• Ahmad Daher and John de Groot.
• Hartford HealthCare Cancer Institute, 85 Seymour Street, Suite 227, Hartford, CT 06106, United States. Electronic address: ahmad.daher@hhchealth.org.
• Exp. Neurol. 2018 Jan 1; 299 (Pt B): 281-288.

AbstractTumor heterogeneity is a major factor in glioblastoma's poor response to therapy and seemingly inevitable recurrence. Only two glioblastoma drugs have received Food and Drug Administration approval since 1998, highlighting the urgent need for new therapies. Profiling "omics" analyses have helped characterize glioblastoma molecularly and have thus identified multiple molecular targets for precision medicine. These molecular targets have influenced clinical trial design; many "actionable" mutation-focused trials are underway, but because they have not yet led to therapeutic breakthroughs, new strategies for treating glioblastoma, especially those with a pharmacological functional component, remain in high demand. In that regard, high-throughput screening that allows for expedited preclinical drug testing and the use of GBM models that represent tumor heterogeneity more accurately than traditional cancer cell lines is necessary to maximize the successful translation of agents into the clinic. High-throughput screening has been successfully used in the testing, discovery, and validation of potential therapeutics in various cancer models, but it has not been extensively utilized in glioblastoma models. In this report, we describe the basic aspects of high-throughput screening and propose a modified high-throughput screening model in which ex vivo and in vivo drug testing is complemented by post-screening pharmacological, pan-omic analysis to expedite anti-glioma drugs' preclinical testing and develop predictive biomarker datasets that can aid in personalizing glioblastoma therapy and inform clinical trial design.Copyright © 2017 Elsevier Inc. All rights reserved.

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