The project is being undertaken at the University of Houston, where two researchers secured a $3.5 million grant (over a five-year period) to build novel technologies to ascertain which are the best chemical combinations to kill the most resistant bacteria, in the form of antibiotics. The grant comes from the U.S. National Institute of Allergy and Infectious Diseases.
Antimicrobial resistance is about the ability of a microorganism to resist the action of antimicrobial drugs. The phrase multi-drug resistance is when an organism is resistant to one or more chemicals. While this state of resistance can occur in nature, the major threat to human health arises with known human pathogens acquiring resistance, a more common method is through gene transfer. Here, genes causing resistance can be transferred between different strains of microorganism. Where this occurs in the healthcare setting, where patients are more vulnerable, concerns arise.
In recent decades the proportion of organisms becoming multi-drug resistance has increased. Much of this arises from the mis-prescribing of antibiotics.
Commenting on the new research project, lead researcher Professor Vincent Tam explains: “People are dying, there’s no question about that. And it’s because bacteria – time and again – have come up with ways to fight back against the antibiotics we are throwing at them and survive.”
The microbiologist adds: “In the war of people versus bacteria, bacteria are winning.” To combat this Professor Tam explains, combining antibiotics is a common practice. However, the complication arises from selecting the correct combination.
What is needed is a faster and more robust process to ensure that the correct chemical combinations are selected. This is the basis of the new research. For this the researchers are collaborating with the commercial company BacterioScan.
The intention is to build a rapid diagnostic device that is capable of testing bacterial responses to various drug combinations. The aim is for a medical professional to place bacterial samples into the device. The device will then assess bacterial growth in the presence of different antibiotics. The data will be captured digitally and analysed, with the clinician provided with the optimal antibiotic combination.
Testing will begin with a selection of hospital pathogens, including Pseudomonas aeruginosa; Acinetobacter baumannii; and Klebsiella pneumoniae. The aim is to pinpoint the different structural classes of antibiotics which will be effective against the organisms at different sites.
