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Recent Advances in the Race to Design a Rapid Diagnostic Test for Antimicrobial Resistance Heidi Leonard, Raul Colodner, Sarel Halachmi, and Ester Segal ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.8b00900 • Publication Date (Web): 23 Oct 2018 Downloaded from http://pubs.acs.org on October 24, 2018
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Recent Advances in the Race to Design a Rapid Diagnostic Test for Antimicrobial Resistance Heidi Leonard1, Raul Colodner2, Sarel Halachmi3, Ester Segal1,4* 1Department
of Biotechnology and Food Engineering, Technion – Israel Institute of Technology,
Haifa, Israel 3200003 2Laboratory
of Clinical Microbiology, Emek Medical Center, Afula, Israel 18101
3Department 4The
of Urology, Bnai Zion Medical Center, Haifa, Israel 3104800
Russell Berrie Nanotechnology Institute, Technion – Israel Institute of Technology, Haifa,
Israel, 3200003 Abstract Even with advances in antibiotic therapies, bacterial infections persistently plague society and have amounted to one of the most prevalent issues in healthcare today. Moreover, the improper and excessive administration of antibiotics has led to resistance of many pathogens to prescribed therapies, rendering such antibiotics ineffective against infections. While the identification and detection of bacteria in a patient’s sample is critical for point-of-care diagnostics and in a clinical setting, the consequent determination of the correct antibiotic for a patient-tailored therapy is equally crucial. As a result, many recent research efforts have been focused on the development of sensors and systems that correctly guide a physician to the best antibiotic to prescribe for an infection, which can in turn, significantly reduce the instances of antibiotic resistance and the evolution of bacteria “superbugs.” This review details the advantages and shortcomings of the recent advances (focusing from 2016 and onward) made in the developments of antimicrobial susceptibility testing (AST) measurements. Detection of antibiotic resistance by genomic AST techniques relies on the prediction of antibiotic resistance via extracted bacterial DNA content, while phenotypic determinations typically track physiological changes in cells and/or populations exposed to antibiotics. Regardless of the method used for AST, factors such as cost, scalability, and assay time need to be weighed into their design. With all of the expansive innovation in the field, which technology and sensing systems demonstrate the potential to detect antimicrobial resistance in a clinical setting?
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Keywords Antimicrobial Resistance, Antibiotics, Bacteria, Susceptibility Testing, Minimum Inhibitory Concentration, Pathogen, Sensing Millions of metric tons of antibiotics have been produced and employed in the past 60 years.1 Though antibiotics and antifungals have revolutionized both medicine and agriculture, an apparent tipping point has been reached in which the abuse of antimicrobial agents has led to a global crisis of antimicrobial resistance (AMR). This crisis is predicted to be the number one cause of death by 2050, causing 10 million deaths, unless a global response is mounted.2 AMR is the ability of a microorganism to survive and replicate in the presence of an antimicrobial agent by a variety of mechanisms,3, 4 rendering antimicrobial treatment ineffective, causing persistent infections, and spreading of the infection to others. Two main approaches have been taken to tackle this impending crisis, including the development of new antimicrobial agents and the implementation of better antimicrobial stewardship. The first of these approaches is usually met with failure after several years of the antibiotic’s widespread usage, as pathogens typically acquire resistance to commonly used antibiotics.1 Furthermore, the extent of the crisis cannot be solved by the addition of a single new antibiotic with the overwhelming diversity of pathogenic species. As an example, a typical municipal hospital, like Bnai Zion Medical Center in Haifa, Israel, may encounter dozens of different species of bacteria and fungi in their urology department alone (Figure 1A). This diversity of pathogenic species, which subtly changes in composition from year to year, will hinder the effectiveness of new antimicrobial compounds, which are concurrently dwindling in development5. Even more alarming about this example is that only about a quarter of the antibiotics were fully susceptible to all isolated microorganisms tested, as depicted in Figure 1B. However, the specific antibiotics included in this category often consist of “last resort” antibiotics, due to their potency that is often accompanied by toxicity to the patient. Even then, resistance in these antibiotic classes has developed and spread, with the evolution of carbapenemresistant Klebsiella and other Enterobacteriaceae species in hospitals across the world.6 Thus, the second, more preventative approach, has been implemented in which physicians and the general public have been advised to decrease their antibiotic intake.7-9 In particular, this includes the cautious prescription of antibiotics that have been deemed effective by a clinical
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diagnostic test. This approach has stimulated innovative research for developing a test to predict antimicrobial resistance in isolated organisms, and in particular, the development of rapid antimicrobial susceptibility testing (AST), which is, herein, the focus of this article.
