Life Sciences Informatics in R&D

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The medical world is looking to new technologies to provide advanced understanding of disease. Effective drugs for treatment and prevention are needed for many disease areas, including cardio-vascular disease, cancer, neurological disorders, infectious diseases, endocrinology, and inflammatory and chronic degenerative diseases. There is excitement about the potential biological revolution that will emerge with understanding the human genome, proteomics and metabolic pathways. The challenge for pharmaceutical research is to unravel the pathophysiology of human diseases and thus, make it possible to identify targets. Now that the first set of complete human genome data has been reported, the focus of pharmaceutical and biotech companies needs to shift from sequencing the genome to understanding the relationships of genome to diseases and finding new, innovative drug molecules. Parallel developments of new biological technologies, such as transcript profiling, allow scientists to examine almost any biological system in high molecular resolution. Contemporary drug discovery research is now focusing on the identification and validation of pharmaceutical targets in the molecular pathways/systems embedded in this information. Novel therapeutic interventions are being developed and evaluated as a result of this research, and these are expected to be the basis of innovative pharmaceuticals of the future.

New R&D technologies have dramatically increased data volume and complexity. The mapping of the human genome, and the increased use of combinatorial chemistry, high throughput screening, and other technologies have dramatically increased the amount of data available to drug discovery organizations. The amount of public and proprietary data being generated today is several orders of magnitude larger than that of the pre-genomics era. Vast amounts of biological data are being accumulated in the areas of gene sequencing, gene expression, protein structure, cellular metabolic processes, cell-signaling pathways, and cell system analysis, and the volume and complexity of this new data defies efforts to transform it into information and to integrate it into existing discovery processes. New technologies have driven a 10,000-fold increase in the number of compounds a company can investigate during the discovery phase, yet the difficulty of managing this unprecedented volume of data has become one of the biggest bottlenecks in the drug discovery process.

Life sciences informatics, or the use of computers and sophisticated algorithms to store, analyze and interpret large volumes of life sciences data (bio-, cheminformatics), is essential in order to capture value from this growing pool of data. By integrating data sources, development organizations can integrate and analyze genomic information from multiple sources to discover genes that may represent the basis for new biological targets, therapeutic proteins, or diagnostic products. Through curation and annotation (the checking and re-tagging of data within a database) even dissimilar information can be integrated, including gene expression, gene sequence information, SNP data, functional genomics studies, preclinical pharmacology and toxicology, and results from clinical development studies.

In order to benefit fully from the range of new technologies available to the life sciences industry, discovery organizations need to develop and implement a cohesive informatics strategy for managing corporate research and discovery processes. Management and research personnel need to cooperate to develop a comprehensive informatics platform that will meet the organization's present and future data requirements. Although most development organizations realize the potential of life sciences informatics to improve research productivity, few understand how they might integrate informatics into their current research architecture. Most also have concerns regarding how the necessary process change, implementation, and training would be realized.

Few pharmaceutical and biotechnology firms have the experienced systems IT personnel necessary to architect and implement an enterprise-wide data infrastructure. However, many of the large IT systems consultants provide only a standard solution, or a solution based around their own product, to the detriment of the client.

Each discovery organization's data management practices are unique, with its own proven processes and methodologies for gathering, mining, and storing research data. There is an opportunity to help management and research personnel of R&D organizations to implement an informatics strategy that complements this legacy process and hardware infrastructure. There is also an opportunity to migrate legacy software applications into multi-tier client/server applications, allowing customers to preserve the core functionality and other benefits of their legacy applications while eliminating the constraints of legacy system architectures.