A scientist in Britain has found a way to use a supercomputer to develop drugs that could avert an “antibiotic apocalypse”.

A computational chemist at Portsmouth University has led an international team which has discovered how to modify existing antibiotics so that bacteria are no longer resistant to them.

Experts warn that if we do not develop new antibiotics now in 10 to 15 years many routine operations could be fatal.

Only two new classes of antibiotics have been introduced in the last 40 years.

Dr Gerhard Koenig, of Portsmouth University, working with scientists in German and the US used a supercomputer to modify a major antibiotic which bacteria is known to have become resistant to.

It worked out what needed to be changed to enable the drug to target a different part of the bacteria to kill it.

The computer could help modify antibiotics (
Image:
Getty Images/iStockphoto)

The landmark results suggested the modified drug was 56 times more “active” against key bacteria than two top performing antibiotics on the World Health Organisation’s list of essential medicines, erythromycin and clarithromycin.

First author Dr Koenig said: “Antibiotics are one of the pillars of modern medicine and antibiotic resistance is one of the biggest threats to human health.

“There’s an urgent need to develop new ways of fighting ever-evolving bacteria.

“Developing a new antibiotic usually involves finding a new target that is essential for the survival of a wide range of different bacteria.

“This is extremely difficult, and only very few new classes of antibiotics have been developed in recent times.

“We have taken a simpler approach by starting from an existing antibiotic, which is ineffective against new resistant strains, and modifying it so it’s now able to overcome resistance mechanisms.”

England’s previous chief medical officer Sally Davies warned “the world is facing an antibiotic apocalypse” if the drugs were not stopped being overused and new versions developed.

Large pharmaceutical companies have closed down their antibiotic research divisions because the drugs are less profitable than others.

They need to be used sparingly to prevent resistance - meaning less are sold.

Today patients undergoing routine treatments for cancer as well as organ transplants and hip and knee replacements take antibiotics to prevent infection.

Some predict that if antibiotics stop working, average life expectancies will drop by 20 years.

The doctor, from the University of Portsmouth, led the team (
Image:
Hampshire Live / Darren Pepe)

The international team, which including Nobel Prize laureate Ada Yonath, carried out the research at the Max-Planck-Institut für Kohlenforschung, the Weizmann Institute, and the universities of Duisburg-Essen, Bochum and Queensland.

It is hoped the technology could be used to modify other antibiotics to predict ways bacteria will evolve and help modern medicine stay one step ahead.

The modified antibiotics could now undergo clinical trials in humans.

Dr Koenig added: “Not only is our best candidate more effective against the tested targets, but it also shows activity against the three top ranked bacteria from the WHO priority list where the tested existing antibiotics don’t work.

“Our computers are becoming faster with every year. So, there is some hope that we will be able to turn the tide.

“If computers can beat the world champion in chess, I don’t see why they should not also be able to defeat bacteria.”

The race has been on for many years to develop new antibiotics to fight disease faster than a disease can evolve.

Computers have been used in drug design for decades, but this is the first study to use a multi-pronged strategy to make a new antibiotic from an existing one which bacteria have outwitted.

The computational work was done in a matter of weeks on one of the top supercomputers in Europe, but it took the international team several years to verify experimentally that their approach was correct.

The findings are published in the US Proceedings of the National Academy of Sciences (PNAS).

Read More

Read More