10004479Rapid discovery of novel antibiotics against A. baumanniiClosedCollaborative R&DInnovate UKAntimicrobial resistance (AMR) is a rapidly escalating healthcare crisis, estimated to cause 700,000 deaths globally per year, and predicted to rise to 10Mn by 2050, potentially a bigger killer than cancer and diabetes, combined. Treating AMR currently costs the NHS £180Mn per year. A combination of overuse and misuse of our current antibiotics has led to increasing resistance from bacteria who constantly evolve to resist them meaning our current repertoire is becoming increasingly ineffective. Antibiotics are routinely used in healthcare from minor surgery, midwifery, organ transplants to cancer therapy. If the issue of AMR is not addressed then modern healthcare as we know it will not be possible. There is an urgent need for novel antibiotics that can target resistant-bacteria responsible for life-threatening infections. _A. baumannii_ is particularly critical due to its resistance to antibiotic-of-last-resort, colistin and high mortality rate \>70%. New antibiotics are urgently needed in order to allow us to enjoy the current standard of healthcare we take for granted. However, high costs of discovery and lower commercial returns compared to other pharmaceuticals have meant that no novel antibiotics have reached the market in ~30 years bringing us closer to potential antibiotic apocalypse as this pipeline runs dry. The majority of antibiotics in use today have been isolated from soil bacteria. Only ~1% of soil bacteria have been exploited due to difficulties in replicating growth conditions in the lab. ~80% of antibiotics in use, derive from this 1%, representing a $100Bn market. However, this bioresource has now been exhausted. Accessing the remaining 99% has enormous potential as an untapped resource of novel antibiotics. Baccucio has developed a platform that can exploit this 99%. In this project we will develop our platform so that it is fully automated allowing rapid discovery of antibiotic-producing bacteria. Machine-learning will lead to an increased library size (1200 novel bacteria), with wider bacterial diversity (from 10 phyla). We have an ambitious goal to identify 5-10 novel compounds that target _A. baumannii_. This will provide a compelling case for further investment from the pharma industry to take identified compounds through to market. Drug discovery costs are a significant barrier especially for antibiotics. As a return we estimate \>€750Mn in annual sales for targeting _A. baumannii_ alone. This aligns with the UK government 5-year action plan to tackle AMR, which includes investing in new therapeutics to re-establish the antibiotic discovery pipeline.