Harnessing the Crowd to Make Better Drugs: Merck’s Friend Nails Down $5M to Propel New Open Source Era

Xconomy Seattle — 

Biology has never really had a social-networking movement like open-source computing, where thousands of loosely-affiliated people around the world pool brainpower to make better software. If Merck’s Stephen Friend gets his way, about five years from now, he will have ushered in a new era in which biologists work together to make drugs that are better than any company can today inside its walls.

Friend, 54, is leaving his high-profile job as Merck’s senior vice president of cancer research, after having nailed down $5 million in anonymous donations to pursue this vision at a nonprofit organization getting started in Seattle called Sage, Xconomy has learned. I heard about this potentially transformative idea during a phone conversation a couple days ago with Friend and his co-founder from Merck, Eric Schadt.

Sage is built on the premise that vast networks of genes get perturbed, or thrown off-kilter, in complex diseases like cancer, diabetes, and obesity. Scientists can’t just pick one faulty gene or protein and make a magic bullet to shut it down. But what if researchers around the world capturing genomic profiles on patients could get all of their data to talk to each other through a free, open database? A researcher in Seattle looking at how all 35,000 genes in breast cancer patients are dialed on or off at a certain stage of illness might be able to make critical comparisons by stacking results up against a deeper and broader data pool that integrates clinical, genetic, and other molecular data from peers in, say, San Francisco, New Haven, CT, or anywhere else.

Besides helping scientists aim higher, this will make medicine more transparent than ever, Friend says. Physicians from around the world could look at genetic profiles from their patients, match it up with the Sage database, and then prescribe the medicine most likely to work, Friend says. The FDA could look for insight into the proper balance between the risk and benefit of a drug. Health insurers could look at drugs for certain patients that have the greatest likelihood of success, and pay for ones that work. Drug companies could use the database to weed out treatments that are bound to fail or cause side effects for patients with certain genetic profiles, potentially saving years of wasted effort and hundreds of millions of dollars.

“We see this becoming like the Google of biological science. It will be such an informative platform, you won’t be able to make decisions without it,” Schadt says. He adds: “We want this to be like the Internet. Nobody owns it.”

Some big names have signed on for the early incubating phase. Besides the full-time efforts of Friend and Schadt, the Sage board includes Nobel Laureate Lee Hartwell of the Fred Hutchinson Cancer Research Center; Paul Ramsey dean of the School of Medicine at the University of Washington; Richard Lifton, the chairman of genetics at Yale University; and Hans Wigzell, director emeritus of Sweden’s Karolinska Institute. For insight into how to apply lessons from the open-source computing world, the board has brought on John Wilbanks, the vice president of science at the San Francisco-based Creative Commons.

To get started, Merck is in the process of donating some needed equipment and software that has been used at the Rosetta Inpharmatics subsidiary in Seattle, Friend says. The Whitehouse Station, NJ-based pharmaceutical giant will also donate important genomic data that doesn’t relate to its proprietary drug discovery programs, Friend says. And, as Sage plans to build up a staff of about 30 people, it will draw partially from the remaining talent pool that worked for Friend and Schadt, since Merck announced last fall it is closing the Seattle facility and transferring some people to Boston.

Sage hopes to follow the road map of Facebook, which started on campus at Harvard University, quickly caught on there … Next Page »

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18 responses to “Harnessing the Crowd to Make Better Drugs: Merck’s Friend Nails Down $5M to Propel New Open Source Era”

  1. Morris says:

    I have mixed feelings. Lets face it, the system is broken. Millions of people have been saved by drugs, but millions of more people have found that drugs they desperately needed, drugs that cost pennies to manufacture (most of the money is spent on advertising, not surprisingly) are out of their reach. I myself had my esophagus destroyed because I could not afford the $600 a month that Prilosec cost in 1997 (twice a day), which is now available for pennies a day..

    Thanks to that high price, I now have to sleep with the head of my bed propped up.

    Something has to give. High drug prices are criminal.

  2. Well, *this* is certainly interesting.. I don’t suppose psychiatric survivors will have a crack at following the progress internally, too, ay..? :)

  3. Obiben says:

    I don’t know, Morris…

    What you say is right, prices on meds are awful, even more so if you live somewhere with no healthcare system (governement paying for meds is one step towards evil socialism, don’t do it!), but this news isn’t related.

