Prediction of drug-ABC-transporter interaction - Recent advances and future challenges

被引:175
|
作者
Montanari, Floriane [1 ]
Ecker, Gerhard F. [1 ]
机构
[1] Univ Vienna, Dept Pharmaceut Chem, A-1090 Vienna, Austria
基金
奥地利科学基金会;
关键词
ABC transporters; Computational models; Bioassays; Machine learning; Pharmacophore modeling; Transport inhibition; BINDING CASSETTE TRANSPORTERS; MEDIATED MULTIDRUG-RESISTANCE; SALT EXPORT PUMP; P-GLYCOPROTEIN; COMPUTATIONAL MODELS; CRYSTAL-STRUCTURE; INHIBITION; EFFLUX; IDENTIFICATION; MODULATION;
D O I
10.1016/j.addr.2015.03.001
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
With the discovery of P-glycoprotein (P-gp), it became evident that ABC-transporters play a vital role in bioavailability and toxicity of drugs. They prevent intracellular accumulation of toxic compounds, which renders them a major defense mechanism against xenotoxic compounds. Their expression in cells of all major barriers (intestine, blood-brain barrier, blood-placenta barrier) as well as in metabolic organs (liver, kidney) also explains their influence on the ADMET properties of drugs and drug candidates. Thus, in silico models for the prediction of the probability of a compound to interact with P-gp or analogous transporters are of high value in the early phase of the drug discovery process. Within this review, we highlight recent developments in the area, with a special focus on the molecular basis of drug-transporter interaction. In addition, with the recent availability of X-ray structures of several ABC-transporters, also structure-based design methods have been applied and will be addressed. (C) 2015 The Authors. Published by Elsevier B.V.
引用
收藏
页码:17 / 26
页数:10
相关论文
共 50 条
  • [1] Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives
    Van Tran, Thi Tuyet
    Wibowo, Agung Surya
    Tayara, Hilal
    Chong, Kil To
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2023, 63 (09) : 2628 - 2643
  • [2] Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives
    Tran, Thi Tuyet Van
    Tayara, Hilal
    Chong, Kil To
    PHARMACEUTICS, 2023, 15 (04)
  • [3] Challenges with the precise prediction of ABC-transporter interactions for improved drug discovery
    Volpe, Donna A.
    Qosa, Hisham
    EXPERT OPINION ON DRUG DISCOVERY, 2018, 13 (08) : 697 - 707
  • [4] Cancer drug development in China: recent advances and future challenges
    Wu, Yi-Long
    Zhang, Helena
    Yang, Yumei
    DRUG DISCOVERY TODAY, 2015, 20 (06) : 766 - 771
  • [5] In Silico ADMET Prediction : Recent Advances, Current Challenges and Future Trends
    Cheng, Feixiong
    Li, Weihua
    Liu, Guixia
    Tang, Yun
    CURRENT TOPICS IN MEDICINAL CHEMISTRY, 2013, 13 (11) : 1273 - 1289
  • [6] Targeted protein degradation in drug development: Recent advances and future challenges
    Song, Jian
    Hu, Mingzheng
    Zhou, Jun
    Xie, Songbo
    Li, Tianliang
    Li, Yan
    EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY, 2023, 261
  • [7] Deep learning in target prediction and drug repositioning: Recent advances and challenges
    Yu, Jun-Lin
    Dai, Qing-Qing
    Li, Guo-Bo
    DRUG DISCOVERY TODAY, 2022, 27 (07) : 1796 - 1814
  • [8] Mobile apps for transporter drug-drug interaction prediction: A tool of the future, now
    Ekins, Sean
    Clark, Alex
    Polli, James
    Wright, Stephen
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2015, 250
  • [9] Molecular interaction fields in drug discovery: recent advances and future perspectives
    Artese, Anna
    Cross, Simon
    Costa, Giosue
    Distinto, Simona
    Parrotta, Lucia
    Alcaro, Stefano
    Ortuso, Francesco
    Cruciani, Gabriele
    WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE, 2013, 3 (06) : 594 - 613
  • [10] Neisseria: recent advances and future challenges
    Criss, Alison K.
    Tang, Christoph
    PATHOGENS AND DISEASE, 2017, 75 (08):