Research Project
9240535
Project Summary/Abstract Tuberculosis (TB) is one of the leading causes of morbidity and mortality from infectious disease worldwide with an estimated 9.6 million cases of active TB and 1.5 million deaths from the disease annually. Despite emerging new technologies, culture remains the gold standard in TB diagnosis and therapeutic monitoring. In 2007, the World Health Organization (WHO) recommended the use of a liquid culturing system using the Mycobacteria Growth Indicator Tube (MGIT) and drug susceptibility test (DST) in middle and low income countries to address challenges due to the high prevalence of HIV co-infection and drug-resistant TB. While automated liquid culture systems can detect the presence of mycobacteria, they do not furnish species-level identification, requiring separate identification tests for positive samples. Specific Technologies has a novel sensor technology that can be easily adapted into an existing liquid culture system to not only detect the presence of mycobacteria, but also to provide species and strain identification. In collaboration with the Stanford University School of Medicine, Specific Technologies has demonstrated the colorimetric sensor array?s ability to detect and identify that 18 clinically-relevant bacterial species in blood culture with 95% accuracy over 1,192 trials. The CSA also allowed discrimination between unrelated strains of methicillin-resistant Staphylococcus aureus, indicating that the metabolomic fingerprint can differentiate different strains of the same species. Recently, these results have been extended to demonstrate the use of CSA sensors for the characterization of 10 mycobacterial species in liquid culture. Here, we propose to further develop and commercially validate this inexpensive, simple and novel sensing paradigm for mycobacterial culture, combining improved speed to detection with the hands free species ID. The strain-level signature we have demonstrated could have profound value for obtaining putative susceptibility profiles, and epidemiologically tracking resistant strains in the low resource environments characteristic of TB infection.
