Kidney cancer affects over 400'000 individuals annually, with Europe particularly impacted. Despite therapeutic advances, many renal cell carcinoma (RCC) patients experience limited or toxic responses to current first-line therapies. This thesis focuses on enhancing the RCC-oriented application of the Therapeutically Guided Optimization (TGMO) method, which combines in vitro data with in silico modelling to identify safe and effective drug combinations. To increase the relevance of the in vitro data, we developed two TGMO-compatible platforms: (1) patient-derived RCC organoids (PDO) for efficacy testing and (2) a toxicity assessment platform using kidney PDOs, H9c2, and HepaRG cells. Testing multidrug combinations TGMO-optimized against RCC cell line models using our novel platform and biologically relevant 3D and hypoxic culture conditions revealed limitations of standard models and the improved predictive value of our two platforms. This highlights the improved translational potential of these models and supports their early integration in the combination therapy screening process.