Nishimura Kazuko, Shibata Hiroko, Aoyama Muneo, Hosogi Jun, Kadotsuji Kenta, Minoura Kyoko, Mori Tamiki, Nakamura Takahiro, Nishimiya Kazuhiro, Nomura Tatsuki, Saito Tetsu, Soma Masako, Wakabayashi Hiroki, Sakamoto Norihisa, Niimi Shingo, Katori Noriko, Saito Yoshiro, Ishii-Watabe Akiko
National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan.
Eisai Co., Ltd, 5-1-3 Tokodai, Tsukuba-shi, Ibaraki 300-2635, Japan.
Bioanalysis. 2019 Mar;11(6):509-524. doi: 10.4155/bio-2018-0178. Epub 2019 Apr 4.
Appropriateness of anti-drug antibody (ADA) assay is critical for immunogenicity assessment of biopharmaceuticals. Although cut point setting in ADA assay has a large impact on the results, a standard statistical approach for its setting has not been well established. In this multi-laboratory study, to elucidate factors influencing the cut point setting, we compared the statistical approaches and calculated cut points for multiple datasets of ADA assays using the individual procedure employed at each laboratory. We showed that outlier exclusion, false-positive rate and investigating data distribution have the greatest impact on both screening and confirmatory cut points. Our results would be useful for industry researchers and regulators engaged in immunogenicity assessment of biopharmaceuticals.
抗药物抗体(ADA)检测的适用性对于生物制药的免疫原性评估至关重要。尽管ADA检测中的切点设定对结果有很大影响,但尚未建立用于设定切点的标准统计方法。在这项多实验室研究中,为了阐明影响切点设定的因素,我们比较了统计方法,并使用每个实验室采用的单独程序计算了多个ADA检测数据集的切点。我们发现,异常值排除、假阳性率和研究数据分布对筛选和确证切点都有最大影响。我们的结果将对从事生物制药免疫原性评估的行业研究人员和监管机构有用。