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Innovation Drives a New Era of Clinical Testing

Disruptive technologies are changing the way we approach patient diagnostics and treatment

Innovative approaches to clinical testing are critical to overcome the most significant challenges faced by the health care system today. Central to these efforts is the introduction of new technologies that change the way patients are diagnosed and treated. The American Association for Clinical Chemistry (AACC) recently recognized six semifinalists for the 2021 AACC Disruptive Technology Award for developing the most innovative testing platforms that will transform patient care. This year’s selection highlights companies taking innovative steps to address some of the most pressing global health concerns, including the COVID-19 pandemic, antimicrobial resistance, and conditions ranging from metabolic diseases to cancer. These groundbreaking technologies have the potential to increase efficiency of patient diagnostics, reduce patient morbidity and mortality, and improve overall patient care.

Addressing contemporary health care issues

Nothing has recently garnered more attention in health care than the global outbreak of the SARS-CoV-2 virus. In response to the pandemic, biotech companies have made drastic efforts to improve testing and care for afflicted patients. In response to the crisis, Mammoth Biosciences adapted its CRISPR-based system, DETECTRTM, which identifies a specific DNA or RNA target, to recognize SARS-CoV-2 in patient samples.1 MeMed Diagnostics was also recognized for its innovative approach to COVID-19 patient care. MeMed’s COVID-19 SeverityTM assay stratifies COVID-19 patients into risk categories based on viral-induced protein signatures, allowing health care professionals to more accurately direct therapeutic protocols.2 Given the limited availability of health care resources, early distinction of potentially severe cases allows hospitals to allocate vital resources to patients with the most need. 

Though antimicrobial resistance has taken a back seat to the COVID-19 pandemic, drug-resistant infection remains a global health crisis. AACC-featured platforms could provide solutions that minimize erroneous use of antibiotics, and therefore reduce selection for resistant bacteria. Day Zero Diagnostics’s OneSeq Dx uses whole genome sequencing to identify and generate susceptibility profiles for severe infections within hours, enabling a quicker response with appropriate antibiotic selections. For example, OneSeq Dx has been used to identify antibiotic-resistant bacteria in a burn unit ICU,3 and a drug-resistant E. coli strain transmitted by fecal microbiota transplant.4 By facilitating rapid and accurate responses to severe infections, these technologies may curb the inappropriate use of antibiotics that contributes to drug-resistant bacterial strains.

Artificial intelligence at the forefront

The 2021 semifinalists feature an emphasis on the use of artificial intelligence (AI). AI can be applied to tasks such as mining and interpreting information from large databases, analyzing sample images, comparing patient data to existing datasets, and predicting patient outcomes. 

MeMed’s COVID-19 Severity uses machine learning to analyze host protein signatures for COVID-19 patient risk stratification,2 and OncoHost’s PROphetTM applies AI to proteomic analysis to predict patient responses to immunotherapy.5 These platforms combine AI with patient data to subclassify patients according to disease progression and response to treatment, respectively. In doing so, they enable health care professionals to make more informed decisions regarding treatment plans, and ideally, optimize patient outcomes. 

Sight Diagnostics introduced Sight OLOTM, which leverages machine vision and AI for complete blood counts (CBCs), one of the most common frontline diagnostic tests. The platform uses machine vision to produce detailed images of blood samples, and incorporates AI to identify cell populations and abnormalities.This technology brings CBCs closer to the patient setting and produces accurate results much faster than current standards.The broad use of AI across the featured innovations illustrates its multiuse potential and suggests that the next generation of clinical testing platforms will rely heavily on this technology.

Innovation supports precision medicine

The use of AI in the AACC semifinalists not only brings efficient, scalable, and effective solutions to an array of health care issues, but also advances clinical precision medicine. These new technologies harness and interpret large patient datasets that dramatically expand our understanding of an individual at the molecular level. Notably, the Numares AXINON® system taps into vast metabolomics datasets to derive molecular information about various organ systems. Using machine learning alongside their metabolomics platform, Numares identified constellations of patient metabolites that detect acute rejection of kidney transplants.7 This AI-driven approach may facilitate faster medical intervention and improved outcomes for those facing organ transplant failure.

Similarly, OncoHost’s PROphet analyzes immunotherapy patients’ proteomic signatures to inform personalized cancer treatment strategies.7 Rather than resorting to standardized immunotherapy protocols, PROphet informs a more individualized clinical strategy for each patient based on molecular profiles, which ideally promotes treatment success.

These exciting new platforms have the potential to change the way we approach clinical testing. They harness new technology that can alleviate the burden of health care staff. Moreover, these systems facilitate rapid, accurate, and personalized testing that can inform better treatment plans for various health conditions, and therefore have the power to drastically improve patient outcomes.

Correction: An earlier version of this article suggested that Sight OLO by Sight Diagnostics was FDA-approved for POCT when it is not yet approved for POCT in the US. Updated September 17, 2021.

References

  1. Broughton, James P., et al. “CRISPR-Cas12-based detection of SARS-CoV-2.” Nature Biotechnology 38.7 (2020): 870–874.
  2. Lev, Shaul, et al. “Observational cohort study of IP-10's potential as a biomarker to aid in inflammation regulation within a clinical decision support protocol for patients with severe COVID-19.” PloS One 16.1 (2021): e0245296.
  3. Shenoy, Erica S., et al. “Community-acquired in name only: A cluster of carbapenem-resistant Acinetobacter baumannii in a burn intensive care unit and beyond.” Infection Control and Hospital Epidemiology  41.5 (2020): 531–538.
  4. DeFilipp, Zachariah, et al. “Drug-resistant E. coli bacteremia transmitted by fecal microbiota transplant.” The New England Journal of Medicine 381.21 (2019): 2043–2050. 
  5. Harel, Michal, et al. “Abstract LB-401: A proteomics-based biomarker discovery platform for predicting clinical response to immune checkpoint inhibitor therapy in non-small cell lung cancer.” Cancer Research 80.16 (2020). 
  6. Bachar, Neta, et al. “An artificial intelligence-assisted diagnostic platform for rapid near-patient hematology.” American Journal of Hematology (2021):10.1002/ajh.26295.
  7. Banas, Miriam C., et al. “A urinary metabolite constellation to detect acute rejection in kidney allografts.” EBioMedicine 48 (2019): 505–512.