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A single nucleotide polymorphism-based formula to predict the risk of propofol TCI concentration being over 4 µg mL−1 at the time of loss of consciousness

The Pharmacogenomics Journal volume 22, pages 109–116 (2022)Cite this article

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Abstract

We aim to develop a formula based on single nucleotide polymorphisms (SNPs) to predict whether the propofol target-controlled infusion (TCI) concentration would be over 4 μg mL−1 at the time of loss of consciousness (LOC). We recruited 184 patients undergoing thyroid or breast surgeries with propofol anaesthesia. A total of 48 SNPs of CYP2B6, CYP2C9, UGT1A9, HNF4A, ABCB1, ABCC4, ABCG2, GABRA2, GABRA4, GABRB1, GABRB3, GABRG2, GABBR2, GAD1, SLC1A3, BDNF, and NRXN1, previously associated with propofol metabolic and pharmacology pathway, were genotyped. The formula was developed in the training cohort using the least absolute shrinkage and selection operator logistic regression model, and then validated in the testing cohort. The SNPs, GABBR2 rs1167768, GABBR2 rs1571927, NRXN1 rs601010, BDNF rs2049046, GABRA4 rs1512135, UGT1A9 rs11692021, GABBR2 rs2808536, HNF4A rs1884613, GABRB3 rs2017247, and CYP2B6 rs3181842 were selected to construct the SNP-based formula, which was used to calculate the risk score for over 4 μg mL−1 TCI concentration of propofol at the time of LOC. Patients in the high-risk group were more likely to require a propofol concentration higher than 4 μg mL−1 and presented a longer LOC latency. The SNP-based formula may significantly improve the safety and effectiveness of propofol-induced anaesthesia.

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Fig. 1: The association between the TCI concentration of propofol at the time of LOC and LOC latency (a) and hypotension (b) in 184 patients.
Fig. 2: Mean AUC of the predictive model in 500 testing cohorts after adding different independent SNPs.
Fig. 3: Influence of SNPs markers on propofol TCI concentration (>4 μg mL−1 or not) at the time of LOC.
Fig. 4: ROC curves, risk score distribution and LOC latency variation in the training and testing cohorts.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant 81601711, 81971877], the Key Training Programs for Young Teachers of Sun Yat-sen University [Grant 19ykzd12], the Guangdong Provincial Key Laboratory of Construction Foundation [Grant 2017B030314030, 2020B1212060034] and the National Key Research and Development Program [Grant 2018YFC0116701].

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Author notes

  1. These authors contributed equally: Zhuoling Zheng, Faling Xue, Zhongxing Wang, Jiali Li.

Authors and Affiliations

  1. Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China

    Zhuoling Zheng, Caibin Zhang, Yanping Guan, Yongzi Wen, Min Huang & Jiali Li

  2. Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China

    Zhuoling Zheng, Caibin Zhang, Yanping Guan, Yongzi Wen, Min Huang & Jiali Li

  3. Department of Pharmacy, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China

    Zhuoling Zheng & Xiaoyan Li

  4. Department of Anaesthesiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China

    Faling Xue, Lingyi Zhang, Wudi Ma, Fang Ye, Wenqi Huang & Zhongxing Wang

  5. Department of Pharmacy, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China

    Haini Wang

  6. Department of Pharmacy, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China

    Yongqi He

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Contributions

ZZ, FX, ZW and JL designed the research. HW, LZ, WH and MH conducted sample selection and data management. YH, WM, FY, and YW performed the DNA isolation and genotyping. ZZ, CZ, YG and XL analyzed data and wrote the manuscript. All authors gave approval of the final version to be published, and agreed to be accountable for all aspects of the work.

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Correspondence to Zhongxing Wang or Jiali Li.

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Supplementary information

Supplementary Table 1. Information of 48 Candidate SNPs

Supplementary Table 2. Forward, reverse, and extend primers for 48 target SNPs

41397_2021_263_MOESM3_ESM.docx

Supplementary Table 3. Influence of demographic factors on propofol TCI concentration at the time of LOC (Chi-square tests)

41397_2021_263_MOESM4_ESM.docx

Supplementary Table 4. Influence of demographic factors on propofol TCI concentration at the time of LOC (Mann-Whitney U test)

Supplementary Table 5. Genotype and allele frequencies of candidate SNPs in the 184 enroled patients

41397_2021_263_MOESM6_ESM.docx

Supplementary Table 6. Influence of gene polymorphisms on propofol TCI concentration at the time of LOC in the training cohort

Supplementary Table 7. Baseline demographic characteristics in the high-risk and low-risk groups

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Zheng, Z., Xue, F., Wang, H. et al. A single nucleotide polymorphism-based formula to predict the risk of propofol TCI concentration being over 4 µg mL−1 at the time of loss of consciousness. Pharmacogenomics J 22, 109–116 (2022). https://doi.org/10.1038/s41397-021-00263-3

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