基于矢量的比较和平均斜率可以改进生物等价性要求:一种机器和深度学习方法
Maria Kokkali1, Vangelis D Karalis1,2
1Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Athens, Greece.
两个新的方法,即吸收率 (AS) 和基于载体的临床终点 (VBC),增强了生物等价性研究. 结合AS和VBC可以提高准确性,减少变异性,降低药物开发成本.
科学领域:
- 药物动力学和生物制药学
- 临床试验中的统计建模.
背景情况:
- 生物等价性 (BE) 研究对于药物开发至关重要,确保治疗等价性.
- 当前的BE研究方法在准确性,效率和成本方面面临挑战.
研究的目的:
- 在生物等价性研究中评估吸收率 (AS) 和基于载体的临床终点 (VBC) 的综合优势.
- 根据标准的统计方法和先进的机器学习技术来评估AS和VBC的性能.
主要方法:
- 利用了14个现实世界数据集来评估AS和VBC的性能.
- 与机器学习和人工神经网络一起应用标准监管统计方法.
- 分析了使用AS的吸收率和使用VBC的临床终点,将它们分解为独立的组件.
主要成果:
- AS和VBC的组合准确地测量了吸收率,同时显著降低了数据的变化.
- 这种综合方法增强了统计能力,并有效地解决了多重性问题.
- 这些方法支持使用较小的样本大小,从而导致简化研究设计.
结论:
- AS和VBC的联合应用提供了一个精确而有效的方法来定义BE研究中的吸收率.
- 这些创新方法改善了研究结果,减少了资源需求,缩短了完成时间.
- AS和VBC代表了现代生物等价分析的宝贵工具,优化了药物开发过程.
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