Virtual Biotech Companies: Built on Solid Bedrock or Unstable Landfill?


Xconomy Seattle — 

A recent meeting focused on “Reinventing Biotech’s Business Model” provided an interesting window into the spectrum of approaches being used to create new biotech companies. Unfortunately, it did little to relieve the concerns I recently voiced regarding the expanding numbers of “virtual” biotechs. This model is becoming a popular archetype for the creation of new biotech companies because they require much smaller initial capital investments than traditional biotechs. The basic idea is to build a company around a small managerial group who then farm out most of the basic operations to contract organizations. These outsourced functions usually include research and development, clinical trials, intellectual property, regulatory, financial, and even human resources.

These disposable companies are not designed to reach adulthood.They are raised for the sole purpose of being gobbled up while still young fry by the larger, wealthier fish in the pond. While some of these companies have been acquired for attractive valuations, I remain concerned about their potential to create useful medicines. There are several other attributes of “virtual” biotechs that contribute to my anxieties beyond the distortions these companies may introduce in the biotech ecosystem. The first has to do with the quality of the science on which at least some of these companies are likely built, and the second concerns the fact that “virtual” companies are not likely to contribute much towards the development of robust biotech clusters.

Most biotech companies, no matter their business model, need to start off with some sort of molecule that they plan on turning into a drug or treatment that will attract funding from investors. “Virtual” companies, however, are not equipped to do independent research, as they have no dedicated lab space. So where do their drug development projects come from? They generally originate from one of the three places that are also plumbed for drug leads by more conventional biotechs:

1) New ideas provided by entrepreneurs directly or indirectly associated with the new company.

2) Castoffs from Big Pharma or biotech companies that did not develop them for a variety of reasons (e.g. change in clinical focus; drug performance wasn’t stellar in early trials; prioritization left no money for development; projects left over after purchase of more valued corporate assets).

3) Published data on potential drug targets, which mostly arise from academic research papers.

It is this last category that is the primary source of my trepidation. One might expect that research chosen as the basis for forming a new company would be described in ground breaking, high profile papers. After all, practically all science builds upon previous discoveries, which is why many researchers spend so much time reading the literature and attending conferences. This concept was famously summarized by Isaac Newton in a letter he wrote in 1676 to his rival Robert Hooke, “If I have seen a little further it is by standing on the shoulders of Giants.” Suppose, however, that instead of standing on the shoulders of Giants, Newton had found himself kneeling on the feet of Dwarfs? What would he have seen and come up with from that position? Would he have “seen a littler further” if his studies in mathematics, optics, celestial mechanics, and gravitation were based on the work of contemporaries and predecessors who had produced mostly bad science?

The process through which many scientific advances are made came to mind after reading an alarming paper recently published in Nature by C. Glenn Begley and Lee M. Ellis. The authors reported that significant efforts were made to reproduce the results found in 53 “landmark” cancer research papers as part of a corporate R&D program at Amgen. The goal was to confirm the findings, which were then expected to serve as platforms for the development of new drugs. The net results of this large-scale effort were dispiriting: they were only able to substantiate the data in a paltry 11 percent (6) of the papers. The key findings in the other 89 percentof the articles could not be reproduced. Scientists at Bayer HealthCare in Germany obtained similar results in an earlier study. Both of these analyses back up the work of John Ioannidis, who has written extensively about “Why Most Published Research Findings are False”.

The authors are not claiming that all of the non-reproducible papers were truly wrong or fraudulent. There can often be minor details in the way experiments are performed that hinder their replication by others. This minutia can be as simple as using a different brand of plastic vial or buying an enzyme from one source instead of another. However, the primary implication of these studies is that a significant percentage of the work was indeed wrong, and therefore should not be relied upon as the basis for drug discovery efforts.

This lack of data reproducibility in academic research is apparently well known in the VC community. According to Bruce Booth of Atlas Venture, “the unspoken rule is that at least 50% of the studies published even in top tier academic journals…. can’t be repeated with the same conclusion by an industrial lab”. As a result, many VC firms don’t invest in early stage research, and those that do generally require additional validation work before investing. The problem of building a company around published data affects both lab-based and “virtual” biotech companies. Amgen and Bayer clearly had both the financial resources and the laboratory facilities to undertake this validation work. “Virtual” drug development companies, however, don’t have the ability to determine directly if the basic research underpinning their own efforts might be tainted and unreliable. Without this internal capability, they may embark on a program based on faulty science that will be doomed to failure from the outset. The smarter companies would certainly make an effort to engage a contract research organization (CRO) to verify the data. But where would these early stage companies get the funds to pay someone else to perform this critical validation work? Would their investors view the money spent replicating published results as a worthwhile use of limited financial resources at the expense of just pushing ahead with the program?

