Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs
Abstract
1. Introduction
2. Results
2.1. Logistic Regression Modeling
2.2. Equilibrium Dialysis
2.3. Drug Metabolism in IPRL
2.4. Fitting the Model-Simulated Curves
3. Discussion
- When protein binding occurs, does the variation in the concentration of the free drug influence the accuracy of model predictions, and if so, in what way?
- WSM designates Cout as the CDF. What are the reasons behind this choice and what mechanisms could validate this assumption?
- While Equation (10) has long been regarded as practical and intuitive from a macroscopic perspective, questions remain regarding its universal applicability.
4. Materials and Methods
4.1. Logistic Regression Modeling for Hepatic Model Selection
4.2. Chemicals
4.3. Rapid Equilibrium Dialysis
4.4. Surgery and Liver Perfusion
4.5. Sample Preparation and Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
CDF | Driving force concentrations |
CDM | Representative drug concentration in DM |
CH | Total drug concentration in liver |
Cin | Drug concentration entering liver |
CLH | Hepatic clearance |
CLint | Intrinsic clearance |
CMWSM | Representative drug concentration in MWSM |
Cout | Drug concentration exiting liver |
CPTM | Logarithmic mean drug concentration in PTM |
CWSM | Representative drug concentration in WSM |
DM | Dispersion model |
ER | Extraction ratio |
FH | Hepatic availability |
fu | Intravascular fraction of unbound drug |
fu,E | Extravascular fraction of unbound drug |
HSA | Human serum albumin |
IPRL | Isolated perfused rat liver |
IVIVE | In vitro-to-in vivo extrapolation |
MWSM | Modified well-stirred model |
PTM | Parallel-tube model |
QH | Hepatic blood flow |
WSM | Well-stirred model |
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Drug | Percent Better Prediction (BP%) | ||
---|---|---|---|
W | S | M | |
Diazepam | 92.92 | 7.04 | 0.04 |
Diclofenac | 55.26 | 30.82 | 13.92 |
Rosuvastatin | 19.95 | 31.46 | 48.59 |
Concentration Sequences | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
---|---|---|---|---|---|---|---|---|---|
Drug | |||||||||
Diazepam a | 0.063 ± 0.031 (15) | 0.127 ± 0.040 (15) | 0.188 ± 0.029 (9) | 0.208 ± 0.050 (15) | 0.277 ± 0.058 (9) | 0.305 ± 0.059 (15) | 0.664 ± 0.070 (6) | 0.920 ± 0.040 (6) | |
Diclofenac b | 0.067 ± 0.010 (6) | 0.180 ± 0.044 (5) | 0.237 ± 0.041 (6) | 0.400 ± 0.084 (6) | 0.654 ± 0.064 (6) | 0.869 ± 0.039 (4) | - | - | |
Rosuvastatin c | 0.029 ± 0.019 (12) | 0.073 ± 0.027 (6) | 0.172 ± 0.015 (6) | 0.395 ± 0.015 (6) | 0.533 ± 0.037 (6) | 0.700 ± 0.032 (6) | 0.833 ± 0.039 (4) | - | |
Fluoxetine d | 0.037 ± 0.008 (7) | 0.076 ± 0.005 (7) | 0.097 ± 0.010 (7) | 0.114 ± 0.011 (7) | 0.183 ± 0.010 (6) | - | - | - | |
Tolbutamide d | 0.808 ± 0.030 (6) | 0.828 ± 0.025 (6) | 0.858 ± 0.015 (6) | 0.880 ± 0.014 (6) | 0.895 ± 0.014 (6) | - | - | - |
Drug | Interval of HSA Concentration (%) | Optimal Model | Modified WSM | WSM | PTM | DM |
---|---|---|---|---|---|---|
Diazepam | 0–0.04 | MWSM | 0.0481 | 0.1123 | 0.0894 | 0.1003 |
0.04–2 | WSM | 1.5150 | 0.1818 | 0.4375 | 0.2781 | |
Diclofenac | 0–0.01 | MWSM | 0.0278 | 0.0885 | 0.0702 | 0.0792 |
0.025–2 | WSM | 0.2923 | 0.1194 | 0.1790 | 0.1400 | |
Rosuvastatin | 0–2 | MWSM | 0.2132 | 1.8440 | 1.0670 | 1.4550 |
Fluoxetine | 0–2 | WSM | 5.9630 | 0.1154 | 0.2096 | 0.2062 |
Tolbutamide | 0–2 | WSM | 0.0923 | 0.0672 | 0.0790 | 0.0715 |
Drug | MW a | AlogP a | PSA a | Rotatable Bonds a | HBA a | HBD a | Aromatic Rings a | Heavy Atoms a | Vss b | MRT b | logCLint,u,in-vivo (pred) | fup | Class a | OATP c | P-gp c | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
hep | mic | |||||||||||||||
Diazepam | 285 | 3.15 | 32.67 | 1 | 3 | 0 | 2 | 20 | 1.00 | 44.00 | 0.93 | 0.95 | 0.021 | N | Without | With |
Diclofenac | 296 | 4.36 | 49.33 | 4 | 3 | 2 | 2 | 19 | 0.22 | 1.00 | 2.24 | 2.17 | 0.004 | A | Without | Without |
Rosuvastatin | 482 | 2.4 | 140.9 | 10 | 9 | 3 | 2 | 33 | 1.70 | 2.60 | 1.30 | NF | 0.129 | A | With | Without |
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Liang, R.-J.; Hsu, S.-H.; Chen, H.-T.; Chen, W.-H.; Fu, H.-Y.; Chen, H.-Y.; Wang, H.-J.; Tang, S.-L. Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs. Pharmaceuticals 2025, 18, 991. https://doi.org/10.3390/ph18070991
Liang R-J, Hsu S-H, Chen H-T, Chen W-H, Fu H-Y, Chen H-Y, Wang H-J, Tang S-L. Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs. Pharmaceuticals. 2025; 18(7):991. https://doi.org/10.3390/ph18070991
Chicago/Turabian StyleLiang, Ren-Jong, Shu-Hao Hsu, Hsueh-Tien Chen, Wan-Han Chen, Han-Yu Fu, Hsin-Ying Chen, Hong-Jaan Wang, and Sung-Ling Tang. 2025. "Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs" Pharmaceuticals 18, no. 7: 991. https://doi.org/10.3390/ph18070991
APA StyleLiang, R.-J., Hsu, S.-H., Chen, H.-T., Chen, W.-H., Fu, H.-Y., Chen, H.-Y., Wang, H.-J., & Tang, S.-L. (2025). Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs. Pharmaceuticals, 18(7), 991. https://doi.org/10.3390/ph18070991