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.

背景和意义世界卫生组织（世卫组织）已将抗生素耐药性确定为一个主要的全球健康威胁，不仅影响健康，而且影响粮食安全。这一问题导致医疗成本增加、住院时间延长和死亡率上升，2019年有120万人死于抗生素耐药性。我们正在接近一个临界点，普通感染和轻伤可能会因耐药性而危及生命。这种情况可能会使常规手术风险很高，可能会破坏现代医学的成就。抗菌肽（AMP）的潜力抗菌肽（AMP）正在成为解决这一危机的许多有前途的解决方案之一。这些天然存在的物质对多重耐药细菌有效。其作用方式的多样性使得各种AMP可以开发成新药，从而有可能绕过现有的细菌耐药机制。我们团队的方法和专业知识我们的团队拥有超过60年的AMP研究经验和50年的人工智能和数据分析经验，处于该领域的最前沿，引用次数超过100，000次。我们的团队拥有合成和评估数千种AMP的独特能力。我们还利用先进的人工智能工具从广泛的基因组数据中预测新的AMP。人工智能应用中的挑战和机遇虽然人工智能在预测AMP活性方面取得了进展，但挑战仍然存在，特别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，例如在人体条件下的毒性和有效性，这些信息对于识别适用于高级药物开发的肽至关重要。愿景和目标克服这些挑战，我们正在编制一个包含数千种肽的广泛数据集。这些将在各种条件下针对多重耐药细菌进行测试，生成105，000个数据点的丰富数据集。这一举措将使我们的人工智能系统能够更有效地识别高价值的肽序列，减少早期药物开发阶段的时间和成本。节省下来的资金可以重新分配到以后的开发阶段，提高临床试验成功的可能性。我们的长期目标是将这项研究扩展到包括所有世卫组织优先生物，进一步完善我们的人工智能驱动的方法，以加快开发针对多重耐药病原体的有效药物。ConclusionOur使命是利用人工智能彻底改变抗菌药物的开发。凭借熟练的团队，创新的技术和战略计划，我们有能力为抗击耐药感染做出重大贡献。该项目是英国研究领域的重要补充。