My consulting experience suggests caution is required. I was once hired to do a scientific review by a (non-virtual) biotech company that was based upon a single publication written by the company’s CEO. It made for a very uncomfortable meeting when I had to explain to him that he had completely misinterpreted his data and that his company was built not on bedrock, but on unstable landfill. I was also engaged to review the data at a “virtual” biotech company that hired a second CRO to confirm some academic findings that could not be replicated by an initial CRO. Data obtained from the second CRO was also very weak and not supportive of the scientific hypothesis. I recommended that both of these companies abandon their respective projects and redirect their financial resources, a suggestion they each ignored to their eventual detriment.

This lack of reproducibility of published scientific data ties in to another disheartening finding: the rate of retraction of scientific articles has jumped alarmingly in the past few years. There are a number of reasons why a science paper might be retracted: the scientists realized an error in their interpretation post-publication; they were unable to reproduce the findings themselves, or some or all of the work was fraudulent. While errors are more common that fraud, retractions based on fraudulent data alone rose seven-fold between 2004 and 2009. The science described in a retracted paper would clearly be a poor choice on which to base a new biotech company. A “virtual” company founded on misinterpreted or fraudulent data, and with no direct means to validate it, may be staring into the abyss in short order.

One other concern I have with the “virtual” biotech model: it does virtually nothing to help establish or expand a vibrant biotech cluster. A cluster is a localized assemblage of companies whose size and proximity to each other helps facilitate the success of the entire group. If one company in a healthy cluster downsizes, the other organizations are likely to hire at least some of their employees. This helps retain a local talent pool and can greatly facilitate hiring in the area. Clusters often revolve around anchor companies, large organizations that serve as “job sinks” in an area. Successful anchor companies tend to grow and spin out other companies, and the whole cluster grows like a snowball rolling downhill. Here in Seattle, Microsoft and Amazon serve as large anchors for software, and Boeing leads the aerospace cluster. Our biotech cluster, however, has diminished in size over the past decade due to the acquisition and downsizing of the largest biotechs and numerous layoffs among the smaller companies.

Leading biotech clusters in the U.S. continue to attract new companies and programs like magnets attract iron filings. A number of Big Pharma companies (e.g. Pfizer, Merck KGaA, Novartis, and AstraZeneca) recently moved jobs to the Boston area, the largest U.S. biotech hub, as they restructured and downsized their R&D programs. Merck has just committed up to $90M creating a new translational research facility, the California Institute for Biomedical Research (Calibr), in the San Diego hub. Companies like to be in vibrant clusters interspersed with strong academic institutions. “Virtual” companies, being small and ephemeral, do not make for a strong cluster, although the CROs that they employ may certainly do so.

I did hear about one new biotech model at the meeting that I thought had some promise. Inception Sciences of San Diego is based on the concept of establishing a core drug discovery platform as a holding company. Individual projects may be sold or spun off as independent companies, but the scientific staff remains engaged with the remaining projects. This model would allow for the direct testing and validation of internal projects because they actually have a dedicated lab group. Research focused on multiple projects means that if one project is based on faulty science, it won’t necessarily kill the company. While the expectation is for the individual projects to succeed and leave, the company itself is meant to survive, thus contributing to the strength of the local cluster. This approach therefore has several potential advantages compared to the “virtual” biotech model. However, for those of you who continue to see the merits in “virtual” biotechs, please remember the Russian proverb favored by both Vladimir Lenin and Ronald Reagan: “Trust, but verify.”

Stewart Lyman is Owner and Manager of Lyman BioPharma Consulting LLC in Seattle. He provides strategic advice to clients on their research programs, collaboration management issues, as well as preclinical data reviews. Follow @

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14 responses to “Virtual Biotech Companies: Built on Solid Bedrock or Unstable Landfill?”

  1. Stuart,

    Thanks for putting this out there; although I may not agree with everything you write, I think it’s great you are sharing your perspective – wish more of us blogged!

