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A Semi-Physiologically Based Pharmacokinetic Model Describing the Altered Metabolism of Midazolam Due to Inflammation in Mice.
Pharmaceutical Research 2018 June 22
PURPOSE: To investigate influence of inflammation on metabolism and pharmacokinetics (PK) of midazolam (MDZ) and construct a semi-physiologically based pharmacokinetic (PBPK) model to predict PK in mice with inflammatory disease.
METHODS: Glucose-6-phosphate isomerase (GPI)-mediated inflammation was used as a preclinical model of arthritis in DBA/1 mice. CYP3A substrate MDZ was selected to study changes in metabolism and PK during the inflammation. The semi-PBPK model was constructed using mouse physiological parameters, liver microsome metabolism, and healthy animal PK data. In addition, serum cytokine, and liver-CYP (cytochrome P450 enzymes) mRNA levels were examined.
RESULTS: The in vitro metabolite formation rate was suppressed in liver microsomes prepared from the GPI-treated mice as compared to the healthy mice. Further, clearance of MDZ was reduced during inflammation as compared to the healthy group. Finally, the semi-PBPK model was used to predict PK of MDZ after GPI-mediated inflammation. IL-6 and TNF-α levels were elevated and liver-cyp3a11 mRNA was reduced after GPI treatment.
CONCLUSION: The semi-PBPK model successfully predicted PK parameters of MDZ in the disease state. The model may be applied to predict PK of other drugs under disease conditions using healthy animal PK and liver microsomal data as inputs.
METHODS: Glucose-6-phosphate isomerase (GPI)-mediated inflammation was used as a preclinical model of arthritis in DBA/1 mice. CYP3A substrate MDZ was selected to study changes in metabolism and PK during the inflammation. The semi-PBPK model was constructed using mouse physiological parameters, liver microsome metabolism, and healthy animal PK data. In addition, serum cytokine, and liver-CYP (cytochrome P450 enzymes) mRNA levels were examined.
RESULTS: The in vitro metabolite formation rate was suppressed in liver microsomes prepared from the GPI-treated mice as compared to the healthy mice. Further, clearance of MDZ was reduced during inflammation as compared to the healthy group. Finally, the semi-PBPK model was used to predict PK of MDZ after GPI-mediated inflammation. IL-6 and TNF-α levels were elevated and liver-cyp3a11 mRNA was reduced after GPI treatment.
CONCLUSION: The semi-PBPK model successfully predicted PK parameters of MDZ in the disease state. The model may be applied to predict PK of other drugs under disease conditions using healthy animal PK and liver microsomal data as inputs.
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