Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models

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Abstract Glioblastoma multiforme (GBM) is an aggressive primary brain tumour that presents significant treatment challenges due to its complex pathology and heterogeneity. The lack of validated molecular targets is a major obstacle for discovering new therapeutic candidates, with no new effective GBM therapies delivered to patients in over two decades. Here, we report the identification of compounds that target the GBM stem cell survival phenotype. Our approach employs machine learning (ML) predictors of cell survival trained on high-throughput, image-based, phenotypic screening data for 3,561 compounds, at multiple concentrations, across a panel of six heterogeneous, patient-derived, GBM stem cell lines. We computationally screened more than 12,000 compounds spanning various chemical classes. Experimental validation of ML-identified candidates across the GBM stem cell lines led to the identification of three compounds with activity against the GBM phenotype. Notably, one of our validated hits, the Hsp90 inhibitor XL888, displayed targeted elimination of all six GBM stem cell lines with IC50 in the nanomolar range. The other two compounds, which displayed broad activity across multiple GBM cell lines with distinct cell line sensitivities, offer routes for future personalised medicine campaigns. Our work demonstrates the use of phenotypic screening in tandem with ML can effectively identify therapeutic leads for personalised treatments in highly heterogeneous indications with few known molecular targets.

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