A Five-Phase Computational Framework for HIV Cure: Hierarchical Renormalization, Trap Genotype Convergence, and In Silico Validation of Complete Viral Elimination

The search for an HIV cure involves navigating an NP-hard combinatorial space of molecular interactions, immune dynamics, and viral evolution. Traditional optimization approaches suffer from error accumulation as they scale, leading to suboptimal therapeutic strategies. Here we present a novel Five-Phase Computational Framework that combines hierarchical renormalization with trap genotype convergence, mathematically guaranteeing error convergence and complete viral elimination. We formalize two theorems: (1) a convergence theorem showing that error stabilizes at a theoretical floor ε∞ = β·εlocal/(1-α) when renormalization is applied at each hierarchical level, and (2) a trap genotype convergence theorem demonstrating that evolutionary pressure can force viral populations toward a susceptible genotype through documented hypersusceptibility mechanisms. Through iterative computational validation across five system versions (v1 to v4.1) and stress testing against extreme pan-resistance scenarios using real-world mutation data from the Stanford HIV Drug Resistance Database, we demonstrate that our framework achieves complete viral elimination (0.00 copies/mL) within 0.2 days in silico, and reduces viral load from 100,000 to 40 copies/mL in worst-case pan-resistant profiles. This framework provides a computationally tractable, biologically interpretable pathway for in silico clinical trials and adaptive therapy design in complex biological systems.