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Review

Computational modeling in glioblastoma: from the prediction of blood–brain barrier permeability to the simulation of tumor behavior

    Ana Miranda

    Pharmaceutical Technology Department, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal

    Pharmacometrics Group of the Centre for Neurosciences & Cell Biology (CNC), University of Coimbra, Rua Larga, Faculty of Medicine, Pólo 1, 1st floor, 3004-504 Coimbra, Portugal

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    ,
    Tânia Cova

    Coimbra Chemistry Center, Department of Chemistry, University of Coimbra, Rua larga, 3004-535 Coimbra, Portugal

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    ,
    João Sousa

    Pharmaceutical Technology Department, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal

    Pharmacometrics Group of the Centre for Neurosciences & Cell Biology (CNC), University of Coimbra, Rua Larga, Faculty of Medicine, Pólo 1, 1st floor, 3004-504 Coimbra, Portugal

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    ,
    Carla Vitorino

    *Author for correspondence: Tel.: +351 239 488 400;

    E-mail Address: csvitorino@ff.uc.pt

    Pharmaceutical Technology Department, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal

    Pharmacometrics Group of the Centre for Neurosciences & Cell Biology (CNC), University of Coimbra, Rua Larga, Faculty of Medicine, Pólo 1, 1st floor, 3004-504 Coimbra, Portugal

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    &
    Alberto Pais

    Coimbra Chemistry Center, Department of Chemistry, University of Coimbra, Rua larga, 3004-535 Coimbra, Portugal

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    Published Online:13 Dec 2017https://doi.org/10.4155/fmc-2017-0128

    Abstract

    The integrated in silico–in vitro–in vivo approaches have fostered the development of new treatment strategies for glioblastoma patients and improved diagnosis, establishing the bridge between biochemical research and clinical practice. These approaches have provided new insights on the identification of bioactive compounds and on the complex mechanisms underlying the interactions among glioblastoma cells, and the tumor microenvironment. This review focuses on the key advances pertaining to computational modeling in glioblastoma, including predictive data on drug permeability across the blood–brain barrier, tumor growth and treatment responses. Structure- and ligand-based methods have been widely adopted, enabling the study of dynamic and evolutionary aspects of glioblastoma. Their potential applications as predictive tools and the advantages over other well-known methodologies are outlined. Challenges regarding in silico approaches for predicting tumor properties are also discussed.

    Papers of special note have been highlighted as: • of interest; •• of considerable interest

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