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Bioinformatics in Drug Discovery
Frost & Sullivan, June 2010, Pages: 62
This research service discusses the role of bioinformatics in drug discovery with a specific focus on in silico applications.
This Frost & Sullivan research service titled Bioinformatics in Drug Discovery provides an overview of key technology trends in this space and the potential implications on the sustainability of businesses in the drug discovery segment. In this research, Frost & Sullivan's expert analysts thoroughly examine the following: molecular modeling, business models in drug discovery, and competitive behavior of the industry.
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Technology Overview
Adoption of In Silico Tools in Drug Discovery Speeds up Time to Market and Confers Cost Advantages
Due to the decrease in the rollout of blockbuster drugs in recent years, there is greater emphasis on reducing the cycle time to shorten the time to market for new drugs. The limited time frame granted for the generation of blockbuster drugs is an important driver for the development of in silico tools. 'The pressure is intensifying on pharmaceutical companies to develop new drugs, which are capable of replacing older ones that are going off patent,' notes the analyst of this research service. 'With the cost of drug discovery and development escalating every year, companies are now looking at using bioinformatics to lessen the time for generating drugs.'
Not surprisingly, the cost for target discovery and validation together account for a little over 42 percent of the total drug discovery expense. This is essentially because the process of target discovery and validation depends on the robustness of the software that is developed to model the desired range of products for the purpose of drug discovery. The cost of the products in in silico drug discovery varies according to the applications for which they are deployed. About 50 to 90 percent of the cash outflow is channelled toward the key steps of algorithm development and proof of concept studies and formulation of the final application. More often than not, the success of a drug discovery experiment depends upon the effectiveness of the in silico tools employed to assess the varied aspects of the drug. These include 3D structures of the ligand and the protein, assessing ADMET (absorption, distribution, metabolism, elimination, toxicology) profiles, docking affinities and most importantly, interaction of the drug and the ligand and the pharmacology profiles. All of these components, integral to the drug discovery space, can be effectively determined through the usage of specific in silico software.
Although several tools have been in use, the lack of standards in the industry has made it difficult to validate these tools. Hence, there arises a need to develop standards that would allow efficient means of organizing, analyzing, and storing data. Additionally, the large amount of data generated through various research processes highlight the need for standard data formats and interfaces. Different annotation systems use different standards, which lead to lack of coordination of available data. Lack of integration of biological data can result in the same data being replicated by several scientists across the globe. Therefore, the industry must look at developing standard data formats that could help prevent such issues. The advent of cloud computing has made it possible to develop coding across a shared space. The ability to do so could well bring down costs drastically, with the availability of several operational modules essential to generate a drug discovery software in the most efficient manner. As trials involve a major portion of development costs, cost reduction can be achieved through open advertising of the software across specialized forums. 'A pool of researchers in the drug discovery community could be engaged to try the efficacy of the software,' says the analyst. 'In doing so, a fundamental advantage that is gained is the ability to understand the existing customer base and thereby expedite software efficiency.'
Technologies
The following technologies are covered in this research:
- Protein modeling
- Molecular modeling
- Molecular screening
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