Frost & Sullivan (http://www.healthcare.frost.com) finds that Virtual Screening Opportunities in Drug Discovery in Europe generated revenues of $93.2 million in 2006 and estimates this to reach $228.4 million in 2013.
Increasing economic pressure on the pharmaceutical industry to develop new drugs in a faster, efficient and more economic way than in the past has led to the emergence of several new methods aimed at a more efficient and rapid lead structure discovery process.
"Recent advances in combinatorial chemistry have made it possible to synthesise large libraries of compounds, and high throughput screening (HTS) provides a considerable reduction of the time needed for the discovery of new molecules possessing biological activity for a certain target," comments Frost & Sullivan Drug Discovery Team Leader Dr. Amarpreet Dhiman. "With experimental efforts to carry out the biological screening of billions of compounds still high, computational techniques and computer-aided drug design approaches have emerged as promising tools in helping researchers decide what to screen and synthesise."
Most pharmaceutical companies now have access to models that seek to predict key properties of the chemical structures that their research teams plan to synthesise. These range from the simple application of the ubiquitous Lipinskis rule of five, to the relatively sophisticated integration and scoring of a range of predictive models. While the rising use of predictive in-silico models is encouraging, much research remains to be performed to demonstrate appropriate confidence in the output of currently developed models and broaden the chemistry space covered.
In the drug development process, the data most readily available is that which concerns efficacy and selectivity. Often, at this point, researchers will transfer the compound to a preclinical stage using animal models, where the data becomes less consistent from study to study.
To create the most accurate predictive models, software developers need to have access to accurate and consistent data, so that they can model interstudy variations and reduce those variations to key characteristics. However, for a number of reasons, information from different laboratories is often not compatible, and thus a reasonable amount of consistent data is needed to build better models.
"Many companies are using in-silico techniques, but there is concern that these models are causing promising compounds to be disregarded based on in-silico model results," adds Dr. Dhiman. "This is resulting is some scepticism and uncertainty in the acceptance of these tools."
Informatics applications for toxicity screening is an area requiring further development and proof of concept studies, as confidence among researchers remains limited. Companies are very sceptical about the reliability of these models compared to actual experimentation. The prediction that in-silico models would replace in vivo and in vitro testing is yet to be fulfilled with current landscape favouring the belief that the best results will occur through a simultaneous use of all available tools.
"To allow companies to reap the benefits from in-silico studies, in the first instance, the data generated needs to be of high quality, reliable and accurate," advises Dr. Dhiman. "A key to achieving this objective would be to develop tools that facilitate data consolidation and information sharing; then systems would be standardised, allowing for solutions that integrate data from numerous tools and experiments."
Moreover, manufacturers should offer their services in data management, training and tool maintenance among others. They should function as both service provider and product developer. By partnering with clinical organisations, informatics and tool companies, manufacturers would be able to create solutions that best meet research needs.
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