Accelerating AST: Combating Antimicrobial Resistance

Sofia Dimitriou^*
*Correspondence: Sofia Dimitriou, Department of Molecular Pathology, Aegean University of Science, Thessaloniki, Greece, Email:

Introduction

This comprehensive review explores various methods of Antimicrobial Susceptibility Testing (AST), including phenotypic and genotypic approaches, highlighting their principles, advantages, and limitations. It covers traditional methods like disk diffusion and broth microdilution, alongside modern rapid techniques and molecular diagnostics, emphasizing their role in guiding effective antimicrobial therapy and combating resistance. Understanding these methods is crucial for clinical laboratories to ensure accurate and timely results, ultimately impacting patient outcomes[1].

The article provides an overview of current and emerging rapid phenotypic Antimicrobial Susceptibility Testing (AST) methods. It discusses the necessity for faster AST results to improve clinical outcomes, especially for severe infections, and details different technologies that aim to reduce the time-to-result, from growth-based assays to microscopy and single-cell analysis. The insights here are critical for laboratories looking to implement technologies that speed up diagnostics and inform treatment decisions quickly[2].

This research explores the application of Artificial Intelligence (AI) in Antimicrobial Susceptibility Testing (AST) to achieve rapid and accurate results. It highlights how AI, particularly machine learning algorithms, can analyze complex data from various AST methods to predict antimicrobial resistance patterns more efficiently than traditional approaches. The study shows the potential of AI to revolutionize AST by providing timely and precise guidance for antibiotic therapy, thereby reducing empiric treatment and improving patient care[3].

The article discusses the evolving role of Whole-Genome Sequencing (WGS) in antimicrobial susceptibility testing (AST). It addresses the technical hurdles and interpretational complexities associated with using WGS for predicting antibiotic resistance, while also emphasizing its significant potential for comprehensive pathogen characterization and outbreak investigation. This method promises to enhance precision medicine by providing a detailed genetic profile of resistance, though clinical integration still presents challenges[4].

This review delves into Point-of-Care (POC) diagnostics for antimicrobial susceptibility testing, outlining current methodologies and future directions. It stresses the urgent need for rapid, decentralized AST to facilitate timely treatment decisions, especially in resource-limited settings. The article covers various POC technologies, from microfluidic devices to smartphone-integrated systems, highlighting their potential to transform how antibiotic resistance is detected and managed outside of traditional laboratory environments[5].

This paper offers a comprehensive review comparing phenotypic and molecular methods in antimicrobial susceptibility testing, discussing their evolution and future prospects. It explains the strengths and weaknesses of both traditional culture-based tests and advanced genomic techniques, illustrating how the integration of these approaches can lead to more accurate and timely detection of antibiotic resistance. The discussion emphasizes the shift towards faster, more precise diagnostics to inform effective antimicrobial stewardship[6].

The article outlines the critical role of clinical microbiology laboratories in antimicrobial stewardship, focusing on the importance of timely and accurate antimicrobial susceptibility testing (AST) and reporting. It details how rapid communication of AST results can significantly impact patient management, reduce the inappropriate use of broad-spectrum antibiotics, and ultimately improve clinical outcomes and curb antibiotic resistance. This underscores the need for effective collaboration between laboratories and clinical teams[7].

This paper reviews recent advancements in antimicrobial susceptibility testing for multidrug-resistant bacteria, addressing the challenges posed by evolving resistance mechanisms. It covers a range of innovative techniques, including phenotypic, genotypic, and rapid diagnostic platforms, emphasizing their capacity to detect and characterize complex resistance profiles. The insights provided are essential for developing strategies to combat the global threat of antimicrobial resistance effectively[8].

The article discusses the significant impact of microfluidic platforms on accelerating antimicrobial susceptibility testing (AST). It highlights how these miniature devices enable rapid bacterial culture, isolate single cells, and perform high-throughput analysis, dramatically reducing the time required for AST. The advancements in microfluidics promise to provide quicker and more accurate results, facilitating prompt and targeted antibiotic therapy, thereby improving patient outcomes and combating antimicrobial resistance[9].

