Fengjiao Bu, Yong-Soon Cho, Qingfeng He, Xiaowen Wang, Saurav Howlader, Dong-Hyun Kim, Mingshe Zhu, Jae Gook Shin, Xiaoqiang Xiang
{"title":"利用基于生理学的药代动力学模型预测安罗替尼作为受害者参与的药代动力学药物间相互作用","authors":"Fengjiao Bu, Yong-Soon Cho, Qingfeng He, Xiaowen Wang, Saurav Howlader, Dong-Hyun Kim, Mingshe Zhu, Jae Gook Shin, Xiaoqiang Xiang","doi":"10.2147/DDDT.S480402","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Anlotinib was approved as a third line therapy for advanced non-small cell lung cancer in China. However, the impact of concurrent administration of various clinical drugs on the drug-drug interaction (DDI) potential of anlotinib remains undetermined. As such, this study aims to evaluate the DDI of anlotinib as a victim by establishing a physiologically based pharmacokinetic (PBPK) model.</p><p><strong>Methods: </strong>The PBPK model of anlotinib as a victim drug was constructed and validated in the Simcyp<sup>®</sup> incorporating parameters derived from in vitro studies, pre-clinical investigations, and clinical research encompassing patients with cancer. Subsequently, plasma exposure of anlotinib in cancer patients was predicted for single- and multi-dose co-administration with typical perpetrators mentioned in Food and Drug Administration (FDA) industrial guidance.</p><p><strong>Results: </strong>Based on predictions, the CYP3A potent inhibitor ketoconazole demonstrated the most significant DDI with anlotinib, regardless of whether anlotinib is administered as a single dose or multiple doses. Ketoconazole increased the area under the concentration-time curve (AUC) and maximum concentration (C<sub>max</sub>) of single-dose anlotinib to 1.41-fold and 1.08-fold, respectively. In contrast, rifampicin, a potent inducer of CYP3A enzymes, exhibited a relatively higher level of DDI, with AUC<sub>R</sub> and C<sub>maxR</sub> values of 0.44 and 0.79, respectively.</p><p><strong>Conclusion: </strong>Based on the PBPK modeling, there is a low risk of DDI between anlotinib and potent CYP3A/1A2 inhibitors, but caution and enhanced monitoring for adverse reactions are advised. To mitigate the risk of anti-tumor treatment failure, it is recommended to avoid concurrent use of strong CYP3A inducers. In conclusion, our study enhances understanding of anlotinib's interaction with medications, aiding scientists, prescribers, and drug labels in gauging the expected impact of CYP3A/1A2 modulators on anlotinib's pharmacokinetics.</p>","PeriodicalId":11290,"journal":{"name":"Drug Design, Development and Therapy","volume":"18 ","pages":"4585-4600"},"PeriodicalIF":4.7000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11490238/pdf/","citationCount":"0","resultStr":"{\"title\":\"Prediction of Pharmacokinetic Drug-Drug Interactions Involving Anlotinib as a Victim by Using Physiologically Based Pharmacokinetic Modeling.\",\"authors\":\"Fengjiao Bu, Yong-Soon Cho, Qingfeng He, Xiaowen Wang, Saurav Howlader, Dong-Hyun Kim, Mingshe Zhu, Jae Gook Shin, Xiaoqiang Xiang\",\"doi\":\"10.2147/DDDT.S480402\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Anlotinib was approved as a third line therapy for advanced non-small cell lung cancer in China. However, the impact of concurrent administration of various clinical drugs on the drug-drug interaction (DDI) potential of anlotinib remains undetermined. As such, this study aims to evaluate the DDI of anlotinib as a victim by establishing a physiologically based pharmacokinetic (PBPK) model.</p><p><strong>Methods: </strong>The PBPK model of anlotinib as a victim drug was constructed and validated in the Simcyp<sup>®</sup> incorporating parameters derived from in vitro studies, pre-clinical investigations, and clinical research encompassing patients with cancer. Subsequently, plasma exposure of anlotinib in cancer patients was predicted for single- and multi-dose co-administration with typical perpetrators mentioned in Food and Drug Administration (FDA) industrial guidance.</p><p><strong>Results: </strong>Based on predictions, the CYP3A potent inhibitor ketoconazole demonstrated the most significant DDI with anlotinib, regardless of whether anlotinib is administered as a single dose or multiple doses. Ketoconazole increased the area under the concentration-time curve (AUC) and maximum concentration (C<sub>max</sub>) of single-dose anlotinib to 1.41-fold and 1.08-fold, respectively. In contrast, rifampicin, a potent inducer of CYP3A enzymes, exhibited a relatively higher level of DDI, with AUC<sub>R</sub> and C<sub>maxR</sub> values of 0.44 and 0.79, respectively.</p><p><strong>Conclusion: </strong>Based on the PBPK modeling, there is a low risk of DDI between anlotinib and potent CYP3A/1A2 inhibitors, but caution and enhanced monitoring for adverse reactions are advised. To mitigate the risk of anti-tumor treatment failure, it is recommended to avoid concurrent use of strong CYP3A inducers. 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Prediction of Pharmacokinetic Drug-Drug Interactions Involving Anlotinib as a Victim by Using Physiologically Based Pharmacokinetic Modeling.
