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

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Toxicology and Safety Assessment
• Computational Biology and Modeling

Background:

• There is a critical need for non-animal methods to assess chemical pharmacokinetics and safety.
• Physiologically-based biokinetic (PBK) modeling, utilizing in vitro data, presents a viable alternative to traditional animal testing.
• Understanding chemical biokinetics is essential for evaluating drug efficacy and safety.

Purpose of the Study:

• To develop and validate bottom-up physiologically-based biokinetic (PBK) models for three HMG-CoA reductase inhibitors: rosuvastatin, fluvastatin, and pitavastatin.
• To assess the accuracy of these models in predicting systemic exposure (AUC0h-t), maximum plasma concentration (Cmax), plasma clearance, and time to reach Cmax (Tmax).
• To evaluate the utility of quantitative proteomics-based mechanistic in vitro-to-in vivo extrapolation (IVIVE) in predicting human biokinetics.

Main Methods:

• Constructed bottom-up PBK models using the Simcyp® Simulator, integrating in vitro metabolism and transporter data (Vmax, Jmax, Km, CLint).
• Employed proteomics-based scaling factors to adjust for differences in transporter expression between in vitro systems and in vivo organs.
• Performed simulations for single intravenous, single oral, and multiple oral doses of the selected statins, with additional middle-out simulations using animal distribution data.

Main Results:

• The developed bottom-up PBK models generally predicted key pharmacokinetic parameters (AUC0h-t, Cmax, CL, Tmax) within a two-fold margin of observed data.
• Exceptions were noted for multiple oral pitavastatin dosing and single oral fluvastatin dosing, indicating areas for model refinement.
• Middle-out simulations using animal distribution data improved plasma-concentration time profiles but did not significantly alter predicted biokinetic parameters.

Conclusions:

• Quantitative proteomics-based mechanistic IVIVE can accurately predict whole organ clearances, accounting for transporter downregulation in vitro.
• Bottom-up PBK modeling, when integrated with mechanistic IVIVE, serves as a robust, animal-free alternative for predicting human biokinetics.
• This approach supports the development of safer and more effective chemicals without reliance on animal testing.