Bottom-up physiologically-based biokinetic modelling as an alternative to animal testing.

James Chan

doi:10.14573/altex.1812051

Abstract

There is a growing need for alternatives to animal testing to derive biokinetic data for evaluating both efficacy and safety of chemicals. One such alternative is bottom-up physiologically-based biokinetic (PBK) modeling which requires only in vitro data. The primary objective of this study is to develop and validate bottom-up PBK models of 3 HMG-CoA reductase inhibitors: rosuvastatin, fluvastatin and pitavastatin. Bottom-up PBK models were built using the Simcyp® Simulator by incorporating in vitro transporter and metabolism data (Vmax, Jmax, Km, CLint) obtained from the literature and proteomics-based scaling factors to account for differences in transporters expression between in vitro systems and in vivo organs. Simulations were performed for single intravenous, single oral and multiple oral dose of these chemicals. The results showed that our bottom-up models predicted systemic exposure (AUC0h-t), maximum plasma concentration (Cmax), plasma clearance and time to reach Cmax (Tmax) within two-fold of the observed data, with the exception of parameters associated with multiple oral pitavastatin dosing and single oral fluvastatin dosing. Additional middle-out simulations were performed using animal distribution data to inform tissue-to-plasma equilibrium distribution ratios for rosuvastatin and pitavastatin. This improved the predicted plasma-concentration time profiles but did not significantly alter the predicted biokinetic parameters. Our study demonstrates that quantitative proteomics-based mechanistic in vitro-to-in vivo extrapolation (IVIVE) could account for downregulation of transporters in culture and predict whole organ clearances without empirical scaling. Hence, bottom-up PBK modeling incorporating mechanistic IVIVE could be a viable alternative to animal testing in predicting human biokinetics.

Highlights

The replacement, refinement, and reduction of animal use in research (3R) was first established in 1959 (Russell and Burch, 1959), with growing efforts in recent times to establish alternatives to animal testing in the risk assessment of xenobiotics (Chapman et al, 2013; Paini et al, 2017)
By leveraging recent developments in transporter kinetics and quantitative proteomics that were not available at the time of development of the published models, we demonstrate the utility of mechanistic scaling factors for in vivo extrapolation (IVIVE) via bottom-up physiologically-based biokinetic (PBK) modeling
Building upon the work by Lundquist, Vildhede and colleagues, we show that the key to greater accuracy in IVIVE of transporters lies in the use of quantitative proteomics to account for differential expression of transporters in in vitro systems compared with isolated hepatocytes and liver tissue (Kimoto et al, 2012; Lundquist et al, 2014; Vildhede et al, 2015, 2018)

Summary

Introduction

The replacement, refinement, and reduction of animal use in research (3R) was first established in 1959 (Russell and Burch, 1959), with growing efforts in recent times to establish alternatives to animal testing in the risk assessment of xenobiotics (Chapman et al, 2013; Paini et al, 2017). In 2006, the REACH Regulation implemented in the EU demanded that testing on animals be done only as a last resort (EU, 2006). Under the EU Cosmetics Regulation (EU, 2009; Creton et al, 2009), a complete ban on testing of cosmetic products and ingredients in animals in the EU was imposed in 2013. The current strategies to extrapolate toxicity testing results obtained with animals to humans struggle with predictivity and, in some cases, lack physiological relevance for humans (Blaauboer and Andersen, 2007). There is a acute need, especially in the area of biokinetics, for viable alternatives to animal testing in risk assessment

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