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Greeting from Seattle. About the emergence of bioinformatics and information infrastructure companies.
While talking about what constitutes “life sciences,” I’d think it’s important to add to the mix a set of enterprises that are emerging as increasingly important players in research, drug production, and medical application. Bioinformatics, in-silico biology, genome and proteome databases, supercomputing, data modeling and visualization, nanotechnology (biotech is wet nanotech), computational biology are parts of that scene.
The big burst of innovation in life science that enabled the birth of biotechnology was discovering how to use biological processes to discover and produce medicines and biologically produced products. Biotechnology has been called the “New Biology” for some time in contrast to the chemistry of early pharmacology. The latest phase is inclusion of information-intensive techniques to decipher, analyze, and manipulate biological processes. Is it too much of a stretch to call it the “New,” New Biology. But with the sequencing of the human genome as the emblematic event, biology has become perhaps the most information-intensive branch of science.
Indeed, biology increasingly is called an “information science.” A new field was born: bioinformatics. Gene sequencing, gene profiling, protein characterization, modeling gene and protein interaction pathways, all these are information linked. Due to the “high-throughput” of microarrays in genomics and other assays, the filed of life science at this time is swamped with data to analyze and to use. The future portends more of the same. The medical interventions and practices to follow from these scientific developments will be highly information intensive too. Companies specialized to support information processing with hardware, databases, communication networks, and innovative computation strategies have emerged as significant factors in life science.
Let’s take three example companies: two household names and one not yet high profile.
IBM Starting right at the turn of the millennium, 2000, IBM launched a major market initiative with a division of life sciences. It put some 1,500 scientists—most not computer engineers—to develop hardware, software, and integration for computation-intensive products and services in biology and biomedicine. Big Blue saw an opportunity in the emergence of post-genomic biology that required vast ability to compile, manipulate, display, and transmit data for genomic, proteomic, and bioinformatics companies. They haven’t done badly. According to the manager for the program, Caroline Kovak, the business has grown at about 18% per annum.
Just a couple of months ago IBM merged their life sciences division with their health care division. Interestingly, the healthcare—because of the notorious resistance of health care providers to adopt modern information technology—only grows at about 7% per annum. Even President Bush called for greater use of IT to contain health care costs in his “State of the Union” speech, but there’s not much movement. However, IBM perhaps sees the bigger picture: a future in which drug research, development, clinical trials, and routine clinical data all become aspects of an integrated information flow, with Big Blue handling the information pipes.
What is IBM’s vision? It an IBM whitepaper on personalized healthcare they predict that by 2010:
1. Medical science breakthroughs will become increasingly common, due to increasing consumerism.
2. Genetic testing will be routine for some population groups, and associated privacy and discrimination policies will be determined through a broad debate involving government, industry and citizens.
3. Several major diseases will be understood at the molecular level, including relevant proteins and pathways, with clearly understood mechanisms.
4. Some subpopulations at risk for advers drug events will be identified for many therapeutics, resulting in increased drug discovery productivity and targeted clinical trials.
5. Healthcare will become wellness care, making pre-symptomatic diagnostics and treatments commonplace.
Something to contemplate for health-focused decision makers, as this company, positioned in both biomedical research and healthcare infrastructure, is anticipating significant developments well before the Society’s 2015 milestone.
Intel Yes, “Intel inside” might take on a whole new dimension with that company’s extension of its great chemical expertise into research and medical devices. While we associate the company with electronics, the company itself says it really is expert in incredibly precise control of chemical processes and molecules. They’ve been doing nanotechnology for years.
The name of their new effort is called Precision Biology, and it is aimed squarely at…cancer. They intend to move today’s highly precise analytical tools inte exquisitely precise tools for identifying individual molecules, perhaps what’s needed to usefully identify biomarkers.
Intel and researchers from the Fred Hutchinson Cancer Research Center recently announced a joint effort to develop improved methods for diagnosing cancer. To launch the effort, Intel is building an Intel Raman Bioanalyzer System at the Fred Hutchinson Cancer Research Center located in Seattle, Washington. The instrument beams lasers onto tiny medical samples, such as blood serum, to create images that reveal the chemical structure of molecules. The goal is to determine if this technology, previously used to detect microscopic imperfections on silicon chips, can also detect subtle traces of disease.
