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Nov. 12, 2002 | What is the real promise of the genomic revolution? It is its power to unleash disruptive innovation — technology that allows a new group of people with different skill sets to do things in a decentralized, less expensive way, compared to what has historically been performed only by more expensive centralized specialists. Our work suggests that this new era of biology may invoke significant structural changes across the pharmaceutical industry. This column is the first in a series that will explore these changes, and give clues as to how they will manifest themselves.

Many of America's most successful companies began as disruptive innovators, bringing to market new products and services. These products and services shared one thing in common — they moved work from unstructured problem solving to a more straightforward, pattern-based way of doing things. As a result, they upset the existing industry order and created massive waves of corporate growth: Bell's telephone let people communicate without the need for telegraph operators; photocopying enabled office workers to do things that historically only professional printers could do; and online brokerages made investing so inexpensive and convenient that even high-school students can actively manage their own portfolios.

We are now at a disruptive turning point in the history of medicine. Medical treatment is really just a theory of cause and effect — if a doctor prescribes a specific medication, or performs a certain procedure, the following result will occur. But, as in all science, the predictive power of the theory, or treatment, is only as good as the classification of the root cause of the problem, or diagnosis. If doctors misclassify the disease, they can't formulate effective treatment.

We are at a disruptive turning point in the history of medicine.
Take cancer, for example, which is typically classified by the affected organ or geographic location in the body. But our improved understanding of the gene expression profile in cancer has revealed that what we previously called "leukemia" is in fact at least six unique diseases. Each form is characterized by a specific molecular profile, and patients can be precisely diagnosed by matching their expression patterns to a template.

Our work in many industries has shown that as scientific progress improves root cause classification schemes, problem-solving ceases to be unstructured, moves to pattern recognition, and eventually becomes rules-based. The improved understanding of the genetic basis for disease will not only profoundly impact patient treatment and care, but also transform the process and economics of drug discovery, development, and clinical trials.

The Coming Changes 
In the next few months, we will explore these transformations in detail in a series of columns, which will focus on, among others, the following predictions:

· A flattening of scale requirements and a "skill-set shift" in drug discovery. The oft-heard comment, "The next set of drugs is sitting in a morass of data," highlights the opportunity for a different skill set to drive the discovery process. The movement from unstructured problem solving towards a pattern-based analytical discovery process will shift the value away from life scientists toward those with strong analytical skills — mathematicians, cryptographers, data-miners, and so on. Individuals who analyze complex data will find a new home in this sector.

This is not to say that scientists will not have an important role to play — they will. However, the power in being able to see, predict, and interpret patterns will likely have the most significant impact on the discovery process going forward. As this change occurs, the scale and skills required to perform these tasks will decrease significantly. In other words, we will likely see organizations with highly differentiated and targeted sets of employees who focus on these functions.

· Change in the length and scope of clinical trials. The power of pattern-based discovery will fundamentally change the structure of the clinical trial business. Going forward, trials will be narrower in scope and shorter in duration, a result from ensuring that trial participants have appropriate disease and genetic profiles. Data gleaned from broad-based screening, coupled with pattern-based analysis, will move the somewhat random, inefficient sampling process toward greater precision. Similar to the discovery process, these changes will reduce the scale and skill set required to design and run clinical trials. One of the key drivers for merger-mania in pharma is scale — ensuring the necessary size to drive the pipeline, as well as support massive clinical trials. If disruption occurs in these two sectors, will mergers lose their power and appeal?

The dynamics of disruption, which eventually lead to disaggregation at certain points in the value chain, have important implications for both the incumbents, which traditionally have been vertically integrated, and companies that provide value at targeted points. Misunderstanding the drivers of industry evolution can have disastrous effects for incumbents as they divest themselves of the very businesses where profitability will likely exist in the future and merge to enhance those advantages that may be transitory. The emerging new biology companies will need to also anticipate these shifts to form effective partnerships with the right players. Lastly, infrastructure providers need to position themselves early at the points in the value chain where profits will migrate.

Clayton Christensen, Matthew Eyring and Mark Johnson contributed to this column. Christensen, a Harvard Business School professor, is a co-founder of Innosight in Belmont, Mass. Mark Johnson, also an Innosight co-founder, is the management consultancy's president, and Matthew Eyring is a managing partner. Debra Goldfarb ( is IDC group vice president of worldwide systems and life science research. 

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