Benevolent Platform Precision Medicine

GBM: Hypothesis Generation and Validation A therapeutic target which functions as a radiosensitiser identified for Glioblastoma (GBM) using knowledge-graph-based relational inference models Benevolent Knowledge Graph enriched and customised to identify targets modulating viability of Glioblastoma Stem Cells (GSCs) or radiosensitisers Predictions enriched with disease relevance by use of Patient datasets (combination of 'Omics platforms) Target ID Entity selection and data build out around GBM stem cells (GSC) and radiosensitisers. Predictions for GBM using relation inference models on the Benevolent Knowledge Graph Target Triage Hypotheses prioritised based on relevance to GSC modulation, suitable safety profile, "druggability" 'Omics Target expression GBM vs normal brain tissue (Patient dataset; Single Seq), mapped across diverse pathways & mechanisms for GSC Target selection - Novel MoA for GBM - Expression in GBM tumours - Subtype preference - Radiosensitiser Unstructured data 1 2 3 4 Tgt11 ☐ Tgt12 Target Safety Tgt1 Safe Drugs? Metadata Score Partial OPC, GCNN 0.71 Tgt2 Unsafe No SQT 0.65 Structured data Graph Inference Tgt3 Safe Yes OPC, CROM 0.51 Tgt4 Phase II Offtgts OPC 0.49 'Omics Patient Stratification Experimental testing སྱཱ ཎྜ སྠཽ ༔ སྠཽ 206 1.2 0 Gy 3 Gy 1.0 0.8 IC 0.2 0.0 P 0.01 0.1 Concentration (M) Al Benevolent 53

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