An Integrated PBPK-Quantitative Systems Pharmacology Model for Statins to Assess the Variability and Implications of Transporter-Mediated Distribution
Prieto Garcia L. (1,2), Nordell P. (2), Lennernäs H. (1), Ahlström C. (2), Sjögren E. (1)
(1) Department of Pharmacy, Uppsala University, SE-75123, Uppsala, Sweden; (2) DMPK, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
Introduction: Quantitative Systems Pharmacology (QSP) models can be used to quantitatively and mechanistically describe the underlying biological/pharmacological processes in disease. Physiologically-Based Pharmacokinetic (PBPK)-QSP models are built on mechanistic framework to drive the pharmacodynamic (PD) effect of a drug via concentrations at the site of the pharmacological target. This is particularly important for drugs where the target tissue distribution is significantly influenced by membrane carriers. Plasma concentration are in these cases not reflecting the concentration at the site of action and is consequently less appropriate as surrogate for driver of PD, as commonly applied. Statins are highly effective cholesterol-lowering drugs, widely prescribed for the treatment of hypercholesterolemia and prevention of cardiovascular events. The mechanism of action is mediated via competitive inhibition of HMG-CoA reductase in liver which is involved in the endogenous production of cholesterol. Several clinical drug-drug interactions (DDI) and drug-gene interactions (DGI) studies demonstrate that statins disposition are mediated by influx and efflux transporters in liver, intestine and kidney [1]. However, statin dose-adjustment recommendations when function of transporters are altered, by coadministered drugs and/or different genotypes, are based solely on changes in statin plasma pharmacokinetics (PK). Thus, a holistic PBPK-QSP approach might be beneficial to guide the statin-dose adjustment in different scenarios.
Objectives:
- Develop a PBPK-QSP model to describe statin disposition and inhibition on liver cholesterol production.
- Assess the implications on PK and PD in different transporter mediated DDI and DGI scenarios using the established PBPK-QSP model.
- Investigate the factors associated with interindividual variability in statins PD in disease population using the established PBPK-QSP model.
Methods: A QSP model for the LDL lowering effect off statins was established based on a reported model on PCSK9 inhibitors [2]. Further mechanistic descriptions of statin liver cholesterol inhibition was implemented using individual LDL data from a randomized, parallel-group, open-label trial in patients (NCT00654537) [3]. Simvastatin and Pravastatin were used as investigational compounds of this exercise. The QSP model was coupled with statin PBPK model in PK-Sim/MoBi®. Population variability was included in expression of enzymes, transporters and other processes involved in statin disposition and pharmacology.
Results: An integrated PBPK-QSP model for statins was developed in PK-Sim/MoBi that coupled the predicted unbound liver concentration to the effect of liver cholesterol inhibition. The QSP model, informed via 2753 measurements of circulating LDL concentrations in 1147 patients, successfully described the time course for the LDL lowering effects of the two statins. The model was externally validated against literature clinical studies and was able to capture the observed mean LDL reduction and variability at different dosing regimens. The established PBPK-QSP model was applied to predict the statin LDL lowering effect in different DDI and DGI scenarios. The simulations suggest that reduction in OATP1B1 hepatic uptake transporter had a 2-4 fold increase in plasma PK while PD was unchanged or even slightly decreased. The simulations also suggested that reduced function of BCRP was unlikely to affect the simvastatin PK or PD. However for pravastatin, reduced function of MRP2 would lead to at least 3-fold higher increase in PD compared to plasma PK. In addition, the impact on statins PD due to variability in different enzymes/transporters expression and physiological/pharmacological process was also assed in disease population.
Conclusion: Statin dose adjustment when co-administered with transporter inhibitors or in different transporter genotypes in patients should consider assessment of both PK and PD, not only PK. PBPK-QSP enables an assessment of what consequences variability in different process in disease population may bring to the pharmacological effect. Finally, the PBPK-QSP model can be used as a framework for other statins in DDI risk assessments and guidance for dose-adjustment in patients.
References:
[1] Elsby R et. al. Clin Pharmacol Ther. 2012 Nov;92(5):584-98.
[2] Gadkar K et. al. CPT Pharmacometrics Syst Pharmacol. 2014 Nov 26;3(11):e149.
[3] Jones PH et. al. Am J Cardiol. 2003 Jul 15;92(2):152-60