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Figure 1. Microbial diversity and antibiotic resistance observed in the Department of Urology at the Bnai Zion Medical Center (Haifa, Israel) between 2010 and 2016. (A) Almost 30 different species of microorganisms were encountered in the department. E. coli, K. pneumonia, E. faecalis, and P. aeruginosa were consistently the four most prevalent bacterial species. (B) Antibiotics were classified based on the percentage of instances that the antibiotic failed to inhibit the growth of the pathogen tested (i.e., the bacteria were resistant to the antibiotic). For example, in 2010, for two of the evaluated antibiotics (i.e., ampicillin and cephalothin), more than 50% of the bacteria tested against them exhibited resistance (dark red category). Only five of the antibiotics assayed that year
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successfully inhibited the growth of all pathogens tested (blue category), namely colistin, fusidic acid, rifampicin, vancomycin, and chloramphenicol. Pathogen identification precedes antibiotic susceptibility testing (AST) To further understand how antimicrobial resistance has manifested as a global health crisis, we begin by analyzing the steps required to receive an antibiotic prescription, as outlined in Figure 2. Typically, a symptomatic patient will arrive to a medical center to meet with a physician. Depending on the symptoms, the physician will often prescribe a broad-spectrum antibiotic (one that can fight against multiple bacterial species, such as ampicillin), which may or may not work directly against the assumed infection. Sometimes, the physician will also order further testing to confirm the type of an infection (i.e., viral, bacterial or fungal) and identification of the pathogen. For confirming the presence of an infection, various first-line, rapid tests may be used, such as dipstick analysis for urinary tract infections10 (e.g., Siemens Multistix® 10 SG urinanalysis strips gave results within minutes, ~70% sensitivity11). Identification in a clinical setting of a pathogenic species is typically performed by isolating colonies on chromogenic agars, Gram-staining, microscopic examination, biochemical testing, polymerase chain reaction (PCR) gene detection, and/or mass spectrometry (MS).12-15 Culture-based methods can take up to 24 hours, while PCR and MS can reduce this time to 4 hours.16-18 Only after confirmation and identification of the pathogen, which usually accompanies isolation of the pathogen from the bodily fluid, the appropriate choice of antibiotic is determined (Figure 2). Clinical microbiology laboratories employ various methods of antimicrobial susceptibility testing (AST), which will help a physician decide the best antibiotic to prescribe. AST determines the minimum inhibitory concentration (MIC) of an antimicrobial against a pathogen, namely antibiotics for bacterial infections and antifungal agents for fungal infections. For bacteria, the MIC is determined as the point in which the antibiotic has effectively inhibited the growth of the bacteria. Clinical breakpoints determined by AST are used by medical doctors to prescribe patients with the most effective antimicrobial therapy. Within these breakpoints, a concentration of antibiotic can be considered “susceptible” with a high likelihood of efficiency, “intermediate” with an uncertain degree of antibiotic efficiency, or “resistant” with a high likelihood of antibiotic failure.19 Additionally, numerous factors affect the determination of the MIC, such as the growth media, time, and inoculum size.20 Typical AST methods, such as those
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described in the Clinical and Laboratory Standards Institute (CLSI) and European Committee on Antimicrobial Susceptibility Testing (EUCAST) procedures use Mueller-Hinton broth or agar to perform broth microdilution tests or disk diffusion tests at 35-37 °C for microorganisms such as E. coli.19 AST can also involve direct or indirect sampling, meaning it can use bacterial colonies isolated from the original sample or the original sample itself (such as urine, stool or blood). While direct sampling may yield faster analyses, EUCAST suggests the use of indirect sampling in order to have full control over the inoculum or avoid matrix effects (such as pH of the sample).21 Several factors must be additionally taken into account when designing a method for AST, such as accuracy, price, time, scalability, feasibility, and biological sample size.
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Figure 2. Current, clinical workflow versus an ideal clinical workflow, particularly regarding samples obtained from urinary tract infections. Currently, after obtaining a urine sample, broadspectrum antibiotics are administered immediately, followed by pathogen identification. Pathogen identification and isolation are typically performed by culturing the sample on chromogenic agar, occasionally leading to ambiguous classifications if multiple pathogens are present, and thus repetition of the experiment. In recent years, more clinical microbiology labs are utilizing Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) mass spectroscopy (MS) or PCR methods to accelerate the identification of pathogenic species to couple of hours as opposed to a full day. Only after pathogen identification is complete, AST is performed, resulting in another day of assay time using currently approved methods. An ideal workflow includes point-of-care testing for pathogen identification, such as multiplexed PCR, followed by comprehensive, but rapid, AST. Integrated methods for identification and AST may also be possible, but tailored antibiotic therapy would be administered within a couple of hours, resulting in improved antibiotic stewardship. Reprinted by permission from Springer Nature: Nature Reviews Urology, Davenport et al10. Copyright 2017.
