Scientific computation is the key to success

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Dr John McAlister finds that scientific computing is not an end in itself but that success depends on it

The past several years have been challenging for most sectors of the economy, and the pharmaceutical industry has been no exception. Although the demand for superior medicines remains high, there is continuing pressure to deliver more, better, and increasingly sophisticated new therapies at decreased cost and under an increasingly stringent regulatory burden. As a result, the industry faces dramatic challenges to increase the efficiency and productivity of its research - challenges that can be met only through substantive changes to the research and development process.

Within this intensely competitive arena, pharmaceutical and biotechnology companies are aggressively seeking new methods that will change the paradigm of pharmaceutical discovery and development to one that focuses on earlier determinative decisions on programmes that reduce unproductive investment and quickly identify pathways with the highest likelihood of yielding successful therapies. This attention to reducing attrition in programmes has a huge pay-back. It is not easy to achieve this goal. It requires a very tight coupling of experiment and analysis, capture of data that were previously ignored, new information systems to support this way of working, and scientifically innovative decision-support tools. In short, the methods that have traditionally been successful in drug discovery must be changed. As therapeutic targets become more complex, and the demands for early identification of potentially adverse events in this environment continue to rise, new ways of answering these questions must be employed. Needless to say, scientific computation plays a large part in making these goals achievable.

This environment heightens the differentiation among companies that will succeed in this industry in favour of those that offer new solutions to problems, rather than merely better methods for old solutions.

Not everyone agrees on how to address the present needs of the market. The challenges are significant, and the industry is asking some important questions. How can we find workable solutions that increase drug discovery efficiencies and demonstrate return on investment? How do we know that these solutions will integrate seamlessly within our infrastructure and significantly improve innovation? How will we measure the impact of these technologies against the reduction in attrition rates?

Of course, no single solution is correct for every situation. There are general principles that will prove valuable in every research organisation. However, the details of the implementation must recognise the unique environment and personality of the individual groups. Ultimately, the best solution will be achieved by integrating process management with science, implementing decision analysis and measuring effectiveness at every step.

Over the past 24 years, we at Tripos have learned and chosen to develop unique computational tools that prove their value quantitatively. Our software products enable our customers to sift through massive amounts of data, quickly bringing new order to information and correctly targeting paths that will be scientifically and commercially valuable. We collaborate with our customers, creating improved tools as part of their specific scientific programs. We have also extended our capabilities to widely available informatics platforms, enabling us to reach more markets and give customers greater flexibility to integrate systems.

That said, we have achieved our greatest breakthroughs and learned more about effective use of these tools as we implemented our own chemistry research operations in the Tripos Discovery Research Centre in Bude, England. As we built this research group from 12 scientists to more than 170 chemists, technicians, and support staff, we were forced to develop supporting infrastructure and processes that maximised the efficiency of the group, captured virtually all the corporate knowledge that was being amassed, and enabled commercial success during this time of immense change. To do this we have used a combination of our existing tools and those of others, and we have developed scientific methods driven by the challenge of these real scientific applications.

What has this experience taught us? Scientific computing is not an end in itself. It must be implemented in the context of problems to be solved and it must serve the greater purpose of the organisation - attrition reduction, faster discovery of new therapeutics, and development of the products amid the regulatory environment. That said, it is impossible to achieve success in the challenging pharmaceutical business in the absence of scientific computation. The key to success in the current and future research environment is full integration of the computational infrastructure with the experimental operations. Decisions must be effectively made by the research team - enabled by access to the assembled corporate knowledge base - not by a single research scientist using only his or her experience base.

Increasing integration with downstream development stages will inevitably follow and increase the impact of computation on the return on investment for new medicines. Computation is a key enabling technology of the scientific process.

Dr John McAlister is CEO and president of Tripos Inc.