Natural product antibiotics targeting Gram-negative MDR pathogens

迫切需要新的抗生素来对抗多重耐药(MDR)的上升，到目前为止，最紧迫的是识别抑制MDR-革兰氏阴性病原体的化合物，其中几种病原体的治疗选择已经变得非常有限。Demuris是一家中小企业，拥有世界领先的放线菌集合，这些细菌极其多样化，几乎没有复制。这些收集正在被用来识别和开发抗生素和抗真菌药物，以对抗抗菌素耐药性(AMR)的上升。通过从标本中筛选7000株多样性和去复制的放线菌，德穆里斯已经鉴定出100多株具有广谱抗菌活性的菌株，这些菌株可以抑制一组多重耐药的大肠杆菌菌株。在这个项目中，德穆里斯将致力于对所有这些广谱抗生素产生菌的基因组进行测序。这将使用牛津纳米孔的长阅读、单分子测序来快速和廉价地进行。我们将创建一个内部技术平台，利用现有软件以低成本对抗生素生产细菌的基因组进行测序，然后进行生物信息筛选。生物信息预测将产生已知广谱抗生素化合物的菌株将从发现管道中移除，这将大大加快去复制过程，并迅速放大产生新抗生素化合物的菌株。将对新型抗生素进行结构特征描述，并将生成一个包含微生物学和细胞毒性的数据包。这些数据将足以对临床开发进行评估。这种“数据优先”的方法承诺提高效率，并将允许所有资源优先用于临床成功机会最大的化合物。

New antibiotics are urgently needed to combat the rise of Multi-Drug Resistance (MDR), and by far the most pressing is the identification of compounds to inhibit MDR-Gram-negative pathogens, where the treatment options for several pathogens have become severely limited.Demuris is an SME with a world-leading collection of actinomycete bacteria which are both extremely diverse and almost devoid of replicates. The collection is being used to identify and develop antibiotic and antifungal agents to combat the rise of antimicrobial resistance (AMR). Through screening >7000 diverse and dereplicated actinomycetes from the collection, Demuris has identified over 100 strains with broad-spectrum antibacterial activity that inhibit a panel of multiply antibiotic resistant E. coli strains.In this project Demuris will aim to sequence the genomes of all of these broad spectrum antibiotic producer strains. This will be performed rapidly and cheaply using long read, single molecule sequencing from Oxford-Nanopore. We will create an in-house technology platform to sequence and then bioinformatically screen the genomes of antibiotic producing bacteria at low cost using existing software. Strains bioinformatically predicted to produce known broad spectrum antibiotic compounds will be removed from the discovery pipeline, dramatically speeding up the dereplication process and zooming in rapidly on strains producing novel antibiotic compounds. Novel antibiotics will be structurally characterised and a data package consisting of microbiology and cytotoxicity will be generated. These data will be sufficient to enable evaluation for clinical development. This "data first" based approach promises to improve efficiency and will allow all resources to be prioritised on the compounds with the best chance of clinical success.