Javier Reig-López (1,2), Alfredo GarcÃa-Arieta (3), Matilde Merino-Sanjuan (1,2) and VÃctor Mangas-Sanjuan (1,2)
(1) Department of Pharmacy Technology and Parasitology, Faculty of Pharmacy, University of Valencia, Valencia, Spain. (2) Interuniversity Institute of Recognition Research Molecular and Technological Development, Valencia, Spain. (3) Service of Pharmacokinetics and Generic Medicines, Division of Pharmacology and Clinical Evaluation, Department of Human Use Medicines, Spanish Agency for Medicines and Health Care Products
Introduction: Atorvastatin (ATS), one of the most prescribed statin worldwide, shows low oral bioavailability and non-linear pharmacokinetics (PK) due to its poor solubility in the gastrointestinal (GI) tract and the contribution of saturable processes in absorption, metabolism, and excretion [1] . Several physiologically based pharmacokinetic models (PBPK) have been published to describe ATS disposition and evaluate drug-drug interactions (DDIs) [2-5]. To the best of our knowledge, there is no PBPK model that has evaluated the impact of different OATP1B1 phenotypes in ATS exposure, as a drug-gene interaction (DGI) has been described between ATS and SLCO1B1*5 gene [6].
Objectives: The aim of this work is to develop a full PBPK model for ATS and its metabolites (open acid and lactone forms) that is able to quantitatively assess the impact of OATP1B1 phenotypes on ATS Cmax and AUC.
Methods: The model has been developed in the PBPK platform Simcyp® Simulator V19 following a “bottom-up” approach. We first developed an ATS PBPK model and compared simulations output with clinical observations after single dose oral administration of 20 mg. Following this first step, we developed the atorvastatin-lactone (ATS-L), 2-hydroxy-atorvastatin (2OH-ATS), 2-hydroxy-atorvastatin-lactone (2OH-ATS-L) and 4-hydroxy-atorvastatin-lactone (4OH-ATS-L) model files sequentially and verified model performance globally in each step with observed clinical data [7]. Healthy volunteers population file available in Simcyp® was used at this stage and a population representative subject was used in all simulations. Model performance was assessed by means of the fold error prediction (Parameterpredicted/Parameterobserved) in exposure PK parameters AUC and Cmax. Model validation was done internally with clinical data from bioequivalence trials at 40 and 80 mg dose level after single dose administration of the calcium salt of ATS (ATS-Ca) and externally after one week of daily administration of 10 mg in order to check steady state conditions. The model was used to simulate clinically relevant DDIs with known CYP450 and OATPs inhibitors that have been observed in vivo [8-10]. Finally, the full PBPK model was used to investigate the different ATS exposure regarding the activity of OATP1B1 in order to quantitatively assess the drug-gene interaction (DGI) between SLCO1B1 polymorphisms and ATS.
Results: Model prediction errors in exposure PK parameters AUC and Cmax in both the development and validation steps were within the 2-fold range for ATS, ATS-L and its metabolites (2OH-ATS-L and 4OH-ATS-L). The main hydroxylated ATS metabolite (2OH-ATS) exposure was slightly under-predicted in all scenarios, with AUC and Cmax fold errors of 0.57, 0.61 and 0.57 and 0.33, 0.30 and 0.26 at 20, 40 and 80 mg dose levels, respectively. Exposure at steady state was accurately predicted by the model with fold errors of 0.87 and 0.64 in AUC and 1.17 and 0.73 in Cmax for ATS and 2OH-ATS, respectively. Fold error prediction in AUC changes when co-administered with itraconazole, clarithromycin and rifampin (single dose) were 0.66, 0.94 and 0.79, respectively, for ATS and 0.96 (itraconazole) and 1.45 (clarithromycin) for ATS-L. Cmax ratios resulted in 0.99 (itraconazole), 0.61 (rifampin) and 0.88 (clarithromycin) for ATS, and 1.20 (itraconazole) and 1.36 (clarithromycin) for ATS-L. The model predicted a 30% lower CL (p<0.01) for poor OATP1B1 transporters (PT) when compared with extensive OATP1B1 transporters (ET), with a 40 and 33% increase (p<0.05) in AUC and Cmax, respectively.
