Predicting the presence and mechanism of busulfan drug-drug interactions in hematopoietic stem cell transplantation using pharmacokinetic interaction network-based molecular structure similarity and network pharmacology

被引:6
|
作者
Hao, Chenxia [1 ]
Ma, Xiaoqin [1 ]
Wang, Lining [2 ]
Zhang, Weixia [1 ]
Hu, Jiong [2 ]
Huang, Jingjing [1 ]
Yang, Wanhua [1 ]
机构
[1] Shanghai Jiao Tong Univ, Ruijin Hosp, Dept Pharm, Sch Med, Shanghai, Peoples R China
[2] Shanghai Jiao Tong Univ, Ruijin Hosp, Dept Hematol, Sch Med, Shanghai, Peoples R China
关键词
Busulfan; Drug-drug interactions; Pharmacokinetics; Structural similarity; Network pharmacology; Hematopoietic stem cell transplantation; CONDITIONING THERAPY; SYSTEMIC EXPOSURE; GENETIC POLYMORPHISMS; INTRAVENOUS BUSULFAN; CLINICAL-USE; FLUDARABINE; CYCLOPHOSPHAMIDE; VORICONAZOLE; METABOLISM; PHARMACOGENETICS;
D O I
10.1007/s00228-020-03034-4
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Purpose This study aimed to predict the presence and mechanism of busulfan drug-drug interactions (DDIs) in hematopoietic stem cell transplantation (HSCT) using pharmacokinetic interaction (PKI) network-based molecular structure similarity and network pharmacology. Methods Logistic function models were established to predict busulfan DDIs based on the assumption that an approved drug tends to interact with the drug used in HSCT (DH) if structurally similar to the drugs in the PKI network of the DH. The PKI network of the DH represented the association between drugs and the proteins related to the PK of the DH. The most appropriate model was applied to predict busulfan DDIs in HSCT. Candidate targets for busulfan DDIs and their interacting were identified by network pharmacology. Results Six of the top ten predicted busulfan DDIs were clinically relevant and involved voriconazole, fludarabine, itraconazole, cyclophosphamide, metronidazole, and melphalan. Candidate targets for these DDIs were CYP450s (3A4, 2B6, 2C9, and 2C19), GSTs (GSTA1, GSTP1, GSTT1, and GSTM1), and ABC transporters (ABCB1, ABCC1, ABCC2, and ABCC3), in the targets of drug-induced liver injury (DILI). The networks of interacting proteins and candidate targets indicated the regulatory potential of pregnane X receptor (PXR), as a nuclear receptor. Enrichment analysis showed the metabolism of drugs and xenobiotics, glutathione metabolism, and bile secretion associated with busulfan DDIs and DILI. Conclusions This study has successfully predicted busulfan DDIs in HSCT through PKI-based molecular structure similarity. The mechanism of busulfan DDI and DILI was attributed mostly to CYP450s, GSTs, and ABC transporters, and PXR was identified as a potential target.
引用
收藏
页码:595 / 605
页数:11
相关论文
共 12 条
  • [1] Predicting the presence and mechanism of busulfan drug-drug interactions in hematopoietic stem cell transplantation using pharmacokinetic interaction network–based molecular structure similarity and network pharmacology
    Chenxia Hao
    Xiaoqin Ma
    Lining Wang
    Weixia Zhang
    Jiong Hu
    Jingjing Huang
    Wanhua Yang
    European Journal of Clinical Pharmacology, 2021, 77 : 595 - 605
  • [2] Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity
    Rohani, Narjes
    Eslahchi, Changiz
    SCIENTIFIC REPORTS, 2019, 9 (1)
  • [3] Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity
    Narjes Rohani
    Changiz Eslahchi
    Scientific Reports, 9
  • [4] Characterization of drug-drug interactions on the pharmacokinetic disposition of busulfan in paediatric patients during haematopoietic stem cell transplantation conditioning
    Dunn, Allison
    Moffett, Brady S.
    Ivaturi, Vijay
    Gobburu, Jogarao V. S.
    BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, 2022, 88 (05) : 2223 - 2235
  • [5] DDIPred: Graph Convolutional Network-based Drug-drug Interactions Prediction using Drug Chemical Structure Embedding
    Sadeghi, Shaghayegh
    Ngom, Alioune
    2022 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (IEEE CIBCB 2022), 2022, : 265 - 270
  • [6] A dual graph neural network for drug-drug interactions prediction based on molecular structure and interactions
    Ma, Mei
    Lei, Xiujuan
    PLOS COMPUTATIONAL BIOLOGY, 2023, 19 (01)
  • [7] Drug-drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model
    Perrier, Jeremy
    Gualano, Virginie
    Helmer, Eric
    Namour, Florence
    Lukacova, Viera
    Taneja, Amit
    CTS-CLINICAL AND TRANSLATIONAL SCIENCE, 2023, 16 (11): : 2222 - 2235
  • [8] An effective framework for predicting drug-drug interactions based on molecular substructures and knowledge graph neural network
    Chen, Siqi
    Semenov, Ivan
    Zhang, Fengyun
    Yang, Yang
    Geng, Jie
    Feng, Xuequan
    Meng, Qinghua
    Lei, Kaiyou
    COMPUTERS IN BIOLOGY AND MEDICINE, 2024, 169
  • [9] Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs
    Feng, Yue-Hua
    Zhang, Shao-Wu
    MOLECULES, 2022, 27 (09):
  • [10] Drug-drug interaction of phenytoin sodium and methylprednisolone on voriconazole: a population pharmacokinetic model in children with thalassemia undergoing allogeneic hematopoietic stem cell transplantation
    Wu, Yun
    Niu, Lu-lu
    Ling, Ya-yun
    Zhou, Si-ru
    Huang, Tian-min
    Qi, Jian-ying
    Wu, Dong-ni
    Cai, Rong-da
    Wu, Ting-qing
    Xiao, Yang
    Liu, Taotao
    EUROPEAN JOURNAL OF CLINICAL PHARMACOLOGY, 2025, 81 (03) : 365 - 374