England's chief medical officer on why the drugs don't work

Large-scale resistance to antibiotics is inevitable, yet new antibacterials aren't emerging. Why?

The Drugs Don’t Work: a Global Threat
Sally C Davies, with Jonathan Grant and Mike Catchpole
Penguin Specials, 112pp, £3.99

Professor Dame Sally Davies, England’s chief medical officer, likens the impending crisis in antimicrobial drug resistance to global warming. In both instances scientists foresee a problem and can offer solutions. In neither case is our response anywhere near sharp enough, Davies fears. Acting on antibiotic resistance should be the easier of the two; no one has a vested interest in denying the risk. Why then are we stumbling towards a selfmade but preventable calamity?

Alexander Fleming is credited with discovering antibiotics. In the summer of 1928, while working at St Mary’s hospital in London, he went on holiday and left an open plate of bacteria behind. Returning to work, he found a fungus growing on the plate that had killed the bacteria with a chemical that he named penicillin. In 1930s Oxford, Howard Florey and Ernst Chain produced enough penicillin to prove its healing ability. The penicillin production programme that followed during the Second World War is a classic tale of ingenuity under adversity. By engaging American pharmaceutical companies, the Allies were able to cure soldiers of otherwise fatally infected wounds.

Bugs create chemicals to kill other bugs as part of an aeons-old microbial arms race, so drug-hunters turned to soil microbes to help fight a range of diseases. Streptomycin, discovered in America in 1943, even cured tuberculosis, one of mankind’s greatest afflictions. Today, however, roughly a third of the world’s population still carries TB. Of the nearly 9,000 cases reported in the UK in 2011 hundreds of sufferers were resistant to at least one drug. Half a dozen cases carried incurable, “extensively drug-resistant” strains of TB. Cholera, leprosy, typhoid fever and syphilis all remain global scourges. Just last year several people in Edinburgh died after inhaling legionnaire’s disease-causing bacteria. Dozens of Germans died in 2011 after eating beansprouts contaminated with E coli.

Luckily, for now at least, we can still treat most bacterial infections, but some bacterial cells can yield over a billion progeny in just 24 hours. Genetic mutations stimulating drug resistance are inevitable. Cases of penicillin resistance appeared almost immediately: methicillin, a more stable derivative of penicillin, enjoyed only a few years of success before resistance emerged. Methicillin-resistant Staphylococcus aureus (MRSA) now kills hundreds in British hospitals every year.

Yet new antibacterials aren’t emerging. The reasons for this are primarily economic. Antimicrobial agents are usually given in shortterm doses. Compare that to statins, taken by affluent westerners with high cholesterol over decades. Most antibiotics are also off-patent, which has driven prices down. The estimated $1bn it costs to develop a drug inflates the cost of new medicines. Cash-strapped health services will use cheaper, old drugs until their utility is all but gone.

Davies fears that time might come quickly. Resistance genes are flourishing out there and bacteria are remarkably happy to share their genes. The widespread imprudent use of antibiotics has created perfect conditions to select those resistance genes and global air travel can carry resistant bugs around the world in hours.

Davies offers possible solutions. Fifteen years ago the pharmaceutical industry had largely abandoned diseases of the poor – malaria, tuberculosis, sleeping sickness, bilharzia and so on. An anti-sleeping sickness drug, called eflornithine, was even about to be withdrawn because sufferers couldn’t pay for it. When eflornithine was shown to prevent unwanted hair growth, however, pharmaceutical companies fell over themselves to produce it. Economics dictated that a drug could be made to “treat” unwanted facial hair but not to save lives. New models were needed to combat diseases of the poor. Groups such as the Medicines for Malaria Venture and Drugs for Neglected Diseases Initiative emerged to help promote drug development. A decade on, the first new drugs are poised to appear. The pharmaceutical industry itself, though, is in crisis and shedding staff at an alarming rate.

If a pestilential Armageddon really is upon us, a cynical company might gamble on huge profits, getting new antimicrobials ready for when the competition fails. But the economic models won’t shift until the evidence becomes overwhelming. Davies also talks of incentivisation – a £50m prize to develop a new antibiotic, for instance. Given development costs, $1bn would be more realistic. Yet even that’s a snip compared to the taxpayers’ bank bailouts. Surely saving life trumps life savings. Whatever it takes, though, action is needed now. The big pharmaceutical companies continue to abandon their anti-infective programmes and with them goes the expertise and capacity that will be needed when the crisis hits.

