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在统计分析BBB透性指数的染色学数据

Karolina Wanat¹, Elżbieta Brzezińska¹

¹Department of Analytical Chemistry, Faculty of Pharmacy, Medical University of Lodz, 90-419 Lodz, Poland.

|July 28, 2023

概括

这项研究将药物特性与血脑屏障 (BBB) 透性相关联. 薄层染色学 (TLC) 数据显著改善了预测药物进入中枢神经系统 (CNS) 的模型.

科学领域:

药理动力学和药物发现
计算化学的计算化学
神经科学是一个神经科学.

背景情况:

血脑屏障 (BBB) 透性对于治疗神经系统疾病和了解中枢神经系统药物副作用至关重要.
准确预测BBB透率对于有效的药物开发至关重要.

研究的目的:

评估活性药物成分 (API) 的物理化学性质,色谱数据和BBB透性之间的相关性.
通过回归分析开发中枢神经系统生物利用性的预测模型.

主要方法:

统计分析和相关性评估APIs的物理化学性质和文献衍生的BBB透性数据 (日志BB,Kp,uu,大脑).
构建回归模型,包括分子描述器 (键接受器/捐赠器,电荷,LUMO能量) 和色谱保留数据 (TLC,HPLC).
应用多重线性回归用于模型开发,特别是利用正常相TLC数据.

主要成果:

影响BBB透性的关键分子描述因素包括键接受器/键捐赠器,生理电荷和最低空置分子轨道 (LUMO) 能量.
正常阶段的TLC数据对日志BB回归模型做出了重大贡献,实现了R2 = 0.87.87的预测值.
对于Kp,uu,大脑预测的模型产生较低的统计数据 (R2 = 0.56) 与23个API,包括k IAM.

结论:

物理化学和染色学特性,特别是TLC数据,是BBB透性的有价值预测因素.
开发的回归模型为预测中枢神经系统中药物的生物可用性提供了基础,有助于药物设计和安全性评估.

关键词:

通过BBB透.克普,乌,大脑这样.血脑屏障血脑屏障血脑屏障血脑屏障染色体保留数据的数据数据挖掘技术数据挖掘技术时间 BB 记录 BB 记录统计建模统计建模

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