Tackling the pandemic of antibiotic-resistant infections: An artificial intelligence approach to new druggable therapeutic targets and drug discovery

应对抗生素耐药性感染的流行：利用人工智能方法实现新的药物治疗靶点和药物发现

• 批准号: MR/X009246/1
• 负责人: Tania Dottorini
• 金额: $ 171.95万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX009246%2F1
• 关键词: Tackling; pandemic; antibiotic; resistant; infections

It is difficult to imagine life before antibiotics were discovered. Infections such as tuberculosis, pneumonia and whooping cough were common killers - and if minor wounds and burns became infected they were fatal. The use of antibiotics to control bacterial infections is perhaps the most important achievement of modern medicine. However, we have failed to keep pace with microbes becoming increasingly resistant to available treatments. The Covid-19 pandemic exemplifies the threat to human health of an infection without an effective treatment. Antibiotic-resistant infections are already another global pandemic claiming almost 5 million deaths per year globally. Of particular concern are the infections caused by Klebsiella pneumoniae, globally, the third leading pathogen associated with deaths (250 000) attributed to any antibiotic-resistant infection. The increasing isolation of strains resistant to "last resort" antimicrobials has significantly narrowed, or in some settings completely removed, the therapeutic options. This is particularly alarming in low and middle-income countries. Unfortunately, new classes of drugs are not being invented and resistance continues to spread inexorably. The stakes are high and we might be entering into a pre-antibiotic era. Public Health England has calculated that the lack of effective antibiotics will render more than the three million operations and cancer treatments life-threatening, and more than 90,000 people are estimated to die in the UK over the next 30 years due to antibiotic-resistant infections.The golden era in antibiotic drug discovery leveraged the antibacterial products produced by soil microorganisms but this approach became exhausted after 20 years of systematic screening. Researchers have mined different sources of natural products such as marine environments, plants, and even the community of harmless microbes inhabiting our gut with encouraging results. Yet, none of the compounds isolated have entered into drug development. A better understanding of the means used by microbes to resist antibiotics may result in the discovery of hitherto unknown targets suitable to develop new drugs against. In this research, we will use artificial intelligence to identify new potential druggable targets from K. pneumoniae that when blocked may render the microbe susceptible to antibiotics and perhaps may even facilitate the clearance of Klebsiella by our defenses. We will train supervised learners to go through data we will generate in the laboratory and to read the genome of the microbe to find these targets that researchers have overlooked. Next, and utilizing other learners, we will identify drugs that can block these targets. Specifically, we will search drugs already approved for use in humans but used for purposes unrelated to antimicrobial activity. We will carry out experiments in the laboratory to confirm the effect of these drugs. From the drug discovery point of view, our approach significantly shortcuts the drug development process hence allowing a potential fast-track transition from the basic research to clinical development. We envision that our results will encourage other academics as well as pharmaceutical companies to follow this new avenue of research to tackle the problem of the lack of therapies for microbes resistant to antibiotics. To facilitate this, we will make freely available our protocols, models and data.

很难想象在发现抗生素之前的生活。结核病，肺炎和百日咳等感染是常见的杀手 - 如果被轻微的伤口和烧伤感染了，他们是致命的。使用抗生素来控制细菌感染也许是现代医学的最重要成就。但是，我们未能跟上微生物对可用治疗的耐药性越来越多。 19009年大流行体现了对感染的人类健康的威胁，而无需进行有效治疗。抗生素耐药性感染已经是另一个全球大流行病，每年在全球范围内近500万人死亡。特别关注的是全球肺炎克雷伯氏菌引起的感染，这是与死亡（250 000）相关的第三个领先的病原体（250 000），归因于任何抗生素耐药性感染。抗抗菌抗菌抗菌抗菌抗菌抗菌抗菌的抗菌菌株的分离越来越大，或者在某些情况下完全去除了治疗选择。在低收入和中等收入国家中，这尤其令人震惊。不幸的是，新的药物没有被发明，耐药性继续传播。赌注很高，我们可能正在进入抗生素前时代。英格兰公共卫生已经计算出，缺乏有效的抗生素将使300万手术和癌症治疗生命危及生命，并且由于抗生素耐药性感染，据估计，在英国估计有9万人死亡。抗生素药物发现中的黄金时代利用了土壤微生物生产的抗菌产品，但是经过20年的系统筛查，这种方法变得精疲力尽。研究人员挖掘了不同的天然产品来源，例如海洋环境，植物，甚至是无害的微生物社区，居住在我们的肠道中，令人鼓舞。但是，孤立的化合物都没有进入药物开发。更好地理解微生物用于抵抗抗生素的手段可能会导致发现迄今为止适合开发针对新药的未知靶标。在这项研究中，我们将使用人工智能从K.肺炎的K.肺炎中确定新的潜在可毒靶标的，该靶标会使微生物易受抗生素的敏感，甚至可能通过我们的防御能力促进Klebsiella的清除。我们将培训受监督的学习者，以了解我们将在实验室中生成的数据，并阅读微生物的基因组，以找到研究人员忽略的这些目标。接下来，利用其他学习者，我们将确定可以阻止这些目标的药物。具体而言，我们将搜索已经批准用于人类的药物，但用于与抗菌活性无关的目的。我们将在实验室进行实验，以确认这些药物的作用。从药物发现的角度来看，我们的方法显着捷径，因此药物开发过程允许从基础研究到临床开发的潜在快速过渡。我们设想，我们的结果将鼓励其他学者和制药公司遵循这一新的研究途径，以解决缺乏对抗生素抗性抗生素疗法的问题。为了促进这一点，我们将免费提供协议，模型和数据。