Gene Network Sciences (GNS) One entry in the new world of information-based adjuncts to biological research is GNS. More than that, GNS is specifically focusing its efforts on cancer. Basically, they are developing a computer-based model of the HCT 116 colon cancer cell. This is what is termed “in-silico” biology, a modeling process to explore the dynamics of cellular processes and to then enable computer simulation of possible molecular perturbations, i.e., drugs. Here is an extraction from a recent paper about the GNS system in the journal of the New York Academy of Sciences
Many genes and pathways that control cell growth and proliferation and underlie cancer have been discovered and the mechanisms by which mutations in these genes and pathways lead to uncontrolled cellular proliferation have been elucidated…However, the apparent progress that has been made in understanding and treating cancer has also revealed many layers of complexity that thwart our abilities to rationally diagnose, treat, and understand the disease. The majority (∼80%) of cancers do not appear to be dependent on the presence of a single predisposing mutant allele, such as the BRCA1 or BRCA2 gene for breast cancers. Rather, a number of subtle mutations may act synergistically, resulting in the initiation of the cancerous phenotype….This complexity makes the process of connecting molecular interactions and events to cellular and tissue level outcomes very difficult, error-prone, and information-intensive….However, the current reality is that this recent explosion of data has exacerbated a problem that was already beyond the control of cancer researchers: how to integrate all of the relevant knowledge in a systematic way that can provide the best strategy for halting the uncontrolled growth of cancer cells and tumors….data). While these approaches are a very powerful way of housing and querying large data sets, these methods will always lack the context dependence and dynamic interconnectedness that is at the core of biological function….
How would medicine improve if we were able to systematically deal with the complexity of cancer biology to make accurate predictions of drug response in cell lines, animals, and human patients? We would be able to ask complicated questions of our data, such as “What is the predicted effect of applying a particular chemotherapy with this new targeted cancer drug to a cell line with a particular genetic background?”; or more subtle questions could be posed, such as “What is the optimal dose or binding rate of a drug that is necessary to achieve a maximal apoptotic effect?”. Being able to answer these types of questions would allow the creation of therapeutic and diagnostic strategies that effectively treat different forms of cancer and are tailored to specific groups of patients.
GNS expresses the approach of a new class of entrants in the life science field, companies that are converting the analog informational qualities of cell processes (DNA code, protein structure and transformation, signaling paths and transduction) into digital models. This modeling approach has worked very well in many areas of science and engineering to analyze and simulate real world situations. The challenge: a single cell is without doubt the most complex system ever tackled. The construction is far from complete. But cancer is very relevant, even central, to cell process understanding.
I think it’s instructive to see that the life science industry continues, of necessity, to sprout additional specialized sectors to enable it to tackle problems that cannot be solved any other way. Life science today may easily encompass traditional biology, chemistry, physics, mathematics, and computation in one effort to make an advance in knowledge or production. Therein lies one of the conundrums of the industry: how to encompass, integrate and intelligently manage all the kinds of resources needed to reach a final product? Not even the Big Pharmas are able to have everything, and those that have spent the money to try are being criticized because they have not been able to make it work well in the time-frame demanded by investors.
The future of the industry, of collaboration and partnerships, of mergers and acquisitions, will, in part, be to find a working way to incorporate all they need to bring to bear on biological problems. It may be that the model of the life sciences industry in not one of giant, monolithic companies that secure and vertically integrate all conceivable resources, but instead a constellation of constantly shifting enterprises with infantile to mature skills related to advancing knowledge fronts of science.
There are indications that the industry behaves more like an ecosystem of entities constantly shifting and adjusting in relationship to each other. It’s pretty common wisdom these days that Big Pharma is not going to be the source of a lot of innovation in drug development no matter how much money they spend on super-labs. Indeed, their size may be a deterrent to innovation. Instead, the smaller, focused biotechs are where the new approaches are coming from. But small companies sometimes do not have the capital or the expertise to finish a product and to take it out successfully into the complex health care system. So commonly the pharmaceutical companies are buying-out, or licensing the intellectual property of the biotechs. Let the small, quick companies start the R&D process and then have the pharmaceuticals finish the products, fund approval testing, and then use established channels to market the final product. Some say the future of Big Pharma is in building a portfolio of properties from biotech companies.
Life Sciences in the Health Care System
Finally, this bestiary of life science corporate species would not be complete without at least a mention of their embedded-ness in the health care system. (Calling what we currently have in the US a system is perhaps an overly generous characterization of the kluge of actors in the arena where we seek adequate health-related support.)
Roger Howe, MD, in his recent analysis of the US health care places the pharmaceutical industry (not distinguishing biotechnology) as one important node in a system of “players” that also includes: fiscal intermediaries, the government, hospitals, the legal system, the medical profession, the news media, purchasers of medical coverage, and the public. There are many complex and debatable issues around the role of the industry that produces medical products—chiefly drugs—in this complex mix. I’ll touch on some of these issues later, but, suffice it to say, that not only is the ecosystem of relationships and forces within the life sciences industry complex, but the industry itself is linked deeply with many other important parties that are users, consumers of their resulting products. The impinging forces and their effects are beyond this report but they are significant factors in the health, function and expectations we have of the industry.
