Bioinformatics within Drug Discovery & Development
Bioinformatics has played a vital role in the advancement of biomolecular sciences, traditionally focusing upon molecular evolution and structural biology. It has allowed accurate statistical analysis of DNA or amino acid sequences, resulting in effective translation between such sequences, to the benefit of genetics, evolution and biochemistry. Bioinformatics sequence analysis has accelerated genome-sequencing efforts, enabling educated deductions of a newly sequenced gene’s product through sequence alignment.
Bioinformatics has proved itself as a powerful tool primarily in the effective generation of predictive proteomic data from analysis of DNA sequences. At present proteomic studies encompass myriad applications and techniques, including profiling expression patterns in response to various stimuli and conditions, time correlation analysis of protein expression, identification of tissue specific or disease specific proteins and perhaps in the future, personal genome and proteome maps. Such proteome maps may ultimately prove much more informative than the genomic versions, particularly in relation to diseases to which an individual has shown genetic susceptibility.
The massive output of computer-generated predictions is beyond the scope of classical biochemistry to attempt to analyze and validate. To meet the demand for effective protein analysis, proteomics has evolved advanced gene expression technologies which produce functional data about gene products at a pace approaching that of the bioinformaticians working to analyze the genetic code. Vast amounts of information can easily be generated using sophisticated technology such as the software analysis tool called Progenesis. Computerized analysis of 2D gels will produce much more information than would be found by eye, for example Progenesis can detect up to 5000 individual protein spots per 2D gel. Current large-scale research efforts can produce hundreds of gels which literally inundate scientists with data, resulting in the possibility of overlooking important results amidst the deluge of information.
Bioinformatics has therefore taken a new turn with the development of pharmacogenomics, rising to the challenge presented by peptide sequences of unknown function. For high-throughput analysis to effectively aid researchers bioinformatics must not merely be capable of producing vast quantities of data, rather the software must provide answers, highlighting important results and revealing the true meaning of the data.
Bioinformatics is being applied at all stages of the drug discovery chain. Currently, the greatest level of contribution is at the target identification stage, followed by target validation. In the future, the contribution of bioinformatics along the discovery chain is expected to shift downstream, with a primary role in target validation and secondary application in lead optimization. Based on assessment by pharmaceutical and biotech end-users, bioinformatics has the potential to reduce the current time of drug discovery by approximately 30%, and to reduce annual costs by 33%. The promise of huge savings has contributed to the industry frenzy around bioinformatics advancements.
Bioinformatics has affected drug development to a much lesser degree than discovery. Its current application is primarily at the preclinical stage, and a more significant role is expected in later stage clinical development (primarily Phase III). Pharmacogenomics will be a main driver for use of bioinformatics in drug development. The value of bioinformatics in drug discovery (i.e., more quality targets and lead compounds) is expected to improve success rates and length of drug development.
Genomics, cheminformatics, proteomics, and pharmacogenomics are the main application areas for bioinformatics. The expected trend in the biopharmaceutical industry is to diversify beyond the current focus on genomics, ramp up on cheminformatics, and move toward proteomics and pharmacogenomics over the next five years. The completion of sequencing the human genome has set the stage for more complex types of analysis, such as:
· Current and near-term areas of interest include: interpretation of gene expression data and its application to metabolic pathways, linkage of bioinformatics and cheminformatic data, and protein sequence and function analysis
· In the longer-term, pharma/biotech organizations plan to increase use of pharmacogenomics in large-scale association studies and for patient stratification in clinical trials. High-throughput protein structure and protein-protein interaction analysis also represent potential areas of commitment
While the expectation is an increased role for bioinformatics in drug discovery and development, outstanding bottlenecks exist. Pharma/biotech companies identify the following bottlenecks as impeding data flow and analysis within their organizations:
· Data interpretation (finding meaning in all this data)
· Integration across data sources (public, commercial, and in-house databases)
· Integration across data types (e.g., gene-gene and protein-protein interaction data) and along the discovery and development chain (biological, chemistry, and clinical data)
· Lack of manpower and specialized expertise
Need for standardization within and across data types as well as across experiments and technology platforms
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