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.
Keywords: blood–brain barrier; cancer treatment; computational modeling; glioblastoma; ligand-based methods; structure-based methods.