PRECISION EVOLUTIONARY MEDICINE: A COMPUTATIONAL GRAPH-THEORETICAL FRAMEWORK FOR PATHOGEN-SPECIFIC ANTIBIOTIC CYCLING IN MULTI-DRUG-RESISTANT GRAM-NEGATIVE INFECTIONS

Abstract

BackgroundThe Proliferation of Multi-drug resistant (MDP) ESKAPE pathogens threatens to compromise the efficacy of standard antibiotic pharmacopoeia. Current antimicrobial stewardship strategies predominantly rely on reactive antibiograms selecting therapeutic agents based on immediate phenotypic susceptibility. This approach, while clinically expedient, often inadvertently selects for cross-resistance, driving the evolutionary trajectory toward pan-drug resistance. A paradigm shift is required toward predictive strategies that exploit evolutionary trade-offs, specifically Collateral Sensitivity (CS), where the acquisition of resistance to one agent induces hypersensitivity to another. MethodsWe developed a computational graph-theory framework to map the evolutionary trajectories of three critical Gram-negative pathogens: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Drawing upon validated CS interaction matrices from experimental evolution literature, we constructed directed weighted graphs where nodes represent antibiotics and edges represent evolutionary sensitivity trade-offs. A closed-loop cycle optimization algorithm was deployed to identify pathogen-specific "Trap Loops" sequences of three or more antibiotics that force the pathogen into a state of high sensitivity. These loops were validated via stochastic in-silico clinical trials simulating 18 months of treatment, explicitly modeling clinical error and biological noise. ResultsThe model identified distinct, optimal cycling protocols for each pathogen. For E. coli, an Aminoglycoside-Beta Lactam loop (Gentamicin to Cefuroxime to Fosfomycin) demonstrated sustained suppression of resistance accumulation in silico. For K. pneumoniae, a novel Rifampicin to Doxycycline to Colistin loop was identified. For P. aeruginosa, a Tobramycin to Ciprofloxacin to Piperacillin sequence proved optimal. Stochastic simulations demonstrated that while standard reactive care resulted in progressive resistance accumulation (Normalized Resistance > 2.5), the graph-optimized protocols suppressed resistance within the therapeutic window (Normalized Resistance < 0.2) for the duration of the simulation. ConclusionWe demonstrate, through computational modeling, that antibiotic resistance trajectories can be strategically constrained by optimizing the temporal sequence of existing agents. This study provides a computational framework to inform the transition from reactive prescribing toward precision evolutionary steering. These protocols are intended to complement, not replace, clinical judgment and local antibiograms, particularly regarding pharmacokinetic constraints.