Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma

// Yoshiaki Yamamoto 1 , Ryouichi Tsunedomi 2 , Yusuke Fujita 3 , Toru Otori 4 , Mitsuyoshi Ohba 5 , Yoshihisa Kawai 1 , Hiroshi Hirata 1 , Hiroaki Matsumoto 1 , Jun Haginaka 6 , Shigeo Suzuki 7 , Rajvir Dahiya 8 , Yoshihiko Hamamoto 3 , Kenji Matsuyama 9 , Shoichi Hazama 10 , Hiroaki Nagano 2 and Hideyasu Matsuyama 1 1 Department of Urology, Graduate School of Medicine, Yamaguchi University, Ube, Yamaguchi, Japan 2 Department of Gastroenterological, Breast and Endocrine Surgery, Yamaguchi University Graduate School of Medicine, Ube, Yamaguchi, Japan 3 Department of Computer Science and Systems Engineering, Yamaguchi University Graduate School of Sciences and Technology for Innovation, Ube, Yamaguchi, Japan 4 Faculty of Pharmacy, Kindai University, Higashiosaka, Osaka, Japan 5 Technical Research Laboratory, Toyo Kohan Company Ltd., Kudamatsu, Yamaguchi, Japan 6 School of Pharmacy and Pharmaceutical Sciences, Mukogawa Women's University, Nishinomiya, Hyogo, Japan 7 Department of Laboratory of Analytical Chemistry for Pharmaceutical Sciences, Kindai University, Higashiosaka, Osaka, Japan 8 Department of Urology, San Francisco Veterans Affairs Medical Center and University of California at San Francisco, San Francisco, California, USA 9 Faculty of Pharmacy, Daiichi College of Pharmaceutical Sciences, Fukuoka, Fukuoka, Japan 10 Department of Translational Research and Developmental Therapeutics Against Cancer, Yamaguchi University Faculty of Medicine, Ube, Yamaguchi, Japan Correspondence to: Hideyasu Matsuyama, email: hidde@yamaguchi-u.ac.jp Keywords: axitinib; pharmacogenetics; renal cell carcinoma; gene polymorphisms; area under the plasma concentration–time curve Received: November 03, 2017      Accepted: February 26, 2018      Published: March 30, 2018 ABSTRACT We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration–time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters ( ABCB1 and ABCG2 ), UGT1A , and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate ( P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC ( P < 0.0001), and correctly predicted objective response rate ( P = 0.0044) as well as adverse events ( P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment ( P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC.