The Health Data Science and Systems Research & Learning Cluster is meeting this Friday, January 8th, 10:30am-noon to hear from Dr. Imran Khan about Multidisciplinary Approach to TB Diagnostics Based on Computational Modeling
Approximately, two billion people worldwide are infected with Mycobacterium tuberculosis (M. tb.), the etiologic agent of tuberculosis (TB). The current frontline diagnostic tests are lack sensitivity, efficiency, cumbersome or too expensive. There is a urgent need for low cost, efficient, high-throughput and accurate diagnostic approaches. We have developed immune biomarker based TB diagnostic system in blood in TB patients as well as nonhuman primate model. Data on immune biomarkers, microbiology, gut-microbiota, and imaging (X-ray & CT scan) from proof-of-concept and subsequent field studies have shown that this approach will enable a scalable, flexible and cost-effective model for diagnostic applications. In addition, cytokine/chemokine biomarkers (e.g., IP-10, MIG, IL-16, IFN-α and G-CSF) progressively decrease in patients which responded to anti-tuberculosis treatment (ATT). These decreases strongly correlate with treatment success and can be used for monitoring efficacy of therapy – ATT is a drawn out process (over many months), and early detection of patients who may not respond to therapy is important. Clinical studies in three TB endemic countries (Pakistan, India and Uganda) have demonstrated that the above multidisciplinary approach to TB diagnostics is highly useful and can be successfully positioned for diagnosis of all forms of TB (pulmonary, extra-pulmonary and pediatric) in comparison to the sputum based diagnostic tests that are limited for use in pulmonary TB. A clinical trial in India is currently under way.
Dr. Khan is Professor at the School of Medicine, University of California, Davis. He earned his Ph.D. (Molecular & Cellular Biology) at Albert Einstein Collge of Medicine, NY. His research program has been focused on infectious diseases for over two decades. Since 2002 he has worked on developing highly efficient and high throughput multiplex approaches for infectious disease biomarkers (e.g., Tuberculosis). He has employed novel approaches to study disease related biomarkers (e.g., immune biomarkers) in bodily fluids (e.g., plasma/serum) by combining the power of multiplexing systems and computational modeling. Results of his research have been published in peer reviewed journals for the development of novel methods for biomarker profiling for infectious diseases, cancer, and inflammatory diseases.