Identification and application of novel lipoglycodepsipeptide antibiotics

Unmet Need

Antibiotic resistance (ABR) is a global public health concern. In 2019, an estimated five million deaths were attributed to antibiotic resistant pathogens. A recent study estimates that ABR could cause nearly ten million deaths per year by 2050, surpassing cancer related deaths and could cost the global economy up to $100 trillion. Current therapies have seen increasing resistance rates in recent years. Gram-positive bacteria, in particular cocci strains (staphylococci, streptococci, and enterococci), exhibit high rates of ABR. The rate of penicillin resistance in these strains is 85%, and multi-drug resistance 44%. Many naturally occurring compounds possess good antibiotic activity in vitro but fail to make the jump to drug candidates owing to poor stability, adsorption, toxicity, or routes of delivery. Modification of naturally occurring antibiotics provides the opportunity to develop second-generation versions that overcome these limitations, as exemplified by the semisynthetic glycopeptides telavancin, oritavancin, and dalbavancin. There is a need for methods of identifying novel antibiotic agents, which may be employed in the development of optimized second-generation antibiotics with improved ADMET profiles and efficacy in treating multidrug resistant pathogens.

Technology

Duke inventors have developed a methodology to identify improved antibiotics. This is intended to be used by researchers during antibiotic drug discovery to identify novel variants of naturally occurring antibiotic agents. Specifically, structure-activity relationship (SAR) guided genome mining is performed on sequenced bacterial genomes to identify gene clusters capable of producing novel antibiotics. This has been demonstrated for lipoglycodepsipeptide antibiotics related to ramoplanin and chersinamycin. Bacteria containing novel lipoglycodepsipeptide gene clusters were cultured to produce novel antibiotic agents which show excellent activity against gram positive bacteria. This strategy provides an opportunity to develop novel antibiotics rapidly and flexibly via editing of biosynthetic gene clusters in genetically tractable organisms.

Other Applications

This technology could also be used to identify derivatives of other naturally occurring antibiotics, antifungals, or other biosynthetic drug products.

Advantages

• Targeted genome mining strategy to expand existing classes of antibiotics
• Novel antibiotic, chersinamycin, demonstrates in vitro efficacy similar to ramoplanin
• Combines Structure-Activity Relationship (SAR) guided drug discovery with bioinformatic genome mining to improve undesirable characteristics of existing antibiotics