4.5 Article

Kinetic modeling and analysis of dynamic bioluminescence imaging of substrates administered by intraperitoneal injection

期刊

出版社

AME PUBL CO
DOI: 10.21037/qims.2020.01.01

关键词

Kinetic model; dynamic bioluminescence imaging (BLI); intraperitoneal injection (IP injection)

资金

  1. National Key R&D Program of China [2018YFC0910602]
  2. National Natural Science Foundation of China [81571725, 81627807, 11727813, 81871397, 91859109]
  3. Fok Ying-Tong Education Foundation of China [161104]
  4. Program for the Young Top-notch Talent of Shaanxi Province
  5. Research Fund for Young Star of Science and Technology in Shaanxi Province [2018KJXX-018]
  6. Best Funded Projects for the Scientific and Technological Activities for Excellent Overseas Researchers in Shaanxi Province [2017017]
  7. Fundamental Research Funds for Central Universities [JB171202, JB171206, JB181203]

向作者/读者索取更多资源

Background: Bioluminescence imaging (BLI) has been found to have diverse applications in the life sciences and medical research due to its ease of use and high sensitivity. From kinetics analysis, dynamic imaging studies have significant advantages for diagnosis when compared to traditional static imaging studies. This work focuses on modeling and quantitatively analyzing the dynamic data produced from the intraperitoneal (IP) injection of D-luciferin in longitudinal BLI, aiming to provide a powerful tool for monitoring the growth of tumors. Methods: We constructed a three-compartment pharmacokinetic (PK) model and employed the standard Michaelis-Menten (M-M) kinetics to investigate the dynamic BLI data produced from the IP injection of D-luciferin. The 3 compartments were the plasma compartment, the non-specific compartment, and the specific compartment. The validity of this PK model was tested by the dynamic BLI data of MKN28M-luc xenograft mice, along with the published longitudinal dynamic BLI data of B16F10-luc xenograft mice. Results: The R-squares between the simulated lines and the measurement were 1 and 0.99, respectively, for the mice data and the published data. In addition, the 2 kinetic macroparameters obtained reflected the rate of tumor growth in vivo. In particular, the values of macroparameters A showed a significant dependence on tumor surface area. Conclusions: The proposed PK model may be an effective tool for use in drug development programs and for monitoring the response of tumors to treatment.

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