Application of physiologically based pharmacokinetic modeling to understand real‐world outcomes in patients receiving imatinib for chronic myeloid leukemia
Abstract We aimed to use physiologically based pharmacokinetic (PBPK) modeling and simulation to predict imatinib steady‐state plasma exposure in patients with chronic myeloid leukemia (CML) to investigate variability in outcomes. A validated imatinib PBPK model (Simcyp Simulator) was used to predic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-08-01
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| Series: | Pharmacology Research & Perspectives |
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| Online Access: | https://doi.org/10.1002/prp2.1082 |
| _version_ | 1797743653730385920 |
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| author | Josephine A. Adattini Jeffry Adiwidjaja Annette S. Gross Andrew J. McLachlan |
| author_facet | Josephine A. Adattini Jeffry Adiwidjaja Annette S. Gross Andrew J. McLachlan |
| author_sort | Josephine A. Adattini |
| collection | DOAJ |
| description | Abstract We aimed to use physiologically based pharmacokinetic (PBPK) modeling and simulation to predict imatinib steady‐state plasma exposure in patients with chronic myeloid leukemia (CML) to investigate variability in outcomes. A validated imatinib PBPK model (Simcyp Simulator) was used to predict imatinib AUCss, Css,min and Css,max for patients with CML (n = 68) from a real‐world retrospective observational study. Differences in imatinib exposure were evaluated based on clinical outcomes, (a) Early Molecular Response (EMR) achievement and (b) occurrence of grade ≥3 adverse drug reactions (ADRs), using the Kruskal‐Wallis rank sum test. Sensitivity analyses explored the influence of patient characteristics and drug interactions on imatinib exposure. Simulated imatinib exposure was significantly higher in patients who achieved EMR compared to patients who did not (geometric mean AUC0‐24,ss 51.2 vs. 42.7 μg h mL−1, p < 0.05; Css,min 1.1 vs. 0.9 μg mL−1, p < 0.05; Css,max 3.4 vs. 2.8 μg mL−1, p < 0.05). Patients who experienced grade ≥3 ADRs had a significantly higher simulated imatinib exposure compared to patients who did not (AUC0‐24,ss 56.1 vs. 45.9 μg h mL−1, p < 0.05; Css,min 1.2 vs. 1.0 μg mL−1, p < 0.05; Css,max 3.7 vs. 3.0 μg mL−1, p < 0.05). Simulations identified a range of patient (sex, age, weight, abundance of hepatic CYP2C8 and CYP3A4, α1‐acid glycoprotein concentrations, liver and kidney function) and medication‐related factors (dose, concomitant CYP2C8 modulators) contributing to the inter‐individual variability in imatinib exposure. Relationships between imatinib plasma exposure, EMR achievement and ADRs support the rationale for therapeutic drug monitoring to guide imatinib dosing to achieve optimal outcomes in CML. |
| first_indexed | 2024-03-12T14:58:31Z |
| format | Article |
| id | doaj.art-639c704b3e3a43ecaec0d443cd48baa2 |
| institution | Directory Open Access Journal |
| issn | 2052-1707 |
| language | English |
| last_indexed | 2024-03-12T14:58:31Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Pharmacology Research & Perspectives |
| spelling | doaj.art-639c704b3e3a43ecaec0d443cd48baa22023-08-14T10:06:44ZengWileyPharmacology Research & Perspectives2052-17072023-08-01114n/an/a10.1002/prp2.1082Application of physiologically based pharmacokinetic modeling to understand real‐world outcomes in patients receiving imatinib for chronic myeloid leukemiaJosephine A. Adattini0Jeffry Adiwidjaja1Annette S. Gross2Andrew J. McLachlan3Sydney Pharmacy School, Faculty of Medicine and Health The University of Sydney Sydney AustraliaSydney Pharmacy School, Faculty of Medicine and Health The University of Sydney Sydney AustraliaSydney Pharmacy School, Faculty of Medicine and Health The University of Sydney Sydney AustraliaSydney Pharmacy School, Faculty of Medicine and Health The University of Sydney Sydney AustraliaAbstract We aimed to use physiologically based pharmacokinetic (PBPK) modeling and simulation to predict imatinib steady‐state plasma exposure in patients with chronic myeloid leukemia (CML) to investigate variability in outcomes. A validated imatinib PBPK model (Simcyp Simulator) was used to predict imatinib AUCss, Css,min and Css,max for patients with CML (n = 68) from a real‐world retrospective observational study. Differences in imatinib exposure were evaluated based on clinical outcomes, (a) Early Molecular Response (EMR) achievement and (b) occurrence of grade ≥3 adverse drug reactions (ADRs), using the Kruskal‐Wallis rank sum test. Sensitivity analyses explored the influence of patient characteristics and drug interactions on imatinib exposure. Simulated imatinib exposure was significantly higher in patients who achieved EMR compared to patients who did not (geometric mean AUC0‐24,ss 51.2 vs. 42.7 μg h mL−1, p < 0.05; Css,min 1.1 vs. 0.9 μg mL−1, p < 0.05; Css,max 3.4 vs. 2.8 μg mL−1, p < 0.05). Patients who experienced grade ≥3 ADRs had a significantly higher simulated imatinib exposure compared to patients who did not (AUC0‐24,ss 56.1 vs. 45.9 μg h mL−1, p < 0.05; Css,min 1.2 vs. 1.0 μg mL−1, p < 0.05; Css,max 3.7 vs. 3.0 μg mL−1, p < 0.05). Simulations identified a range of patient (sex, age, weight, abundance of hepatic CYP2C8 and CYP3A4, α1‐acid glycoprotein concentrations, liver and kidney function) and medication‐related factors (dose, concomitant CYP2C8 modulators) contributing to the inter‐individual variability in imatinib exposure. Relationships between imatinib plasma exposure, EMR achievement and ADRs support the rationale for therapeutic drug monitoring to guide imatinib dosing to achieve optimal outcomes in CML.https://doi.org/10.1002/prp2.1082adverse drug reactionsdrug interactionsearly molecular responseexposure‐responseimatinibphysiologically based pharmacokinetic (PBPK) |
| spellingShingle | Josephine A. Adattini Jeffry Adiwidjaja Annette S. Gross Andrew J. McLachlan Application of physiologically based pharmacokinetic modeling to understand real‐world outcomes in patients receiving imatinib for chronic myeloid leukemia Pharmacology Research & Perspectives adverse drug reactions drug interactions early molecular response exposure‐response imatinib physiologically based pharmacokinetic (PBPK) |
| title | Application of physiologically based pharmacokinetic modeling to understand real‐world outcomes in patients receiving imatinib for chronic myeloid leukemia |
| title_full | Application of physiologically based pharmacokinetic modeling to understand real‐world outcomes in patients receiving imatinib for chronic myeloid leukemia |
| title_fullStr | Application of physiologically based pharmacokinetic modeling to understand real‐world outcomes in patients receiving imatinib for chronic myeloid leukemia |
| title_full_unstemmed | Application of physiologically based pharmacokinetic modeling to understand real‐world outcomes in patients receiving imatinib for chronic myeloid leukemia |
| title_short | Application of physiologically based pharmacokinetic modeling to understand real‐world outcomes in patients receiving imatinib for chronic myeloid leukemia |
| title_sort | application of physiologically based pharmacokinetic modeling to understand real world outcomes in patients receiving imatinib for chronic myeloid leukemia |
| topic | adverse drug reactions drug interactions early molecular response exposure‐response imatinib physiologically based pharmacokinetic (PBPK) |
| url | https://doi.org/10.1002/prp2.1082 |
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