Cryptosporidium Artificial Intelligence Network Analysis of Drug Action (CANADA)

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Bringing a new drug to market can take up to twelve years and cost 2.6 billion USD (a 140 percent increase in the past ten years), with a total drug development cost up to $60 billion/year. This is far from being sustainable or accessible and creates an economic barrier that prevents pharmaceutical companies from investing in non-lucrative and yet very important "fields of research in domains including pandemic prevention and antimicrobial resistance, with major current and future costs for society". ["Artificial Intelligence for Public Good Drug Discovery", GPAI, 2021]. In this project, we share the long-term vision of removing this barrier, sharing the McKinsey vision of an urgent need to "transform R&D for new-drug development holistically-500 days faster, better tailored to patient needs, and 25 percent cheaper".With this long-term goal in mind, this project focuses on developing a proof-of-concept for overcoming the bottleneck in the drug development process, which is the testing of new compounds for parasitic diseases. Traditionally, this testing process has been labour and resource intensive. The proposed solution is to develop an AI-based drug design system that automates the process of predicting the effect of existing compounds on protein-protein interaction networks. This system will use machine learning algorithms to analyse the interactions between proteins and predict the drug action. By understanding these interactions, the system will be able to identify compounds that can effectively target key genes in the network while minimising toxicity. The project will specifically focus on testing this AI-based drug design system on the Cryptosporidium parvum parasite, which is a gastrointestinal parasite which causes diarrhoea, malnutrition, and sometimes death particularly in children. The hypotheses and methods used in this project are based on previous studies conducted by the Principal Investigators (PIs) and will be further refined and tested using proven biological approaches.The ultimate goal of this project is to develop a system that can predict which compounds will be the most effective in treating parasitic diseases with the minimum levels of toxicity to the host