Experimentally calibrated multiscale model predicts schedule dependent drug combination effects.

Therapeutic synergy emerges from interactions between molecular drug action, intracellular signaling, and tissue-level transport dynamics. We developed a multiscale model integrating these scales to predict schedule-dependent drug combination effect in the AGS cell line. Calibrated solely on single-drug growth curves, the model accurately predicted population-level outcomes of drug combinations without combination-specific training. This demonstrates the model's capacity to suggest mechanistic multiscale insights into the logic of drug combinations within the AGS cell line, establishing a computational platform for the systematic in silico exploration of virtual multiscale experiments on drug diffusion and dosing schedules. Cross-scale analysis revealed that combination therapy efficacy flows across scales: population-level pharmacokinetics dictate the sequence of molecular target engagement within individual cells, determining collective cell-fate decisions. Our simulations predict that inhibiting the PI3K/AKT axis before MEK is more effective than the reverse order, disabling a pro-survival rebound and locking cells into an apoptotic state. Ultimately, by capturing phenomena that single-scale approaches cannot, this framework generates translationally relevant hypotheses, providing a versatile platform for optimizing drug scheduling, formulation, and combination strategies where matched molecular Boolean models and phenotypic data are available.