Development of Rapid and Sensitive Diagnostics for Tuberculosis Karen E. Kempsell†, Seshadri Vasan†, Catherine Arnold‡ Public Health England, Centre for Infections, 61 Colindale Avenue, London NW9 5EQ‡ Manor Farm Road, Porton Down, Salisbury, SP4 0JG † THE PROBLEM OUR SOLUTION Tuberculosis (TB) is a continuing health problem both here in the UK and globally 8,751 cases of TB were reported in the UK in 2012, an overall incidence rate of 13.9/100,000 1 The majority of TB cases (73%) occurred among people not born in the UK, usually from high-burden countries These can provide foci of infection in the UK population and contribute to the socio-economic disease burden Most cases of TB in the UK are concentrated in large urban centres Globally TB is second only to HIV/AIDS as the greatest killer worldwide due to a single infectious agent 2 TB poses significant disease management issues, especially in low- and middle-income countries Significant improvements in TB diagnosis would greatly facilitate patient management and care and reduce disease spread Current methods for TB diagnosis are laborious and time-consuming and also have issues with sensitivity, leading to higher rates of false -negative reporting Delays or misdiagnosis of TB can impede treatment initiation and contribute to disease spread Rapid Point of Care (PoC) or more sensitive tests would be of great value in this challenging diagnostic landscape These would need to demonstrate utility in both high and low-resource settings We have developed a panel of TB-specific host-derived biomarkers which are in development for active and latent disease diagnosis These could be used on simple, rapid PoC devices and using a readily accessible sample i.e. blood We have also developed an ultra-sensitive, rapid diagnostic test for detection of TB in sputum, which may offer improvements in sensitivity and cost compared with current ‘gold standard’ molecular diagnostic method (WHO endorsed GeneXpert) These will also provide valuable additional information on drug sensitivity Incidence rate per 100 000 population 0.00 - 4.99 5.00 - 9.99 10.00 - 14.99 15.00 - 24.99 25.00 - 39.99 40.00 - 69.99 70.00 - 114.99 Figure 1: Three-year average tuberculosis case rates by local authority (England), health board (Scotland and Wales) and Northern Ireland, UK, 2010-2012 Figure 2. Tuberculosis patient showing characteristic disease wasting Figure 3. Chest X-ray of patient with pulmonary Tuberculosis BENEFITS AND OPPORTUNITIES A B B A B1 B2 B1 B2 Figure 4. Unsupervised Euclidean hierarchical clustering on a Human blood expression dataset on conditions (disease state i.e. Active (ATB), Latent TB (LTBI) or negative controls), using a panel of eight biomarkers derived from the PHE patent. The patients are separated into two clear clusters A and B, with two sub-clusters in cluster B, B1 and B2. Most ATB patients co-localise in cluster A using these markers, however a number of LTBI patients also segregate in cluster A. These may be at greater risk of disease progression. The remaining LTBI, control and a limited number of ATB patients segregate in cluster B and may be at lower risk. A number of ATB patients also co-localise with LTBI and controls in cluster B, however these do not show clear signs of active TB gene signatures, using this or any other panel and may be classed as false negatives. Figure 5. Unsupervised Squared-Euclidean hierarchical clustering on a Human blood expression dataset on conditions and entities using the latent and control samples only from Figure 1 and eleven gene biomarkers from the PHE patent. These biomarker gene candidates segregate the latent and control samples into two main clusters A and B, cluster B having two sub-clusters, B1 and B2. Cluster A represents LTBI patents who express markers strongly associated with ATB (n = 10, ~14.6% of all LTBI), which may be borderline Latent/Active TB and at greater risk of developing ATB. Cluster B1 represents LTBI samples which co-segregate with most control samples (n = 20, ~29.4%). These most likely represent near-negative LTBI, therefore at lowest risk. Cluster B2 represents LTBI samples which express a gene profile more consistent with true Latent TB (n = 38, 56%) and may be at intermediate risk. Figure 1. Unsupervised Euclidean hierarchical clustering on a Human blood expression dataset on conditions (disease state i.e. Active (ATB), Latent TB (LTBI) or negative controls), using a panel of eight biomarkers derived from the PHE patent. The patients are separated into two clear clusters A and B, with two sub-clusters in cluster B, B1 and B2. Most ATB patients co-localise in cluster A using these markers, however a number of LTBI patients also segregate in cluster A. These may be at greater risk of disease progression. The remaining LTBI, control and a limited number of ATB patients segregate in cluster B and may be at lower risk. A number of ATB patients also co-localise with LTBI and controls in cluster B, however these do not show clear signs of active TB gene signatures, using this or any other panel and may be classed as false negatives. Biomarker-Based Diagnostic Tests for Tuberculosis Peripheral blood Leukocyte (PBL)-associated biomarkers have been discovered in a non-human primate model of pulmonary TB and confirmed in human tuberculosis These could be used to develop nucleic-acid or protein-based diagnostics tests for active and latent human TB Biomarkers have shown utility in TB diagnosis in HIV positive and negative individuals Turn-around time for many emerging PoC devices ~ 15-30 minutes or less Used with easily accessible sample i.e. blood Could be used additionally to monitor disease progression/response to therapy Figure 6. Unsupervised Squared-Euclidean hierarchical clustering on a second Tuberculosis Human blood expression dataset including patients with other infectious diseases and HIV positive and HIV negative individuals for each infectious disease state. Group averaged data is clustered on conditions and entities using eleven gene biomarkers from the PHE patent. These preferred markers segregate latent TB (cluster A) from active TB and other infectious diseases (cluster B) . The markers also differentiate active TB and other infectious diseases into two sub-clusters B1 and B2. These markers function with equal efficacy in both HIV positive and HIV negative individuals. Expression of some highly differentially regulated markers are higher in HIV positive, active TB than HIV negative, active TB individuals demonstrating utility for diagnosis in both patient groups. A B B1 B2 NEXT STEPS IP CONTACT US Complete biomarker validation studies in a new cohort of TB patients Compare profile with those from other infectious diseases Select commercial platform-provider for assay development Secure funding and engage with stakeholders for commercial development and implementation Patent in progress for biomarker development for active and latent TB Patent in progress for biomarker development for early TB Knowledge and know-how in Biomarker discovery and validation Knowledge and know-how in diagnostic test development Dr Karen Kempsell Senior Research Scientist, Research and Technical Services Public Health England Porton Down, Wiltshire SP4 0JG United Kingdom +44 1980 619816 Karen.Kempsell@phe.gov.uk Dr S.S. Vasan Senior Business Development Manager (Research & Innovation) +44 1980 616709 Seshadri.Vasan@phe.gov.uk Dr Catherine Arnold Unit Head, Genomic Services and Development 61 Colindale Avenue, London NW9 5EQ United Kingdom +44 20 8327 6068 Catherine.Arnold@phe.gov.uk REFERENCES ACKNOWLEDGEMENTS https://www.gov.uk/government/news/tuberculosis-rates-remain-among-highest-in-western-europe 2. http://www.who.int/mediacentre/factsheets/fs104/en/ This study is funded by both the PHE Research & Development Pipeline Fund and The Department of Health. The views expressed in this publication are those of the authors and not necessarily those of PHE or the Department of Health.