Next-generation rapid phenotypic antimicrobial susceptibility testing

Abstract

Slow progress towards implementation of conventional clinical bacteriology in low resource settings and strong interest in greater speed for antimicrobial susceptibility testing (AST) more generally has focused attention on next-generation rapid AST technologies. In this Review, we systematically synthesize publications and submissions to regulatory agencies describing technologies that provide phenotypic AST faster than conventional methods. We characterize over ninety technologies in terms of underlying technical innovations, technology readiness level, extent of clinical validation, and time-to-results. This work provides a guide for technology developers and clinical microbiologists to understand the rapid phenotypic AST technology landscape, current development pipeline, and AST-specific validation milestones.

Fig. 1. PRISMA flow diagram of search strategy.

A systematic search strategy encompassed PubMed and the FDA 510(k) Premarket Notification and FDA Premarket Approval (PMA) databases. Iterative internet searches and consultation with content experts in microbiology and bio/biomedical engineering were used to probe for lacunes in our search strategy. Technologies were included if they relied on phenotypic antimicrobial susceptibility profiling of bacteria, regardless of the recognition element used. Rapid technologies were defined as those offering a faster time-to-final-result than those possible with conventional clinical microbiology methods. Phenotypic tests were defined as those that measure microbial growth or viability in the presence of antimicrobials to determine susceptibility. Hypothesis-free nucleic acid-based tests were defined as those using genomic recognition elements to detect or quantify bacteria in the presence of different antimicrobial conditions without pre-defined targets. We considered methods using nucleic acid-based recognition elements in a distinct category of technologies to facilitate comparison between them. We only included technologies with publications that specifically addressed their application to phenotypic AST. Finally, we only considered technologies used for non-mycobacterial vegetative bacteria routinely isolated in clinical laboratories. We considered as “commercialized” any AST technology with any of the following: FDA authorization, approval, or a pre-market notification; European Economic Area CE marking; or authorization by another WHO-Listed Authority (WLA) if specified by authors. All others were considered “non-commercialized”.

Fig. 2. Proposed classification for diagnostic validation studies of rapid antimicrobial susceptibility testing platforms and how research studies map onto the Phase of Clinical Validation framework.

This framework differs from the Technology Readiness Levels by focusing on the extent of clinical validation available from diagnostic study designs rather than stages of technological development. When a publication described the requirements for multiple phases, it was put only in the highest-level phase. All papers and FDA submissions were included in this figure. All 3b studies were from FDA submissions. One of these did not specify if the organisms were fresh or stock but referenced the FDA Class II Special Controls Document which requires >50% of organisms to be fresh, so it is assumed this criterion was met.

Fig. 3. Turnaround time of identified antimicrobial susceptibility testing (AST) technologies overlaid on a conventional AST workflow.

The conventional workflow shows a timeline of the standard steps from specimen collection to final results for blood culture specimens (A). Cultures of urine can be assumed to require at least 24 h less than the conventional workflow shown for blood specimens. The time from specimen collection to final AST readout is shown for commercialized phenotypic platforms (B) and non-commercialized platforms (C). For commercialized phenotypic platforms, the shortest time was taken as the shortest time reported by the company and the longest time was taken from the longest time reported by the company or from a paper in our review which evaluated the platform. For non-commercialized tests, when a test reported a range of time-to-results, the shortest and longest time were recorded, otherwise only the shortest time was recorded. For tests that reported a time to result from positive blood culture, an assumption of 24 h was used to estimate the time from sample collection to the start of the test. For tests that reported a time to result from colony isolation, an assumption of 48 h was used to estimate the time from sample collection to the start of the test. When a test was performed directly from specimen collection, no assumptions of extra time were added to the total turnaround time. One assay was performed directly on blood, while the other direct-from-specimen tests were all performed on urine samples. 41/81 non-commercial phenotypic tests were excluded from this graph because they did not report the time from beginning the test to a minimal inhibitory concentration measurement. Graphics created with BioRender.com.

Fig. 4. Spotlight on selected innovations underlying some of the recent advances leading to rapid phenotypic AST.

Single-cell bacterial imaging (A) combines optical detection of bacterial growth with small reaction chambers to reduce incubation times, and optimizes geometry for specimen processing. Nano-scale growth chambers are a common feature across several rapid AST technologies. Plasmonic bacterial sensing (B) can enhance the speed and sensitivity of optical readouts. It can be categorized into nanosurface or nanoparticle plasmonic sensing. Nanosurface plasmonic sensing (i) incorporates nano-structured surfaces exhibiting plasmonic structural colours that enhance AST metabolic assay TAT through bright field microscopy. Alternatively, nanosurface plasmonic sensing can utilize surface plasmon resonance (SPR), where a light source illuminates the sensor under-surface through a prism using a wide beam within the range of total internal reflection. The SPR angle shifts in response to changes in the refractive index at the surface of the chip, triggered by the binding of an analyte (e.g. a biomolecule). Nanoparticle plasmonic sensing (ii) employs plasmonic nanoparticles (e.g. gold nanoparticles) for colorimetric AST detection. Changes in the configuration of the nanoparticles (monodispersed or aggregate) yield detectable colour changes (e.g. from red to violet). Different assays integrate plasmonic nanoparticle sensing where the metabolic activity of viable bacterial cells would lead to change of the nanoparticle composition (from monodispersed to aggregate or vice versa) which can be monitored by naked eye or through absorption measurements. Highly-multiplex nucleic acid probe-based bacterial detection (C) uses the incorporation of multiple target-specific probes into replicating bacteria and leverages spectral analysis to assign a quantification and specific identification of bacteria present. Other examples of bacterial probes include the use of isotope labels in Raman-spectroscopy-based methods. Regardless of signal detection method, deep learning is increasingly used for enhanced signal processing. Created with BioRender.com.