Two professors from the University of Houston received a $519,000 grant funded by the National Institutes of Health to be invested in the field of antibiotics and bacteria research and development.
Mike Nikolaou, professor of chemical and biomolecular engineering, and Vincent Tam, professor of clinical sciences pharmacy, have been longtime research collaborators. Together, they developed a patented equation to address and measure the effects of antibiotics on bacteria. As a result of this new formula, the researchers expect to develop their first prototype by 2015, using a methodology that will allow the creation of software that will help understand which are the most effective combinations of antibiotics for a patient with a specific clinical scenario. Furthermore, the software will accelerate the development of new antibiotics and, consequently, the approval of new antibiotics to treat patients.
Nikolaou said: “This is a race of humans developing antibiotics against nature’s evolving bacteria. It’s very difficult to win that race because bacteria evolve fairly rapidly.” Adding to the difficulty of keeping up with bacterial evolution, both the development and approval processes of antibiotics is painstaking; finding drugs to kill bacteria is difficult, since it has to be effective against microorganisms and still be safe for humans. As a result, developing antibiotics can take a decade or more. Thanks to this new software that Nikolaou and Tam created, data can more easily be analyzed and antibiotic combinations can be quickly discovered and developed.
“Single antibiotics are becoming less and less effective against bacteria, so very frequently you have to use combinations of antibiotics. In recent years, we’ve been using more combinations of antibiotics so that we can have a combined effect that can make the antibiotics more potent and perhaps kill bacteria that would otherwise be resistant,” said Nikolaou.
Tam said: “Physicians are presented with the challenge of considering overwhelming permutations of antibiotic combinations and dosing regimens for patients. Time restrictions necessitate eyeballing results and making best guesses about treatments based on their expertise and intuition.”
Since time is precious in infection-related conditions, this software can reduce the “guessing period” and optimize researchers’ work.
“Our approach is empirical, so it relies on experimental data rather than detailed prior knowledge,” Nikolaou noted. “This way, you don’t need to know the type of bacteria, the type of killing mechanism or the mechanism of resistance.”
Thanks to Tam’s practical observations on how antibiotics work, Nikolaou and his team composed mathematical equations to predict the course of unknown antibiotics. “The equations do not define logic. Instead, they augment logic and intuition more accurately,” Nikolaou explained. “The user will simply have to push the button, and the software will do the calculations that guide the doctor on what antibiotic or combination of antibiotics to use.”
With automation, more information can be processed, predictability becomes more accurate, and taking into account differences between laboratory tests and in vivo environments (patients) becomes a reality.
“By doing this, we’ll be gaining efficiency. And, ideally, that would eliminate the need for physicians to run excessive numbers of tests in a clinical setting,” concluded Tam.