Michael Barrett is Professor of Biochemical Parasitology at the University of Glasgow

Who decides which drugs are made, and which ones we have access to? Image: Getty

This article first appeared in the 30 October 2013 issue of the New Statesman, Should you bother to vote?

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How science and statistics are taking over sport

An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others.

In the mid-1990s, statistics undergraduates at Lancaster University were asked to analyse goal-scoring in a hypothetical football match. When Mark Dixon, a researcher in the department, heard about the task, he grew curious. The analysis employed was a bit simplistic, but with a few tweaks it could become a powerful tool. Along with his fellow statistician Stuart Coles, he expanded the methods, and in doing so transformed how researchers – and gamblers – think about football.

The UK has always lagged behind the US when it comes to the mathematical analysis of sport. This is partly because of a lack of publicly available match data, and partly because of the structure of popular sports. A game such as baseball, with its one-on-one contests between pitcher and batter, can be separated into distinct events. Football is far messier, with a jumble of clashes affecting the outcome. It is also relatively low-scoring, in contrast to baseball or basketball – further reducing the number of notable events. Before Dixon and Coles came along, analysts such as Charles Reep had even concluded that “chance dominates the game”, making predictions all but impossible.

Successful prediction is about locating the right degree of abstraction. Strip away too much detail and the analysis becomes unrealistic. Include too many processes and it becomes hard to pin them down without vast amounts of data. The trick is to distil reality into key components: “As simple as possible, but no simpler,” as Einstein put it.

Dixon and Coles did this by focusing on three factors – attacking and defensive ability for each team, plus the fabled “home advantage”. With ever more datasets now available, betting syndicates and sports analytics firms are developing these ideas further, even including individual players in the analysis. This requires access to a great deal of computing power. Betting teams are hiring increasing numbers of science graduates, with statisticians putting together predictive models and computer scientists developing high-speed software.

But it’s not just betters who are turning to statistics. Many of the techniques are also making their way into sports management. Baseball led the way, with quantitative Moneyball tactics taking the Oakland Athletics to the play-offs in 2002 and 2003, but other sports are adopting scientific methods, too. Premier League football teams have gradually built up analytics departments in recent years, and all now employ statisticians. After winning the 2016 Masters, the golfer Danny Willett thanked the new analytics firm 15th Club, an offshoot of the football consultancy 21st Club.

Bringing statistics into sport has many advantages. First, we can test out common folklore. How big, say, is the “home advantage”? According to Ray Stefani, a sports researcher, it depends: rugby union teams, on average, are 25 per cent more likely to win than to lose at home. In NHL ice hockey, this advantage is only 10 per cent. Then there is the notion of “momentum”, often cited by pundits. Can a few good performances give a weaker team the boost it needs to keep winning? From baseball to football, numerous studies suggest it’s unlikely.

Statistical models can also help measure player quality. Teams typically examine past results before buying players, though it is future performances that count. What if a prospective signing had just enjoyed a few lucky games, or been propped up by talented team-mates? An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others. In many sports, scoring goals is subject to a greater degree of randomness than creating shots. When the ice hockey analyst Brian King used this information to identify the players in his local NHL squad who had profited most from sheer luck, he found that these were also the players being awarded new contracts.

Sometimes it’s not clear how a specific skill should be measured. Successful defenders – whether in British or American football – don’t always make a lot of tackles. Instead, they divert attacks by being in the right position. It is difficult to quantify this. When evaluating individual performances, it can be useful to estimate how well a team would have done without a particular player, which can produce surprising results.

The season before Gareth Bale moved from Tottenham Hotspur to Real Madrid for a record £85m in 2013, the sports consultancy Onside Analysis looked at which players were more important to the team: whose absence would cause most disruption? Although Bale was the clear star, it was actually the midfielder Moussa Dembélé who had the greatest impact on results.

As more data is made available, our ability to measure players and their overall performance will improve. Statistical models cannot capture everything. Not only would complete understanding of sport be dull – it would be impossible. Analytics groups know this and often employ experts to keep their models grounded in reality.

There will never be a magic formula that covers all aspects of human behaviour and psychology. However, for the analysts helping teams punch above their weight and the scientific betting syndicates taking on the bookmakers, this is not the aim. Rather, analytics is one more way to get an edge. In sport, as in betting, the best teams don’t get it right every time. But they know how to win more often than their opponents. 

Adam Kucharski is author of The Perfect Bet: How Science and Maths are Taking the Luck Out of Gambling (Profile Books)

This article first appeared in the 28 April 2016 issue of the New Statesman, The new fascism