Cryptosporidium Artificial Intelligence Network Analysis of Drug Action (CANADA)

隐孢子虫药物作用人工智能网络分析（加拿大）

• 批准号: EP/Z532885/1
• 负责人: Laura TONI
• 金额: $ 18.95万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FZ532885%2F1
• 关键词: Cryptosporidium; Artificial; Intelligence; Network; Analysis

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

将一种新药推向市场可能需要长达 12 年的时间，耗资 26 亿美元（过去 10 年增长了 140%），药物开发总成本高达 600 亿美元/年。这远非可持续或可及的，并且造成了经济障碍，阻止制药公司投资于非盈利但非常重要的“包括流行病预防和抗菌素耐药性在内的领域的研究领域，这给社会带来了当前和未来的重大成本”。 [“人工智能促进公益药物发现”，GPAI，2021 年]。在这个项目中，我们分享消除这一障碍的长期愿景，分享麦肯锡的愿景，即迫切需要“全面转变新药开发的研发——加快 500 天，更好地满足患者需求，并且成本降低 25%”考虑到这一长期目标，该项目的重点是开发一种概念验证，以克服药物开发过程中的瓶颈，即测试用于寄生虫病的新化合物。传统上，这个测试过程是劳动力和资源密集型的。提出的解决方案是开发一种基于人工智能的药物设计系统，该系统可以自动预测现有化合物对蛋白质-蛋白质相互作用网络的影响。该系统将使用机器学习算法来分析蛋白质之间的相互作用并预测药物作用。通过了解这些相互作用，系统将能够识别能够有效靶向网络中关键基因的化合物，同时最大限度地降低毒性。该项目将专门针对小隐孢子虫寄生虫测试这种基于人工智能的药物设计系统，这种寄生虫是一种胃肠道寄生虫，会导致腹泻、营养不良，有时甚至导致死亡，尤其是儿童。该项目中使用的假设和方法基于主要研究者 (PI) 之前进行的研究，并将使用经过验证的生物学方法进一步完善和测试。该项目的最终目标是开发一个系统，可以预测哪些化合物将治疗寄生虫病最有效，对宿主的毒性最小