Article Figures & Data
Figures
- Fig. 1.
Comparison of RST AUCR for 22 drugs calculated by basic model using IC50 values with probe substrates (A) E217βG vs. RST for OATP1B1 inhibition and (B) CCK8 vs. RST for OATP1B3 in HEK293 cells overexpressing these transporters.
- Fig. 2.
Quantitative predictions of AUCR for RST mediated by 22 studied drugs using (A) static model, which only considered OATP1B1/3 inhibition, and (B) combined static model, which took into account intestinal BCRP inhibition in addition to OATP1B1/3 inhibition.
- Fig. 3.
DDI simulations between RST and rifampicin, velpatasvir, and asunaprevir using PBPK modeling. For rifampicin, the error bars represent the range of AUCRs reported from three studies (Prueksaritanont et al., 2014; Lai et al., 2016; Shen et al., 2017). For asuanprevir and velpatasvir, the error bars represent the 90% confidence interval as reported in the respective studies (Eley et al., 2015; Mogalian et al., 2016).
Tables
- TABLE 1
PPVs and NPVs when 1.1 or 1.04 as the AUCR cutoff criteria for DDI prediction using either OATP1B1 + OATP1B3 inhibition alone (static model) or in combination with BCRP inhibition (combined static model)
Either E217β/CCK8 or RST were used as in vitro probes for OATP1B inhibition.
AUCR (Only OATP1B1 and OATP1B3) AUCR (OATP1B1, OATP1B3, and BCRP for Igut/IC50 > 10) Cutoff criteria AUCR ≥ 1.04 AUCR ≥ 1.1 AUCR ≥ 1.04 AUCR ≥ 1.1 In vitro substrate RST E217βG/CCK8 RST E217βG/CCK8 RST E217βG/CCK8 RST E217βG/CCK8 PPV 0.714 0.643 0.875 0.889 0.647 0.556 0.688 0.667 Number of TP/number of TP + FP 10/14 9/14 7/8 8/9 11/17 10/18 11/16 10/15 NPV 0.857 0.714 0.692 0.750 1 0.667 1 0.833 Number of TN/number of TN + FN 6/7 5/7 9/13 9/12 4/4 2/3 5/5 5/6 RMSE 1.31 (RST as probe); 1.24 (E217βG/CCK8 as probes) 0.767 (RST as probe); 0.812 (E217βG/CCK8 as probes)
Data Supplement
- Supplemental Data -
Supplementary Table 1 - Summary of IC50 and AUCR values predicted using the basic model for 22 studied drugs.
Supplementary Table 2 - AUCR as predicted using static and combined static model for DDI predictions for 22 studied drugs. The cutoff criteria of R ≥ 1.1 was applied here.
Supplementary Table 3 - Drug-dependent parameters for rosuvastatin PBPK model development.
Supplementary Table 4 - Drug-dependent parameters for rifampicin, velpatasvir and asunaprevir.
Supplementary Table 5 - Summary of population and dosing characteristics of DDI trial designs.
Supplementary Table 6 - List of drugs with unbound fraction < 0.01 and in vivo AUCR > 1.25 with rosuvastatin.
Supplementary Figure 1 - Observed and simulated plasma concentration-time profiles of rosuvastatin (A), rifampicin (B), velpatasvir (C) and asunaprevir (D).
Supplementary Figure 2 - Simulated plasma concentration-time profiles of rosuvastatin in the presence and absence of rifampicin (A and B), velpatasvir (C and D) and asunaprevir (E and F).
Supplementary References
- Supplemental Data -
