Department of Pharmaceutics (N.A., N.I., K.E.T.) and Medicinal Chemistry (A.N.), School of Pharmacy, University of Washington, Seattle, Washington
Department of Pharmaceutics (N.A., N.I., K.E.T.) and Medicinal Chemistry (A.N.), School of Pharmacy, University of Washington, Seattle, Washington.
Drug Metab Dispos. 2023 Nov;51(11):1455-1462. doi: 10.1124/dmd.123.001260. Epub 2023 Aug 10.
In pharmacogenomic studies, the use of human liver microsomes as a model system to evaluate the impact of complex genomic traits (i.e., linkage-disequilibrium patterns, coding, and non-coding variation, etc.) on efficiency of drug metabolism is challenging. To accurately predict the true effect size of genomic traits requires large richly sampled datasets representative of the study population. Moreover, the acquisition of this data can be labor-intensive if the study design or bioanalytical methods are not high throughput, and it is potentially unfeasible if the abundance of sample needed for experiments is limited. To overcome these challenges, we developed a novel strategic approach using non-linear mixed effects models (NLME) to determine enzyme kinetic parameters for individual liver specimens using sparse data. This method can facilitate evaluation of the impact that complex genomic traits have on the metabolism of xenobiotics in vitro when tissue and other resources are limited. In addition to facilitating the accrual of data, it allows for rigorous testing of covariates as sources of kinetic parameter variability. In this study, we present a practical application of such an approach using previously published in vitro cytochrome P450 (CYP) 2D6 data and explore the impact of sparse sampling, and experimental error on known kinetic parameter estimates of CYP2D6 mediated formation of 4-hydroxy-atomoxetine in human liver microsomes. SIGNIFICANCE STATEMENT: This study presents a novel non-linear mixed effects model (NLME)-based framework for evaluating the impact of complex genomic traits on saturable processes described by a Michaelis-Menten kinetics in vitro using sparse data. The utility of this approach extends beyond gene variant associations, including determination of covariate effects on in vitro kinetic parameters and reduced demand for precious experimental material.
在药物基因组学研究中，使用人肝微粒体作为模型系统来评估复杂基因组特征（即连锁不平衡模式、编码和非编码变异等）对药物代谢效率的影响具有挑战性。要准确预测基因组特征的真实效应大小，需要具有代表性的研究人群的大型丰富采样数据集。此外，如果研究设计或生物分析方法不是高通量的，那么获取这些数据可能需要大量的劳动，并且如果实验所需的样本量有限，那么获取这些数据可能是不可行的。为了克服这些挑战，我们开发了一种新的策略方法，使用非线性混合效应模型（NLME）来使用稀疏数据确定个体肝标本的酶动力学参数。这种方法可以在组织和其他资源有限的情况下，方便评估复杂基因组特征对体外外源性物质代谢的影响。除了方便数据的积累外，它还允许严格测试协变量作为动力学参数变异性的来源。在这项研究中，我们使用先前发表的体外细胞色素 P450 (CYP) 2D6 数据展示了这种方法的实际应用，并探讨了稀疏采样和实验误差对已知 CYP2D6 介导的人肝微粒体中 4-羟基-阿托西汀形成的动力学参数估计的影响。 意义陈述：本研究提出了一种新的基于非线性混合效应模型（NLME）的框架，用于使用稀疏数据评估复杂基因组特征对米氏动力学描述的体外饱和过程的影响。这种方法的实用性不仅限于基因变异关联，还包括确定协变量对体外动力学参数的影响以及减少对珍贵实验材料的需求。
