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Integration analysis of metabolites and single nucleotide polymorphisms improves the prediction of drug response of celecoxib

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Abstract

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.

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (Nos. 81673390, 81603219, 81603196), Jiangsu Provincial Natural Science Foundation (No. BK20191322); the Open Project Program of MOE Key Laboratory of Drug Quality Control and Pharmacovigilance (No. DQCP2017MS01); “Double First-Class” University project (Nos. CPU2018GY05 and CPU2018GY34).

Author information

Author notes

  1. Xiaoqing Xing and Pengcheng Ma have contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China

    Xiaoqing Xing, Guohua Zhou & Qinxin Song

  2. Department of Pharmacology, Jinling Hospital, Medical School of Nanjing University, No. 305, Zhongshan East Road, Nanjing, 210002, China

    Xiemin Qi, Bingjie Zou & Guohua Zhou

  3. Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, 210042, China

    Pengcheng Ma, Jun Wei, Lei Tao & Lingjun Li

  4. Jiangsu Institute for Food and Drug Control, Nanjing, 210008, China

    Qing Huang

  5. Department of Pharmacy, Hebei General Hospital, Shijiazhuang, 050051, China

    Xiaoqing Xing

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  1. Xiaoqing Xing
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Contributions

XX supervised the experiment, manuscript writing and performed statistical data analysis; PM, JW, LT, LL supervised all experimental procedures of the clinical trial; QH supervised the experiment, analyzed the raw data and performed the metabolite identification; XQ, BZ supervised the experiment; GZ, QS supervised and designed the experiment and manuscript writing and all authors reviewed the manuscript.

Corresponding authors

Correspondence to Guohua Zhou or Qinxin Song.

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Authors declare that there are no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Xing, X., Ma, P., Huang, Q. et al. Integration analysis of metabolites and single nucleotide polymorphisms improves the prediction of drug response of celecoxib. Metabolomics 16, 41 (2020). https://doi.org/10.1007/s11306-020-01659-1

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