Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Eisai Co., Ltd., 5-1-3 Tokodai, Tsukuba-shi, Ibaraki, 300-2635, Japan.
Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Sumitomo Dainippon Pharma Co., Ltd., 3-1-98 Kasugade-naka, Konohana-ku, Osaka, 554-0022, Japan.
Regul Toxicol Pharmacol. 2021 Jun;122:104919. doi: 10.1016/j.yrtph.2021.104919. Epub 2021 Mar 20.
The Standard for Exchange of Nonclinical Data (SEND) has been adopted by the US FDA, which has required pharmaceutical companies who are developing new drugs for the US market to implement SEND. The Japan Pharmaceutical Manufacturers Association (JPMA) SEND Taskforce Team responded to this situation by starting a project to better understand the contents of SEND datasets. The project focused on domains generally included in the SEND domains for single- and repeat-dose general toxicology studies, and surveyed what kind of information are populated in which domains and in what way. The qualitative analysis of the results indicated that variations exist based on whether or not an individual variable was populated and on how the variable was populated. The Taskforce Team recommends reducing variations not only in the SEND datasets but also in the descriptions in the study protocol and/or final study report. Reduction of such variations should lead to higher quality datasets with powerful and increased searchability so that accumulated SEND datasets should become more valuable. These efforts would provide regulatory agencies with easier review of SEND datasets, which contributes to efficient development of new drug candidates.
美国 FDA 已采用《非临床数据交换标准》(SEND),要求开发面向美国市场新药的制药公司实施 SEND。日本制药商协会(JPMA)SEND 工作组针对这种情况启动了一个项目,以便更好地了解 SEND 数据集的内容。该项目侧重于 SEND 单剂量和重复剂量一般毒理学研究域中通常包含的域,并调查了在哪些域中填入了何种信息以及以何种方式填入。结果的定性分析表明,存在基于是否填入单个变量以及如何填入变量的差异。工作组建议不仅要减少 SEND 数据集的差异,还要减少研究方案和/或最终研究报告中的描述中的差异。减少这些差异应能提高具有强大和可搜索性的高质量数据集,从而使累积的 SEND 数据集更具价值。这些努力将为监管机构提供更轻松地审查 SEND 数据集的机会,这有助于提高新药候选物的开发效率。