Antibiotics were one of the first drug classes, for which systematic tests predicting drug response have become available. Such in-vitro antibiotic susceptibility tests (AST) have been introduced into routine care in the 1960s and rely on culturing the bacteria in the presence of antibiotics. A major drawback of culture-based AST is the time-to-result of several days, which seriously limits the ability of the test to guide first-line therapy. This diagnostic gap has led to massive overuse of antibiotics that confounds the rapid evolution of antimicrobial resistance (AMR), one of the most severe public health threats globally. Aiming at resolving this dilemma, rapid molecular diagnostic polymerase chain reaction (PCR) tests are increasingly used to detect genetic markers of antibiotic resistance. While providing considerable benefit short-term, PCR-based tests however, have limited multiplexing capabilities and can only address few known mechanisms of resistance, still leaving a significant diagnostic gap and lagging behind the dynamics of AMR evolution and epidemiology. In this context, high resolution technologies such as next generation sequencing (NGS) are increasingly rendered applicable for diagnosis and drug response prediction due to recent technology improvements, workflow integration and automation.
This talk will discuss concepts for and progress in applying NGS in combination with advanced bioinformatics and artificial intelligence methods to translate the proven success stories of precision medicine in cancer to microbial infections. The presented approach and methodology could significantly improve patient outcomes as well as antibiotic stewardship.