Article Figures & Data
Figures
- Fig. 1.
Comparison of apparent permeability in LLC-PK1 cells to active uptake in human hepatocytes. (A) Papp values (nanometers per second) in the apical to basal direction in LLC-PK1 cells are plotted against active uptake component in plated hepatocytes for a set of discovery compounds (MW > 400) with a carboxylic acid moiety. Each data point is as expressed an average of triplicate measurements for one compound. (B) ECCS classification of the 36 marketed drugs used for CLint,h,u prediction. ECCS class 1b compounds in parentheses represent the assignment according to Varma et al. (2015). Digoxin can be assigned to ECCS class 2 or class 4 depending on reported Papp values (Table 1).
- Fig. 2.
Apparent in vitro intrinsic clearance for marketed drugs measured in suspended human hepatocytes and HepatoPac. For 36 marketed drugs at 1 µM, apparent in vitro intrinsic clearance CLint,h [without correction by unbound fraction in incubates fu(inc)] generated with suspended hepatocytes are compared with these generated in the HepatoPac model. The CLint,h values are calculated based on linear depletion rate of the parent drug from the media during the incubation time: 2 hours for hepatocyte suspension and 96 hours for HepatoPac (n = 1 or 2). For intrinsic clearance, n = 1 denotes a single intrinsic clearance value determined using seven (suspended hepatocytes) or eight (HepatoPac) different independent incubation wells with different incubation times. A hepatocyte cell density of 1.0 mg protein/106 cells is assumed irrespective of the two different assay systems (hepatocyte suspension and HepatoPac). CLint,h values of 20 compounds assigned as ECCS class 1a and 2 (i.e., predicted to be eliminated mainly by metabolism), five compounds as ECCS class 3b (i.e., likely eliminated mainly by hepatic active uptake and/or renal excretion), and remaining four compounds as ECCS class 3a and 4 (i.e., likely eliminated mainly by renal excretion) are plotted in (A–C), respectively. Compound names with numbering for each plot are summarized in Table 1. CLint,h values for 23. fluvastatin, 27. famotidine, 29. fexofenadine, 30. memantine, 32. furosemide, 34. cimetidine, and 35. digoxin are not shown due to a lack of measurement of depletion of the drug concentration from the media in suspended hepatocytes (Table 2). Solid, broken, and bold lines represent 1:1, 1:2, and 1:3 correspondence, respectively.
- Fig. 3.
Comparison of the reference and scaled-up hepatic intrinsic clearance values for marketed drugs. The in vivo CLint,h,u values (milliliters per minute per kilogram) for 36 marketed drugs are plotted against the in vitro CLint,h,u values (milliliters per minute per kilogram) from human suspended hepatocytes (A, C, and E) and HepatoPac (B, D, and F) assays. Twenty ECCS class 1a/2 compounds (i.e., predicted to be eliminated mainly by metabolism), five ECCS class 3b compounds (i.e., likely eliminated mainly by hepatic active uptake and/or renal excretion), and remaining four ECCS class 3a/4 compounds (i.e., likely eliminated mainly by renal excretion) are shown in panels (A–F), respectively. The in vitro and in vivo CLint,h,u values to illustrate the panels are listed in Table 2. Solid, broken, and bold lines represent 1:1, 1:2, and 1:3 correspondence, respectively.
- Fig. 4.
Decision trees for the selection of the optimal in vitro assays for the in vitro to in vivo extrapolation of human hepatic clearance by active uptake and metabolism. The decision tree suggests an ECCS class-dependent selection of the optimal in vitro assay (liver microsomes, hepatocytes, and HepatoPac). The assessment of uptake and metabolic clearance processes is followed by IVIVE to predict the hepatic clearance. (A and B) represent decision trees for ECCS class1b/3b and ECCS class 1a/2/3a/4 compounds, respectively. The two schemes also indicate when the potential investigation of other disposition pathways such as biliary and renal excretion may be mandated. To compensate differences of human hepatocytes and liver microsomes for clearance optimization, hepatocellularity (120 × 106 cells/g liver) and liver microsomal contents (38 mg human liver microsome/g liver; Johnson et al., 2005) can be used.
