A decade ago a good research chemist could produce 50-100 new compounds a year. Today, with standard combinatorial chemistry, the same chemist can turn out a couple of thousand compounds a year. Meanwhile, high throughput screening has massively accelerated the speed at which compounds can be tested to identify the most promising molecules. In short, technology has transformed the early part of the pharmaceutical Research and Development (R&D) process. But this is only the first chapter in a story that has yet to be finished. Pharmaceutical companies still conduct much of their primary science in laboratories and still have to perform studies on people. They have made very little progress in automating the "D", let alone integrating it with the "R". However, some of the industry leaders have now begun to use in silico techniques in development. Companies like Glaxo Wellcome have, for example, adopted "cassette" dosing -- concurrent testing of as many as 20 compounds for properties like pharmacokinetics. Other firms like Pharsight are providing the technology to go a stage further, with clinical trials based on virtual patients. This move towards "e-R&D" -- the term we have coined to describe the computerization of the R&D process -- is partly a response to greater financial pressures. Pharma 2005: An Industrial Revolution in R&D, the report PricewaterhouseCoopers* published in November 1998, shows how soaring R&D costs, sluggish sales growth and shorter lifecycles have all started to take their toll on the sector. But it is also a consequence of advances in chemistry, biology, computing and automation -- together with the amalgamation of these disciplines -- that would have been unimaginable just a few years before. With pharmacogenomics -- the study of genetic variations between individuals and how they influence the way in which people respond to a particular drug -- it will soon be possible to customize drugs for defined sub-populations of patients. It may eventually even be possible to produce bespoke therapies tailored to the biological traits of specific individuals. Since many of the drugs developed today work for 60 percent of patients at most, this would be a huge improvement. But though pharmacogenomics promises to deliver numerous benefits, it will also revise the financial basis for much of the industry's R&D. On the one hand, the development of customized drugs for sub-populations with different versions of the same disease or different responses to the same medication will fragment the market. On the other hand, it will produce new opportunities both to treat patients for whom the existing drugs are ineffective and to resuscitate drugs that have been abandoned because they produce dangerous side effects in a few people, although they work well for many others. In other words, pharmacogenomics will increase the complexity of the R&D process and potentially reduce the number of patients from whom the industry can recoup its investment in any one treatment, even as it expands the overall number of patients who can be treated. This has major commercial implications. Costs per approved drug have risen steadily over the past decade; indeed, the latest estimate suggests they could be at least $600m. With the impending erosion of the current blockbuster model, the industry must therefore find more economic ways of producing safe and efficacious new drugs. The introduction of e-R&D is a critical step in doing so. In silico technologies will enable drug manufacturers to accelerate the selection process, reduce the cost of preclinical and clinical studies and increase their overall chances of success. We estimate that they could collectively save at least $200m and two to three years per drug. Yet most pharmaceutical companies are ill equipped to make the transition -- partly because their IT is under-funded and overworked. They are already grappling with Y2K compliance, the new technologies involved in early research and the corresponding increase in the output of data. Some companies are also struggling to integrate legacy systems, following the latest spate of mergers and acquisitions. They are doing all these things with smaller budgets than many of their peers in other information-intensive industries. But if the industry is to exploit the real power of e-R&D, it must invest in innovative new technologies, build networked organizations and harness its knowledge capital. It must re-invent the role of the IT function.1 Above all, it must jettison the old, empirical way of doing things for systematic, predictive processes based on a more complete understanding of how the human body works. To read the complete study, download the PDF file at the top of this page. | 