    Two comments/objections with your key points.

    First, backing bad science has nothing to do with the operating model you put it into, virtual or integrated. Failure to replicate academic work is a real concern, but I know of fully in-house shops based on poor science or skewed ways of looking at data (or even worse, artifactual results). High quality virtual shops have networks of great labs they work with to confirm findings, conduct MoA and assay work, etc… It’s not always about cost. Why hire a B+ team of inhouse generalists when you can get the world’s A++ specialist to do it for you? Virtual development has been around successfully for some time; virtual discovery is in its infancy but we’re seeing great results so far at companies like Nimbus. Smart medicinal and computational chemists working together on new designs, made by capable CROs, and tested there, and the iterative drug discovery cycle happens just as it would if all were within one shop. It works, and it can be down both efficiently and for world class science. I’m sure there are poorly run shops attempting virtual, but there are poorly run inhouse shops too (frankly if they worked so well we wouldn’t be experimenting with virtual ones!)

    Second, it’s a fair point on effect on ecosystems. Virtual plays dont need 100s of inhouse scientists and don’t typically help support big clusters. But the converse is true – for those biotech folks outside of the big clusters, virtual operating models should enable them to compete on high caliber programs with the best of the big clusters. In some ways, virtual models should level the playing field so lots of subscale biotech areas can develop winning drugs…

    Thanks Stuart. Thought provoking as usual.

  2. Abdallah Al-Hakim says:


    Overall I like the post and the points it raised. I do, however, have to admit that I am generally supportive of the virtual biotech model. The huge talent pool of scientists outside of the typical biotech clusters (which are very few – Boston, San Diego, Philadelphia, Bay area, hard to think of others!!) makes this model a viable one. This is especially true if proper university support is in place for instance by making it easier for academic scientist to embark on such collaborative projects.

    Regarding the point of lack of reproducibility. Unfortunately, this is common knowledge within the scientific circles. However, it is not difficult to identify reliable labs that produce solid data versus those that producing non-reproducible data. It is surprisingly easy to figure out once you are within the scientific community in any particular field which is more reason to focus on working with top talent in the academic circles.

    Finally, virtual biotech companies might be first step in addressing major crisis in the scientific circles which is the growing pool of post-doctoral scientist that are facing increasingly less choice for jobs whether it be academia or industry. It would be great if such pools of talented and experienced individuals become more involved in virtual biotech business model.

    Thanks for posting this thought provoking article

  3. Bruce and Abdallah,

    Thanks to both of you for posting your comments and for sharing your perspectives. Let me be clear: I’m not rooting against virtual biotechs, I’m simply saying that they need to prove out a bit more before I’m willing to endorse them in a big way. At the moment it’s just another model, and I don’t think it’s prudent to have a massive shift into this model unless and until it can demonstrate that it really generates novel drugs (and not just big deals). I’m a scientist, and I need to see the data to be convinced.

    I don’t have much experience dealing with VCs who are crafting different models of biotech companies, so I will defer to your experience in this area. Based on the anecdotes I cited in my op-ed, I would say that the due diligence employed by at least some of them has been less than stellar. At the end of the day, I think we would all agree that in the drug discovery business, it all comes down to hiring highly qualified, experienced individuals who can work well together. Across the industry, this appears to be something that is in short supply. That applies to VCs, to biotechs, to CROs, to consultants like me, and to the academic labs as well. The trick, it would seem, is picking out the wheat from the chaff, and it is clear that there is a tremendous amount of chaff out there. VCs haven’t been doing the best job, or half of them wouldn’t have gone out of business in the past 10 years. Pharma and biotechs of all types clearly face challenges, as evidenced by the fact that the success rate of moving drugs through clinical trials and getting them approved is less than 10%. There is a wide spectrum of quality and experience in CROs, and finding the right one to help is key. You both mention high quality lab groups, which many people would have assumed were those folks who published their data in those hot papers in Cell, Science, and Nature. As the articles I cited pointed out here, the ability to replicate the data produced by these “top” labs has been quite limited. I would imagine if one were to survey the C-level management of biotech companies as to the quality of the scientists that they either employ or work with in academia, I think they would all give them extremely high marks. It’s just like Lake Wobegon, where all of the children are above average!

