Metabolic-Temporal Dissociation: A Kinetic Framework for Pre-Treatment Drug Resistance

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Authors: Tuğçenur Uzun
Cited by: 0

Abstract

This preprint introduces the Metabolic-Temporal Dissociation (MTD) framework (v3.0), a multi-tier quantitative model proposing a microenvironment-driven, deterministic kinetic mechanism for the emergence of EGFR T790M gatekeeper resistance mutations in treatment-naive lung adenocarcinomas. Unlike purely stochastic models of genetic innovation, MTD maps how transient, functional Replication Protein A (RPA) insufficiency—induced by a 24–48 hour cyclic perivascular hypoxic priming phase—creates a critical temporal mismatch (\Delta T) at replication forks during acute reoxygenation. This mismatch leaves naked single-stranded DNA (ssDNA) vulnerable to localized cytosine deamination at GC-rich/CpG hotspots (specifically EGFR Exon 20) before checkpoint activation. Key Mathematical Validations in this Version: Stochastic Gillespie Simulations: Confirms long-tail kinetic behavior where rare, extreme replication forks undergo prolonged mutagenic windows (\Delta T > 2 minutes) under restricted RPA pools. Flux Balance Analysis (FBA): Verifies the metabolic coupling between hypoxic G6PD/Pentose Phosphate Pathway (PPP) upregulation and immediate post-reoxygenation survival (the GSH Antioxidant Shield). Spatial Partial Differential Equations (PDEs): Identifies a strict perivascular 40-micrometer "Golden Zone" where oxygen diffusion dynamics satisfy the preconditions for MTD. Current Objective: The computational framework and inner mathematical consistency of MTD are fully realized. We are actively seeking in vitro (wet-lab) experimental collaborators to biologically validate these parameters (e.g., via targeted RPA overexpression rescue assays and time-resolved ultra-deep sequencing during cycling hypoxia).

Fields of Study & Subjects

Lung Cancer Treatments and Mutations Cancer Genomics and Diagnostics Cancer Hypoxia and Metabolism

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
15 January 2026
Document type
Preprint