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ABSTRACT.Early prediction of human clearance is often challenging, in particular for the growing number of low-clearance compounds. Long-term in vitro models have been developed which enable sophisticated hepatic drug disposition studies and improved clearance predictions. Here, the cell line HepG2, iPSC-derived hepatocytes (iCell®), the hepatic stem cell line HepaRG™, and human hepatocyte co-cultures (HμREL™ and HepatoPac®) were compared to primary hepatocyte suspension cultures with respect to their key metabolic activities. Similar metabolic activities were found for the long-term models HepaRG™, HμREL™, and HepatoPac® and the short-term suspension cultures when averaged across all 11 enzyme markers, although differences were seen in the activities of CYP2D6 and non-CYP enzymes. For iCell® and HepG2, the metabolic activity was more than tenfold lower. The micropatterned HepatoPac® model was further evaluated with respect to clearance prediction. To assess the in vitro parameters, pharmacokinetic modeling was applied. The determination of intrinsic clearance by nonlinear mixed-effects modeling in a long-term model significantly increased the confidence in the parameter estimation and extended the sensitive range towards 3% of liver blood flow, i.e., >10-fold lower as compared to suspension cultures. For in vitro to in vivo extrapolation, the well-stirred model was used. The micropatterned model gave rise to clearance prediction in man within a twofold error for the majority of low-clearance compounds. Further research is needed to understand whether transporter activity and drug metabolism by non-CYP enzymes, such as UGTs, SULTs, AO, and FMO, is comparable to the in vivo situation in these long-term culture models.KEY WORDS: in vitro clearance; in vitro liver models; IVIVE; nonlinear mixed-effects modeling.

Abstract. This case study compares the usefulness and applicability of eight computer tools with respect to the validation of logic control programs for continuous processes. Six simulation packages (Taylor's Matlab-based simulator, Simulink/StateFlow, gPROMS, Shift, Dymola, and BaSiP) and two verification tools (SMV and HyTech) were applied to a single process control example with non-trivial continuous dynamics. The paper presents a detailed description of this benchmark example. Short introductions to the tools are given and the application results are decribed and discussed with emphasis on the suitability to the problem and the numerical performance.

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Monoclonal antibodies (mAbs) are a rapidly growing drug class for which great efforts have been made to optimize certain molecular features to achieve the desired pharmacokinetic (PK) properties. One approach is to engineer the interactions of the mAb with the neonatal Fc receptor (FcRn) by introducing specific amino acid sequence mutations, and to assess their effect on the PK profile with in vivo studies. Indeed, FcRn protects mAbs from intracellular degradation, thereby prolongs antibody circulation time in plasma and modulates its systemic clearance. To allow more efficient and focused mAb optimization, in vitro input that helps to identify and quantitatively predict the contribution of different processes driving non-target mediated mAb clearance in vivo and supporting translational PK modeling activities is essential. With this aim, we evaluated the applicability and in vivo-relevance of an in vitro cellular FcRn-mediated transcytosis assay to explain the PK behavior of 25 mAbs in rat or monkey. The assay was able to capture species-specific differences in IgG-FcRn interactions and overall correctly ranked Fc mutants according to their in vivo clearance. However, it could not explain the PK behavior of all tested IgGs, indicating that mAb disposition in vivo is a complex interplay of additional processes besides the FcRn interaction. Overall, the transcytosis assay was considered suitable to rank mAb candidates for their FcRn-mediated clearance component before extensive in vivo testing, and represents a first step toward a multi-factorial in vivo clearance prediction approach based on in vitro data.