Thursday, March 20, 2008

The Failure of Industrialized Research (Part 3)

Drug discovery is unpredictable and unmanageable. So why do large pharmaceutical companies spend so much money on it?

The glimmerings of the approach were visible in the development of captopril. Squibb’s chemists made systematic alterations in organic molecules that were then tested for activity against a targeted enzyme. A new route was added in the 80’s, when the nascent techniques of recombinant DNA allowed a protein of interest to be traced to its gene, the gene inserted into a cell, the cell bred in the billions, and the protein extracted. Among the first proteins so manufactured was human insulin, a predictable success given that the role of insulin in treating diabetes was already understood.

The drug industry expected its biologists to discover, in short order, a host of protein therapeutics, ushering in an era of discovery based on biology, rather than chemistry. Indeed, several blockbuster protein drugs were produced, almost all conforming to the model of human insulin: scientists knew ahead of time that the drugs would work, if only the protein could be made economically. But for each success, there were a great number of costly failures.

The 90’s saw an immense increase in spending on industrialized research in chemical approaches. Generally, new technologies employed robotic systems to process large numbers of drug candidates in a blind search for a desired biological activity. Combinatorial chemistry starts with a molecule of interest, then adds to it randomly to form a burgeoning family of molecules. The technique formed the basis of biotech startups, like Darwin Molecular (now Chiroscience R&D), AXYS Pharmaceuticals (since acquired by the Celera Genomics Group), and Affymax. To manage the plethora of resulting molecules, other firms used technologies pioneered in Silicon Valley to build expertise in high-throughput screening.

Also in the 90’s, robotics was combined with the polymerase chain reaction, an established technique that quickly and reliably copies selected sections of DNA, to create machines capable of mass-sequencing genes. This effort produced a wealth of raw data that promises to yield new drug targets for both biological and chemical approaches. A number of companies besides Celera, including Millennium Pharmaceuticals and Human Genome Sciences, are mining data for new targets, hoping either to develop drugs themselves or sell the information to pharmaceutical firms.

More advanced drug discovery tools appear every year. Proteomics companies like CuraGen, Myriad Genetics, and ProteoMetrics were founded to categorize the blizzard of proteins synthesized in human cells, a far greater challenge than transcribing the human genome. Functional genomics, another buzz phrase, has been seized upon as the “the next big thing” by companies like Affymetrix, Celera, and Human Genome Sciences, all of which are trying to link raw genetic data to their precise physiological function in the cell. To accelerate, and thus cheapen, the multifarious chemical analyses, other startups hope to integrate them all into “labs on a chip,” in which tiny, etched channels pipe droplets of reagents from one reaction chamber to another. Still more high-tech is “virtual screening,” the plan to replace actual laboratories – even those reduced to a chip – with computer simulations.

It is impossible to measure the success of these technologies against the sums spent on them. Private investment in genomics alone came to $1 billion in 1996 and $2 billion in 1997, rising in multiples every year thereafter until the bubble burst in 2000. It is not unreasonable to conclude that for genomics alone, some $15 billion in funds were raised. Meanwhile, proponents of industrialized research cannot yet point to astounding drug discoveries their ideas have engendered.

The failure of industrialized research to produce blockbuster results for pharmaceutical companies suggests several conclusions. The billions of dollars spent in pursuit of innovator molecules has (largely) been wasted or will be rewarded over a much longer time frame than desired. Drug discovery is neither predictable nor inherently manageable. Serendipity remains the most obvious explanation for the majority of new discoveries.

At university laboratories, where serendipity is understood, creativity is valued, and researchers are not subject to corporate management. Moreover, these labs are more numerous than industrial labs, and remain the most productive source of genuinely new ideas. Small, single-minded biotechnology firms are best suited to the early development of NMEs and biologics. As these firms become larger and more successful, they become turgid, less able to develop new ideas. And pharmaceutical companies are the organizations that can most effectively validate new research, shepherd novel drugs through the later stages of development, manage their regulation, and commercialize and market new therapeutics. To that end – and in the hope of a lucky break in discovery – it is reasonable for them to invest in large staffs of researchers.

But the scale and unrestrained growth of the pharmaceutical industry’s investment in research is irrational. Such investment will not produce the wealth of new drugs for which the industry longs. The unclogging of the drug pipeline will come from intelligent investment in biotech firms and academia. To prescribe how much money large pharmaceutical companies should spend on R&D would be a speculative exercise at best. However, if the real function of pharmaceutical R&D is not discovery, but rather the validation, development, and reformulation of existing molecules, then much of what is currently spent on discovery would be better spent on licensing, venture investment, and acquisitions – as well as in development itself.

Pharmaceutical companies will always have discovery research labs. They will never exactly resemble high-technology businesses (where networking companies like Cisco Systems effectively outsource all of their science to startups and universities), but they will look more like high-tech firms than the ossified institutions they are now.

Part 3 of 3.

Contributed by Barry Sherman and Philip Ross. Originally published by them in The Acumen Journal of Sciences, Volume I and reprinted with their permission.

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