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

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and built critical mass, and then expanded to other universities, before going mainstream. Sage will look for its initial spark at three institutions: The University of Washington, the Fred Hutchinson Cancer Research Center in Seattle, and Yale University. (Sage has at least one strong Boston tie already, with important brain tissue donations from Massachusetts General Hospital that will enrich the database.)

The last time Friend got together to collaborate with Hartwell, they co-founded Rosetta Inpharmatics in 1996 with biotech pioneer Leroy Hood. They sold that company to Merck for $620 million in 2001. Since getting absorbed into Merck, among its many projects, the company has been stitching together reams of genomic data from cancer patients that shows the interplay between the underlying DNA, the RNA messages that arise from it, and the proteins that ultimately carry out all those instructions and turn tissue into cancer that can be seen under a microscope, Friend says. Rosetta’s ability to put together those puzzle pieces into a coherent picture, much of which has been published in scientific journals, has been the difference in making mere correlations between biology and disease and actually establishing the root cause of the problem. This work by Schadt’s genetics group is what makes Sage possible, Friend says.

This progress within Merck hasn’t led to any specific drug Friend can point to on the market, but many candidates in the Merck pipeline have been enabled by efforts by the molecular profiling and then oncology groups led by Friend. This progress has inspired him and Schadt to imagine what they can do in the future if they could harness researchers from around the world to think about the same problems.

“This is a huge deal, because of who Steve Friend is, what he’s done, and what he wants to do,” says Chad Waite, a managing director with OVP Venture Partners, an early investor in Rosetta. “Steve dares to be great, over and over and over.”

As with any far-out vision, plenty of things can derail it along the way. What if researchers use different gene analysis machines, from Affymetrix, Illumina, or Applied Biosystems? How will Sage reconcile differences in how experiments are designed by different scientists? How will researchers be enticed to let go of their precious data, currently stored on password-protected hard drives and servers? How will Sage manage the intellectual property that arises from the database? Why would companies want to participate and run the risk of putting valuable proprietary data out in public? How will this get financed?

Some of these things Friend can answer, and some still need to be worked out. Software is already making it possible to manage differences between the various instruments scientists use, and deal with the differences in experimental design, Friend says. The intellectual property question is one important aspect that has to be settled as governing rules get established, he says. Companies may not want to dump all their data on drugs in early stages of development, but they may want to support other experiments that will boost the entire field of drug discovery without undercutting their competitive standing, he says. Sage is still thinking about how to create the best incentives to get scientists to join, and it seems to hinge on a couple of ideas. People who enrich the database will get peer recognition and rewards of professional status through the community, as happens in open source computing. Plus, early adopters will have an informational advantage over everyone else.

“Companies, and academic researchers, will come to the table because they don’t want to have a less informed view of the data,” Friend says.

Sage expects to draw financial support from foundations, government grants, pharmaceutical partnerships, and IT partnerships, Friend says. He wouldn’t disclose the names, or occupations of the individuals who have pumped in the initial $5 million in seed funding. But once Sage is up and running, it will need a $20 million annual budget to operate, he says. The partnerships are expected to come together over the next three to five years of an incubation phase, as the community of scientists grows and starts taking responsibility of the database, like with open source computing, and then costs will go down, Friend says.

Eric Schadt, Sage's chief scientific officer

Eric Schadt, Sage's chief scientific officer

This effort will also require cooperation from one especially unpredictable partner—society at large. All sorts of samples of tissue samples from patients showing signs of disease will need to get crunched into data that illustrates the complex symphony of DNA, RNA, and proteins that interact with the environment to cause the problem. If Sage can get its hands on this sensitive material, assure everyone it won’t be misused, and attract scientists to join, then society will benefit. The potential payoff is through fighting disease more in a more personalized way than today, in which the average drug is prescribed on a trial-and-error, one-size-fits-all, let’s-see-what-happens basis.

“This will fundamentally change biology,” Friend says. “We will have a more coherent way to think about disease.”

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  • Morris

    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.

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

  • 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…

  • Azald

    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.