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

将新药带到市场上可能需要长达十二年，并且耗资26亿美元（在过去十年中增长了140％），总药物开发耗资高达600亿美元/年。这远非可持续或易于使用，并且创造了一种经济障碍，该障碍可以防止制药公司投资于非持续性但非常重要的“研究领域的研究领域，包括大流行预防和抗菌素抵抗，并具有重大的社会和未来成本”。 [“公共良好药物发现的人工智能”，GPAI，2021年]。 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疾病。传统上，这种测试过程是劳动和资源密集型。提出的解决方案是开发一种基于AI的药物设计系统，该系统可以自动化预测现有化合物对蛋白质 - 蛋白质相互作用网络的影响的过程。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，系统将能够识别可以有效靶向网络中关键基因的化合物，同时最小化毒性。该项目将特别着重于在隐孢子虫寄生虫上测试该基于AI的药物设计系统，该系统是一种胃肠道寄生虫，会导致腹泻，营养不良，有时甚至在儿童中死亡。该项目中使用的假设和方法是基于先前的主要研究者（PIS）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种可以预测哪些化合物对寄生疾病的最小毒性水平，该系统最有效