Tables
- TABLE 1
Data supporting ECCS classification of 36 marketed drugs and collected data on main elimination pathways
Table 1 summarizes physicochemical parameters used for the ECCS classification of 36 marketed drugs. The pathways (enzymes and transporters) involved in hepatic (and renal) elimination are provided. Pathway descriptions are provided in parentheses if the clinical relevance of the elimination pathways is not confirmed. Passive permeability (Papp) in the apical to basal direction in LLC-PK1 cells was measured in the present study or was taken from the literature (Caco-2 permeability) in the instances for which the reference is provided. Documentation for physiologically based pharmacokinetic models for the marketed drugs are provided with the SimCYP software platform and have been verified to show good performance for prediction of PK profiles and drug-drug interaction potential. Therefore, the model input parameters and drug disposition pathways were used for the current assessment.
No. Compound Molecular weight (g/mol) Ionization classification at pH 7.4 Papp (A to B) Main elimination pathways ECCS (nm/s) Reference Enzyme Transporter Reference 1 Midazolam 325.78 Base 135 CYP3A4 — FDA, 2020 2 2 Quinidine 324.42 Base 280 CYP3A4 — FDA, 2020 2 3 Dextromethorphan 271.40 Base 291 CYP2D6 — FDA, 2020 2 4 Diclofenac 296.15 Acid 132.8 Lee et al., 2017 CYP2C9 — FDA, 2020 1a 5 Tolbutamide 270.35 Acid 173 CYP2C9 — FDA, 2020 1a 6 Bupropion 239.74 Base 388 CYP2B6 — FDA, 2020 2 7 Propranolol 259.34 Base 287 CYP2D6/multiple — McGinnity et al., 2004 2 8 Verapamil 454.60 Base 197 CYP3A4 — McGinnity et al., 2004 2 9 Clozapine 326.82 Base 199 CYP1A2/multiple — McGinnity et al., 2004 2 10 Efavirenz 315.68 Acid 158 CYP2B6/2A6 > 3A4 and 1A2 — Simcyp 2 11 Rosiglitazone 357.43 Acid 152 CYP2C8/3A4 — Simcyp 1a 12 Omeprazole 345.42 Neutral 253 CYP2C19/3A4 — Simcyp 2 13 Ethinylestradiol 296.40 Neutral 233 CYP1A2/2C9/3A4, UGT1A1 and SULTs — Simcyp 2 14 Mycophenolic acid 320.34 Acid 50 UGT1A9/2B7 Minor/(MRP2) Picard et al., 2005; Patel et al., 2017 1a 15 Clopidogrel 321.82 Base 257 CES1/CYP2C19 — Polasek et al., 2011 2 16 Oseltamivir 312.40 Base 45 CES1 — FDA review 3a 17 Irinotecan 586.68 Base 21 CES1/2/CYP3A P-gp/MRP2 Mathijssen et al., 2001; Toshimoto et al., 2017 4 18 SN-38 392.40 Base 37 UGT1A1 P-gp/MRP2/BCRP Mathijssen et al., 2001; Toshimoto et al., 2017 4 19 Benzydamine 309.41 Base 302 FMO(1/)3 (and UGT) — Baldock et al., 1990; Bohnert et al., 2016 2 20 Cyclosporine A 1202.60 Neutral 68 CYP3A4 (P-gp) Simcyp; (Thiel et al., 2015) 2 21 Ketoconazole 531.43 Base 171 CYP3A4 (+UGT/FMO/deacetylation) — Kim et al., 2017 2 22 Atorvastatin 558.64 Acid 11 CYP3A4 OATP1B1 Maeda et al., 2011 3b 23 Fluvastatin 411.47 Acid 26 Li et al., 2011 CYP2C9 (OATP1B1/)BCRP Fujino et al., 2004; Keskitalo et al., 2009; Kunze