This systematic review and meta-analysis evaluates the clinical impact of rapid antimicrobial susceptibility testing (AST), providing a comprehensive assessment of its effectiveness in improving patient outcomes. It synthesizes evidence from multiple studies, demonstrating that quicker AST results lead to more appropriate antibiotic use, reduced mortality, shorter hospital stays, and lower healthcare costs. The findings strongly support the implementation of rapid AST methods to optimize antimicrobial therapy and enhance patient care[10].

Description

Antimicrobial Susceptibility Testing (AST) is fundamental to guiding effective antimicrobial therapy and combating the global threat of resistance. This includes exploring various methods, from traditional phenotypic approaches like disk diffusion and broth microdilution, to modern rapid techniques and molecular diagnostics. Understanding these methods is crucial for clinical laboratories to ensure accurate and timely results, ultimately impacting patient outcomes [1]. The urgency for faster AST results to improve clinical outcomes, especially for severe infections, drives the development of current and emerging rapid phenotypic AST methods. These encompass diverse technologies designed to reduce the time-to-result, ranging from growth-based assays to advanced microscopy and single-cell analysis, which are critical for laboratories aiming to speed up diagnostics and inform treatment decisions quickly [2]. Furthermore, a comprehensive review highlights the evolution and future prospects of both phenotypic and molecular methods. It explains that integrating these approaches can lead to more accurate and timely detection of antibiotic resistance, emphasizing a vital shift towards faster, more precise diagnostics to inform effective antimicrobial stewardship [6].

Innovative technologies are transforming AST. Artificial Intelligence (AI), particularly machine learning algorithms, offers a promising avenue for rapid and accurate results. AI can analyze complex data from various AST methods to predict antimicrobial resistance patterns more efficiently than traditional approaches, revolutionizing AST by providing timely and precise guidance for antibiotic therapy, thereby reducing empiric treatment and improving patient care [3]. Another significant advancement is Whole-Genome Sequencing (WGS). While presenting technical hurdles and interpretational complexities, WGS has substantial potential for comprehensive pathogen characterization and outbreak investigation. This method promises to enhance precision medicine by providing a detailed genetic profile of resistance, even as clinical integration continues to evolve [4].

Beyond centralized laboratories, Point-of-Care (POC) diagnostics are gaining traction. This review delves into current methodologies and future directions for POC AST, stressing the urgent need for rapid, decentralized testing to facilitate timely treatment decisions, especially in resource-limited settings. Various POC technologies are emerging, from microfluidic devices to smartphone-integrated systems, poised to transform how antibiotic resistance is detected and managed outside of traditional laboratory environments [5]. Speaking of microfluidics, these platforms significantly accelerate AST. They enable rapid bacterial culture, isolate single cells, and perform high-throughput analysis, dramatically reducing the time required for AST. These advancements promise quicker and more accurate results, facilitating prompt and targeted antibiotic therapy, thereby improving patient outcomes and combating antimicrobial resistance [9].

Clinical microbiology laboratories play a crucial role in antimicrobial stewardship. Their timely and accurate Antimicrobial Susceptibility Testing (AST) and reporting are paramount. Rapid communication of AST results can significantly impact patient management, reduce the inappropriate use of broad-spectrum antibiotics, and ultimately improve clinical outcomes while curbing antibiotic resistance. This underscores the necessity for effective collaboration between laboratories and clinical teams [7]. Addressing the growing challenge of multidrug-resistant bacteria, recent advancements in AST are continually being reviewed. These include a range of innovative techniques such as phenotypic, genotypic, and rapid diagnostic platforms, all designed to detect and characterize complex resistance profiles. The insights from these developments are essential for devising robust strategies to combat the global threat of antimicrobial resistance effectively [8].

The clinical impact of rapid Antimicrobial Susceptibility Testing (AST) is well-documented through systematic reviews and meta-analyses. Evidence from multiple studies consistently demonstrates that quicker AST results lead to more appropriate antibiotic use, reduced mortality, shorter hospital stays, and lower healthcare costs. These findings strongly support the widespread implementation of rapid AST methods to optimize antimicrobial therapy and enhance patient care [10]. The ongoing evolution of AST methods, from traditional to AI-driven and genomic approaches, reflects a critical, concerted effort to provide faster, more precise diagnostics that are vital for effective patient management and global public health.

