Original Article
Kinetic modeling and analysis of dynamic bioluminescence imaging of substrates administered by intraperitoneal injection
Abstract
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.
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.