et al., 2014 3b 24 Simvastatin 418.57 Neutral 52 CYP3A4 (OATP1B1/1B3) Simcyp; (Kunze et al., 2014) 2 25 Pravastatin 424.53 Acid 3.22 Minor OATP1B1/MRP2 Thiel et al., 2015 3b 26 Rosuvastatin 481.54 Acid 0 (BLOQ) Minor OATP1B1/NTCP/BCRP Simcyp 3b 27 Famotidine 337.45 Base 12 Minor (S-oxide formation) OCT2 (renal) Echizen and Ishizaki, 1991; FDA, 2020 4 28 Gemfibrozil 250.33 Acid 82 UGT2B7 — Simcyp 1a 29 Fexofenadine 501.68 Zwitterions 0 (BLOQ) — OATP1B3 and unknown mechanism Shimizu et al., 2005 3b 30 Memantine 179.30 Base 320 — OCT2 (renal) FDA, 2020 2 31 Repaglinide 452.59 Zwitterions 41 CYP3A4/2C8 OATP1B1 Drug label 3b 32 Furosemide 330.75 Acid 36 Biliary excretion OAT3 (renal) FDA, 2020 3a 33 Valsartan 435.52 Acid 0 (BLOQ) P450 (minor) OATP1B1/1B3/MRP2 Waldmeier et al., 1997; FDA, 2020 3b 34 Cimetidine 252.34 Base 35 P450 (minor) OAT3/OCT2 (renal) Somogyi and Gugler, 1983; McGinnity et al., 2004; FDA, 2020 4 35 Digoxin 780.94 Neutral 54 Zhang and Morris, 2003 Minor P-gp (Caldwell and Cline, 1976); Simcyp 2 35–45 4 36 Talinolol 363.50 Base 29 Anderle et al., 1998 (CYP3A4) P-gp (and MRP2) Giessmann et al., 2004; FDA, 2020 4 BLOQ, below limit of quantification; NTCP, Na+-taurocholate cotransporting polypeptide.
- TABLE 2
Measured and scaled hepatic intrinsic clearance from the in vitro assays, in comparison with the respective in vivo data among 36 marketed drugs
Not detected: calculated values are below the internal resolution limit criteria (<0.1 µl/min per milligram). For IVIVE with the in vitro data, the following scaling factors were applied irrespective of differences of the two in vitro assay systems (hepatocyte suspension and HepatoPac): hepatocellularity, 120 (106 cells = mg protein/g liver); liver weight, 22 (g liver/kgbw); and body weight, 70 (kgbw). These were generally derived from a system parameter database (Johnson et al., 2005). The in vivo intrinsic hepatic clearance as reference was calculated with the well-stirred model, the in vivo CLh,p, and hepatic blood flow rate (23.2 ml/min per milligram; Johnson et al., 2005). For the marketed drugs, perfusion-limited conditions were applied even though permeability limitations in hepatic elimination may have been reported for some. Key PK parameters used to estimate the in vitro CLint,h,u (upscaled) and in vivo CLint,h,u (reference) were also provided in this table. Derivations of the in vivo CLtot,p are listed in Supplemental Table 1 with the respective literature source information. Documentation for physiologically based pharmacokinetic models for the marketed drugs are provided with the SimCYP software platform and have been verified to show good performance for prediction of PK profiles and drug-drug interaction potential. Therefore, the model input parameters and drug disposition pathways were used for the current assessment.
