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How IT Helps Solve Pharma's R&D Problems: in silico Drug Discovery

NEW YORK, Nov. 8, 2010 /PRNewswire/ -- announces that a new market research report is available in its catalogue:

IntroductionIn an era of dwindling product pipelines and looming patent expirations, the pain of drug development is particularly acute for pharma. To increase the R&D productivity ratio and reduce the risk of failure in late stage clinical trials or post-production, the life sciences industry is leveraging the use of in silico technologies to support decision-making in the early stages of drug discovery.

Scope*Identifies the key forces driving the adoption of in silico technologies by the life sciences industry

*Analyzes the crucial issues that will impede the use of computer models and simulations during drug discovery

*Discusses the benefits of using in silico tools and how to get the most out of them

*Offers insight into the in silico technology competitive landscape

HighlightsThe use of computer simulations during drug discovery and development can drastically reduce the cost of bringing a drug to market. Computational and mathematical models to better understand how a drug will react in the human body will allow for more informed go/no-go decisions prior to investment in expensive trials, thereby reducing R&D costs.

Although the current approach to drug discovery gives scientists a general idea of how drug candidates will work, in vitro/in vivo models are not the same as humans. Prior to conducting potentially harmful clinical trials, researchers must build models that simulate physiological and biological behaviors in response to varying properties of a drug.

In silico research involves using a variety of technology solutions that range from HPC to simulation and predictive modeling tools to data mining systems to bio and cheminformatics, which are provided by a number of vendors that have expertise in different areas of discovery research.

Reasons to Purchase*Validate your market messaging and positioning in the pharmaceutical industry

*Identify strategies that will increase adoption of in silico technologies by life sciences companies

*Understand the trends that are shaping the future of pharma R&D


Catalyst 1

Ovum View 1

Key Messages 2

To restore growth, pharma companies must integrate in silico technologies into the discovery process 2

The in silico technologies market is diverse, much like the needs of the life sciences industry 2

Both companies and vendors alike need to increase collaboration with the larger research community 3

Vendors should cultivate pharma relationships by leveraging their computational science expertise 3


Table of Figures 4


The patent cliff of 2011 is just around the corner 5

The pharma industry's collapsing sales growth is impacting shareholder expectations 6

R&D productivity is steadily decreasing as companies battle rising costs 7

Increased R&D spending has not resulted in more drugs pushed through the pipeline 7

Dwindling product pipelines is a major cause for concern 9

An industry filled with unknowns, it is difficult to build reliable models 10

A lack of computational scientists hinders the use of computer-aided research 11


Executives are more likely to invest in IT that supports R&D: the soul of pharma 12

R&D processes must change to integrate virtual experiments 14

Drug discovery must be more predictive to increase the number of drug successes 15

The use of virtual models is rare during target identification and validation 16

The greatest bottlenecks reside in lead generation and optimization 16

Poor ADMET is the cause of majority of drug failures in late-stage development 17

Systems biology and the 'omics' add another layer to in silico technologies 18


Drug discovery requires high performance computing 19

Cloud computing is facilitating the use of predictive modeling and molecular simulations 19

Amazon is the current mainstream cloud provider for the life sciences industry 20

Pre-built versus custom models - different needs for different companies 20

Companies are debating between building their own models or seeking external services 21

Data management is the backbone of in silico research 21

In silico technologies must offer benefits across the R&D spectrum 21

Target identification and validation are not high on the priority list for in silico model development 22

Computer simulations and modeling must complement high throughput screening 22

In silico needs to provide predictive ADMET earlier in the discovery and development process 22

In silico must go beyond traditional drug discovery 22

The complexity of drug discovery has resulted in a diverse vendor market 23


Recommendations for pharma and biotech companies 25

With R&D productivity hitting rock bottom, research must incorporate in silico methods 25

Scientists must test the waters before making a final decision on in silico technologies 25

Collaboration with the larger research community and technology vendors is a necessity for growth 26

Recommendations for vendors 26

Vendors must engage with customers as if they too were part of the life sciences industry 26

Solutions to improve lead generation and predictive ADMET need to be priorities 27

Develop a sound service strategy as computational biologists and chemists are in high demand 27


Ask the analyst 28

Definitions 28

Further reading 29

Methodology 29

Disclaimer 30

LIST OF TABLES Table 1: Definitions of key drug discovery processes 16

LIST OF FIGURESFigure 1: Deficit in global annual sales for the top 50 pharma companies due to patent expiries through 2014 6

Figure 2: The pharma industry's sales growth with flat-line to 2014 7

Figure 3: The growing cost of drug discovery and allocation of R&D investment by function (%), 1975-2008 8

Figure 4: Productivity continues to decline across the US, Europe and Japan , 1989-2008 9

Figure 5: Top priorities of life sciences executives 10

Figure 6: PhRMA R&D as a proportion of sales (%), 1975-2008 13

Figure 7: Percent of life sciences companies' IT budgets allocated to various phases of the drug lifecycle 13

Figure 8: The impact of in silico models on drug compound testing and drug time-to-market 14

Figure 9: Current drug discovery and development process 15

Figure 10: R&D process with in silico research incorporated 18

Figure 11: Matrix of vendors that provide in silico technologies and support 24

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