    This thing looks great from all angles, and I sure do hope it works.

    I’m most curious about data accuracy will be ensured, however. From miles away, I can see people with interests using this as a merchandising tool – we’ll have to see…

  4. Azald says:

    I respectfully urge that the fancy technology and long scientific-sounding words that purport to validate the omnibus project proposed here be challenged by second and third opinions. Before the public is left with the impression that all these gentlemen say is true and worthwhile, let’s have balanced and fair reporting of the current scientific status of the genomics effort in drug discovery and common disease understanding, rather than writing articles that serve as a shill for new projects based on a failed research approach.
    Efforts to have genetic information, specifically gene expression data, assist drug discovery and further the understanding of complex, common human diseases have been a bust so far. The public should be intensely angry! Tens of billions of irreplaceable NIH and pharma R&D dollars have been flushed straight down the drain. The dearth of important innovative drugs hitting the market over the last ten-fifteen years can be traced in no small part to the wasteful focusing by large pharmaceutical companies of megabucks on this strategy. The idea has passed the point at which throwing more money at it is the answer. The money has been thrown and nothing came back. Approaches have been tried and tried and tried, and yielded nothing useful. Novel, yes. Publishable, yes. Interesting, yes. Useful, no.
    One of the fundamental problems, and perhaps the fatal flaw in the overall strategy, is that when these techniques are applied to collections of one cell type, as in a laboratory culture of isolated single cell types, the gene expression data are useful. It is often possible to identify gene expression patterns, and even changes in gene expression patterns in response to chemical perturbations (e.g., “drugs”) or disease processes, because the cells respond in unison with like changes, since all are of the same type. In this simple situation, the activity of the genes can be linked to the behavior of cells of a specific type.
    However, all human tissues contain multiple cell types. Yes, inside humans, there are few or no isolated cell types that perform useful functions. One might think of human tissue as an orchestra, and the multiple cell types that make the tissue function as the instrumental sections. When tissue, rather than groups of like cells of one type, is analyzed for gene expression, each cell type is caught in the act of making its own unique pattern or response. The gene expression of a whole soup of cell types if measured at once. When whole chunks of tissue are analyzed, though a gene expression pattern emerges for the tissue, it is no longer possible to attribute any particular gene activity to any particular cell type in the tissue. It has proven impossible to find out who is doing what.
    In addition, the same gene exists in multiple cell types; the technique that detects gene expression cannot tell the cell type generating the gene expression product, only that it is present. Some cell types may be doing nothing relevant or even nothing at all. Others may be the real culprits. The same gene may be expressed in multiple cell types and mean nothing in some cell types and be all-important in others. To expand the orchestra analogy, one cannot, in effect, tell the activity of the violins from the oboes from the brass, because the orchestra, not its instrumental sections, has been sampled. It is not possible to temporarily drop out the function of one cell type (e.g., have the French horns stop playing), because the tissue would no longer be functioning in its native state. The cell types interact and influence each other!
    When it is only one cell type that is really the target for drug treatment or has malfunctioned in disease, this remains an insurmountable problem even after a generation of research. A drug may, in fact, influence gene expression in the tissue, and even in the offending cell type. However, it alters cell types that must stay unaltered to maintain healthy tissue, too. That may not be good.
    Drug company insiders at high levels know what has happened within their four walls, but are loath to disclose publicly their experience to their competitors. Occasionally, these insiders are the generators of the idea and have little encouragement to admit their personal failures. For financial reasons, they are even unwilling to admit it to their colleagues on the business sides of their companies. Pharmaceutical scientists and university-based researchers in the trenches know it, but too many still make their livelihoods by receiving significant funding for their efforts in the area. There are tens of thousands of scientific publications reporting gene expression data. The number that have assisted materially in the development of drugs that manage/cure human disease is so small that the approach should be abandoned as cost-ineffective.
    This must become known to the investors, governmental research agencies, private research agencies, and the general public, so that new research funds can be funneled in different directions, in an environment where failure of the methodology and technology has already been proven.