AI optimization of tuberculosis drug cocktail
Tuberculosis (TB) is the second most prolific infectious disease in the world, affecting 10 million people worldwide with a mortality rate of 1.5 million per year. The research team at the Tufts University of Medicine (MA, USA) have utilized artificial intelligence to optimize drug combinations for TB medication, thus aiming to streamline the treatment process.
Typical treatment of TB requires a trial-and-error strategy to achieve the correct three to four drugs (out of a possible 20 compounds) to create the perfect drug concoction to kill off the TB bacteria. This is particularly challenging as TB bacteria can behave very differently depending on the environment in cells and can even evolve to become resistant to antibiotics. Therefore, these drugs may need to be taken over months or years, in some cases, to eradicate the bacteria from a patient.