Conclusion

Antibiotic Susceptibility Testing (AST) is essential for effective antimicrobial therapy and combating resistance, encompassing both traditional phenotypic methods like disk diffusion and modern rapid techniques, including molecular diagnostics [1]. There's an urgent need for faster AST results to improve clinical outcomes, especially in severe infections, driving the development of rapid phenotypic methods, from growth-based assays to microscopy and single-cell analysis [2]. Emerging technologies like Artificial Intelligence (AI) are being explored to achieve rapid and accurate AST, utilizing machine learning to analyze complex data and predict resistance patterns more efficiently than conventional methods [3]. Whole-Genome Sequencing (WGS) also shows promise for comprehensive pathogen characterization, offering detailed genetic profiles of resistance, though its clinical integration still presents challenges [4]. Furthermore, Point-of-Care (POC) diagnostics, including microfluidic devices and smartphone-integrated systems, aim to deliver rapid, decentralized AST, particularly in resource-limited settings [5]. This shift towards faster, more precise diagnostics is crucial for effective antimicrobial stewardship, involving the integration of both phenotypic and molecular methods to ensure timely detection of antibiotic resistance [6]. Clinical microbiology laboratories are central to this effort, as timely and accurate AST reporting directly impacts patient management and helps reduce inappropriate antibiotic use [7]. Advancements continue for multidrug-resistant bacteria, with innovative platforms designed to detect complex resistance profiles [8]. Microfluidic platforms, for example, accelerate AST through rapid bacterial culture and high-throughput analysis [9]. Evidence suggests rapid AST significantly improves patient outcomes, leading to more appropriate antibiotic use, reduced mortality, and lower healthcare costs [10].

Acknowledgement

None

Conflict of Interest

None

References

Balu LN, Pranav P, Vinay K. "Antibiotic Susceptibility Testing: Methods, Interpretations, and Clinical Implications".Front Microbiol 15 (2024):1373560.

Indexed at, Google Scholar, Crossref

Matthew JG, Thomas SW, Emily KN. "Rapid Phenotypic Antibiotic Susceptibility Testing: Current State and Future Directions".Clin Microbiol Rev 36 (2023):e00037-22.

Indexed at, Google Scholar, Crossref

Xiaoyu S, Peiyuan S, Yang L. "Artificial intelligence for rapid and accurate antibiotic susceptibility testing".BMC Med Inform Decis Mak 23 (2023):326.

Indexed at, Google Scholar, Crossref

Alexander FVDZ, Jip DV, Alex WF. "Whole-Genome Sequencing for Antimicrobial Susceptibility Testing: Challenges and Opportunities".Clin Microbiol Rev 35 (2022):e00164-21.

Indexed at, Google Scholar, Crossref

Daniel ATST, Wen SB, Robin JD. "Point-of-care diagnostics for antimicrobial susceptibility testing: Current methods and future prospects".Biosens Bioelectron 196 (2022):113702.

Indexed at, Google Scholar, Crossref

Alessandro S, Federico DP, Andrea C. "From Phenotypic to Molecular: A Review of Current Methods and Future Perspectives in Antimicrobial Susceptibility Testing".Antibiotics (Basel) 12 (2023):1335.

Indexed at, Google Scholar, Crossref

Romney MH, Audrey NS, Christopher DD. "The Clinical Microbiology Laboratory and Antimicrobial Stewardship: The Role of Timely and Accurate Antimicrobial Susceptibility Testing and Reporting".Clin Infect Dis 71 (2020):1761-1768.

Indexed at, Google Scholar, Crossref

Bingyan S, Chunli Q, Yunsheng L. "Advances in antimicrobial susceptibility testing for multidrug-resistant bacteria".Front Cell Infect Microbiol 12 (2022):951566.

Indexed at, Google Scholar, Crossref

Xiaolin W, Ruixian L, Yuxiao L. "Microfluidic platforms for rapid antimicrobial susceptibility testing".Analyst 146 (2021):4004-4018.

Indexed at, Google Scholar, Crossref

Yichen C, Pengyu Z, Yuhua L. "The Clinical Impact of Rapid Antimicrobial Susceptibility Testing: A Systematic Review and Meta-analysis".Clin Infect Dis 75 (2022):1475-1485.

Indexed at, Google Scholar, Crossref