Extensively drug-resistant tuberculosis (XDR-TB), which has been associated with up to 80% mortality, is considered "virtually untreatable" in many parts of the world, and is threatening global TB control. Approximately 6,000 cases of XDR-TB from 85 countries were reported to World Health Organization (WHO) in 2011. WHO estimates this is likely <10% of the true number of the cases due to a lack of global laboratory capacity to diagnose XDR-TB. The goal of this project is to produce and deliver an integrated tabletop diagnostic platform for detecting XDR-TB in sputum in less than eight hours using existing Gel Element Microarray (GEM) technology developed by our industry partner, Akonni Biosystems. There is currently no FDA approved rapid diagnostic for XDR-TB, which is why the 2013 CDC fact sheet states that diagnosis of XDR-TB (a NIAID Category C priority pathogen) currently requires "6 to 16 weeks" to complete. US civilians are at increased risk for exposure to XDR-TB from a growing list of potential transmission environments, from exposure to XDR-TB infected persons entering the US undetected, to airplane flights and increased exposure to high TB prevalence countries for leisure and business. US troops are also at increased risk of exposure in environments encountered on tactical and humanitarian missions. There is a clear and critical need for an integrated, table-top platform for high sensitivity, high specificity, rapid diagnosis of drug resistant TB from direct patient samples. We propose to combine the experience, resources and existing diagnostic testing pipeline of the Global Consortium for Drug Resistant Tuberculosis Diagnostics (GCDD - NIAID U01AI082229) with the technological innovation and industry knowledge of our project partner, Akonni Biosystems to develop and test a rapid XDR-TB diagnostic platform. Based on our extensive experience with the genetic basis of XDR-TB, and Akonni's high fidelity prototype platform for identifying specific single nucleotide polymorphisms (SNPs) in clinical samples, we hypothesize that we will be able to detect XDR-TB isolates with clinically relevant phenotypic resistance to INH, RIF, FQ, AMK, KAN, and CAP from a direct sputum sample, with 90-98% sensitivity and ~100% specificity in under eight hours. We will achieve this goal by completing the following three objectives;1) Expand and optimize an existing prototype Gel Element Microarray (GEM) platform to detect all of the clinically relevant resistant alleles found in XDR- TB isolates;2) Verify the expanded XDR GEM platform performance in a laboratory setting against 300 isolates from an existing GCDD collection of multinational drug resistant isolates already characterized with phenotypic drug susceptibility testing, sequencing and whole genome sequencing;and 3) Field evaluate the XDR GEM platform ability to detect XDR-TB isolates in sputum samples from patients at risk for drug resistant TB at a high-throughput clinical laboratory in Mumbai, India.

Public Health Relevance

Diagnosis and treatment of drug resistant tuberculosis is currently limited by reliance on slow-growing, culture-based drug susceptibility testing. The goal of this project is to produce and deliver an integrated tabletop diagnostic platform for detecting extensively drug-resistant tuberculosis (XDR-TB) in sputum in less than eight hours using existing Gel Element Microarray (GEM) technology developed by our industry partner, Akonni Biosystems. The technical and clinical knowledge gained in this project will translate directly to the ultimate goal of a sample-in, answer-out system that is deployed closer to the point of need than current diagnostics, enabling not only rapid diagnosis, but also rapid response to the threat posed by XDR-TB in healthcare and biodefense settings.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
1R01AI111435-01
Application #
8693519
Study Section
Special Emphasis Panel (ZAI1)
Program Officer
Jacobs, Gail G
Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California San Diego
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
City
La Jolla
State
CA
Country
United States
Zip Code
92093