Natural product antibiotics targeting Gram-negative MDR pathogens

迫切需要新的抗生素来对抗多药耐药（MDR）的上升，到目前为止，最紧迫的是确定抑制耐多药革兰氏阴性病原体的化合物，在这种情况下，几种病原体的治疗选择已经严重有限。Demuris是一家中小型企业，拥有世界领先的放线菌细菌收集，这些放线菌细菌种类繁多，几乎没有复制。收集的数据被用于识别和开发抗生素和抗真菌药物，以应对抗菌素耐药性的上升。Demuris从收集物中筛选了约7000种不同的和去复制的放线菌，发现了100多种具有广谱抗菌活性的菌株，可以抑制一组多重耐药大肠杆菌菌株。在这个项目中，Demuris的目标是对所有这些广谱抗生素产生菌株的基因组进行测序。使用牛津纳米孔公司的长读单分子测序技术，这将快速而廉价地完成。我们将创建一个内部技术平台，对产生抗生素的细菌进行测序，然后利用现有软件以低成本进行生物信息学筛选。生物信息学预测产生已知广谱抗生素化合物的菌株将从发现管道中移除，大大加快了去复制过程，并迅速放大了产生新型抗生素化合物的菌株。将对新型抗生素进行结构表征，并生成由微生物学和细胞毒性组成的数据包。这些数据将足以为临床开发进行评估。这种基于“数据优先”的方法有望提高效率，并将允许所有资源优先用于最有可能取得临床成功的化合物。

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.