Sara Carolina Henriques (1), Carolina Ameijeiras-Rodriguez (2), Serafim Guimarães (3), Iñaki F. Trocóniz (4), Nuno E. Silva (1)
(1) Research Institute for Medicine and Pharmaceutical Science (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Lisboa, Portugal, (2) Department of Biomedicine, Pharmacy and Therapeutics Unit, Faculty of Medicine, University of Porto, Porto, Portugal, (3) BlueClinical Ltd., Porto, Portugal, (4) Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
Introduction: The application of population pharmacokinetics (PopPK) modelling to data from bioequivalence studies can provide valuable insight on the pharmacokinetics (PK) and identify covariates that might explain the variability in exposure of drugs, as population used on these trials usually includes healthy subjects that are highly standardized.
Objectives: The objective of this analysis was to assess the PK and to identify covariates that might explain variability in exposure following oral administration of Sunitinib in healthy subjects. Sunitinib malate is an oral multitargeted tyrosine kinase inhibitor approved for advanced renal cell carcinoma and imatinib-resistant or imatinib-intolerant gastrointestinal stromal tumour [1-3]. Sunitinib is classified as BCS III and BDDCS I [4].
Methods: This analysis, which was approved by the pertinent Ethics Committee, used data from a previous single-dose, randomized, two-sequence, two-treatment, two-period crossover study in 20 healthy volunteers (9 male and 11 female) under fasting conditions that have demonstrated bioequivalence between 50 mg Sunitinib Test and Reference formulations. Observed plasma concentration-time profiles were analysed using a noncompartmental analysis (NCA), applied on R version 4.0.3. Moreover, plasma concentration-time data were analysed using nonlinear mixed-effects modelling to estimate PopPK parameters, as well as relationships between these parameters and formulation, sex, age, weight, and hormonal contraception use. Simulations were performed to determine the predicted effect of these covariates on exposure. Stochastic approximation expectation maximization (SAEM) algorithm was used for estimation of the population parameters, alongside conditional distribution methods for estimation of the individual estimates. Both methods were used in combination with a Markov Chain Monte Carlo (MCMC) procedure for maximum likelihood estimation in nonlinear mixed-effects models without linearization. This method was implemented in Monolix 2019R2.
Results: Based on observed NCA parameters, female subjects (n=11) in general presented higher exposure (Cmax: 45.47 µg/L [16.7%], AUC0-t: 1512.43 µg.h/L [17.7%]) in comparation to male population (n=9) (Cmax: 28.94 µg/L [27.1%], AUC0-t: 947.33 µg.h/L [37.6%]). However, female under stable hormonal contraception (n=5) presented even higher mean values and variability for the same parameters in comparation to female subjects without hormonal contraception (n=6) (Cmax: 52.03 µg/L [15.2%] vs 40.64 µg/L [6.0%], AUC0-t: 1688.35 µg.h/L [20.2%] vs 1379.92 µg.h/L [7.8%]). Following oral absorption, sunitinib disposition was described by a one-compartment model with zero-order absorption and latency time (tlag). The model included individual random effects for the rate of absorption (Tk0), clearance (Cl/F), and bioavailability (F), and occasion random effects for tlag, Tk0, Cl/F, and F. For the healthy individuals in the present study, sunitinib volume of distribution (V/F) was estimated to be 1542.43 L (11.6%) and Cl/F as 41.30 L/h (14.7%). Body weight explained the variability observed for V/F, and sex the variability in Cl/F. Sunitinib oral absorption was characterized through a Tk0 of 5.37 h (5.1%), preceded by a tlag of 0.627 h (5.8%). No covariates were associated with these absorption parameters. Formulation was not associated with differences between occasions within individuals, which corroborates that the two formulations (Test and Reference) are bioequivalent. Furthermore, the use of hormonal contraception (n=5) was correlated with an increase in F.
Conclusions: The developed model adequately described plasma concentration-time profiles of sunitinib on healthy subjects. Model covariates and model estimates for sunitinib PK parameters are similar to the described in literature [3]. However, V/F population estimates were below the ones reported in the literature (2230 L) [3]. Additionally, hormonal contraception use was correlated with an increase of sunitinib’s F, following oral administration. Such an investigation is of interest and warrants further analysis, considering the increase in sunitinib exposure at this population.
References:
[1] European Medicines Agency (EMA) Sutent® Summary of Product Characteristics. Revised Version: 14 March 2019. https://www.ema.europa.eu/en/documents/product-information/sutent-epar-product-information_en.pdf
[2] U.S. Food and Drug Administration (FDA) SUTENT® (sunitinib malate) capsules, for oral use. Revised Version: May 2019. https://www.accessdata.fda.gov/drugsatfda_docs/label/2019/021938s036lbl.pdf
[3] U.S. Food and Drug Administration (FDA) Sutent (Sunitinib Malate) Capsules Clinical Pharmacology and Biopharmaceutics Review. Application No.: NDA 21-938 (GIST) & NDA 21-968 (MRCC). Approval Date: 26 Jan 2006. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2006/021938_S000_Sutent_BioPharmR.pdf
[4] Benet LZ, Broccatelli F, Oprea TI (2011) BDDCS Applied to Over 900 Drugs. AAPS J 13:519–547. https://doi.org/10.1208/s12248-011-9290-9
Reference: PAGE 29 (2021) Abstr 9604 [www.page-meeting.org/?abstract=9604]
Poster: Methodology - Covariate/Variability Models