Evidence map›Paper›PMID 39429896›Full record

ArticleDrug design, development and therapy2024

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

Fengjiao Bu, Yong-Soon Cho, Qingfeng He, Xiaowen Wang, Saurav Howlader, Dong-Hyun Kim, Mingshe Zhu, Jae Gook Shin, Xiaoqiang Xiang

Abstract read
In one paragraph

Article in Drug design, development and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Fengjiao Bu *Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, People's Republic of China.
Yong-Soon Cho *Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.
Qingfeng HeDepartment of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, People's Republic of China.
Xiaowen WangDepartment of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, People's Republic of China.
Saurav HowladerDepartment of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.
Dong-Hyun KimDepartment of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.
Mingshe ZhuDepartment of DMPK, MassDefect Technologies, Princeton, NJ, USA.
Jae Gook ShinDepartment of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.
Xiaoqiang XiangDepartment of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, People's Republic of China.ORCID 0000-0002-8683-2603

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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 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.

Indexed as

Antineoplastic AgentsDrug InteractionsIndolesQuinolinesCytochrome P-450 CYP3A InhibitorsDose-Response Relationship, DrugHumansKetoconazoleModels, BiologicalProtein Kinase InhibitorsanlotinibAntineoplastic AgentsCytochrome P-450 CYP3A InhibitorsIndolesKetoconazoleProtein Kinase InhibitorsQuinolinesanlotinibCYP1A2CYP3ADDIsPBPK

Identifiers

PMID39429896
PMCPMC11490238

What Socratic holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.