Conclusions: The PBPK model here presented is able to properly describe the time-course of ATS and its metabolites plasma concentrations after oral administration of 10, 20, 40 and 80 mg of ATS-Ca in healthy volunteers and when co-administered with known DDI perpetrators. The model slightly under-predicted 2OH-ATS concentration as its formation is only parameterized by direct CYP-mediated hydroxylation of ATS instead of the main formation pathway (e.g. lactonization, hydroxylation and hydrolysis) previously suggested [11], because of structure limitations in the PBPK platform. The ATS PBPK model quantifies the change in ATS exposure due to polymorphisms in SLCO1B1, as 30% lower clearance has been predicted in PT. These findings could help to explain the frequent treatment discontinuation and statin-associated musculoskeletal symptoms observed in patients carrying SLCO1B1*5 allele in the context of routine care.
References:
[1] Lennernas, H. Clinical Pharmacokinetics of Atorvastatin. Clin. Pharmacokinet. 2003, 42, 1141-1160.
[2] Zhang, T. Physiologically Based Pharmacokinetic Modeling of Disposition and Drug–drug Interactions for Atorvastatin and its Metabolites. European Journal of Pharmaceutical Sciences 2015, 77, 216-229.
[3] Duan, P.; Duan, P.; Zhao, P.; Zhao, P.; Zhang, L.; Zhang, L. Physiologically Based Pharmacokinetic (PBPK) Modeling of Pitavastatin and Atorvastatin to Predict Drug-Drug Interactions (DDIs). Eur J Drug Metab Pharmacokinet 2017, 42, 689-705.
[4] Li, S.; Yu, Y.; Jin, Z.; Dai, Y.; Lin, H.; Jiao, Z.; Ma, G.; Cai, W.; Han, B.; Xiang, X. Prediction of Pharmacokinetic Drug-Drug Interactions Causing Atorvastatin-Induced Rhabdomyolysis using Physiologically Based Pharmacokinetic Modelling. Biomedicine & Pharmacotherapy 2019, 119, 109416.
[5] Morse, B.L.; Alberts, J.J.; Posada, M.M.; Rehmel, J.; Kolur, A.; Tham, L.S.; Loghin, C.; Hillgren, K.M.; Hall, S.D.; Dickinson, G.L. Physiologically‐Based Pharmacokinetic Modeling of Atorvastatin Incorporating Delayed Gastric Emptying and Acid‐to‐Lactone Conversion. CPT: Pharmacometrics & Systems Pharmacology 2019, 8, 664-675.
[6] Voora, D.; Baye, J.; Mcdermaid, A.; Narayana Gowda, S.; Wilke, R.A.; Nicole Myrmoe, A.; Hajek, C.; Larson, E.A. SLCO1B1*5 Allele is Associated with Atorvastatin Discontinuation and Adverse Muscle Symptoms in the Context of Routine Care. Clin Pharma and Therapeutics 2022.
[7] Riedmaier, S.; Klein, K.; Hofmann, U.; Keskitalo, J.E.; Neuvonen, P.J.; Schwab, M.; Niemi, M.; Zanger, U.M. UDP-Glucuronosyltransferase (UGT) Polymorphisms Affect Atorvastatin Lactonization in Vitro and in Vivo. Clin Pharmacol Ther 2009, 87, 65.
[8] Kantola, T.; Kivistö, K.T.; Neuvonen, P.J. Effect of Itraconazole on the Pharmacokinetics of Atorvastatin. Clinical pharmacology and therapeutics 1998, 64, 58-65.
[9] Lau, Y.Y.; Huang, Y.; Frassetto, L.; Benet, L.Z. Effect of OATP1B Transporter Inhibition on the Pharmacokinetics of Atorvastatin in Healthy Volunteers. Clinical pharmacology and therapeutics 2007, 81, 194-204.
[10] Shin, J.; Pauly, D.F.; Pacanowski, M.A.; Langaee, T.; Frye, R.F.; Johnson, J.A. Effect of Cytochrome P450 3A5 Genotype on Atorvastatin Pharmacokinetics and its Interaction with Clarithromycin. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy 2011, 31, 942-950.
[11] Reig-López, J.; García-Arieta, A.; Mangas-Sanjuán, V.; Merino-Sanjuán, M. Current Evidence, Challenges, and Opportunities of Physiologically Based Pharmacokinetic Models of Atorvastatin for Decision Making. Pharmaceutics 2021, 13, 709.
Reference: PAGE 30 (2022) Abstr 10077 [www.page-meeting.org/?abstract=10077]
Poster: Drug/Disease Modelling - Absorption & PBPK