Nanotechnology to improve the diagnosis of tuberculosis in children Najeeha Iqbal1, Kumail Ahmad1, Farah Qamar1, Eric Houpt2, Tania Thomas2 1.Aga Khan University 2.University of Virginia

Mycobacterium tuberculosis: leading global killer 2017 Incidence: 10,000,000 Children affected 1,000,000 Children infected with “Latent TB” 67,000,000 Mycobacterium tuberculosis, the pathogen that causes TB, has earned the dubious distinction of being the leading global killer from a single pathogen. In 2017, there were an estimated 10 million cases of TB, of which 1million occurred in children younger than 15 years of age. It was estimated that 230,000 children died from TB, which is largely due to under-diagnosis. Pediatric deaths 230,000 WHO, Global TB Report 2018 Houben et al, PLoSMed, 2016

Although I fully agree that much of this is preventable with screening, tracing contacts of infectious cases, and administering more preventive therapy to people harboring the “latent stage” of TB, our jobs would be much easier if there were an accurate diagnostic test that could be used in children. CDC Global Health:

Lipoarabinomannan (LAM): a biomarker of TB Pediatric studies: Sensitivity: 5-83% Specificity: 61-93% Adults w/ HIV/AIDS Sensitivity: ~56% Specificity: ~87% Lipoarabinomannan, abbreviated as LAM, is a lipopolysaccharide antigen that is found on the outer cell wall of Mycobacteria species. There has been considerable efforts to create assays to detect LAM as a biomarker of TB disease from bodily fluids including blood, cerebrospinal fluids and urine. It is a huge advance to the field to have this point-of-care, lateral-flow test strip which uses a few drops of urine, much like a urine pregnancy test, to diagnose TB. However, numerous studies in TB endemic regions have demonstrated that these assays do not detect TB reliably unless it is used in someone who is co-infected with advanced HIV/AIDS, then giving a sensitivity of ~56%. This has been evaluated in a few pediatric studies, mainly in Africa and the results have been variable, with sensitivity ranging from 5-83%. WHO/HTM/TB/2015.25 Fig courtesy of Amanda Welin

Nanotechnology to improve yield from current assays Nanotrap®- a molecular cage to capture LAM using high-affinity copper complex dye Limitations with current assays: Low binding affinity Low concentration of LAM Presence of interfering substances In partnership with CERES Technology, we are evaluating how nanotechnology can improve the yield of current LAM assays. These nanotraps, have been developed as molecular cages to capture LAM antigens and with a high-affinity chemical dye, These nanotraps are used as a pre-processing step to augment the yield from currently available LAM assays. The nanotraps address several limitations of current assays: It is challenging to find an antibody that can capture carbohydrates such as LAM well. The nanotrap employs a novel copper-complex reactive dye as bait, which binds and sequesters LAM antigens with high affinity. Thus, it is able to concentrate LAM from urine specimen into a few drops, which can be used on a lateral flow assay. The shell structure eliminates the entry of interfering proteins based on critical size exclusion. Slide courtesy of Ross Dunlap, CERES

Nanotrap® to detect childhood TB AIM: Compare the performance of the lateral flow LAM assay to detect LAM antigens from urine before and after processing specimens with Nanotrap N=60 cases and controls who provided urine samples at enrollment

Pairwise comparison of LAM results qualitative & quantitative Test characteristics by disease group confirmed TB, clinical TB, control Regression analysis to identify factors associated with a positive assay Age, gender, anthropometrics, microbiologic confirmation

Next steps: Validate this assay among larger samples Geographically diverse areas (Bangladesh, Tanzania, Philippines) Children with pulmonary and extrapulmonary TB Prospective evaluation as a child-friendly rapid diagnostic for TB: Can the Nanotrap allow for earlier diagnosis of TB?

Acknowledgements UVA TB Team: Eric Houpt Scott Heysell Jean Gratz Jie Liu Shino Mirawdaly Supporn Pholwat Kristen Petros de Geux Suzanne Stroup Serhiy Vitko UVA GIDI AKU Team: Najeeha Iqbal Farah Qamar Kumail Ahmad Fariha Shaheen Aisha Mehnaz Shazia Sultana Ceres Technology: Ben Lepene Robbie Barbero