Population pharmacokinetics of apixaban in a real-life hospitalized population
Frédéric Gaspar123, Jean Terrier567, Samantha Favre123, Pauline Gosselin56, Pierre Fontana68, Youssef Daali67, Camille Lenoir7, Caroline Flora Samer79, Victoria Rollason79, Jean‑Luc Reny*56, Chantal Csajka*123, Monia Guidi*134
1 Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland 2 School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland 3 Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, Geneva, Lausanne, Switzerland 4 Service of Clinical Pharmacology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland 5 Division of General Internal Medicine, Geneva University Hospitals, Geneva, Switzerland 6 Geneva Platelet Group, Faculty of Medicine, University of Geneva, Geneva, Switzerland 7 Division of Clinical Pharmacology and Toxicology, Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine Department, Geneva University Hospitals, Geneva, Switzerland 8 Division of Angiology and Haemostasis, Geneva University Hospitals, Geneva, Switzerland 9 Faculty of Medicine, University of Geneva, 1206 Geneva, Switzerland.
Objectives: Previous studies indicate that individual intrinsic and extrinsic factors have a limited effect on apixaban exposure [1-5]. However, the cumulative influence of factors affecting apixaban concentrations remains unknown. This study aimed to characterise apixaban pharmacokinetics (PK) and its variability in a real-world clinical setting of hospitalized patients, while assessing the clinical significance of identified factors on apixaban exposure and the delay required for a safe discontinuation of the drug before an invasive procedure.
Methods:
Data were collected from 100 consecutive hospitalized patients from the OptimAT study (NCT03477331, clinicaltrials.gov) who received apixaban at various doses. A classic stepwise strategy was used to determine the population pharmacokinetic (popPK) model that best described apixaban PK, its inter- individual (IIV) and occasion (IOV), as well as residual unexplained (RUV) variability, and to identify the underlying factors responsible for such observed variability using the MONOLIX software [6]. Tested covariates were demographic (e.g. body weight, age, and gender), laboratory (e.g. alanine aminotransferase, aspartate aminotransferase, bilirubin, albumin, gamma-glutamyl transferase, phosphatase, creatinine clearance (CLcr), urea), and genetic factors (CYP3A4/5 and P-glycoprotein (PgP) phenotypic activity). The renal function of patients was assessed using Clcr and categorized according to the stage of chronic kidney disease (CKD) (stage 1 CLcr > 90 mL/min; stage 2 CLcr = 60–89 mL/min; stage 3 CLcr = 30–59 mL/min; stage 4 CLcr = 15–29 mL/min, and stage 5 CLcr < 15 mL/min). PgP activity was quantified based on the fexofenadine area under the curve from 2 to 6 h (AUC2-6) using the Geneva cocktail method [7]. Reduced (PM), normal (NM), and increased (UM) PgP activity were characterized by AUC2-6 concentrations (mean ± SD) of 285.5 ± 67.1, 100.1 ± 47.5, and 50.4 ± 15.3 mg∙h/L, respectively. Model-based simulations were performed to predict apixaban exposure and to assess the delay between the last apixaban intake and the target plasma concentration of 50 ng/mL, defined as the security threshold usually accepted for most of the extracranial procedures [8], under various clinical conditions affecting drug PK.
Results: A two-compartment model with first-order absorption and elimination best described the data. The estimated parameters with theirassociated IIV or IOV were: absorption rate constant 0.82 h−1 (IIV, 58%), lag-time 0.16 h (IIV, 108%), apparent clearance (CL) 3.2 L/h (IOV, 30%), central volume of distribution 27 L, peripheral volume of distribution 51 L (IIV, 88%) and inter-compartment clearance 16 L/h (IIV, 65%). Covariate analyses found CLcr and PgP as significant factors, explaining 41% and 17% of initial variability in apixaban clearance, respectively. Model-based simulations suggest that renal function is crucial to achieve safe apixaban concentration intervals, with 65-80% higher Cmin in stage 3 compared to stage 1 CKD patients considering different PgP phenotype. Reduced PgP activity has a minor effect but increases bleeding risk when combined with renal impairment. Additionally, these simulations predict that apixaban withdrawal times before high-risk and low-risk bleeding surgeries or procedures may need to be longer for patients with impaired renal function and reduced PgP activity. Patients with Stage 3 and 4 CKD require 2- and 4-fold longer withdrawal times, respectively, and those with both Stage 4 CKD and reduced PgP activity may require up to 107 hours. This raises questions about the adequacy of current preoperative withdrawal time recommendations for these patient populations.
Conclusions: A high inter-individual and inter-occasion variability in apixaban PK was observed in a real-life setting, which was partially explained by renal function and by PgP phenotypic activity. Our study shows that standard apixaban dosing is effective in achieving target concentrations, but overexposure can occur with cumulative factors. The validity of current recommendations for anticoagulant management during procedures for patients with renal impairment is questioned. Apixaban monitoring may be useful for CKD patients with factors affecting PgP activity. A model-based tool for tailored dosing and discontinuation time before surgery could minimize the risk of thrombosis and bleeding in high-risk situations.
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