Modeling the Genetic Etiology of Pharmacokinetic-Pharmacodynamic Links with the Arma Process
Modeling the Genetic Etiology of Pharmacokinetic-Pharmacodynamic Links with the Arma Process
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DOI：
10.1080/10543400903572795
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发表时间：
2010-01-01
影响因子：
1.1
通讯作者：
Wu, Rongling
中科院分区：
文献类型：
--
作者：
Lin, Min;Berg, Arthur;Wu, Rongling
不同患者对药物的反应存在很大差异。识别导致人际分化的遗传因素一直是药物遗传学研究和药物发现的重要任务。在这篇文章中，我们已经推导出一个高维统计模型，揭示药物反应的遗传机制，通过整合两个不同的，但生物学相关的processespharmacokinetics（PK）和药效学（PD）到一个遗传作图框架。使用PK和PD的集成模型，我们可以识别特定的DNA序列变体，并测试它们如何与身体对药物（PK）的差异效应以及药物对身体（PD）的影响相关。为了在PD和PK水平上有效建模协方差矩阵的两阶段分层结构，我们首次将自回归移动平均（阿尔马）过程引入到基于混合的似然函数中，用于序列映射。将基于ARMA的协方差矩阵的行列式和逆的封闭形式估计纳入估计步骤中，这显着提高了计算效率。已经进行了模拟研究，以测试我们的模型的统计行为。该模型的潜在应用药物遗传学研究进行了讨论。
Substantial variability exists among different patients in response to drugs. The identification of genetic factors that contribute to the interpersonal differentiation has been an important task for pharmacogenetic research and drug discovery. In this article, we have derived a high-dimensional statistical model for unveiling the genetic machinery for drug response by integrating two different but biologically related processespharmacokinetics (PK) and pharmacodynamics (PD)into a genetic mapping framework. Using an integrated model of PK and PD, we can identify specific DNA sequence variants and test how they relate to the differential effect of the body to the drug (PK) and the effect of the drug on the body (PD). To effectively model a two-stage hierarchic structure of the covariance matrix at the PD and PK level, we have for the first time introduced an autoregressive moving-average (ARMA) process to the mixture-based likelihood function for sequence mapping. Closed-form estimates of the determinant and inverse of the ARMA-based covariance matrix are incorporated into the estimation step, which significantly increases the computational efficiency. Simulation studies have been performed to test the statistical behavior of our model. Potential applications of this model to pharmacogenetic research are discussed.