    It is true that the virtual model will should allow small groups in isolated locations to compete favorably with the best of the big clusters. However, putting together a highly qualified management team may be challenging in those areas (though they too could be virtually linked). And if they are not in a cluster, it will not hurt their economic environment if they fail and fade away. It is clear that biopharma is currently locked in an unsustainable paradigm, as I (and many others) have written about before. The number of new drug approvals has stayed essentially steady over the past decade or so while R&D expenditures have increased steeply. Virtual biotechs may be a great solution to the current problem, but what I have been arguing is that they don’t have the track record yet to conclude that they are the solution. My crystal ball tells me that a few of the very high quality virtual biotechs may succeed and the rest will fail (just like regular biotechs). The economics pluses and minuses of these new models still need to be calculated by someone with access to a great deal more data than I have access to.

    Bruce, I congratulate you on identifying so many high quality groups to work with, and I hope they are successful in coming up with novel drugs. Assuming they are, I hope that this result can be replicated across biotech and pharma. I hope the Nimbus approach works out, but at the moment they have exactly zero drugs on the market. In the financial sector, computational approaches were supposed to transform a number of investment and hedge funds (remember Long Term Capital Management)? Companies hired Nobel laureates (among others) to develop mathematical models that would allow them to achieve stellar results. While initially successful, most of them flamed out despite the hope and hype. And biology and drug development are a much more difficult undertaking, as I have also written about in the past.

    Abdullah, I don’t see how virtual biotechs are going to provide many jobs for post-doctoral scientists. Could you explain this further? Lacking industry experience, they wouldn’t be good candidates to work at virtual biotechs, and I don’t see that employing them indirectly through academic labs is a good solution either. For example, they need to publish papers on novel work to establish their reputations and get a permanent job. This could be a problem if they can’t do that due to IP issues, and the types of science that may need to be done in a company setting (e.g. massive chemical library screening) would not necessarily train a post-doc for an independent type of position.

  4. Thought-provoking piece.

    But as CSO of a long-time virtual biotech company (Funxional Therapeutics in the UK) that has taken an academic project and moved it from the bench to Phase II in 5 years with sector-leading capital efficiency, Im afraid I have to disagree with both your key points.

    Its not to say that you can do virtual biotech badly – plenty do. But whether you do it well or badly is independent of your business model. Perhaps there are more lower-quality groups working virtual because the lower cost base might fool some investors into backing low quality just because its cheap, but there is nothing fundamental about the virtual model that makes it work that way.

    Take your two main points: irreproducibility of academic (and probably all) biological science is endemic, as I have noted before: But there is no reason why virtual companies can’t do “wet diligence” on their project before they begin. You can out-source a replication study (or better still, an experiment to test a simple, unavoidable consequence of the the hypothesis you are basing your company on). Indeed, there are specialist CROs set up exactly to deliver this service: in the UK is just one example.

    Secondly, ecosystems are valuable to a point, but they don’t have enough to recommend them to want to build more capacity just to expand the ecosystem. In the same way traffic always fills new roads, the same happens with biotech companies. BUT the average quality falls. There is plenty of “built capacity” in the world-wide biotech sector – it is using that capability more efficiently that is key to making attractive returns. It is late-stage, capital hungry, “real” biotech firms that are not quite good enough that kill returns in biotech – definitely not the virtual guys:

    Bottom-line: done well, virtual biotech has much to recommend it. I set out the case four years ago, before it was really trendy: The improvement in capital efficiency far outweighs the other disadvantages. As long as you have a management team who REALLY understand how to operate such a business.

  5. Harpreet says:


    Great Article.

    i too are in favour of virtual bio tech. Smaller team and more efficent. I would sugges to look at a company called Verona Pharma
    listed on Uk Aim Market ( vrp )

    till now they had 5 sucessfull trials, 4 trials on the main dug, a dual pe3/pd4 Asthama / COPD and chronic cough. it worked wonders so far

  6. I think your concerns regarding virtual biotech are totally misplaced.

    The usefulness of medicines being developed by any company is solely a measure of the ability of its people to discern and create greater value. All drugs have warts. It takes time and money to define limits, what these mean to usefulness, and ultimately, if limits are manageable, whether at the end of the day there is a product from which to profit.

