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快速分解药片 (FDT) 预制剂试验的数据集开发:数据聚合数据聚合
Mehri Momeni1, Saleh Rakhshani2, Mohammadreza Abbaspour2
1Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
BMC research notes
|July 3, 2023
概括
这项研究将1982年的快速分解药片配方整合到一个全面的数据集中. 这一数据集有助于开发用于加速平板电脑制造的预测模型.
科学领域:
- 制药科学 制药科学
- 计算化学的计算化学
- 药物开发 药物开发
背景情况:
- 平板电脑制造是资源密集型,需要大量的时间和精力.
- 人工智能 (AI) 和预测建模有可能简化平板电脑生产.
- 缺乏全面的数据集阻碍了在平板电脑配方中应用预测模型.
研究的目的:
- 将快速分解片 (FDT) 配方汇总和整合到一个统一的数据集中.
- 解决开发准确预测模型所需的大量数据的需求.
- 通过人工智能驱动的洞察力,促进和加速平板电脑制造过程.
主要方法:
- 在2010-2020年期间,使用与平板电脑配方和分解相关的关键词进行了系统的文献搜索.
- 从四个数据库中检索了1503篇文章,根据研究标准选择了232篇.
- 数据预处理涉及统一名称和单位,专家审查以删除不适当的配方,以及数据整理以创建1982年FDT配方的清洁数据集.
主要成果:
- 成功创建了1982年快速分解药片配方的综合数据集.
- 数据集包括对制药研究和药物发现至关重要的宝贵信息.
- 该方法可用于汇总其他剂型的数据集.
结论:
- 开发的FDT配方数据集是制药研究的宝贵资源.
- 这些综合数据可以在发现和开发新药方面发挥重要作用.
- 该方法提供了一个可扩展的方法,用于构建各种药物剂量形式的数据集.
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