Real-time evaluation of macozinone activity against through bacterial nanomotion analysis.

Novel drugs and improved diagnostics for (MTB) are urgently needed and go hand in hand. We evaluated the activity of two benzothiazinone drug candidates (MCZ, PBTZ169; BTZ043) and their main metabolites against MTB using advanced nanomotion technology. The results demonstrated significant reductions in MTB viability within 7 h, indicating the potential for rapid, precise antibiotic susceptibility testing based on a phenotypic read-out in real time.

Nanomotion technology: an innovative method to study cell metabolism in , as a potential indicator for tolerance.

Antibiotic tolerance corresponds to the bacterial ability to survive a transient exposure to antibiotics and is often associated with treatment failure. Current methods of identifying tolerance based on bacterial growth are time-consuming. This study explores the use of a growth-independent method utilizing nanomotion technology to detect antibiotic-tolerant bacteria.

Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform.

Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner.

Nanomotion technology in combination with machine learning: a new approach for a rapid antibiotic susceptibility test for Mycobacterium tuberculosis.

Nanomotion technology is a growth-independent approach that can be used to detect and record the vibrations of bacteria attached to cantilevers. We have developed a nanomotion-based antibiotic susceptibility test (AST) protocol for Mycobacterium tuberculosis (MTB). The protocol was used to predict strain phenotype towards isoniazid (INH) and rifampicin (RIF) using a leave-one-out cross-validation (LOOCV) and machine learning techniques.