Pediatric diffuse midline glioma (DMG) is an aggressive, infiltrative brain tumor [1]. Therapeutic response is assessed longitudinally using MRI [2]. Functional drug screening in patient-derived samples is increasingly employed to optimize treatments but often relies on static endpoint-based metrics that fail to capture response dynamics[3]. This lack of quantitative metrics that summarize response trajectories in a clinically interpretable manner represents a critical gap in translational drug evaluation. We performed functional drug screening in 33 DMG-relevant therapeutics tested at three concentrations each in spheroids derived from ten DMG cell-lines cultured for two weeks on an automated live-cell imaging and drug administration platform (3DTwin® Profiler). 1,100 longitudinal response profiles were analysed with a flexible ordinary differential equation (ODE)-framework to capture spheroid growth, treatment-induced cell kill, and post-treatment dynamics. The Bayesian Information Criterion was used to balance goodness-of-fit and model complexity based on a least-squares fit assessed by coefficients of determination (R2). Inferred parameter distributions were analyzed using unsupervised clustering and compared to clusters derived from data-driven time–series–based trajectory analysis. Parameter distributions and trajectory clusters were compared to those from DMG patient tumor growth trajectories. We obtained excellent fits across cell lines and compounds (Rcell_line2 =0.969 (IQR [0.960,0.972]), Rcompound2=0.97 (IQR [0.93,0.98])). Clustering of model parameters identified distinct groups by cell lines and drugs characterized by qualitatively different response dynamics, which aligned with known mechanisms of drug action, such as cytostatic proliferation arrest or rapid cytotoxicity. These mechanistic clusters showed strong concordance with trajectory-based clustering, supporting the robustness of the derived response metrics. Our comparison to DMG patient response trajectories further demonstrates the multi-scale applicability of the suggested response metric. We establish a trajectory-aware, mechanistically interpretable metric of drug-response. By aligning preclinical and clinical response paradigms, this framework provides a foundation for improved translational relevance of functional precision oncology approaches in DMG.