Dr. James R. Brown, Director, Computational Biology, Quantitative Sciences, GlaxoSmithKline gave a seminar entitled "Computational Biology in Drug Discovery" in the Virginia Bioinformatics Institute (VBI) Conference Center on Friday, April 23.
Brown outlined why he believes that there is a key role for computational and evolutionary biology approaches to guide and influence the drug discovery process. Discovering new medicines has always been a challenging endeavor. However, pharmaceutical research and development are confronted by an ever-expanding biological knowledge base and increasing demands for even greater productivity in drug discovery.
In his talk, Brown described the role of a computational biology group in a large pharmaceutical company and provided some specific examples of the impact of comparative genomic analyses on infectious disease and cancer research. Within its research portfolio, GlaxoSmithKline is committed to infectious disease research and he outlined some of the lessons that have been learned from the genomics era.
Large-scale high throughput drug discovery efforts were first launched in the 1990s but the return on lead molecules has been meager. This outcome suggests the need for further chemical diversity to be integrated into high throughput screens, which is consistent with the considerable genetic diversity of bacteria. He emphasized that more genomic sequence data are required to cope with this diversity and, increasingly, next-generation sequencing methods are being integrated into the large-scale drug discovery landscape. Brown gave several examples where basic research and the knowledge of evolutionary relationships in bacterial and cancer research could contribute to the drug discovery process for targets such as gyrases and protein kinases (aurora kinases). Although there is still much to do, lessons have been learned from comparative genomics that have been helpful in guiding the drug discovery process.
Dr. Brown's key take away messages were:
To be successful the pharmaceutical and biotechnology industries must integrate diverse data sets
There is an increasing relevance on data available from public sources, public/private alliances, and pre-competitive partnerships to drive drug discovery
Computational Biology has a key role in leveraging data analysis for decision support
Computational Biology needs to continuously adapt and innovate in the challenging scientific community and social environments
Brown also highlighted that GlaxoSmithKline has opportunities for graduate students as interns or postdoctoral researchers at the company and expressed his interest in exploring research and training collaborations with VBI.
April 23, 2010