Adam’s Next Big Venture

Kudos to Adam Rubenstein, blogger extraordinaire, for landing a plum job at cutting edge pharmacogenomic firm ARCA biopharma. In addition to typing his fingers to the bone on seventeen blogs, Adam has been raising money for Fitzsimons BioBusiness Partners, getting MycoLogics off the ground and consulting with High Country Venture. Working at one place may seem like a vacation.

Good luck Adam!

The Progressive Blockbuster

Retiring CEO of GlaxoSmithKline Jean-Pierre Garnier has published his thoughts on the shifting pharmaceutical business model in this month's Harvard Business Review. True to his roots, Garnier advocates returning the management of R&D to the scientists.

But Garnier doesn't stop at restoring R&D management to scientists; he also has some strategies for reorganizing the drug development process. The first is to separate the processes of creating first-in-class from best-in-class drugs. Although discovering a first-in-class may be more satisfying scientifically, pharmas incur less risk and equal, or greater, rewards from best-in-class.

These two R&D processes are currently intertwined in most pharmas which, according to Garnier, leads to reduced productivity in both processes. Of course many of the functions of the processes are shared; complete separation would be impossible, but improved performance could be achieved with a concerted effort.

Garnier's next idea is what he calls "progressive blockbuster." Blockbuster status is the goal that every pharma is seeking yet is becoming more elusive as we enter the smaller patient populations of the personalized medicine era. Enter the progressive blockbuster. This is a drug that has been tested on a carefully selected sub-group of patients which have been shown to benefit and have low side-effects from the drug. The FDA would approve the drug only for patients in this specific sub-group. Once the drug has been approved and marketed to this sub-group, additional sub-groups can be added one by one as they show good results and safety. Eventually you will be marketing to enough sub-groups to make it a blockbuster. Voilà: the progressive blockbuster.

It does sound a bit more palatable to the general population than the current situation; where you begin with a blockbuster population and wait for adverse events to occur. But correlating patient response to genetic profile or phenotype will remain a tough nut to crack.

The Promises of Web 3.0

Put off by the unfulfilled hype of Web 2.0, some in the knowledge management community are now clamoring for Web 3.0. Dr. David J. Roberts, Chief Scientist at iBASEt, writes in Oracle's Profit Online that Web 2.0 technologies aren't really worth the bother and that the real value sought by enterprises lies in Web 3.0 technologies.

The compelling promise offered by Web 3.0 technologies is the ability to make inferences between contextually linked information thereby pulling new, creative combinations out of knowledge bases automatically. This is the holy grail of knowledge managers; to get machines to be able to reason, even just slightly, would offer a great deal of value.

With such a compelling value proposition as reasoning will Web 3.0 technologies render Web 2.0 worthless? There are still some very large obstacles to Web 3.0 as Roberts has described, i.e. machines can't handle ambiguity and major pieces of the Web 3.0 language (ontologies) have yet to be produced.

But is the interim value of Web 2.0 technologies really that low? Web 2.0 technologies provide integration for well defined processes, and it’s the fact that they must be well defined that renders them inflexible and in need of constant maintenance. Yet this integration is indeed quite valuable, too valuable to be left on the shelf until Web 3.0 is ready for prime time. The future world of Web 3.0 is indeed rosy but don't count Web 2.0 out just yet.

Consumer Genome Tests

With the U.S. Senate passing the Genetic Information Nondiscrimination Act (GINA) last week the bill now moves on to President Bush for signature into law. One more impediment to the genomics age will be removed. Does this pave the way for personal genomics companies such as 23andme and Navigenics?

The Wall Street Journal took up the issue of accuracy with these consumer products last week. As with many new (although I can't really call it disruptive) technologies, this one is somewhat of an expensive novelty. The main criticism seems to be a lack of accuracy in identifying just what genetic diseases an individual may be susceptible to. Although there are numerous tests for susceptibility to heart disease, the current genetic tests don't provide much more accuracy than can be provided by measuring blood pressure.

Some fairly rare genetic diseases do show up clearly on the tests, e.g. a condition associated with a higher instance of blood clotting: factor V Leiden. There is a gray area where the tests will indicate a higher susceptibility to a disease but don't go so far as to say you will contract it. This would be valuable to an individual by allowing them to increase their level of monitoring and taking preventative measures.

So far the insurance companies have yet to buy in on the whole genome tests although they have been covering disease specific tests such as those for breast cancer prognosis.

What the personal genomics companies are selling now is information, information in a quantity and form with which scientists and clinicians, let alone consumers, can barely get their arms around. As Howard McLeod, professor of pharmacology at the University of North Carolina, is quoted as saying "You get a lot of information but very little knowledge." The real challenge, and true value, will be to extract knowledge from these tests.

Genetic Sleuthing

No sooner did the U.S. Senate pass a bill barring genetic discrimination than the state of California announces that they will track down suspected criminals through the use of their relatives' genetic information. Certainly using genetic information to restrict access to healthcare and employment could create ambivalence towards the collection and recording of this information and the Genetic Information Nondiscrimination Act of 2007 (GINA) has gone a long way to reduce that. But now knowing that your genetic information can be used to find and convict a family member of a crime, with or without your cooperation, introduces yet another impediment to the collection of this information.

