Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs
<b>Background/Objectives</b>: Hepatic clearance is important in determining clinical drug administration strategies. Achieving accurate hepatic clearance predictions through in vitro-to-in vivo extrapolation (IVIVE) relies on appropriate model selection, which is a critical step. Althoug...
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2025-07-01
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| author | Ren-Jong Liang Shu-Hao Hsu Hsueh-Tien Chen Wan-Han Chen Han-Yu Fu Hsin-Ying Chen Hong-Jaan Wang Sung-Ling Tang |
| author_facet | Ren-Jong Liang Shu-Hao Hsu Hsueh-Tien Chen Wan-Han Chen Han-Yu Fu Hsin-Ying Chen Hong-Jaan Wang Sung-Ling Tang |
| author_sort | Ren-Jong Liang |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: Hepatic clearance is important in determining clinical drug administration strategies. Achieving accurate hepatic clearance predictions through in vitro-to-in vivo extrapolation (IVIVE) relies on appropriate model selection, which is a critical step. Although numerous models have been developed to estimate drug dosage, some may fail to predict liver drug clearance owing to inappropriate hepatic clearance models during IVIVE. To address this limitation, an in silico-based model selection approach for optimizing hepatic clearance predictions was introduced in a previous study. The current study extends this strategy by verifying the accuracy of the selected models using ex situ experimental data, particularly for drugs whose model choices are influenced by protein binding. <b>Methods</b>: Commonly prescribed drugs were classified according to their hepatic extraction ratios and protein-binding properties. Building on previous studies that employed multinomial logistic regression analysis for model selection, a three-phase classification method was implemented to identify five representative drugs: diazepam, diclofenac, rosuvastatin, fluoxetine, and tolbutamide. Subsequently, an isolated perfused rat liver (IPRL) system was used to evaluate the accuracy of the in silico method. <b>Results</b>: As the unbound fraction increased for diazepam and diclofenac, the most suitable predictive model shifted from the initially preferred well-stirred model (WSM) to the modified well-stirred model (MWSM). For rosuvastatin, the MWSM provided a more accurate prediction. These three capacity-limited, binding-sensitive drugs conformed to the outcomes predicted by the multinomial logistic regression analysis. Fluoxetine was best described by the WSM, which is consistent with its flow-limited classification. For tolbutamide, a representative capacity-limited, binding-insensitive drug, no significant differences were observed among the various models. <b>Conclusions</b>: These findings demonstrate the accuracy of an in silico-based model selection approach for predicting liver metabolism and highlight its potential for guiding dosage adjustments. Furthermore, the IPRL system serves as a practical tool for validating the accuracy of the results derived from this approach. |
| format | Article |
| id | doaj-art-0ffa20a7e0af47fd8d7d3c4b16b7068a |
| institution | Kabale University |
| issn | 1424-8247 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Pharmaceuticals |
| spelling | doaj-art-0ffa20a7e0af47fd8d7d3c4b16b7068a2025-08-20T03:56:49ZengMDPI AGPharmaceuticals1424-82472025-07-0118799110.3390/ph18070991Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive DrugsRen-Jong Liang0Shu-Hao Hsu1Hsueh-Tien Chen2Wan-Han Chen3Han-Yu Fu4Hsin-Ying Chen5Hong-Jaan Wang6Sung-Ling Tang7Clinical Pharmacy Department, Tri-Service General Hospital Keelung Branch, Keelung City 202006, TaiwanSchool of Pharmacy, National Defense Medical Center, Taipei 114201, TaiwanSchool of Pharmacy, National Defense Medical Center, Taipei 114201, TaiwanSchool of Pharmacy, National Defense Medical Center, Taipei 114201, TaiwanSchool of Pharmacy, National Defense Medical Center, Taipei 114201, TaiwanGraduate Institute of Life Science, National Defense Medical Center, Taipei 114201, TaiwanGraduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114201, TaiwanGraduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114201, Taiwan<b>Background/Objectives</b>: Hepatic clearance is important in determining clinical drug administration strategies. Achieving accurate hepatic clearance predictions through in vitro-to-in vivo extrapolation (IVIVE) relies on appropriate model selection, which is a critical step. Although numerous models have been developed to estimate drug dosage, some may fail to predict liver drug clearance owing to inappropriate hepatic clearance models during IVIVE. To address this limitation, an in silico-based model selection approach for optimizing hepatic clearance predictions was introduced in a previous study. The current study extends this strategy by verifying the accuracy of the selected models using ex situ experimental data, particularly for drugs whose model choices are influenced by protein binding. <b>Methods</b>: Commonly prescribed drugs were classified according to their hepatic extraction ratios and protein-binding properties. Building on previous studies that employed multinomial logistic regression analysis for model selection, a three-phase classification method was implemented to identify five representative drugs: diazepam, diclofenac, rosuvastatin, fluoxetine, and tolbutamide. Subsequently, an isolated perfused rat liver (IPRL) system was used to evaluate the accuracy of the in silico method. <b>Results</b>: As the unbound fraction increased for diazepam and diclofenac, the most suitable predictive model shifted from the initially preferred well-stirred model (WSM) to the modified well-stirred model (MWSM). For rosuvastatin, the MWSM provided a more accurate prediction. These three capacity-limited, binding-sensitive drugs conformed to the outcomes predicted by the multinomial logistic regression analysis. Fluoxetine was best described by the WSM, which is consistent with its flow-limited classification. For tolbutamide, a representative capacity-limited, binding-insensitive drug, no significant differences were observed among the various models. <b>Conclusions</b>: These findings demonstrate the accuracy of an in silico-based model selection approach for predicting liver metabolism and highlight its potential for guiding dosage adjustments. Furthermore, the IPRL system serves as a practical tool for validating the accuracy of the results derived from this approach.https://www.mdpi.com/1424-8247/18/7/991hepatic clearancein vitro-to-in vivo extrapolation (IVIVE)isolated perfused rat liver (IPRL)well-stirred model (WSM)modified well-stirred model (MWSM)protein binding |
| spellingShingle | Ren-Jong Liang Shu-Hao Hsu Hsueh-Tien Chen Wan-Han Chen Han-Yu Fu Hsin-Ying Chen Hong-Jaan Wang Sung-Ling Tang Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs Pharmaceuticals hepatic clearance in vitro-to-in vivo extrapolation (IVIVE) isolated perfused rat liver (IPRL) well-stirred model (WSM) modified well-stirred model (MWSM) protein binding |
| title | Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs |
| title_full | Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs |
| title_fullStr | Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs |
| title_full_unstemmed | Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs |
| title_short | Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs |
| title_sort | selection of an optimal metabolic model for accurately predicting the hepatic clearance of albumin binding sensitive drugs |
| topic | hepatic clearance in vitro-to-in vivo extrapolation (IVIVE) isolated perfused rat liver (IPRL) well-stirred model (WSM) modified well-stirred model (MWSM) protein binding |
| url | https://www.mdpi.com/1424-8247/18/7/991 |
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