Advancing from Gut Resistome Understanding to Smart Therapeutics Using CRISPR and Artificial Intelligence to Combat Antimicrobial Resistance

Antimicrobial resistance (AMR) is recognized as a top global health threat, directly responsible for over 1.27 million deaths annually and projected to cause several million deaths each year by 2050. While the impact of drug-resistant pathogens on health systems is well documented, recent research highlights the human gut resistome, the collective reservoir of antimicrobial resistance genes as a clinically significant driver in microbial ecology and resistance dissemination. The gut resistome evolves through diet, lifestyle, and environmental factors, accelerating horizontal gene transfer and complicating resistance containment strategies. Conventional surveillance and treatment practices often underestimate this complexity, leaving critical gaps in AMR management. Advances in biotechnology and computational science are addressing these gaps. Engineered CRISPR-Cas genome editing systems now offer precise targeting and inactivation of selected resistance genes and multidrug-resistant bacteria. Parallelly, artificial intelligence (AI) and deep learning have revolutionized microbiome analysis by enabling ultra-high resolution and throughput in metagenomic and metaproteomic studies. These innovations underpin integrated strategies that combine resistome profiling, predictive modeling, and targeted genetic interventions. This review synthesizes current understanding of the human gut resistome, explores the potential of CRISPR-based antimicrobial technologies, and evaluates the impact of AI on microbiome research and AMR management. It also addresses existing challenges, ethical concerns, and envisions future applications of overlapping technologies in combating AMR.