A Large Concept Model for Mechanistic Simulation of Disease Trajectories: A Hypothesis-Generating Exemplar for...

Background: Many diseases evolve through complex, nonlinear trajectories shaped by interacting genetic, cellular, and environmental factors over time. Such dynamics are difficult to represent using static risk models, particularly in biologically heterogeneous conditions such as pediatric acute lymphoblastic leukemia (ALL). Here, we present a large concept model (LCM) as a mechanistic, hypothesis-generating framework for simulating longitudinal disease trajectories using pediatric ALL relapse dynamics as a proof-of-concept exemplar. Methods: We developed a causal longitudinal modeling framework implemented within the aiHumanoid v11.0 platform to characterize post-remission relapse dynamics. Seven clinically relevant ETV6::RUNX1-based genotypic profiles were simulated from remission baseline (T0) through 2 post-remission intervals (T1 = 3 months; T2 = 6 months). Longitudinal remission-to-relapse changes were evaluated across genotype- and age-defined virtual cohorts using descriptive nonparametric effect-size-oriented measures. Relapse dynamics were summarized using 2 composite system-level metrics: the relapse risk score and relapse pressure index. Results: The model generated distinct genotype- and age-associated trajectory patterns across relapse-relevant biological domains and produced composite measures reflecting modeled relapse pressure within the simulation environment. Greater relapse-associated biological divergence was observed in selected genotype-age strata, particularly in domains related to clonal evolution, treatment resistance, and minimal residual disease. Conclusions: This ALL-focused proof of concept demonstrates the architectural and analytic potential of mechanistic trajectory simulation for hypothesis generation, longitudinal systems modeling, and future integration with real-world longitudinal datasets.