From Molecular Chaos to Apoptotic Inevitability: A Digital Twin Framework for Multi-Pathway Containment in Lung Cancer

Lung cancer remains the leading cause of cancer-related mortality globally — not because effective pharmacological agents are absent, but because current treatment strategies deploy them sequentially against a target whose adaptive capacity outpaces single-axis intervention. This paper presents a conceptual computational architecture — the Molecular Digital Twin — designed to integrate patient-specific multi-omics data with deterministic kinetic modeling and probabilistic Bayesian inference to achieve a state of apoptotic inevitability. The framework introduces four simultaneous containment axes (PI3K/AKT, MAPK/ERK, TP53, BCL-2) converging at the MOMP threshold, governed by coupled ordinary differential equations and a Bayesian resistance prediction module. A parallel Virtual Normal Twin (VNT) safety subsystem optimizes dynamic pulse dosing schedules to maintain healthy tissue recovery while accumulating irreversible tumor stress. The Digital Twin Score (DTS), a probabilistic index (0 ≤ DTS ≤ 1), quantifies population-level likelihood of apoptotic commitment. A simulated NSCLC case study (EGFR-mut/PTEN-del) demonstrates DTS progression from 0.35 under monotherapy to 0.82 ± 0.05 under four-axis containment. The framework explicitly acknowledges three epistemic limitations: the omics-tracking disconnect between ctDNA monitoring and ODE-based kinetic updating; kinetic simplification via mass-action approximations; and the absence of a formal PBPK multiscale bridge. These define the primary objectives of the proposed validation research phase. This is a strictly conceptual preprint. No experimental or clinical trial data is presented.