Quantitative analysis of complex drug-drug interactions (DDIs) between cerivastatin and inhibitors using physiologically based pharmacokinetic (PBPK) modeling

Data Supplement

• Supplemental Data -

  Supplemental Figure 1 - Fitted blood concentration-time profile of cyclosporine A (CsA)

  Supplemental Figure 2 - Fitted blood concentration-time profile of cerivastatin (CER) under control conditions in the study of DDI with CsA (A) (0.2 mg CER was administered on day 6) or with gemfibrozil (GEM) (B) (0.3 mg CER was administered on day 3)

  Supplemental Figure 3 - Simulated blood concentration-time profiles of CER under control and DDI conditions using the geometric mean of in vitro inhibition constants after the optimization of model parameters to explain PK
  
  data under control conditions (Supplemental Table 5) (Kim et al., 2017)

  Supplemental Figure 4 - Sensitivity analyses of the in vitro parameters of CER and CsA under DDI conditions with CsA (multiple oral administration, 111 mg (fitted value, same as Supplemental Fig. 3(A)) twice a day for 6 days) after single oral administration of 0.2 mg CER on day 6

  Supplemental Figure 5 - Sensitivity analyses of the in vitro parameters of CER and GEM/GEM-glu under DDI conditions with GEM (multiple oral administration, 600 mg twice a day for 3 days) after single oral administration of 0.3 mg CER on day 3
  
  

  Supplemental Figure 6 - Simulated blood concentration-time profile of CER under control and DDI conditions with GEM (600 mg of GEM was orally administered twice a day for 3 days) using the best-fit in vitro parameter sets (Table
  
  3) after the sensitivity analyses (Figs. 5 and 6) of model parameters to explain PK data under DDI conditions

  Supplemental Figure 7 - Simulated blood concentration-time profiles of cerivastatin under control and DDI conditions with cyclosporine A in each liver model using the geometric mean of in vitro inhibition constants after the
  
  optimization of model parameters to explain PK data under control conditions (Supplemental Table 5; Ki_OATP1B1 and Ki_CYP3A4 were 0.024 μM and 3.71 μM, respectively.)

  Supplemental Figure 8 - The results of PBPK analyses of CER in the model without EHC

  Supplemental Table 1 - Pharmacokinetic parameters for cerivastatin (CER)

  Supplemental Table 2 - Optimized parameter values of cyclosporine A (CsA) after the fitting analysis by Napp
  
  

  Supplemental Table 3 - Pharmacokinetic parameters for gemfibrozil (GEM)/gemfibrozil 1-O-β-glucuronide (GEM-glu)
  
  

  Supplemental Table 4 - A) Optimized parameters of CER in the DDI study with CsA under control conditions after the fitting analysis by Napp
  
  

  Supplemental Table 5 - Summary of in vitro inhibition constants reported by Kim et al. (Kim et al., 2017)
  
  

  Supplemental Table 6 - Average, SD and CV% of the 30 most-optimized drug-dependent parameter sets of CER
  
  in 1- and 3-liver models selected to minimize the WSS from the observed i.v. and p.o. data after the optimization of model parameters by CNM to explain the PK data under i.v. conditions
  
  

  Supplemental Table 7 - Optimized parameters of CER in the DDI study with CsA under control conditions after the fitting analysis by Napp
  
  

  Supplemental Table 8 - Optimized parameters of CsA after the fitting analysis by Napp in 1- and 3-liver models
  
  

  Supplemental Table 9 - Average, SD and CV% of the 30 most-optimized drug-dependent parameter sets of CER
  
  in the model without EHC selected to minimize the WSS from the observed i.v. and p.o. data after the optimization of model parameters by CNM to explain the PK data under i.v. conditions
  
  

  Supplemental Table 10 - Optimized parameters of CER in the DDI study with CsA under control conditions after the fitting analysis by Napp in the model without EHC
  
  

  References
  
  

  Model equations for cerivastatin (CER)