AMPQuest - Journeying to new horizons in treating drug-resistant infections.

AMPQuest - 迈向治疗耐药感染的新视野。

• 批准号: BB/Y514019/1
• 负责人: Kai Hilpert
• 金额: $ 32.89万
• 依托单位: St George's, University of 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=BB%2FY514019%2F1
• 关键词: AMPQuest; Journeying; new; horizons; treating

Context and SignificanceThe World Health Organization (WHO) has identified antibiotic resistance as a major global health threat, impacting not only health but also food security. This issue has led to increased medical costs, longer hospital stays, and a rise in mortality rates, with 1.2 million deaths in 2019 attributed to antibiotic resistance. We are approaching a critical point where common infections and minor injuries might become life-threatening due to drug resistance. This scenario could make routine surgeries highly risky, potentially undermining the achievements of modern medicine.The Potential of Antimicrobial Peptides (AMPs)Antimicrobial peptides (AMPs) are emerging as one of many promising solutions to this crisis. These naturally occurring substances are effective against multi-drug resistant bacteria. The diversity in their modes of action allows for the development of various AMPs into new drugs, potentially bypassing existing bacterial resistance mechanisms.Our Team's Approach and ExpertiseOur team, with over 60 years of combined experience in AMP research and 50 years in AI and data analysis, is at the forefront of this field demonstrated by more than 100,000 citations. Our team possesses the unique capability to synthesize and evaluate thousands of AMPs. We also utilize an advanced AI tool to predict new AMPs from extensive genomic data.Challenges and Opportunities in AI ApplicationWhile AI has advanced in predicting AMP activity, challenges remain, particularly due to variability in existing datasets. These datasets often lack comprehensive information, such as toxicity and effectiveness in human conditions, which are critical for identifying peptides suitable for advanced drug development.Vision and ObjectiveTo overcome these challenges, we are compiling an extensive dataset of thousands of peptides. These will be tested against a multi-drug resistant bacterium under various conditions, generating a rich dataset of 105,000 data points. This initiative will enable our AI system to identify high-value peptide sequences more efficiently, reducing the time and cost of early drug development phases. The savings can then be reallocated to later development stages, enhancing the likelihood of success in clinical trials. Our long-term goal is to extend this research to include all WHO-priority organisms, further refining our AI-driven approach to expedite the development of effective drugs against multi-drug resistant pathogens.ConclusionOur mission is to harness AI to revolutionize antimicrobial drug development. With a skilled team, innovative technology, and a strategic plan, we are well-positioned to make significant contributions to combating drug-resistant infections. This project represents a vital addition to the UK's research landscape.

环境和显着性世界卫生组织（WHO）将抗生素耐药性确定为全球卫生威胁，不仅影响健康，而且影响粮食安全。这个问题导致医疗费用增加，住院时间更长，死亡率升高，2019年死亡归因于抗生素抗药性。我们正在关键点，由于耐药性，常见的感染和轻伤可能会危及生命。这种情况可能会使常规手术高度冒险，可能破坏现代医学的成就。抗菌肽（AMPS）抗菌肽（AMP）的潜力正在成为这场危机的许多有希望的解决方案之一。这些天然发生的物质可有效防止多药物抗性细菌。其作用方式的多样性允许将各种AMP开发为新药，可能绕过现有的细菌抗性机制。我们的团队的方法和专业团队，在AMP研究中拥有超过60年的AI和数据分析经验超过50年，在该领域的最新情况下，有超过100,000个引文。我们的团队具有综合和评估数千个安培的独特能力。我们还利用一种先进的AI工具来预测广泛的基因组数据中的新放大器。在AI应用程序中，AI的挑战和机遇在预测AMP活动方面已提高了挑战，挑战仍然存在，尤其是由于现有数据集的可变性。这些数据集通常缺乏全面的信息，例如人类条件下的毒性和有效性，这对于识别适合先进药物开发的肽至关重要。Vision和Objectiveto克服这些挑战，我们正在编译成千上万肽的广泛数据集。这些将在各种条件下针对多药耐药细菌进行测试，从而产生105,000个数据点的丰富数据集。该计划将使我们的AI系统能够更有效地识别高价值肽序列，从而减少早期药物发育阶段的时间和成本。然后可以将节省重新分配到以后的开发阶段，从而增强了临床试验中成功的可能性。我们的长期目标是扩展这项研究，以包括所有WHO WHO生物体，进一步完善我们的AI驱动方法，以加快针对多药耐药病原体的有效药物的开发。结论的使命是利用AI来革新抗菌药物开发。凭借熟练的团队，创新技术和战略计划，我们有很好的位置，可以为对抗抗药性感染做出重大贡献。该项目代表了英国研究格局的重要补充。