    Large pharma is playing to hit home runs with multi-billion dollar blockbuster drugs involving the most complex and promising drug candidates. The investors and labs are high value, and they get big money and attention. But big pharma and its drugs face the same risks to market as any other company and drug. And along the way, they take more time, spend more money and this translates into higher prices to patients and the healthcare system.

    When this Soviet Central planning works, things are great, but major companies don’t always pick winners. And when they fail, costs still need to be recovered. So that means all drug winners carry tag-along costs from the losers.

    The big box development approach from the 1930’s, where bigger is better and one or two blockbuster drugs will pay for the failures has marginal utility. For certain Manhattan Project-style targets, this may be the only rational way to develop given obscene regulatory funding and market requirements.

    But for the majority of potential drugs, this approach is downright dumb. For the one great drug coming out of the pipeline from start to finish at any major company, tens of thousands of other drug candidates and their inventors sit home waiting for opportunity to find them.

    Virtual bios have the ability to find, investigate, filter and facilitate many more drug opportunities at a far cheaper cost and in a much faster timeframe than most major companies. This means taking many more shots, more quickly, and at a far lower cost to find something socially beneficial and profitable.

    You take issue with data quality, and rightly so, but data is always an issue to everyone. No matter the size of the company, it’s the ability of the team to sift through the research and validate gems that distinguishes between good and bad companies and investments.

    Virtual bios are using the existing FDA regulatory process to screen drug candidates. And in many cases, big pharma is acquiring targets from virtual bios to fund Phase II and Phase III trials and to use their existing marketing channels to get these drugs to market. So the process works. The virtual bio model reduces costs, maintains quality, spreads risk and promotes widespread research that can only help the US healthcare system as a whole.

    Your second concern regarding the value of virtual bio-companies to the development of robust biotech clusters is a complete red herring.

    Judging the value of virtual companies based on the number of jobs it creates by the number of people it directly hires totally misses its value, because you are measuring the wrong thing.

    That’s like saying that its bad that a growing majority of Microsoft jobs are shifting to Asia – even though it’s increasing US pension returns. Or that companies that hire the most people even though they are totally inefficient is a good thing because people have jobs. No, virtual bios are like the old BASF ad – they make other things possible.

    Route 128 didn’t start as a biocluster. Over time elements coalesced for convenience. Virtual companies are one important feature in this landscape that should be exploited to enhance efficiency, improve time to market, and facilitate new drugs, because they can make lots of money doing what they do best. In turn, they prime the pump: maximize chances for university researchers to get projects funded, help big companies validate new opportunities, enable venture folks to earn excellent returns and improve healthcare while reducing costs.

    Virtual biotechs are not the be-all, end-all, but their activities can make the difference between Silicon Valley and Death Valley for many promising new drug candidates.

  7. Abdallah Al-Hakim says:


    Two points that I would like to make:

    1. The high quality labs are not those necessarily publishing in Science, Cell, Nature – as a mater of fact it is those in those papers that you run into many problems when reproducing data. Rather, high quality labs are known by reputation within the academic circles and one of their main characteristic is generating reliable and reproducible data.

    2. Not all post-doctoral scientist are aiming to become group leaders or principal investigators. In fact the majority would just want a good steady job that makes use of their scientific skills and expertise within a dynamic environment. For my idea to work, there has to be considerable university/academic and government support in particular cluster (eg. Toronto) to create drug discovery units that made up of number of post-docs who can collectively push forward a project. There are plenty of experienced post-docs (some with 10 years experience or more) who are more than capable to run a project. Also, you can get principal investigators involved as mentors. I realize that this idea requires much more thinking but that would be the basic foundation.

  8. R. Jones says:

    The amount of bad science in industry and academia that everyone admits to here is not something someone else is doing somewhere else. The virtual company’s CRO is doing it. The in house researchers are doing it. It is a part of our culture. It keeps people employed for another day. It gets investors off your back. It beefs up your pipeline, website and patent portfolio. When the executives head off to Wall Street to beg for money, they are bringing a whole lot of bullsh*t with them.

    In the long run, it hurts all of us. In the short term, we love bullsh*t.

  9. Thanks for all of the comments. One thing we all need to keep in mind is that there can be many different models that are tagged as being “virtual”, so it is a good idea to distinguish how one is using the term whenever possible.

