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My latest Mind and Matter column for the Wall Street Journal is on drug development and network analysis:

Here’s a paradox. Every week seems to bring news from a research laboratory of an ingenious candidate cure about to enter clinical trials for a serious disease. Yet the productivity of drugs coming out of clinical trials has been plummeting, and the cost per drug has been rocketing skyward. The more knowledge swells, the more pharmaceutical innovation fails. What’s going on?

This week’s promising drug candidate is colchicine, a toxin found in Colchicum, the strange flower that comes up in the fall after its leaves have disappeared (also known as the naked lady or the autumn crocus). By attaching colchicine to a trigger that activates in the presence of a tumor, researchers at the University of Bradford in England have developed a potentially potent cancer therapy.

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RIDLEY disease

RIDLEY disease

John S. Dykes

Meanwhile, somebody in the pharmaceutical industry has had the bright idea of funding research on sponges, having concluded that these simple organisms cannot have survived the best part of a billion years on the ocean floor without inventing smart chemical tricks for defeating bacteria. More than 100 promising antibacterial compounds have already emerged from sponge research.

Genetics is also a big part of our golden age for possible new cures. Reading the genes of pathogens and cancer cells helps to identify targets for therapy, and gene sequencing has gotten cheaper far faster than would be predicted by Moore’s Law, which famously holds that transistor density on a silicon chip doubles-while the cost halves-every two years.

But the very opposite of Moore’s Law is happening at the downstream end of the R&D pipeline. The number of new molecules approved per billion dollars of inflation-adjusted R&D has declined inexorably at 9% a year and is now 1/100th of what it was in 1950. The nine biggest drug companies spend more than $60 billion a year on R&D but are finding new therapies at such a slow rate that, as a group, they’ve little chance of recouping that money.

Meanwhile, blockbuster drugs are losing patent protection at an accelerating rate. The next few years will take the industry over a “patent cliff” of $170 billion in global annual revenue. On top of this, natural selection is producing resistant disease strains that undermine the efficacy not only of existing antibiotics and antivirals but (even faster) of anti-cancer drugs. Many people believe that something is terribly wrong with the way the industry works.

The problem, some think, is that science-to mix clichés-is scraping the bottom of the biological barrel after plucking the low-hanging fruit. Others say that generations of research biochemists have led each other into an intellectual cul-de-sac. This may be right. Human biochemistry is supremely intricate and robust. It employs redundancy and network complexity to achieve these features, so it’s unlikely to be changed easily by the simple or solitary molecules that have been deployed to achieve most of our cures.

On this view, the goal of most pharmaceutical research-identifying a “target” for drug action-is misconceived. Biochemical networks are designed to work around the loss of any one node: “There is no single soldier we can shoot whose demise would significantly affect the performance of the army,” says Malcolm Young of the drug-discovery firm e-Therapeutics.

Drugs must be designed to nudge whole networks rather than single targets. For instance, to develop a treatment for the hospital infection Clostridium difficile, e-Therapeutics drew a sort of spider’s web of how all the proteins on the outside of the bacterium interacted. From that web, they identified crucial nodes in the network and, by trial and error, selected a combination of molecules that could attack those nodes.


A similar approach is showing promise for cancer and even neurological disease. It means hitting multiple targets simultaneously, the targets chosen by network analysis. Where diseases are complex, the cures will be complex, too.

By Matt Ridley | Tagged:  rational-optimist