Quantitative Proteomics‐based Bottom‐up PBK Modeling: A Case Study of 3 Statins

Abstract

In the consumer care, cosmetics and chemical industries, there is a growing need for alternatives to animal testing to derive biokinetic data to evaluate both efficacy and safety of chemicals. One promising alternative is bottom‐up physiologically‐based biokinetic (PBK) modeling, which utilizes in vitro‐to‐in vivo extrapolation (IVIVE) for prediction of biokinetic parameters. The main challenges of IVIVE lie in suboptimal biokinetic predictions, particularly when using in vitro transporter data which often requires empirical fitting to human biokinetic data. As part of an Organization for Economic Cooperation and Development (OECD) case study, the primary objective of this work is to develop and validate quantitative proteomics‐based bottom‐up PBK models using a set of chemicals rich with in vitro and human in vivo data. Three HMG‐CoA reductase inhibitors that undergo differential elimination processes were chosen: rosuvastatin (transporter‐dependent), fluvastatin (metabolism‐dependent) and pitavastatin (mixed‐mode). PBK models were built using the Simcyp® Simulator by incorporating: (1) in vitro transporter and metabolism data (Vmax, Jmax, Km and CLint) and (2) animal tissue distribution data from literature to inform tissue‐to‐plasma equilibrium distribution ratio (Kp). Simulations were performed for single intravenous, single oral and multiple oral dosing of these chemicals. The successful prediction was based on a two‐fold criterion when compared against human biokinetic parameters. Our results showed that predicted systemic exposure (AUC0‐∞h), maximum plasma concentration (Cmax), plasma clearance (CL) and time to reach Cmax (Tmax) were within two‐fold of the observed data, with the exception of parameters associated with multiple oral pitavastatin dosing and Tmax of single oral fluvastatin dosing. The use of animal Kp data improved the predicted plasma‐concentration time profiles but did not significantly alter predicted biokinetic parameters (Figure 1). Our study demonstrated that quantitative proteomics‐based mechanistic IVIVE could account for differences in transporter and metabolic enzyme expression levels between in vitro systems and in vivo organs, allowing the prediction of whole organ clearances without any empirical scaling. We conclude that bottom‐up PBK modeling incorporating mechanistic IVIVE could be a viable alternative to animal testing in obtaining predictions of human biokinetics of chemicals.Support or Funding InformationThis work was supported by the Innovations in Food and Chemical Safety Programme [Grant number H18/01/a0/C14] and NUS Department of Pharmacy [Grant number C‐148‐000‐003‐001]. Travel funding for this conference was provided by the Innovations in Food and Chemical Safety Programme and the Skin Research Institute of Singapore.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.