Background: Anlotinib was approved as a third line therapy for advanced non-small cell lung cancer in China. However, the impact of concurrent administration of various clinical drugs on the drug-drug interaction (DDI) potential of anlotinib remains undetermined. As such, this study aims to evaluate the DDI of anlotinib as a victim by establishing a physiologically based pharmacokinetic (PBPK) model.
Methods: The PBPK model of anlotinib as a victim drug was constructed and validated in the Simcyp® incorporating parameters derived from in vitro studies, pre-clinical investigations, and clinical research encompassing patients with cancer. Subsequently, plasma exposure of anlotinib in cancer patients was predicted for single- and multi-dose co-administration with typical perpetrators mentioned in Food and Drug Administration (FDA) industrial guidance.
Results: Based on predictions, the CYP3A potent inhibitor ketoconazole demonstrated the most significant DDI with anlotinib, regardless of whether anlotinib is administered as a single dose or multiple doses. Ketoconazole increased the area under the concentration-time curve (AUC) and maximum concentration (Cmax) of single-dose anlotinib to 1.41-fold and 1.08-fold, respectively. In contrast, rifampicin, a potent inducer of CYP3A enzymes, exhibited a relatively higher level of DDI, with AUCR and CmaxR values of 0.44 and 0.79, respectively.
Conclusion: Based on the PBPK modeling, there is a low risk of DDI between anlotinib and potent CYP3A/1A2 inhibitors, but caution and enhanced monitoring for adverse reactions are advised. To mitigate the risk of anti-tumor treatment failure, it is recommended to avoid concurrent use of strong CYP3A inducers. In conclusion, our study enhances understanding of anlotinib's interaction with medications, aiding scientists, prescribers, and drug labels in gauging the expected impact of CYP3A/1A2 modulators on anlotinib's pharmacokinetics.
期刊介绍:
Drug Design, Development and Therapy is an international, peer-reviewed, open access journal that spans the spectrum of drug design, discovery and development through to clinical applications.
The journal is characterized by the rapid reporting of high-quality original research, reviews, expert opinions, commentary and clinical studies in all therapeutic areas.
Specific topics covered by the journal include:
Drug target identification and validation
Phenotypic screening and target deconvolution
Biochemical analyses of drug targets and their pathways
New methods or relevant applications in molecular/drug design and computer-aided drug discovery*
Design, synthesis, and biological evaluation of novel biologically active compounds (including diagnostics or chemical probes)
Structural or molecular biological studies elucidating molecular recognition processes
Fragment-based drug discovery
Pharmaceutical/red biotechnology
Isolation, structural characterization, (bio)synthesis, bioengineering and pharmacological evaluation of natural products**
Distribution, pharmacokinetics and metabolic transformations of drugs or biologically active compounds in drug development
Drug delivery and formulation (design and characterization of dosage forms, release mechanisms and in vivo testing)
Preclinical development studies
Translational animal models
Mechanisms of action and signalling pathways
Toxicology
Gene therapy, cell therapy and immunotherapy
Personalized medicine and pharmacogenomics
Clinical drug evaluation
Patient safety and sustained use of medicines.