Abstract
Microphysiological systems (MPS) are complex and more physiologically realistic cellular in vitro tools that aim to provide more relevant human in vitro data for quantitative prediction of clinical pharmacokinetics while also reducing the need for animal testing. The PhysioMimix liver-on-a-chip integrates medium flow with hepatocyte culture and has the potential to be adopted for in vitro studies investigating the hepatic disposition characteristics of drug candidates. The current study focusses on liver-on-a-chip system exploration for multiple drug metabolism applications. Characterization of cytochrome P450 (CYP), UDP-glucuronosyl transferase (UGT) and aldehyde oxidase (AO) activities was performed using 15 drugs and in vitro to in vivo extrapolation (IVIVE) was assessed for 12 of them. Next, the utility of the liver-on-a-chip for estimation of the fraction metabolized (fm) via specific biotransformation pathways of quinidine and diclofenac was established. Finally, the metabolite identification opportunities were also explored using efavirenz as an example drug with complex primary and secondary metabolism involving a combination of CYP, UGT and sulfotransferase enzymes. A key aspect of these investigations was the application of mathematical modelling for improved parameter calculation. Such approaches will be required for quantitative assessment of metabolism and/or transporter processes in systems where medium flow and system compartments result in non-homogeneous drug concentrations. In particular, modelling was used to explore the effect of evaporation from the medium and it was found that the intrinsic clearance (CLint) might be underestimated by up to 40% for low clearance compounds if evaporation is not accounted for. Modelling of liver-on-a-chip in vitro data also enhanced the approach to fm estimation allowing objective assessment of metabolism models of different complexity. The resultant diclofenac fm,UGT of 0.64 was highly comparable with values reported previously in the literature. The current study demonstrates the integration of mathematical modelling with experimental liver-on-a-chip studies and illustrates how this approach supports generation of high quality of data from complex in vitro cellular systems.
Highlights
High quality estimates of pharmacokinetic (PK) parameters are essential during drug discovery for candidate selection and human dose predictions
In vitro drug metabolism studies mainly explore the biotransformation and transport of drugs by the cells
Comparison of the number of hepatocytes measured in the control wells at day 2 with the number initially seeded at day – 4 showed an average seeding efficiency of 52.0 ± 7.1% (CV: 14%) with values ranging from 39.0 to 66.5% (Fig. 2B)
Summary
High quality estimates of pharmacokinetic (PK) parameters are essential during drug discovery for candidate selection and human dose predictions. One limitation of hepatocytes cultured in a 2D format is the rapid loss of enzyme activity during the experiment which hinders accurate prediction of metabolic clearance, especially for metabolically stable drugs.[1,2] Additional limitations include the assessment of other ADMErelated drug properties such as biliary and sinusoidal efflux, accurate in vitro to in vivo scaling of active uptake clearance for substrates of drug transporters, prediction of in vivo relevant metabolites for metabolically stable drugs, and the reliable prediction of time-dependent inhibition and induction.[2,3,4,5] Microphysiological systems (MPS) hold promise to address more complex in vitro ADME, toxicology and pharmacology questions in a more physiologically relevant manner.[6,7] The PhysioMimix liver-on-a-chip is a microfluidic device that enables continuous perfusion of the hepatocytes. The seeding efficiency of the system was determined by measuring the number of active hepatocytes residing in the scaffold This evaluation is essential for quantitative translation of data from in vitro to in vivo, since the assumption that all cells seeded into a system are retained and active can be highly inaccurate. Modelling and simulation has been applied throughout as an essential part of experiment planning and data evaluation
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