California is the first state to aggressively employ the new familial, or "partial match" search technology although several other states use it to a limited degree. So far California is only using DNA profiles within its own offender database; DNA taken only from convicted offenders, but plans are in place to extend the database to include arrestees.

The technology of genetic genealogy has progressed rapidly providing insight into human migration and ancestry. Apparently now the technology has progressed to the point where its use can be vital in a criminal investigation.

Although currently used to make exact matches between a DNA sample and a DNA profile in the database, the new policy allows the crime lab to tell investigators who might be related to the person who left the DNA sample. It's not enough for a conviction, but enough to identify, contact and sample the DNA of relatives of the suspect with the eventual goal of finding the suspect.

Familial searches have been common in the UK for the past few years due in part because of the over 4 million profiles of charged criminals and suspects. The practice is not without controversy and even Alec Jeffreys, the discoverer of DNA profiling, believes that using DNA from people who have never been involved with the law raises "potentially rather thorny" civil liberties issues.

Mining the Text Mountain

I've had my doubts about text mining in the past; such as why we spend so much energy wrapping facts into coherent sentences only to have machines struggle to unwrap them. Not that there is any doubt that there is a huge amount of unmined text out there; decades of work for text miners. It's just that it seems of questionable value when this knowledge could be tagged as it were entered thus obviating the need to mine it later. But there is this vision, a vision that with sophisticated text mining tools some bigger picture can evolve from a mass of unstructured data.

In a recent article (subscription required) in Genome Technology William Hayes, director of informatics at Biogen Idec, alludes to this vision stating that writers cannot pre-tag the information while writing as the information can only be viewed within some larger context. The jury is still out on this but regardless of how knowledge is gathered and transferred in the future, we’ve got years of literature to mine from.

Hayes also claims that "almost all of our [biomedical] knowledge…is captured in the literature." I'm sure we can claim that researchers do publish their "good" results in the literature, but this is far less than our total knowledge. Because the process of writing and publishing knowledge is so difficult and expensive, only the very most promising results are usually published, leaving the vast remainder in lab notebooks, hard drives and researchers’ heads where it is difficult to distribute.

Larry Hunter, director of the Computational Bioscience Program at the University of Colorado, sees text mining as a way to improve current databases but remains skeptical that such systems can automatically keep a scientist up to date with the literature. Don't throw those reading glasses away just yet.

Mining the Text Mountain

I've had my doubts about text mining in the past; such as why we spend so much energy wrapping facts into coherent sentences only to have machines struggle to unwrap them. Not that there is any doubt that there is a huge amount of unmined text out there; decades of work for text miners. It's just that it seems of questionable value when this knowledge could be tagged as it were entered thus obviating the need to mine it later. But there is this vision, a vision that with sophisticated text mining tools some bigger picture can evolve from a mass of unstructured data.

In a recent article (subscription required) in Genome Technology William Hayes, director of informatics at Biogen Idec, alludes to this vision stating that writers cannot pre-tag the information while writing as the information can only be viewed within some larger context. The jury is still out on this but regardless of how knowledge is gathered and transferred in the future, we’ve got years of literature to mine from.

Hayes also claims that "almost all of our [biomedical] knowledge…is captured in the literature." I'm sure we can claim that researchers do publish their "good" results in the literature, but this is far less than our total knowledge. Because the process of writing and publishing knowledge is so difficult and expensive, only the very most promising results are usually published, leaving the vast remainder in lab notebooks, hard drives and researchers’ heads where it is difficult to distribute.

Larry Hunter, director of the Computational Bioscience Program at the University of Colorado, sees text mining as a way to improve current databases but remains skeptical that such systems can automatically keep a scientist up to date with the literature. Don't throw those reading glasses away just yet.

Right Brain, Left Brain Thinking

For those of you who may be interested in an anatomical explanation of linear thinking there is an excellent video on the TED Web site of a recent talk given by neuroanatomist Jill Bolte Taylor, Ph.D. Not only has Dr. Taylor studied the brain in great detail but she also had the (mis) fortune to observe her own stroke first hand.

The brain, as Dr. Taylor shows us, is composed of two almost completely separated hemispheres. The analogy she makes is that the right hemisphere functions as a parallel processor and the left a serial processor. The right hemisphere operates in the present moment, thinks in pictures and processes sensory inputs.

The left hemisphere, the serial processor, is the half responsible for linear thinking. This part of the brain picks out details from the information streaming into the brain and categorizes and organizes it. It separates time into past and future. It thinks in language and is responsible for our ego.

Dr. Taylor's stroke occurred in the left hemisphere of her brain. Gradually, as the stroke progressed, she lost more and more of her left brain functionality and existed solely in a right brain world, a world where her language skills were lost, where her ability to carry out simple functions like dialing a phone were seriously impaired but a world where as her ego melted away she felt more connected to the world around her.