The Chandrasekar Formulation (CSF-11): Evolution from an AI-Optimized Biomechanical Hypothesis to a TCGA-Grounded Intravenous Biophysical Protocol for Multi-Target Lung Adenocarcinoma Oncology
Abstract
Abstract This paper presents the complete evolutionary arc of the Chandrasekar Formulation (CSF-11), an open-source computational oncology protocol engineered from mechanical engineering first principles. The foundational v1.0 framework employed an AI-Optimized Genetic Algorithm to derive a phytochemical composition Allicin (67.568%), Rosmarinic Acid (22.258%), Curcumin (3.180%), Quercetin (2.782%), Aegeline (1.431%) - combined with Melittin (2.782%) as a pore-forming peptide. Structural FEA simulations established a precise membrane yield threshold at Maximum Von Mises Stress of 12.2644 MPa under an alkaline synergistic environment (pH 8.5), amplifying intracellular delivery by 26x. Coupled with a 30-day fractionated radiotherapy schedule (44.0 Gy), the v1.0 ensemble simulation achieved a median kill rate of 79.89% (±2.60%) across 33 cancer targets, a TCP of 1.0000, NTCP of 0.0055, and a Therapeutic Index of 4.439x. The v2.0 upgrade reformulated the protocol as a purified 11-component Intravenous (IV) API cocktail. To ground this in biological reality, TCGA-LUAD clinical data (n=347, GDC API) was extracted and validated by Kaplan-Meier analysis (log-rank p
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