,u), of fluconazole and ketoconazole was 1.0 and 13.0, indicating that ketoconazole accumulates in liver. The ratios of inhibition constants in rat liver microsomes (Ki,mic,u) to that in rat cryopreserved hepatocytes (Ki,hep,u) for fluconazole and ketoconazole were 1.5 and 25.5, which were similar to the Kp,uu and suggested that cryopreserved hepatocytes could mimic the hepatic accumulation of inhibitors. 3. The increases in AUC of nifedipine predicted by the minimal PBPK model using [I]liver,u/Ki,mic,u and [I]sys,u/Ki,hep,u were within 1.5-fold of the observed values for both inhibitors, whereas the model using [I]sys,u/Ki,mic,u underestimated the AUC increase caused by ketoconazole 21-fold. 4. These results indicated that hepatic accumulation factor of an inhibitor is required for a precise DDI projection and that cryopreserved hepatocytes would be useful to obtain the Ki including hepatic accumulation factor. It was demonstrated that PBPK model using Ki,hep,u could be a valuable approach for quantitative DDI projection.

Role of Cytochrome P450 2C8 in Drug Metabolism and Interactions

During the last 10-15 years, cytochrome P450 (CYP) 2C8 has emerged as an important drug-metabolizing enzyme. CYP2C8 is highly expressed in human liver and is known to metabolize more than 100 drugs. CYP2C8 substrate drugs include amodiaquine, cerivastatin, dasabuvir, enzalutamide, imatinib, loperamide, montelukast, paclitaxel, pioglitazone, repaglinide, and rosiglitazone, and the number is increasing. Similarly, many drugs have been identified as CYP2C8 inhibitors or inducers. In vivo, already a small dose of gemfibrozil, i.e., 10% of its therapeutic dose, is a strong, irreversible inhibitor of CYP2C8. Interestingly, recent findings indicate that the acyl-β-glucuronides of gemfibrozil and clopidogrel cause metabolism-dependent inactivation of CYP2C8, leading to a strong potential for drug interactions. Also several other glucuronide metabolites interact with CYP2C8 as substrates or inhibitors, suggesting that an interplay between CYP2C8 and glucuronides is common. Lack of fully selective and safe probe substrates, inhibitors, and inducers challenges execution and interpretation of drug-drug interaction studies in humans. Apart from drug-drug interactions, some CYP2C8 genetic variants are associated with altered CYP2C8 activity and exhibit significant interethnic frequency differences. Herein, we review the current knowledge on substrates, inhibitors, inducers, and pharmacogenetics of CYP2C8, as well as its role in clinically relevant drug interactions. In addition, implications for selection of CYP2C8 marker and perpetrator drugs to investigate CYP2C8-mediated drug metabolism and interactions in preclinical and clinical studies are discussed.

Use of HμREL Human Coculture System for Prediction of Intrinsic Clearance and Metabolite Formation for Slowly Metabolized Compounds

Design of slowly metabolized compounds is an important goal in many drug discovery projects. Standard hepatocyte suspension intrinsic clearance (CLint) methods can only provide reliable CLint values above 2.5 μL/min/million cells. A method that permits extended incubation time with maintained performance and metabolic activity of the in vitro system is warranted to allow in vivo clearance predictions and metabolite identification of slowly metabolized drugs. The aim of this study was to evaluate the static HμREL coculture of human hepatocytes with stromal cells to be set up in-house as a standard method for in vivo clearance prediction and metabolite identification of slowly metabolized drugs. Fourteen low CLint compounds were incubated for 3 days, and seven intermediate to high CLint compounds and a cocktail of cytochrome P450 (P450) marker substrates were incubated for 3 h. In vivo clearance was predicted for 20 compounds applying the regression line approach, and HμREL coculture predicted the human intrinsic clearance for 45% of the drugs within 2-fold and 70% of the drugs within 3-fold of the clinical values. CLint values as low as 0.3 μL/min/million hepatocytes were robustly produced, giving 8-fold improved sensitivity of robust low CLint determination, over the cutoff in hepatocyte suspension CLint methods. The CLint values of intermediate to high CLint compounds were at similar levels both in HμREL coculture and in freshly thawed hepatocytes. In the HμREL coculture formation rates for five P450-isoform marker reactions, paracetamol (CYP1A2), 1-OH-bupropion (CYP2B6), 4-OH-diclofenac (CYP2C9), and 1-OH-midazolam (3A4) were within the range of literature values for freshly thawed hepatocytes, whereas 1-OH-bufuralol (CYP2D6) formation rate was lower. Further, both phase I and phase II metabolites were detected and an increased number of metabolites were observed in the HμREL coculture compared to hepatocyte suspension. In conclusion, HμREL coculture can be applied to accurately estimate intrinsic clearance of slowly metabolized drugs and is now utilized as a standard method for in vivo clearance prediction of such compounds in-house.