Integrating scientific data for drug discovery and development using the Life Sciences Grid

Abstract

Background: There are many daunting challenges for companies who wish to bring novel drugs to market. The information complexity around potential drug targets has increased greatly with the introduction of microarrays, high-throughput screening and other technological advances over the past decade, but has not yet fundamentally increased our understanding of how to modify a disease with pharmaceuticals. Further, the bar has been raised in getting a successful drug to market as just being new is no longer enough: the drug must demonstrate improved performance compared with the ever increasing generic pharmacopeia to gain support from payers and government authorities. In addition, partly as a consequence of a climate of concern regarding the safety of drugs, regulatory authorities have approved fewer new molecular entities compared to historical norms over the past few years. Objective: To overcome these challenges, the pharmaceutical industry must fully embrace information technology to bring better understood compounds to market. An important first step in addressing an unmet medical need is in understanding the disease and identifying the physiological target(s) to be modulated by the drug. Deciding which targets to pursue for a given disease requires a multidisciplinary effort that integrates heterogeneous data from many sources, including genetic variations of populations, changes in gene expression and biochemical assays. Method: The Life Science Grid was developed to provide a flexible framework to integrate such diverse biological, chemical and disease information to help scientists make better-informed decisions. Results/conclusion: The Life Science Grid has been used to rapidly and effectively integrate scientific information in the pharmaceutical industry and has been placed in the open source community to foster collaboration in the life sciences community.