Many industries have Big Data problems. But pharma has seen its Big Data problems emerge faster and loom larger than any other. That’s because the need for a complete change in the way drugs are developed has coincided with the ability to generate more and more data about drugs. Think about it. The dotcom boom and the completion of the Human Genome Project occurred at the same time—and the rise of these technologies has coincided with pharma’s downturn, propelled both by the global economic crisis and pharma’s “patent cliff” as key blockbuster drugs have gone generic.
[blockquote cite=”Director, research informatics at a major U.S. Pharma company”]W hat my company, and probably all pharma companies, need in 2012 is a breakthrough solution for drug repurposing. We could also really use some help tracking regulation compliance and adverse drug effects. The “Concept Web” solutions from Code-N look very promising.[/blockquote]
Drug discovery has always been complex, but the volumes of data now available on drug mechanisms of action, clinical outcomes, and the genetic basis of disease have exposed the shortcomings of even the most sophisticated informatics. Clearly, current drug development methodologies are unsustainable; Forbes recently took failure rates into account and found that the best pharmas spent $4 billion for every new drug approved—and many doubled or even tripled that spending.
Research Spending Per New Drug
Hundreds of times per year, our chemists perform complex searches that take days to complete. It’s a pain because we have to traverse dozens of databases, each with a different syntax. Pfizer wants a 100x faster solution to speed up drug discovery.
– Chris Waller, senior director of enterprise architecture, Pfizer
|Sources: InnoThink Center For Research In Biomedical Innovation; Thomson Reuters Fundamentals via FactSet Research Systems|
|Eli Lilly & Co.||$4.6B|
|Merck & Co||$4.2B|
Pharma needs breakthrough solutions to help scientists traverse the informational landscape of R&D—solutions that can help organizations find new areas to operate, repurpose existing drug candidates, and reduce the time and experimentation associated with early drug development.
Code-N’s mission is to leverage the breakthroughs of the Concept Web to accelerate “in silico” discovery. By mining collaboratively constructed Concept Clouds for specific drug-protein-gene-disease combinations, scientists will be able capitalize on Big Data to rapidly identify viable disease targets and potential therapeutic agents.
Code-N applications will streamline time-consuming, repetitive tasks such as
- Uncovering what rivals are doing.” With Code-N’s Green Field Competitive Profiler, scientists, managers and execs can rapidly determine what your competitors are doing with just 1-click. Or better yet, have this intelligence arrive fresh in your InBox every morning.
- Performing federated searches across disparate databases. Hundreds of times per year, scientists in R&D organizations conduct complex chemical searches that take days to complete. Code-N business applications will connect scientists to Concept Clouds so that they can instantly find compounds matching multiple criteria, such as chemical structure, molarity ranges, or species tested.
- Identifying alerts and adverse events. Parsing the metabolites and effects of thousands of prescription and over-the-counter drugs can take days with outmoded database search systems. Code-N business applications will enable scientists to retrieve information on a candidate compound’s potential in vivo performance within seconds.
Better insight into drug targets and candidate compounds will enable organizations to “shelve” unsafe or ineffective drug candidates faster. And scientists will be able to focus their efforts on those candidates with a better chance of success.