The Fight Against Antibiotic Resistant Bacteria
Two University of Houston professors address the relentless game of cat and mouse played between antibiotics and bacteria with $519,000 grant from the National Institutes of Health
Longtime collaborators on combatting drug-resistant bacteria, chemical and biomolecular engineering Professor Mike Nikolaou and clinical sciences pharmacy Professor Vincent Tam produced a patented equation to assess the effects of antibiotics on bacteria.
Discovering a bacteria-killing molecule that is safe and effective for patients can easily take a decade. Nikolaou and Tam’s work can more efficiently analyze data to find drug combinations that are both effective against bacteria and safe for patients. By the end of 2015, they anticipate developing the first working prototype of a methodology and associated software that improves the process of determining effective antibiotic cocktails for patients in clinical settings, as well as expedites the development and approval of new antibiotics.
Single antibiotics are becoming less and less effective against bacteria, so doctors frequently must use combinations of antibiotics. Physicians, however, are presented with the challenge of considering overwhelming permutations of antibiotic combinations and dosing regimens for patients. Nikolaou and Tam are working to optimize the process by reducing the amount of time and guesswork needed to assess which drug combinations are the most effective at killing bacteria.
The professors plan to use existing image analysis technology to automatically record the effects of various antibiotic cocktails on bacteria. A laser detector modified for their purposes can feed the data to computer software the team is in the process of developing that runs the patented mathematical model.
“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,” Nikolaou said.
Automation provides opportunities to collect additional data at more frequent intervals. The software also can account for differences between patients and test tubes.
“By doing this, we’ll be gaining efficiency,” Tam said. “And, ideally, that would eliminate the need for physicians to run excessive numbers of tests in a clinical setting.
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