By Calvin A. Omolo
Humans are in a constant arms race with infectious bacteria. For elimination of microbes, antibiotics; have been the ultimate weapon and once declared the most valuable invention in modern medicine.
However, the bacteria have been developing resistance against these drugs. This has been the cat and mouse game since the discovery of penicillin by Sir Alexander Fleming and the mass production of the drug by Howard Florey and Ernest Chain in 1945 and subsequent introduction in the market to date.
So far, the bacteria have been winning this war with the rise of antimicrobial resistance (AMR). AMR is mainly driven by two factors; one having better adaptability to the environmental change as research shows bacteria have been in existence for two billion years on this planet compared to human whose fossils shows of about 200,000 years of existence.
Second issue is the genetic makeup of the bacterial. During DNA transcription there are enzymes called DNA polymerase. In multicellular organisms these enzymes ensure low misincorporation rates with proofreading activity of genetic material that give faithful replication of the target DNA of interest.
However, single celled organism like bacteria the DNA polymerase enzymes have low fidelity transcribers which leads to easy mutation that results in change in genome and subsequent development of resistant mutants.
Once these new genes are coded bacteria can be easily transfer to other bacteria via horizontal gene transfer and the genetic pool of the resistant strains is easily spread. Thus, having been enhancing the potency of our antibiotics, the bacteria seem to have been adapting comfortably to whatever is thrown at them which has led to the birth of superbugs. Increasingly, these medications are failing to eliminate these highly adapted bacteria, and this has resulted to a growing demand for antibiotics, and a diminishing supply.
Despite the desperate need for new infectious disease therapies, limited progress has been made in the discovery of first-in-class antibacterial drugs to balance the relentless resistance development.
Due to the scale of the problem that is antimicrobial resistance and how shrewd the opponent is, there is a need for change of tact in fight against resistant bacteria. WHO warns of a return to the pre antibiotic era if innovative strategies aren’t devised to manage this crisis. One of the strategies that could be employed to combat antimicrobial resistance is collateral sensitivity. Collateral sensitivity means mutations that causes multidrug resistance in bacteria may simultaneously enhance sensitivity to many other unrelated drugs. Such collateral sensitivity may be exploited to develop novel, sustainable antibiotic treatment strategies aimed at containing the current, dramatic spread of drug resistance.
Through research in the genomic evolution of microbial resistance towards that also increases susceptibility to other drugs arises. This technique is being advocated for as doesn’t require the design of new drugs that usually requires regulatory approval that is costly, time consuming and with low approval rates for market use. Collateral sensitivity involves using bacteria’s own tactic against them as they have been using their genome change to overcome antimicrobials. The tables can be turned against them to use their genome change to reintroduce and introduce other treatment regimes.
The idea of collateral sensitivity is not new, it was introduced almost 70 years ago due to phenomenal work by SZYBALSKI and Bryson published in the Journal of bacteriology. Over the years their work didn’t attract a lot of interest as it was the golden era for antibiotics. However, due to the high turnover of antibiotics versus the new ones coming to the market and recent technological advances in laboratory automation and genome sequencing have led to a renewed interest in collateral sensitivity as a solution to antimicrobial resistance.
As simple as it seems manipulating drug resistance is a formidable task, as it requires detailed knowledge of the microbial genome and relevant ecological conditions. As poor use of the technique could result to cross resistance. Understanding resistance to a certain antibiotic and how it increases the sensitivity to other antibiotics is of great medical importance. This requires understanding of evolutionary trade‐offs in genome that suppress some genetic traits leading to sensitizing and enhancing some other traits leading to resistance. For example, findings by Imamovic and Sommer published in the Science Translational Medicine showed that penetration of gentamycin into the bacterial for its action demands an active proton motive force (PMF). Gentamycin resistance by bacteria is achieved through disabling of this PMF, due to genetic trade off by disabling of the PMF also incapacitates means by which bacteria achieves resistance to other antibiotics by pumping them out of the bacteria.
Thus, studies have that shown bacteria that upon gaining resistance to gentamycin, the bacteria became sensitive to polymyxins, fosfomycin, and azithromycin. More studies have also shown 74% of multidrug resistance may yield hypersensitivity to one or more classes of antibiotics indicating collateral sensitivity frequently occurs.
Recent technological advances in laboratory automation and genome sequencing have led to a renewed interest in collateral sensitivity. If Collateral sensitivity could be employed effectively it could lead to continuous antibiotic cycling that is indefinite.
If the system is developed carefully to have reciprocal collateral sensitivity profiles, collateral sensitivity cycling could limit evolution of drug resistance in the target bacterial population without the need for new antibiotic. Collateral sensitivities if well designed and developed may inform, or even direct, future therapeutic interventions, leading to a better usage of existing antibiotics with longer lead time between sensitivity and resistance development. As the technique is still in its infancy more studies need to be done to have a proper functioning system as most results have been stochastic.
The writer is Bpharm St John’s University of Tanzania, MPharm (University of Kwazulu-Natal), PhD candidate (University of Kwazulu-Natal)
