Hi readers! Happy new year. Here is me, back after knee transplant surgery. Feeling better with support of your good wishes and prayers to God Almighty. Thank you for this goodwill gesture.
Dear readers, do know what is a superbug?
A superbug is a microorganism that has become resistant to multiple antibiotics or other antimicrobials which doesn’t allow antibiotics to treat the ailment for which it is used.
It can be a bacteria or fungi, and they both can develop resistance to antibiotics or antifungal medications, respectively. The severity of a superbug depends on how many different antimicrobials it is resistant to.
Superbugs can be a major health risk. In the United States, the CDC (Center for Disease Control and prevention) estimated that more than 2.8 million people are infected with superbugs each year, and more than 35,000 die from them.
Superbugs develop resistance naturally, and while it’s possible to slow resistance, it can’t be stopped altogether. Once a germ is resistant to the first-choice medicine, other medicines may have to be used, which can have worse side effects or be more expensive.
On November 19th, 2024, Abdullahi Tunde Aborode (a PhD student in department of Chemistry, Mississippi State University, USA) published a story in LiveScience titled
“AI (artificial Intelligence) could identify the next superbug-fighting drug” ( For details, pl. visit the following website)
(https://www.livescience.com/health/medicine-drugs/ai-could-identify-the-next-superbug-fighting-drug?)
The story says, that by analyzing the resistance genes and proteins of Escherichia coli (commonly called E.coli: a bacterium) researchers can optimize treatments to address both current and future antimicrobial resistance.
For developing new antibiotics, researchers identify the genes that make bacteria resistant. They look for mutations in the genetic makeup of resistant bacterial strains that allow them to survive. But this method is time consuming and may not reveal a full picture of how bacteria become resistant?
Abdullahi Tunde Aborode and his colleagues developed a new approach to identify E. coli resistance genes by computer modeling to design new compounds that can block these genes and make existing treatments more effective.
To predict which gene (s) contribute to resistance, Abdullahi et al., analyzed genomes of various E. coli strains to identify genetic patterns and markers associated with resistance. They used machine learning (ML) algorithm trained on existing data to highlight novel genes or mutations shared across resistant strains that might contribute to resistance.
After identifying resistance genes, they designed inhibitors that specifically target and block the proteins that these resistant genes produce. By analyzing the structure of the proteins these genes code for, they were able to optimize their inhibitors to strongly bind to these specific proteins.
They also worked successfully to reduce the likelihood that bacteria would evolve resistance to these inhibitors as well.
Finally, they tested how effectively their inhibitors could overcome antibiotic resistance in E. coli. They used computer simulations to assess how strongly a number of inhibitors bind to the target proteins over time. One inhibitor called hesperidin was able to strongly bind to the three genes in E. coli involved in resistance suggesting it may be able to help fight antibiotic-resistant strains.
The World Health Organization ranks antimicrobial resistance as one of the top 10 threats to global health. In 2019, bacterial antibiotic resistance killed an estimated 4.95 million people worldwide.
By targeting the specific genes responsible for resistance to existing drugs, the approach developed by Abdullahi Tunde Aborode et al., could lead to treatments for challenging bacterial infections that are not only more effective but also less likely to contribute to further resistance. It can also help researchers keep up with bacterial threats as they evolve.
Dear readers! what a great service to humanity. Can we reciprocate?
Ask yourself a question and you will get the answer.
That’s all for now.
See you next week. Take care. Bye.
