Introduction

Pharmacogenetics and pharmacometabolomics are the common methods for personalized medicine, either genetic or metabolic biomarkers have limited predictive power for drug response.

Objectives

In order to better predict drug response, the study attempted to integrate genetic and metabolic biomarkers for drug pharmacokinetics prediction.

Methods

The study chose celecoxib as study object, the pharmacokinetic behavior of celecoxib was assessed in 48 healthy volunteers based on UPLC–MS/MS platform, and celecoxib related single nucleotide polymorphisms (SNPs) were also detected. Three mathematic models were constructed for celecoxib pharmacokinetics prediction, the first one was mainly based on celecoxib-related SNPs; the second was based on the metabolites selected from a pharmacometabolomic analysis by using GC–MS/MS method, the last model was based on the combination of the celecoxib-related SNPs and metabolites above.

Results

The result proved that the last model showed an improved prediction power, the integration model could explain 71.0% AUC variation and predict 62.3% AUC variation. To facilitate clinical application, ten potential celecoxib-related biomarkers were further screened, which could explain 68.3% and predict 54.6% AUC variation, the predicted AUC was well correlated with the measured values (r = 0.838).

Conclusion

This study provides a new route for personalized medicine, the integration of genetic and metabolic biomarkers can predict drug response with a higher accuracy.

中文翻译：

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代谢物和单核苷酸多态性的整合分析改善了塞来昔布药物反应的预测

介绍

药物遗传学和药物代谢代谢组学是个性化医学的常用方法，无论是遗传或代谢生物标志物对于药物反应的预测能力均有限。

目标

为了更好地预测药物反应，该研究试图将遗传和代谢生物标志物整合到药物药代动力学预测中。

方法

该研究以塞来昔布为研究对象，基于UPLC-MS / MS平台评估了48名健康志愿者中塞来昔布的药代动力学行为，并检测了塞来昔布相关的单核苷酸多态性（SNPs）。建立了三种用于塞来昔布药代动力学预测的数学模型，第一个主要基于塞来昔布相关的SNP。第二个模型基于使用GC-MS / MS方法从药代动力学分析中选择的代谢物，最后一个模型基于上述塞来昔布相关的SNP和代谢物的组合。

结果

结果证明，最后一个模型具有更好的预测能力，集成模型可以解释71.0％的AUC变化，并预测62.3％的AUC变化。为便于临床应用，进一步筛选了十种潜在的塞来昔布相关生物标志物，可以解释68.3％并预测54.6％的AUC变化，预测的AUC与测量值具有很好的相关性（r = 0.838）。

结论

这项研究为个性化医学提供了一条新途径，遗传和代谢生物标志物的整合可以更准确地预测药物反应。