Traditional AST methods result in next-day results Several non-automated methods are considered to be gold standards for comparison purposes when developing a new AST method, including agar dilution testing, broth microdilution
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(BMD) testing, disk diffusion testing, and the bioMérieux Etest.18 While these tests are relatively inexpensive to perform, they must be performed manually and take approximately 18 ± 2 hours to perform (corresponding to overnight). These methods are not considered rapid AST methods, but do give reliable values for MIC determination (see TABLE 1). Currently, in the United States, the Food and Drug Administration (FDA) has cleared five automated systems for clinical use, including the VITEK® 2 (bioMérieux), PhoenixTM (Becton Dickinson Diagnostic Systems), Microscan WalkAway® (Siemens Medical Solutions Diagnostics), Sensititre ARIS 2X (Trek Diagnostic Systems)22, 23, and most recently, in 2017, the PhenoTest BC Kit (Accelerate Diagnostics, Inc)24. These instruments can analyze dozens of bacteria samples in one run and some can even identify the type of bacteria in the sample. However, these automated systems, such as the commonly employed Vitek 2, generally take greater than 8 hours to determine the MIC of each antibiotic, which often leads to clinicians running the samples overnight22, 25 (TABLE 1). The growth of bacteria is generally tracked by changes in turbidity, colorimetric changes, and fluorescence in these instruments.22 In particular, the VITEK® 2 measures transmittance of an inoculum in liquid samples every time point. Decreasing transmittance corresponds to increasing turbidity and thus, charts growth over time to determine the MIC of antibiotics.25 Contrarily, the MicroScan WalkAway® relies on fluorescence measurements to determine the MIC and takes half the time of the VITEK® instruments.26 The instrument contains microtiter plates filled with growth medium, a fluorogenic substrate, and antibiotic. As the bacteria grow, they produce more enzymes that act on the substrate, increasing the fluorescence signal. Thus, the fluorescence measurements do not correlate directly to bacteria growth, but instead to their enzymatic activity.25 Nevertheless, although currently established automated, high-throughput systems provide significant advantages, their relatively long time-toreadout is often insufficient for proper management of critically ill hospitalized patients. When the readout time is more than a day, a patient will often be treated with a broad-spectrum antibiotic, which may be ineffective, leading to poor outcomes, including greater morbidity and mortality.27, 28
The most recently FDA approved system, the PhenoTest BC Kit by Accelerate Diagnostics, can both identify and analyze antibiotic susceptibility.29 In 2018, there was a recall due to its high number of false positives with Staphylococcus aureus;30 however, all of the automated systems have encountered similar minor hiccups in their beginnings that were easily
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overcome31 and the Accelerate PhenoTest system gives hope that a new, faster technique for AST may be readily used soon. TABLE 1. Summary of the main commercially available laboratory AST methods. Examples of Difficult Organisms
Test time*
Colony growth on solid agar with antibiotic
-
>16 h
18, 32
Broth microdilution
Turbidity in liquid medium with antibiotic
-
>16 h
18, 32, 33
Disk diffusion
Inhibition zone diameter around a disk of antibiotic
-
>16 h
18, 32, 34
Etest
Inhibition zone diameter on agar from antibiotic gradient strip
-
16 - 20 h
32, 35
Live/dead staining
Fluorescence or fluorescent imaging in liquid medium
Anaerobic bacteria
2-4h
18, 36
PCR gene detection
DNA amplification
_
2-4h
18, 32, 37, 38
MALDI-TOF MS
Mass spectral profiles and/or shifts within
Aspergillus spp.
1-3 h
Real-time microscopy
Time lapse imaging
Filamentous fungi
2-3h
43, 44
**BD PhoenixTM
Colorimetric and turbidity measurements
8 - 10 h
45-48
**VITEK® 2
Transmittance of light due to turbidity
Colistin-resistant bacteria Aspergillus spp.
>7.5 h
25, 46
**MicroScan WalkAway®
Fluorescence corresponding to metabolic growth or transmittance of light due to turbidity
-
4-7h 5 - 18 h
**PhenoTest BC Kit
Identification proceeded by real-time imaging
S. aureus, P. aeruginosa
