Integrating statistical genetics with nonlinear fixed effect pharmacokinetic models to advance high-throughput personalized drug therapy

药代动力学药物遗传学（PGPK）研究ADME过程中DNA序列变异与其他药物相关结果之间的关系。欧洲药品管理局（EMA）在2007年的一份反思文件中正式承认了PGPK的重要性。例如，他们建议检测UGT 1A 1和TPMT的多态性，这两种基因分别编码参与伊立替康和巯基嘌呤处置的酶。在HIV抗逆转录病毒治疗中，发现CYP 2B 6 G516 T杂合子和罕见纯合子的依法韦仑浓度分别高1.4和3倍，而阿扎那韦的血浆水平（最近开发的蛋白酶抑制剂）在具有常见纯合子的患者中显示出比MDR 1 C3435 T变体的杂合子或罕见纯合子高2.8和3.5。非线性混合效应（NLME）模型被引入到生物医学领域，并且已经实质上改进了从临床前和临床药代动力学（PK）试验中获得的信息。事实上，NLME模型允许量化影响剂量-反应关系的复杂生理过程的参数。最近，NLME建模社区对多个单核苷酸多态性（SNP）分析表现出越来越大的兴趣。然而，PK模型的非线性结构和每个SNP与一个或多个生理参数的关联潜力代表了巨大的统计和计算挑战。本研究项目旨在通过将遗传统计学中开发的尖端方法整合到处理药代动力学特征所需的NLME框架中，弥合PK数据分析与不断增长的遗传学信息之间的差距。将在模拟的基础上评估所研究的统计方法的计算负担、稳健性和功效（目标1和2）。进一步的统计发展将应用于公共和工业项目中进行的PGPK研究的分析（目标3）。该研究项目将由Julie Bertrand博士在大卫鲍尔丁教授的监督下进行，涉及多个公共和工业合作，包括英国的伦敦大学学院（UCL），法国国家艾滋病研究机构（ANRS）和法国的施维雅制药公司。

Pharmacogenetics in pharmacokinetics (PGPK) studies the relationship between variations in DNA sequence on ADME processes and other drug-related outcomes. The European Medicine Agency (EMA) has formally acknowledged the importance of PGPK in a reflection paper in 2007. For example, they recommend the testing of polymorphisms in UGT1A1 and TPMT, which encode enzymes involved in the disposition of irinotecan and mercaptopurine, respectively. In HIV antiretroviral therapy, the CYP2B6 G516T heterozygote and rare homozygote have been found to have 1.4 and 3 times higher concentrations of efavirenz whereas Plasma levels of atazanavir (recently developed protease inhibitor) were shown to be 2.8 and 3.5 higher in patients with the common homozygote than in heterozygotes or rare homozygotes for the MDR1 C3435T variant.About 30 years ago, nonlinear mixed effects (NLME) models were introduced to the biomedical field and have substantially improved the information learned from preclinical and clinical pharmacokinetic (PK) trials. Indeed, NLME models allow quantification of parameters influencing the complex physiological processes underlying the dose-response relationship. Recently the NLME modelling community has shown a growing interest in multiple single nucleotide polymorphisms (SNPs) analyses. However, the nonlinear structure of the PK models and the potential of association from each SNP to one or more physiological parameters represent great statistical and computational challenges. This research project aims at bridging the gap between the analysis of PK data and the growing body of genetics information by integrating the cutting-edge methods developed in genetic statistics into the NLME framework required to handle pharmacokinetic profiles. The computational burden, robustness and power of the statistical methods under study will be assessed on the basis of simulations (aims 1 and 2). Further the statistical developments will be applied to the analysis of PGPK studies performed in both public and industrial projects (aim 3).This research project will be conducted by Dr. Julie Bertrand under the supervision of Pr. David Balding and involves multiple public and industrial collaborations including the University College London (UCL) in the UK, the French National Agency of research in AIDS (ANRS) in France and the pharmaceutical company Servier in France.