Chen Ning1, Pieter Annaert1,2
1Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 2BioNotus CommV
Introduction: Atorvastatin, a widely prescribed hydroxymethylglutaryl-CoA(HMG-CoA) reductase inhibitor for hyperlipidemia, exhibits potent lipid-lowering efficacy but is associated with dose-dependent muscular side effects, ranging from common mild muscle pain to rare instances of severe myopathy or rhabdomyolysis.[1] Administered as a calcium salt, its pharmacokinetic complexity arises from pH-dependent acid-lactone interconversion and UDP-glucuronosyltransferase(UGT)-mediated lactonization, with the lactonization serving as the critical first step preceding extensive CYP3A4-mediated metabolism.[2,3] Moreover, elevated lactone metabolite exposure and loss-of-function organic anion transporting polypeptide 1B1(OATP1B1) transporter alleles have been significantly associated with increased myotoxicity risks. [4,5] These dynamic interconversions, coupled with subsequent metabolic and dispositional processes, complicate the characterization of atorvastatin’s pharmacokinetic profile and toxicity risks. Objectives: This study aimed to develop a parent-metabolite PBPK model integrating in vitro-derived acid-lactone interconversion kinetics of atorvastatin and its metabolites, filling a key gap in existing models by explicitly simulating dynamic interconversions. This model aims to predict pharmacokinetics across ethnic populations, genetic polymorphisms, and polypharmacy scenarios, serving as a platform for refining therapeutic regimens and ensuring patient safety. Methods: Acid-lactone interconversion kinetics of atorvastatin and 2-hydroxy atorvastatin were quantified in biorelevant media and human plasma. Clinical pharmacokinetic data from 37 trials from Europe, east Asia and America were compiled, partitioned into training (20 profiles) and verification (59 profiles) datasets. A parent-metabolite PBPK model was developed using Open Systems Pharmacology Suite (PK-Sim®/MoBi® v12.0), integrating metabolic/transport pathways, interconversion kinetics, and polymorphic enzyme/transporter expressions to assess pharmacokinetics across genetic variants.[6] Drug-drug interaction risks with clarithromycin, gemfibrozil, and itraconazole were evaluated using established perpetrator models. [7, 8] Results: The developed atorvastatin PBPK model accurately predicted the pharmacokinetics of atorvastatin acid, atorvastatin lactone, 2-hydroxy atorvastatin, and 2-hydroxy atorvastatin lactone with more than 90% of the predicted Cmax values and 100% of the predicted AUC values within a 2-fold error. The average fold error (AFE) and absolute average fold error (AAFE) were as follows: atorvastatin acid (Cmax 0.88/1.37; AUC 1.01/1.26), atorvastatin lactone (Cmax 0.89/1.20; AUC 1.00/1.21), 2-hydroxy atorvastatin acid (Cmax 0.96/1.45; AUC 1.05/1.25), and 2-hydroxy atorvastatin lactone (Cmax 0.67/1.61; AUC 0.86/1.29). Furthermore, the model demonstrated robust performance across European, east Asian and American populations, among carriers of loss-of-function alleles, and in polypharmacy scenarios, underscoring its reliability in diverse clinical contexts. Conclusion: The PBPK model provides a mechanistic understanding of atorvastatin disposition and its major metabolic pathways. The model’s predictive accuracy supports its potential use for dosing regimen optimization and informing future risk assessments. Overall, this study underscores the importance of integrating comprehensive in vitro kinetic data, including non-enzymatic conversion processes such as the pH-dependent lactonization of statins, into PBPK modeling.
Reference 1. Lennernas, H., et al. Clin Pharmacokinet, 2003. 42(13): p. 1141-60. 2. Hoffmann, M., et al. Org Biomol Chem, 2008. 6(19): p. 3527-31. 3. Jacobsen, W., et al. Drug Metab Dispos, 2000. 28(11): p. 1369-78. 4. Hermann, M., et al. Clinical Pharmacology & Therapeutics, 2006. 79(6): p. 532-539. 5. Lee, H.H., et al. Br J Clin Pharmacol, 2017. 83(6): p. 1176-1184. 6. Lippert, J., et al. CPT Pharmacometrics Syst Pharmacol, 2019. 8(12): p. 878-882. 7. Hanke, N., et al. CPT Pharmacometrics Syst Pharmacol, 2018. 7(10): p. 647-659. 8. Turk, D., et al. Clin Pharmacokinet, 2019. 58(12): p. 1595-1607.
Reference: PAGE 33 (2025) Abstr 11596 [www.page-meeting.org/?abstract=11596]
Poster: Drug/Disease Modelling - Absorption & PBPK