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

很难想象在抗生素被发现之前的生活。结核病、肺炎和百日咳等感染是常见的杀手--如果轻微的伤口和烧伤被感染，它们就是致命的。使用抗生素来控制细菌感染可能是现代医学最重要的成就。然而，我们未能跟上微生物对现有治疗方法日益产生抗药性的步伐。新冠肺炎大流行说明了一种没有有效治疗的感染对人类健康的威胁。抗生素耐药性感染已经是另一种全球大流行，每年在全球范围内造成近500万人死亡。特别令人关切的是肺炎克雷伯氏菌引起的感染，这是全球第三大致病菌，与任何抗药性感染有关的死亡(25万人)有关。对“最后手段”抗菌素耐药菌株的日益分离大大缩小了治疗选择的范围，甚至在某些情况下完全消除了这种选择。这在低收入和中等收入国家尤其令人担忧。不幸的是，没有发明新的药物类别，耐药性继续无情地传播。赌注很高，我们可能正在进入前抗生素时代。英国公共卫生组织计算出，缺乏有效的抗生素将使300多万手术和癌症治疗方案危及生命，预计未来30年英国将有超过9万人死于抗生素耐药感染。抗生素药物发现的黄金时代利用了土壤微生物产生的抗菌产品，但这种方法在20年的系统筛选后耗尽。研究人员挖掘了不同来源的天然产品，如海洋环境、植物，甚至栖息在我们肠道中的无害微生物群落，取得了令人鼓舞的结果。然而，分离出的化合物中没有一种进入药物开发。更好地了解微生物抵抗抗生素的手段，可能会发现适合开发新药的迄今未知的靶点。在这项研究中，我们将使用人工智能来从肺炎克雷伯菌中识别新的潜在可药物靶点，当这些靶点被阻止时，可能会使微生物对抗生素敏感，甚至可能有助于我们的防御系统清除克雷伯氏菌。我们将训练受监督的学习者仔细阅读我们将在实验室产生的数据，并阅读微生物的基因组，以找到研究人员忽视的这些目标。下一步，利用其他学习者，我们将识别可以阻断这些靶点的药物。具体地说，我们将搜索已经批准用于人类但用于与抗菌活性无关的目的的药物。我们将在实验室进行实验，以确认这些药物的效果。从药物发现的角度来看，我们的方法大大缩短了药物开发过程，从而实现了从基础研究到临床开发的潜在快速过渡。我们预计，我们的结果将鼓励其他学者以及制药公司遵循这一新的研究途径，以解决对抗生素耐药的微生物缺乏治疗的问题。为了促进这一点，我们将免费提供我们的协议、模型和数据。

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.