subject: How It Helps Solve Pharma's R&d Problems: In Silico Drug Discovery [print this page] Introduction Introduction
In 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
Highlights
The 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
Table of Contents :
SUMMARY 1
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 CONTENTS 4
Table of Figures 4
MARKET CONTEXT: THE LIFE SCIECNES INDUSTRY IS AT A TURNING POINT 5
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
BUSINESS FOCUS: IN SILICO RESEARCH IS CENTRAL TO ENHANCING R&D 12
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
TECHNOLOGY FOCUS: IN SILICO TECHNOLOGIES MUST ENHANCE DRUG DISCOVERY 19
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 25
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
