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Bavarian Research Network - DynamicKit - Search for new combination therapies against multidrug-resistant tuberculosis

  • Leader: PD Dr. Andreas Wieser, Prof. Dr. med. Michael Hoelscher, Dr. Michael Menden
  • Institution: Max von Pettenkofer Institut, Abteilung für Infektionskrankheiten und Tropenmedizin, Institut für Computational Biology
  • Promotion: 2020 to 2025

Tuberculosis (TB) is the deadliest infectious disease in humans, claiming around 1.5 million lives worldwide each year. To successfully treat this lung disease, a mixture of different drugs must be administered over several months. However, this is problematic because the bacterial pathogens become resistant and highly resistant subpopulations can be detected even in treatable bacterial populations. To prevent the spread of the disease, it is therefore not only necessary to develop new antibiotics, but also to keep finding new combinations of active ingredients. So far, such combinations can only be identified empirically in elaborate clinical trials. New digital tools in combination with novel analytical tools, such as self-learning artificial intelligence algorithms, have the potential to decipher the interplay of different antibiotics on mycobacterial metabolism more quickly and cost-effectively, so that appropriate drug cocktails can be identified to overcome TB drug resistance and improve current treatment regimens.

Drug resistance is a dramatic challenge for the world's deadliest infectious disease, tuberculosis (TB). The research group uses self-learning algorithms to understand the interaction of different drugs in their effect on the metabolism of mycobacteria, the pathogens that cause tuberculosis. This will not only predict new appropriate drug combinations for tuberculosis treatment, but also determine biological molecules that reflect resistance mechanisms so that the research group can figure out how to target drugs to reverse this. This combined approach yields a much-needed preclinical laboratory model that can be used to stop the further spread of the disease.

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