Computers playing a driving role in R&D is more recent and started in the area of drug design. Most people still associate computer technologies with drug discovery, but they are exerting a growing impact further up the R&D chain into clinical trials. With many companies expanding the application of technology solutions to a wider range of R&D areas, commercial departments are also using computer-generated information to help them in their R&D decision-making as these models draw together disparate pieces of information related to R&D, such as potential sales, technical risk, competitor activity, opportunity costs and strategic fit within the company's product portfolio. Several companies have developed their own customized models in-house that couple the best features of external models with their specific corporate needs. Financial and marketing models are also frequently used by companies at both early and late stages of drug development.
A development that should accelerate the adoption of computer-based simulations is the decision of regulatory agencies in Europe and the US to explore the use of these technologies for trials. If this technology can help enhance drug safety, it will provide an additional tool to help the agencies meet public expectations.Genomics drives technology uptake
Within the discovery stage of R&D, computer technologies have found common use in areas such as pharmacophore identification, virtual screening, and quantitative structure activity relationship methods for lead optimization and absorption, distribution, metabolism, excretion and toxicity prediction.1 For example, computer simulations can be used to develop drugs based on the 3D-structures of the proteins targeted by the test compounds being developed. These systems can be used to screen the most promising compounds from those that are being developed. Advances in 3D computer simulation mean that the technology can be quickly used to predict the interaction between a protein and the candidate compound structures. Previously, more conventional simulations were often insufficient and unreliable in predicting such interactions and had proved of little value in the drug development process. To obtain the information they need, many companies have now turned to alternative simulation-based tests.
After 2000, following completion of the Human Genome Project, advanced computing solutions were sought by companies embarking on genomics programmes. Huge amounts of data are predicted to emerge from these programmes, and companies must determine how to process the vast array of genetic information and how to make decisions efficiently on what to move forward with and what to discard. In 2002, researchers from Curagen (CT, USA) calculated that existing drugs on the market only focused on 272 discrete molecular targets.2 In contrast, the amount of druggable targets, based on the human genome, was considered to be much higher at 8000. According to this analysis, 4990 were potential small-molecule targets, 2329 were antibody targets and 794 were targets for protein therapeutics.
Despite the opportunities offered by genomics, companies must ensure that they are set up to handle and manipulate the overwhelming amount of data; without an appropriate strategy to manage the IT approaches used, the availability of too many targets has the potential to actually slow the R&D process down and increase costs by congesting the new drug pipeline.3