A team led by researchers from the Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) of the Chinese Academy of Sciences (CAS) and Peking Union Medical College Hospital reports that it has developed an automated system that provides quick, accurate results for determining the best antibiotics at the right dose.
The system, the Clinical Antimicrobials Susceptibility Test Ramanometry (CAST-R), is based on Raman microspectroscopy, a spectroscopic technique that can identify specific molecules. It can offer great potential for the treatment of bloodstream infections in humans, according to the scientists, who published their study, “Rapid, automated, and reliable antimicrobial susceptibility test from positive blood culture by CAST-Rn,” in mLife.
“Antimicrobial susceptibility tests (ASTs) are pivotal in combating multidrug resistant pathogens, yet they can be time-consuming, labor-intensive, and unstable. Using the AST of tigecycline for sepsis as the main model, here we establish an automated system of CAST-R, based on D2O-probed Raman microspectroscopy,” the investigators wrote.
“Featuring a liquid robot for sample pretreatment and a machine learning-based control scheme for data acquisition and quality control, the three-hour, automated CAST-R process accelerates AST by >10-fold, processes 96 paralleled antibiotic-exposure reactions, and produces high-quality Raman spectra. The Expedited Minimal Inhibitory Concentration via Metabolic Activity is proposed as a quantitative and broadly applicable parameter for metabolism-based AST, which shows 99% essential agreement and 93% categorical agreement with the broth microdilution method (BMD) when tested on 100 Acinetobacter baumannii isolates.
“Further tests on 26 clinically positive blood samples for eight antimicrobials, including tigecycline, meropenem, ceftazidime, ampicillin/sulbactam, oxacillin, clindamycin, vancomycin, and levofloxacin reveal 93% categorical agreement with BMD-based results. The automation, speed, reliability, and general applicability of CAST-R suggest its potential utility for guiding the clinical administration of antimicrobials.”
Sepsis, a severe blood infection, carries a 20–40% mortality rate, and each hour’s delay in obtaining an AST report would raise the mortality rate by 7.6%. Tigecycline is a last-resort antimicrobial used to treat blood infections. “Unfortunately, resistance to tigecycline has emerged in many blood pathogens,” said Yang Qiwen, PhD, senior author of the study and associate director of the department of clinical laboratory, Peking Union Medical College Hospital.
The AST of tigecycline has a turnaround time of 36 to 48 hours, from positive blood culture to AST results. “Rapid and reliable methods for tigecycline resistance are urgent,” added Zhu Pengfei, PhD, a scientist with QIBEBT, CAS.
The research team employed tigecycline as its main model and established an automated system of CAST-R, using the D2O-probed Raman microspectroscopy technology to perform AST for bloodstream infections. CAST-R can help clinicians quickly determine which antibiotic drug to use and how much to prescribe, according to the researchers.
The team’s process uses a liquid robot for sample pretreatment and a machine learning-based control scheme for acquiring the data and maintaining quality control. Each Single-Cell Raman Spectrum (SCRS) that their process produced contained thousands of Raman peaks providing rich information about cells, like a molecular fingerprint of each cell.
In their tests, the three-hour CAST-R process achieved excellent accuracy and proved to be over ten times faster than the conventional AST process. Their process handled 96 paralleled antibiotic-exposure reactions and produced Raman spectra with quality equivalent to that achieved through the more time-consuming manual process. “The automation, speed, reliability, and broad applicability suggest CAST-R as a clinically valuable AST for bloodstream infections,” said Ren Lihui, PhD, an associate professor at the QIBEBT, CAS.
“The strengths of an SCRS, which also include information richness, single-cell resolution, and ability to couple with downstream single-cell sequencing, have yet to be fully exploited in this study, and that will be our priority ahead,” said Xu Jian, PhD, another senior author of the study and director of the single-cell center at QIBEBT.
The team plans to improve the CAST-R process. “As we refine the CAST-R workflow process, our goal is to provide a series of new applications in clinical diagnosis based on microbial single-cell technologies, in order to combat superbugs and support personalized treatment of infections,” said Yang.