    David, Thanks for your comments. Your version of this model is clearly different than the one I have been discussing, because the phrase “long-time virtual company” would be an oxymoron. Under this model (details in my previous op-ed, not this one) virtual companies last but 2-3 years before being bought out, and they work on but a single project. The other difference is you suggest hiring specialist CROs to test out the company’s hypothesis, but I’m not sure how that would happen here in the States. This costs money, and VCs won’t invest and provide the money until they know the hypothesis is sound. You are then stuck in the valley of death, since you can’t afford to prove the idea is sound without the VC’s money. Work arounds include getting a small business grant or angel investors, but they too may be deterred by a non-confirmed hypothesis. Finding a CRO with the proper skill set to confirm the biological data may be much more involved then simple biomarker studies. We can readily agree that it is critically important to have a management team that understands how to operate such a business. It would be difficult to find people with this experience (working in virtual biotechs), of course, since the model is new, so you will be making a decision based on how people performed in a different model. I wish you luck with Funxional Therapeutics and will be rooting for your success.

    Harpreet, you, too, are discussing a different model. The one that has been proposed here would not be able to afford to run 5 trials! If this company did not look attractive enough for an acquisition by now, what would make it attractive to big pharma? How much money have they gone through?

    Rick, It would be terrific if virtual bios can “find, investigate, filter, and facilitate many more drug opportunities at a far cheaper cost and in a much faster timeframe than most major companies”. What I was saying here is that they need to prove that they can, in fact, do this. If big pharma buys a drug from them, and then screws up the phase III trials, does this mean the model doesn’t work? Of course not. Alternatively, the data coming out of the virtual company might be the problem that leads to failure, and distinguishing between these two possibilities will be difficult for outsiders. The virtual model has potential, which was stated in both of my articles on the subject. It is just not a done deal yet. And not having labs to do the studies may be a serious problem for virtual companies. Is the current big pharma model working? The answer is clearly no, but I think that many models need to be considered before everyone jumps on the virtual bandwagon. In this article I mentioned Merck’s new initiative in San Diego, which again is a different model that may (or may not) be a better approach to getting potential drugs out of academic labs. It, too, will need to prove itself.

    Abdallah, while you and I may be able to identify high quality labs, I am not sure this is a trait shared by the entire biotech community. And as to jobs for post-docs, anyone who has been one for 10 years is highly experienced, but they should be looking for a real job. A post-doc is meant to be an extended training period, and with virtual companies, the only places they will find themselves working is at CROs or in academia. That’s OK if that is what they want, but I think the virtual model will limit job choices if widely adapted.

    R. Jones, you are right when you say that bad science hurts all of us. The trick is learning how to avoid it and/or detect it. The fact that this stuff gets published suggests that the training of scientists has been lacking at many institutions, and that at least some investigators are ethically challenged. It may also mean that reviewers are not doing a sufficiently good job as well.

  10. Jonathan says:

    Dear R. Jones,

    Well said. So many times I’ve seen CEO’s at investor conferences really exaggerate the ability of their technology to “cure” cancer or some other ailment. The method of outcomes research should be applied to drug development to determine if the high failure rate of drugs is due to the underlying technology being poor or the application of good technology was well beyond its utility. I think if a virtual biotech picks-up good validated technology and uses their clinical expertise in a specific therapeutic area for further development and clinical trials then its an obvious win-win. Beyond that I don’t think this virtual model will really move the field further, and falls into the class of newco that Stewart warns about.

    Thanks Stewart for a thought provoking op-ed.

  11. Part of Steve Jobs’ genius was to be involved in every part and process of making his products. He must have had tremendous insights and overviews of every nook and cranny of the Apple machinery, making him able to fix and improve in lots of different ways. Perhaps in certain areas of research this approach helps, especially when one is doing the difficult stuff. Perhaps one can spot those compounds that give a negative result in a screen, but an extra methyl group will make them work. Your expertise/insight might be difficult to give to the CRO sometimes, and you’ll have less of a feel for why it did not work.

  12. Dave says:

    Bruce et al.-
    I think we can agree that the clear winners in the virtual biotech movement are the CROs. In the vast majority of situations, they take no risk and have significant pricing advantages when working with smaller companies. Plus, every relationship with a small company and is an opportunity to learn new science and to build up their expertice. If the virtual model is going to be successful, there needs to be a mechanism/forum whereby companies can get the best work for the best price from their CRO partners, optimally one that recognizes the mutual gain realized by both parties in a research relationship.