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Prediction of Pharmacokinetic Drug-Drug Interactions Involving Anlotinib as a Victim by Using Physiologically Based

Fengjiao Bu^1,2, Yong-Soon Cho^3,4, Qingfeng He¹

¹Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, People's Republic of China.

Drug Design, Development and Therapy

|October 21, 2024

Abstract

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 (C_max) 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_R and C_maxR 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.

Insights

This study used physiologically based pharmacokinetic (PBPK) modeling to assess anlotinib drug-drug interactions (DDIs). Ketoconazole showed significant interactions, while strong CYP3A inducers should be avoided to prevent treatment failure.

Area of Science:

Pharmacokinetics and Drug Metabolism
Oncology Drug Development
Computational Pharmacology

Background:

Anlotinib is approved for advanced non-small cell lung cancer, but its drug-drug interaction (DDI) potential with other clinical drugs is unknown.
Understanding these interactions is crucial for safe and effective anlotinib use in cancer patients.

Purpose of the Study:

To evaluate the drug-drug interaction (DDI) potential of anlotinib as a victim drug.
To establish and validate a physiologically based pharmacokinetic (PBPK) model for anlotinib.

Main Methods:

A PBPK model for anlotinib was constructed and validated using in vitro, pre-clinical, and clinical data.
Simulated anlotinib exposure with co-administration of common CYP3A/1A2 inhibitors and inducers based on FDA guidance.

Main Results:

Ketoconazole (a potent CYP3A inhibitor) significantly increased anlotinib exposure (AUC by 1.41-fold, Cmax by 1.08-fold).
Rifampicin (a potent CYP3A inducer) showed a notable interaction, with AUCr of 0.44 and Cmaxr of 0.79.
Overall, a low risk of DDI was predicted with potent CYP3A/1A2 inhibitors, but caution is advised.

Conclusions:

Concurrent use of strong CYP3A inducers with anlotinib should be avoided to prevent anti-tumor treatment failure.
While potent CYP3A/1A2 inhibitors pose a low DDI risk, enhanced monitoring for adverse reactions is recommended.
This PBPK modeling provides valuable insights for clinicians and drug developers regarding anlotinib's pharmacokinetic interactions.

Read full analysis

Keywords:

CYP1A2 CYP3A DDIs PBPK anlotinib

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