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

• Published in: Oncotarget 2018-03, Vol.9 (24), p.17160-17170
• Main Authors: Yamamoto, Yoshiaki, Tsunedomi, Ryouichi, Fujita, Yusuke, Otori, Toru, Ohba, Mitsuyoshi, Kawai, Yoshihisa, Hirata, Hiroshi, Matsumoto, Hiroaki, Haginaka, Jun, Suzuki, Shigeo, Dahiya, Rajvir, Hamamoto, Yoshihiko, Matsuyama, Kenji, Hazama, Shoichi, Nagano, Hiroaki, Matsuyama, Hideyasu
• Format: Article
• Language: English
• Subjects: Clinical Research Paper
• ISSN: 1949-2553; 1949-2553
• DOI: 10.18632/oncotarget.24715

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 ( and ), , and 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 ( = 0.0002) and adverse events (hand-foot syndrome, = 0.0055; and hypothyroidism, = 0.0381). Calculated AUC significantly correlated with actual AUC ( < 0.0001), and correctly predicted objective response rate ( = 0.0044) as well as adverse events ( = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment ( = 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.