Antibiotics are the main weapon of medicine in the fight against infectious diseases caused by bacteria. However, doctors all over the world are slowly running out of ammunition. Because more and more bacteria are developing resistance to life-saving drugs. In many cases, even the spare antibiotics that are withheld for emergencies are no longer as effective as they used to be. This is mainly due to the fact that the means are used far too often. They are sometimes prescribed unnecessarily. And in livestock farming, they are even used prophylactically. However, with each use, the likelihood of new resistance increases. One possible solution to the problem is to continuously develop new antibiotics. But for many pharmaceutical companies, this is too risky. Because the success of the research work is by no means certain. In addition, initially, only low sales figures beckon because new active ingredients are initially kept in reserve.
The genomes of 10,000 bacterial strains were analyzed
A new method that scientists at Rockefeller University in New York have successfully applied for the first time could help. This is what is known as genome mining. Classic antibiotics come from fungi or bacteria that produce special repellents to control other strains. The necessary genetic blueprint is stored in so-called biosynthetic gene clusters. Now if you want to investigate a new antibiotic, the microorganisms in the lab have to be coaxed to activate just the right gene cluster. The active substance released in this way can then be isolated and further processed. However, the whole process is extremely complex. A team from Rockefeller University led by Zongquian Wang has therefore taken a different path. Using genome editing, they first looked at the genome of some 10,000 bacterial strains. They searched for gene sequences containing the corresponding blueprints for lipopeptides. In addition, it was checked whether there was an evolutionary relationship with already known antibiotics.
Algorithms enable replication in the lab
For example, the researchers focused on the bacterium Paenibacillus mucilaginosus. Instead of obtaining the active ingredient through laborious work in the lab, algorithms were then used. These predicted what the substance most likely looks like and is composed of. It was then simply artificially recreated using targeted chemical processes. This is how a new antibiotic called cilagicin came about. Initial tests have shown that it can be used to fight a range of bacteria. Even resistant enterococci, which cause major problems in many hospitals, have been successfully controlled in mice and in Petri dishes. The new antibiotic becomes effective by disrupting the structure of the cell walls of the bacteria in question. This happens in two ways, which could at the very least make it more difficult for new resistance to emerge. However, two limitations should be noted: Clinical studies in humans are still pending. And: It seems that the drug only works on gram-positive bacteria. However, the basic procedure can be repeated in a relatively straightforward manner to find other effective new antibiotics.