Abstract
The increasing resistance of infectious agents to available drugs urges the continuous and rapid development of new and more efficient treatment options. This process, in turn, requires accurate and high-throughput techniques for antimicrobials' testing. Conventional methods of drug susceptibility testing (DST) are reliable and standardized by competent entities and have been thoroughly applied to a wide range of microorganisms. However, they require much manual work and time, especially in the case of slow-growing organisms, such as mycobacteria. Aiming at a better prediction of the clinical efficacy of new drugs, in vitro infection models have evolved to closely mimic the environment that microorganisms experience inside the host. Automated methods allow in vitro DST on a big scale, and they can integrate models that recreate the interactions that the bacteria establish with host cells in vivo. Nonetheless, they are expensive and require a high level of expertise, which makes them still not applicable to routine laboratory work. In this review, we discuss conventional DST methods and how they should be used as a first screen to select active compounds. We also highlight their limitations and how they can be overcome by more complex and sophisticated in vitro models that reflect the dynamics present in the host during infection. Special attention is given to mycobacteria, which are simultaneously difficult to treat and especially challenging to study in the context of DST.