A Standards-Based Bioinformatics Workflow for Digital Twins of Patient-Derived GBM 3D Cultures: Integrating SBML Systems Pharmacology, Viability Screens and MGMT Promoter Methylation Metadata for Rapid Glioblastoma Drug-Combination Prioritization

Rationale. Bioinformatics workflows increasingly need to integrate heterogeneous biomedical data, including experimental assay outputs, patient-derived model metadata, mechanistic models and molecular-pathology information. In glioblastoma (GBM), such integration can help connect treatment responses measured in patient-derived 3D cultures with molecular context and mechanistic representations of tumor metabolism, while maintaining a clear distinction between experimental observations and model-derived hypotheses. Results. We describe a standards-based workflow that integrates patient-derived GBM drug-response screens with a machine-readable QSP model, MGMT promoter methylation metadata and an explicit evidence-tier framework. MGMT-promoter methylation was confirmed in all patient-derived 3D cultures (Ge258, Ge518 and Ge904). Phenformin (PTF) showed the most pronounced single-agent activity, with a concentration-dependent reduction in viability, while 2-deoxy-D-glucose (2-DG) produced a more moderate effect. Among the higher-order combinations, MTF + BPTES + 2-DG + TMZ produced the most pronounced combination response and a statistically significant reduction in viability under the tested conditions. These findings are interpreted as treatment-response and prioritization signals rather than definitive evidence of pharmacological synergy or clinical efficacy. Availability and implementation. The workflow is designed around open and independently checkable data and model files, including raw plate exports, processed block-level tables, molecular metadata, an SBML/SED-ML/COMBINE model package, an evidence-tier table and a next-experiment protocol. Together, these components provide a reproducible framework for integrating computational predictions with experimental evidence and prioritizing testable drug-combination hypotheses.