No. Compound fu,p Rb fu(inc)a In vitro In vivo CLint,h (hep sus) CLint,h,u CLint,h (Hepato Pac) CLint,h,u CLint,h,u CLh,p CLr,p CLothers,p CLtot,p - Ref. - Ref. - (µl/min per milligram) (ml/min per milligram) (µl/min per milligram) (ml/min per kilogram) (ml/min per kilogram ECCS class 1a and 2b 1 Midazolam 0.0320 Simcyp 0.60 Simcyp 0.25 32.50 345.35 43.60 463.29 631.27 8.27 0.02 0.00 8.29 2 Quinidine 0.2020 Simcyp 0.82 Simcyp 0.72 2.59 9.54 12.50 46.04 52.34 6.80 1.67 0.00 8.47 3 Dextromethorphan 0.5000 Simcyp 1.32 Simcyp 0.91 9.60 27.88 13.70 39.78 57.00 14.76 0.00 0.00 14.76 4 Diclofenac 0.0038 Ye et al., 2016 0.55 Ye et al., 2016 0.04 3.10 222.73 4.30 308.95 561.90 1.83 2.74 0.00 4.57 5 Tolbutamide 0.0440 Simcyp 0.60 Simcyp 0.32 1.17 9.80 1.30 10.89 4.85 0.21 0.00 0.00 0.21 6 Bupropion 0.1600 Simcyp 0.82 Simcyp 0.66 0.70 2.82 27.10 109.10 119.20 9.52 0.00 0.00 9.52 7 Propranolol 0.0990 Poulin and Theil, 2002 0.89 Shibata et al., 2002 0.52 3.00 15.13 11.50 57.99 129.88 7.92 0.04 0.00 7.96 8 Verapamil 0.1000 Lombardo et al., 2004 0.77 Shibata et al., 2002 0.53 23.40 117.37 23.40 117.37 12,484.95 17.61 0.36 0.00 17.97 9 Clozapine 0.0550 Simcyp 0.85 Simcyp 0.37 5.00 35.88 12.90 92.57 43.40 2.13 0.0065 0.00 2.13 10 Efavirenz 0.0290 Simcyp 0.74 Simcyp 0.23 2.60 29.85 2.40 27.55 21.92 0.61 0.00 0.00 0.61 11 Rosiglitazone 0.0030 Simcyp 0.96 Simcyp 0.03 2.77 250.34 3.00 271.13 245.70 0.71 0.0007 0.00 0.71 12 Omeprazole 0.0430 Simcyp 0.59 Simcyp 0.31 6.30 53.65 25.60 218.00 90.69 3.04 0.01 0.00 3.04 13 Ethinylestradiol 0.0150 Simcyp 1.00 Simcyp 0.13 37.92 757.49 17.90 357.57 403.36 4.80 1.0011 0.00 5.80 14 Mycophenolic acid 0.0300 Bullingham et al., 1998 0.70 Malmborg and Ploeger, 2013 0.24 5.38 60.13 18.00 201.17 209.22 4.53 0.0158 0.00 4.54 15 Clopidogrel 0.0370 In silico pred. 1.00 -c 0.28 200.00 1902.23 59.60 566.86 1229.14 15.36 0.00 0.00 15.36 19 Benzydamine 0.1460 Reddy et al., 2018 0.76 Reddy et al., 2018 0.63 2.63 11.00 5.50 23.01 7.04 0.97 1.31 0.00 2.29 20 Cyclosporine A 0.0365 Simcyp 1.62 Simcyp 0.27 3.10 29.79 10.30 98.97 460.25 11.61 0.01 0.00 11.62 21 Ketoconazole 0.0290 Simcyp 0.62 Simcyp 0.23 23.50 269.77 28.40 326.02 108.50 2.58 0.04 0.00 2.62 24 Simvastatin 0.0200 Simcyp 1.00 Simcyp 0.17 88.84 1383.77 49.70 774.13 422.83 6.20 0.92 0.00 7.12 28 Gemfibrozil 0.0080 Simcyp 0.75 Simcyp 0.07 2.34 82.78 8.10 286.55 159.50 1.19 0.01 0.09d 1.28 30 Memantine 0.5500 Product monograph_Lundbeck 1.00 -c 0.92 Not detected 0.00 0.50 1.43 0.71 0.39 1.82 0.00 2.20 35 Digoxin 0.7100 Simcyp 1.07 Simcyp 0.96 0.00 0.70 1.92 1.97 1.32 