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Nathan Alade¹, Abhinav Nath², Nina Isoherranen²

¹Department of Pharmaceutics (N.A., N.I., K.E.T.) and Medicinal Chemistry (A.N.), School of Pharmacy, University of Washington, Seattle, Washington alade@uw.edu.

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概括

本研究引入了一种新的非线性混合效应模型 (NLME) 来从稀疏的数据中确定酶动力学参数,帮助药物基因组学研究. 这种方法有效地评估基因组特征对药物代谢的影响,资源有限.

科学领域:

药物基因组学药物基因组学
药物新陈代谢药物新陈代谢
计算生物学计算生物学

背景情况:

使用人类肝脏显微体评估基因组特征对药物代谢的影响是具有挑战性的,因为数据要求.
准确的预测需要大型的,具有代表性的数据集,这些数据集往往是资源密集的或无法获得的.
现有的方法在有限的样本可用性和高吞吐量需求方面扎.

研究的目的:

开发一种使用非线性混合效应模型 (NLME) 来从稀疏数据中确定酶动态参数的新方法.
为了促进对复杂的基因组特征的评估,在资源限制下在体外对异生菌代谢.
为了能够对影响动力参数变量的共变量进行严格的测试.

主要方法:

开发了一个非线性混合效应 (NLME) 建模框架.
将模型应用于人类肝脏显微体的稀疏体内数据.
利用以前发表的细胞染色体P450 (CYP) 2D6数据进行in silico验证.
研究了稀疏采样和实验误差对动力参数估计的影响.

主要成果:

通过使用稀疏数据,NLME方法成功确定了酶动力学参数.
该模型在评估基因组特征对迈凯利斯-门动力学影响方面表现出实用性.
该框架允许共变量分析,并减少了对大量实验材料的需求.
在 silico 验证证实了该模型能够处理稀疏采样和实验错误的能力.

结论:

一个基于NLME的新框架使得基因组特征对药物代谢影响的有效体外评估能够使用稀疏的数据.
这种方法克服了传统方法的局限性,减少了实验需求.
该方法适用于药物基因组学中的和过程和共变量分析.

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