People involved: Anshu Bhardwaj
Keywords: genomics, biomarker, pathogen, nontuberculous mycobacteria, AI chatbot, gaming
Antimicrobial resistance (AMR), the phenomenon of clinically relevant pathogens developing multi-drug resistance (particularly to antibiotics), has emerged as a grave threat to public health that could plunge the world into a ‘post-antibiotic era’. Neglecting AMR would result in global annual loss of 10 million lives and trillions of dollars by 2050 (2015 O’Neil). The Global Action Plan for AMR has identified five objectives to address this scourge. The first two objectives – “ (a) Improve awareness and understanding of antimicrobial resistance through effective communication, education and training and (b) Strengthen the knowledge and evidence base through surveillance and research” are the basis of the current proposal which aims to address these using genomics and artificial intelligence tools. At the core of the project is the pressing need to identify infections from Nontuberculous mycobacteria (NTM) or Mycobacterial Other Than TB (MOTT). NTMs include more than 160 ubiquitous Mycobacterium species that do not cause tuberculosis or leprosy. NTMs are present in the environment (water or soil) and can infect humans or animals leading to a range of pathological conditions like pulmonary, skin, bone, joint, and disseminated diseases. NTM species are gaining visibility due an increasing number of strains responsible for treatment-resistant diseases. NTMs are taxonomical diverse and increasing number of new species offer challenges in identification of NTMs in clinical settings. NTM species like Mycobacterium abscessus are now recognized as a major threat and FDA identified NTMs as their focus disease area in 2016-17. Despite their increasing role in human diseases (in India prevalence rate increased from 0.7% to 34%) limited data is available to delineate and identify species of NTMs in the clinical settings. It is crucial to know the NTM species for prescribing treatment options as the disease presentation and clinical investigation parameters are very similar to Tuberculosis. Moreover, there are species-specific differences from context of resistance to different antibiotics. The current proposal entitled ‘G-MOTT’ aims to develop a comparative genomics pipeline to identify genomic signatures that may help in delineating different NTM species. The second part of the proposal aims to build a conversational artificial intelligence (AI) chatbot wrapped as a gaming application. The idea behind building this tool is to engage the mobile users into playing a game to understand the concept and challenges of AMR. This is first of its kind unique effort to utilize the power of AI to strengthen the human-machine interface aligning with the goals of the Global Action Plan on creating awareness, education and training on AMR.