2.48 0.00 3.80 ECCS class 3be 22 Atorvastatin 0.0570 Waldmeier et al., 1997 0.68 PMDA database 0.38 1.29 9.04 9.90 69.38 179.64 6.21 0.00 0.55 6.75f 23 Fluvastatin 0.0100 Tse et al., 1993 0.71 Tse et al., 1993 0.09 Not detected 0.00 19.20 552.50 401.46 3.23 0.33 2.54 6.10f 25 Pravastatin 0.4850 Simcyp 0.56 Simcyp 0.90 1.00 2.92 0.40 1.17 22.15 5.86 4.60 0.00 10.47 26 Rosuvastatin 0.1070 Simcyp 0.63 Simcyp 0.55 1.24 6.01 1.20 5.81 186.86 8.40 3.24 0.00 11.64 29 Fexofenadine 0.3000 Product monograph_Sanofi (a median of the range; in vivo fu,p) 1.00 -c 0.81 Not detected 0.00 0.10 0.33 8.04 2.19 0.91 0.00 3.10 31 Repaglinide 0.0230 Simcyp 0.62 Simcyp 0.19 3.38 46.83 8.80 121.92 1057.47 9.04 0.01 0.00 9.05 33 Valsartan 0.0560 Simcyp 0.56 Simcyp 0.37 0.30 2.13 0.40 2.84 6.87 0.37 0.15 0.00 0.52 ECCS class 3a and 4g 16 Oseltamivir (prodrug) 0.8130 In silico pred. 1.00 -c 0.98 25.72 69.46 19.50 52.66 204.91 20.36 6.17 0.00 26.53 17 Irinotecan 0.2980 Toshimoto et al., 2017 0.97 Toshimoto et al., 2017 0.81 0.80 2.61 3.30 10.76 32.01 6.70h 1.87 0.00 8.57 18 SN-38 0.0283 Toshimoto et al., 2017 1.31 Toshimoto et al., 2017 0.23 0.50 5.85 6.60 77.25 264.80h — — — — 27 Famotidine 0.8105 (Echizen and Ishizaki, 1991) (a median of the range; in vivo fp) 1.00 -c 0.98 Not detected 0.00 Not detected 0.00 1.82 1.38 3.63 0.00 5.01 32 Furosemide 0.0240 Camenisch and Umehara, 2012 0.75 Camenisch and Umehara, 2012 0.20 Not detected 0.00 0.00 0.00 8.31 0.20 0.71 1.06i 1.97 34 Cimetidine 0.7785 (Somogyi and Gugler, 1983) (mean of the range) 0.98 Somogyi and Gugler, 1983 0.97 Not detected 0.00 0.50 1.36 6.09 3.92 6.81 0.00 10.74 36 Talinolol 0.3000 Thiel et al., 2015 1.00 -c 0.81 1.00 3.26 4.50 14.65 7.70 2.10 2.80 0.00 4.90 ↵a Predicted with dilution method (Schuhmacher et al., 2000) using the respectively reported or measured unbound fractions in plasma. In hepatocyte suspension and HepatoPac assays, 10% FCS was added to the incubation media.
↵b ECCS class 1a (n = 5, in bold) and class 2 (n = 17): primarily elimination = metabolism.
↵c Informed assumption since there is no literature information available.
↵d Contribution of UGT2B7 in kidney (Simcyp).
↵e ECCS class 3b (n = 7): primarily elimination = hepatic uptake (or renal excretion).
↵f Estimation using Simcyp to capture the plasma concentration profiles over time.
↵g ECCS class 3a (n = 2, in bold) and class 4 (n = 5): primarily elimination = renal excretion.
↵h Estimation with physiologically based PK modeling using top-down approach (Toshimoto et al., 2017).
↵i Contribution of biliary excretion.
