DynamicKit

Tuberculosis (TB) is the deadliest infectious disease in humans and claims around 1.5 million lives worldwide every year. To successfully treat this lung disease, a mix of different drugs has to be administered for several months. This however, is problematic as the bacterial pathogens become resistant, and even in treatable bacterial populations, highly resistant subpopulations can be detected. In order to prevent the spread of the disease, it is therefore not only necessary to develop new antibiotics, but also to constantly find new combinations of active substances. Such combinations can so far only be identified empirically in expensive clinical studies. New digital tools in combination with novel analytical tools, such as artificial intelligence self-learning algorithms, have the potential to decipher the interplay of different antibiotics on mycobacterial metabolism in a faster, more cost efficient way, making it possible to identify suitable drug cocktails to overcome TB drug-resistance and improve current treatment regimens.

Multi-drug-resistance (MDR) is a dramatic challenge for the most deadly infectious disease of the world, tuberculosis (TB). In our project, we use self-learning algorithms to understand the interaction of different drugs in their effect on the metabolism of mycobacteria, the causative agents of tuberculosis. This way, we are not only able to predict new suitable drug combinations for tuberculosis treatment, but also determine biological molecules that reflect resistance mechanisms, so that we can find out how we can specifically reverse this with drugs. This combined approach yields an urgently needed preclinical laboratory model that will enable us to stop the further spread of the disease.

Our main goal was to develop fundamentally new approaches against resistant as well as susceptible tuberculosis leveraging the potential of new experimental methods and artificial intelligence.

In this way, we wanted to find out which active ingredients are an ideal match to be used as combination treatment for tuberculosis. This could provide less toxic and shorter treatment regimens. Furthermore, we aimed to identify new drug combinations that may efficiently battle drug-resistant TB.

DynamicKit enables, for the first time, the gentle isolation and time-resolved analysis of intact proteins from tuberculosis pathogens, creating a new platform to study their molecular responses under antibiotic stress. By combining innovative extraction methods, high-resolution mass spectrometry (MALDI-TOF and HPLC-MS), automated AI-supported data analysis, and an open, high-performance software pipeline, the project has identified characteristic protein signatures that pave the way for personalized therapies against drug-resistant tuberculosis. Strengthening Bavaria as a hub for research and pharmaceutical innovation, DynamicKit actively involves young scientists and provides insights already feeding into clinical studies, marking a decisive step toward more effective treatment strategies worldwide.