RPCF-AMR: From Resistance Surveillance to Predictive Control
Antimicrobial resistance (AMR) is no longer only a clinical problem; it is a systems-level challenge that demands predictive, integrated, and cross-sector governance. In response, I propose the Resistance–Predictive Convergence Framework for Antimicrobial Resistance (RPCF-AMR), a conceptual model that moves AMR management from post-detection surveillance toward predictive control.
RPCF-AMR is built on four converging layers: surveillance, predictive intelligence, nanobiotechnological and therapeutic intervention, and governance. Together, these layers support early resistance signal detection, forecasting of resistance evolution, integration of genomic, microbiome, clinical, and environmental data, and AI-assisted decision support. Within a One Health architecture, the framework emphasizes adaptive policy control and precision intervention rather than broad-spectrum reaction.
The core premise is simple: AMR cannot be addressed by antibiotics alone. It requires a convergence architecture that connects intelligence, biology, and governance into one anticipatory system. RPCF-AMR is an attempt to conceptualize this transition.
A key conceptual foundation for this framework lies in the convergence of nanobiotechnology and advanced computational intelligence for antimicrobial resistance management. Recent perspectives highlight a shift toward precision therapeutics within a One Health paradigm, where antimicrobial strategies extend beyond conventional pharmacology into integrated nanoscale delivery systems and adaptive intelligence models. This aligns with emerging approaches that emphasize predictive and personalized intervention strategies supported by multi-layer biological and computational integration (Reyed, 2026). Within this context, AMR is reframed as a dynamic, evolving system that requires continuous modeling, real-time adaptation, and convergence between biological data streams and intelligent decision-support architectures, reinforcing the transition from reactive surveillance to predictive and controllable precision governance.
Reyed, R. M. (2026). Nanobiotechnology and AI Convergence for Antimicrobial Resistance and Predictive Precision Therapeutics: Precision Therapeutics and One Health. In R. Reyed (Ed.), AI, Nanobiotechnology, and the Future of Precision Antibiotics (pp. 1-74). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-5268-8.ch001