Effect of Renal Impairment on the Pharmacokinetics and Safety of Axitinib

Abstract

Background: Axitinib, an inhibitor of vascular endothelial growth factor (VEGF) receptors, is approved as second-line treatment for advanced renal cell carcinoma (RCC). Agents targeting the VEGF pathway may induce renal toxicities, which may be influenced by pre-existing renal dysfunction.

Objective: The objective was to characterize axitinib pharmacokinetics and safety in patients with renal impairment.

Patients and methods: Effect of renal function (baseline creatinine clearance [CrCL]) on axitinib clearance was evaluated in a population pharmacokinetic model in 207 patients with advanced solid tumors who received a standard axitinib starting dose, and in 383 healthy volunteers. Axitinib safety according to baseline CrCL was assessed in previously treated patients with RCC (n = 350) who received axitinib in the phase 3 AXIS study.

Results: Median axitinib clearance was 14.0, 10.7, 12.3, 7.81, and 12.6 L/h, respectively, in individuals with normal renal function (≥90 ml/min; n = 381), mild renal impairment (60-89 ml/min; n = 139), moderate renal impairment (30-59 ml/min; n = 64), severe renal impairment (15-29 ml/min; n = 5), and end-stage renal disease (<15 ml/min; n = 1). The population pharmacokinetic model adequately predicted axitinib clearance in individuals with severe renal impairment or end-stage renal disease. Grade ≥3 adverse events (AEs) were reported in 63 % of patients with normal renal function or mild impairment, 77 % with moderate impairment, and 50 % with severe impairment; study discontinuations due to AEs were 10 %, 11 %, and 0 %, respectively.

Conclusions: Axitinib pharmacokinetics and safety were similar regardless of baseline renal function; no starting-dose adjustment is needed for patients with pre-existing mild to severe renal impairment.

Fig. 1

Final model estimated axitinib clearance according to renal function (baseline creatinine clearance). Shrinkage refers to the phenomenon that occurs when a model is over-parameterized for the amount of information contained in the data. ESRD, end-stage renal disease; ETA, empirical Bayes prediction of the interindividual random effect.