Precision Medicine in the Era of Antimicrobial Resistance
Year: 2024
Type: article
Abstract: Antibiotic overuse contributes to the spread of antimicrobial resistance (AMR). The CDC estimates that 30% of antibiotics prescribed in acute care hospitals are unnecessary or suboptimal. Antibiotic stewardship focuses on reducing inappropriate prescribing while promoting prudent use. Evidence has emerged for the effectiveness of antibiotic stewardship programs. However, ongoing challenges exist for hospital-based programs. The COVID-19 pandemic has caused difficulties for appropriate antimicrobial use and stewardship, such as over-prescribing antibiotics to prevent bacterial coinfections. COVID-19 has led to increased hospitalizations, which increase the risk of acquiring AMR infections.[1] AMR is a significant global issue. Current policies focus on limiting antibiotic use and developing new antimicrobials. However, these measures alone are insufficient. Evolutionary adaptation is central to the AMR crisis, and future treatment strategies must follow evolutionary principles. New antimicrobials provide temporary relief, but resistance will eventually emerge. Approaches must be refined to consider individual pathogen lineages, patients, and microbiota.[1] Precision medicine can help by characterizing bacterial infections and tailoring treatments. However, this requires balancing individual and public interests and may involve ethical dilemmas. Evolution-informed therapies may offer a solution by ensuring pathogen elimination and minimizing resistance spread.[2] Precision medicine offers several approaches to optimize antibiotic use and combat antibiotic resistance. Therapeutic drug monitoring (TDM) can help achieve pharmacokinetic/pharmacodynamic targets that suppress the emergence of resistance.[2] This approach has been shown to reduce toxicity and shorten therapy length. De-escalation from broad-spectrum antibiotics to narrower agents based on susceptibility testing is a priority of antibiotic stewardship. Precision antimicrobials offer an alternative by specifically targeting pathogenic organisms with minimal off-target effects. The CRISPR-Cas system also holds promise for selectively removing antibiotic resistance genes and sensitizing bacteria to antibiotics.[3] Traditional approaches to optimize antibiotic therapy include in-vitro and animal infection models that define drug activity based on the rate and extent of pathogen killing. However, these approaches are unable to pinpoint mechanisms of drug action or pathogen-resistance development and might overestimate pathogen killing. Optimal antibiotic therapy relies on a precise understanding of a drug’s mechanism of action within the pathogen it targets and the pathogen’s subsequent response to the drug. Bacterial resistance genes can be detected in the clinic using genomic-based assays like molecular rapid diagnostic tests. These tests allow clinicians to shorten the time required to select effective antibiotic therapy.[4] With the rapid development of high-throughput sequencing techniques and ultra-sensitive mass spectrometry technology, it is now feasible to simultaneously quantify global gene expression profiles, proteins, and metabolites in bacterial cells in response to antibiotic treatments. Integrative analysis of bacterial genomics, transcriptomics, proteomics, and metabolomics data following antibiotic exposure can elucidate the dynamics of cellular responses to antimicrobial exposure at the systems level. Future research should aim to enrich our understanding of the effect that antibiotic exposure profiles have on global bacterial responses that dictate killing and development of resistance.[5] The importance of personalized and precision approaches to prescribing antibiotics to preserve global antibiotic effectiveness and reduce the selection and spread of resistance should be addressed. Empirical treatment without reference to pathogen susceptibility measurements remains a standard practice in many settings. Electronic patient records can be used to predict the personal risk that a patient’s infection is resistant to specific antibiotics. Treatment-induced emergence of resistance is common and driven by rapid reinfection with a resistant strain pre-existing in the patient’s own microbiota. Machine learning–personalized antibiotic recommendations could minimize the risk of treatment-induced gain of resistance. Future precision approaches may be useful for improving treatment outcomes and preserving antibiotic effectiveness.[6] Author contribution Contributor 1: Definition of intellectual content, manuscript preparation, manuscript editing, manuscript review; Contributor 2: Concepts, design, literature search, manuscript preparation, manuscript editing, manuscript review; Contributor 3: Manuscript preparation, manuscript editing, manuscript review; Contributor 4: Concepts, design, definition of intellectual content, literature search, manuscript preparation, manuscript editing, manuscript review, guarantor. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Language: English
Subfield: Applied Microbiology and Biotechnology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4402580006