Johanna Melin (1), Joanna Parkinson (1), Dinko Rekić (1), Bengt Hamrén (1), Robert C Penland (2), David W Boulton (3), Weifeng Tang (3)
Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca: (1) Gothenburg, Sweden; (2) Boston, US; (3) Gaithersburg, US.
Objectives: Dapagliflozin is a highly potent, selective, and reversible inhibitor of sodium-glucose cotransporter 2 (SGLT2) that improves glycaemic control in patients with type 2 diabetes mellitus (T2DM) by reducing renal glucose reabsorption, leading to urinary excretion of excess glucose [1]. Since the mode of action is independent of insulin, investigations using dapagliflozin for treatment of patients with type 1 diabetes mellitus (T1DM) with inadequate glycemic control are currently ongoing. A previous analysis indicated that the pharmacokinetic (PK) properties of dapagliflozin is similar in T1DM and T2DM patients [1]. The objectives of the analysis were to characterize the PK of dapagliflozin in T1DM patients, to understand influence of covariates on the PK of dapagliflozin, and to compare PK exposure of dapagliflozin between T1DM and T2DM patients based on phase IIa/III data using a population PK approach.
Methods: The population PK analysis was performed in NONMEM 7.3 [2] using dapagliflozin plasma concentrations in adults with T1DM from 1 Phase IIa study (NCT01498185) and 2 Phase III trials (NCT02268214 and NCT02460978). In total, 5797 samples with quantifiable concentrations from 1151 patients administered 5 mg or 10 mg dapagliflozin up to 24 weeks were used for the analysis. The PK of dapagliflozin was described by a 2-compartment model with first order absorption and linear clearance. Exponential interindividual variability was estimated for apparent clearance (CL/F), apparent central volume of distribution (Vc/F), and apparent intercompartmental clearance (Q/F). The residuals were described by a proportional error model. The effect of covariates on dapagliflozin PK was investigated using stepwise covariate modeling as implemented in PsN 4.4.8 [3]. Non-significant covariates were added at a later stage to evaluate their effect/lack of effect on area under the concentration curve (AUC), which was derived using the Empirical Bayes estimate for CL/F. AUC for T2DM patients from previous studies were extracted and compared to AUC of the current analysis.
Results: The final population PK model adequately described the dapagliflozin concentrations in adult T1DM patients. The estimated CL/F was 20.5 L/h, which was comparable to the previous estimate in adult patients with T2DM and healthy subjects (22.9 L/h). Model-predicted systemic exposure for 5 mg and 10 mg of dapagliflozin was dose-proportional and was comparable between T1DM and T2DM patients.
Previously identified covariate relationships in adult T2DM patients and healthy subjects were confirmed in T1DM patients. The identified covariate relationships were: patients with better renal function measured as estimated glomerular filtration rate (eGFR) had higher CL/F, males had higher CL/F and Vc/F than females, heavier patients had higher CL/F and Vc/F, and older patients had lower Vc/F than younger patients. Within the studied range of covariates, no covariates affected systemic dapagliflozin exposure more than 25% compared to the reference individual. Based on previous exposure-response relationship and ongoing investigations in T1DM, no covariates were deemed clinically relevant.
Conclusions: The PK of dapagliflozin in T1DM patients was adequately described by the established population PK model, and no clinically relevant covariates were identified. Moreover, the identified covariates in T1DM patients were similar to the covariates identified in T2DM patients. Systemic dapagliflozin PK exposure in T1DM patients following administration of 5 mg and 10 mg dapagliflozin was found to be dose-proportional and comparable to the PK exposure of T2DM patients. This confirms that PK properties of dapagliflozin are similar for both patient populations, and suggests that there is no pharmacokinetic reason to adjust doses in T1DM patients.
References:
[1] W. Tang, T. A. Leil, E. Johnsson, D. W. Boulton, F. LaCreta. Comparison of the pharmacokinetics and pharmacodynamics of dapagliflozin in patients with type 1 versus type 2 diabetes mellitus. Diabetes Obes Metab (2016) 18: 236-240
[2] S. L. Beal, L. B. Sheiner, A. J. Boeckmann, R. J. Bauer (Eds.) NONMEM Users Guides. 1989-2011. Icon Development Solutions, Ellicott City, Maryland, USA.
[3] L. Lindbom, P. Pihlgren, N. Jonsson. “PsN-Toolkit—a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM.” Computer methods and programs in biomedicine 79.3 (2005): 241-257.
Reference: PAGE 27 (2018) Abstr 8427 [www.page-meeting.org/?abstract=8427]
Poster: Drug/Disease Modelling - Endocrine