“If you treat TB bacteria with a new drug and it goes splat in the same way it does for other drugs that destroy the cell wall, then you may assume it destroys the cell wall as well,” says Aldridge.
Using AI, the team now has gone a step further, linking these visual clues to detailed readouts of bacterial gene activity, known as transcriptional profiles. The researchers trained a model to spot which molecular changes, such as bacterial genes switching on or off, occur alongside specific visual changes.
roughly how a drug killed TB using morphological profiling. Now we can bring more exact insights into how drugs are impacting the cells and why the bacteria are dying,” says Aldridge. For example, in testing DECIPHAER, she says the team found that a TB drug in clinical development didn’t work as expected.
“Based on similar existing compounds, we had assumed the drug worked by destroying the cell wall,” she says. “But it actually kills TB bacteria by impairing the respiratory chain and cells’ ability to make energy.”
Because the AI tool can predict a drug’s molecular impact from images alone—which is far cheaper than using RNA sequencing—it can faster reveal how potential TB treatments work in different growth conditions, genetic strains, or drug combinations.
“We plan to keep using it in our own lab’s drug combination studies and hope it will support collaborations worldwide to accelerate development of new TB drugs,” says Aldridge. While the need is especially urgent for TB, she adds that DECIPHAER’s approach also could be applied to other infectious diseases and cancer.