informatics in pharma
Dr Othmar Pfannes, CEO at Genedata
T
he automation of R&D processes in general, and
data analysis workflows in particular, is a driving force within pharma as the time it takes to move research projects forward is critical. Frequently, raw data is collected from different sources over a long period of time and understandably once the data is there researchers don’t want to spend another few months integrating that data and waiting for the analysis results. Most large pharmaceutical companies are recognising the need to standardise data analytic processes, therefore, and are working with partners to do just that. In the past, these companies could afford to run internal soſtware development projects in order to have customised data analytics workflows. Today, however, pharmaceutical companies need to look more
COMPANIES ARE ALSO BEGINNING TO EMBRACE ENTERPRISE PLATFORMS
closely at the return on investment and consider solutions that work out-of-the-box with little configuration effort. Not only is this a cheaper option, but the deployment process becomes significantly easier and shorter. Companies are also beginning to embrace
enterprise platforms. Tis is driven somewhat by necessity as desktop soſtware solutions are no longer able to cope with the rising amount of data and do not support collaborative research. An enterprise approach also ensures that external partners can gain easier access to the same algorithms and types of analytic workflows. Tere needs to be a slightly different mind-set when investing in an informatics solution for an entire organisation rather than an individual laboratory, but the key considerations remain the same: security, to ensure that data is protected from manipulation and theſt, and that any outside partners only see the data sets they are supposed to; and, of course, scalability, which gives researchers the flexibility needed to grow. I would recommend that any company that is in a position to evaluate its informatics strategy pay close attention to enterprise solutions.
10 SCIENTIFIC COMPUTING WORLD
and networked research models. When using this model it’s important that any outsourced activities are made as much a part of the internal workflow as possible. Te same level of care needs to be given to preparation, understanding and time tables for delivery, and a robust informatics platform is necessary for managing that flow of information. Externalisation and the use of partners such as contract research organisations remains prevalent, but we are beginning to see a change as pharma companies now look to pull their assays back in-house. Te cost advantages that drove the move to externalisation in the first place are becoming less obvious now and although I don’t believe we’ll see a wholesale reversal, the balance is shiſting towards in- house research. Tis change is forcing pharma companies to
A
look at their informatics deployments because one side of the business has been essentially virtualised in order to simply load up received data. Now that a higher level of control and functionality is needed, companies are
Andrew Lemon, managing director at The Edge
lmost 10 years ago, the pharmaceutical industry went through a revolution with the push towards more outsourced
investing in systems that can support the transitional model where some aspects remain outsourced while others are brought in-house. Progression of each project needs to be tracked and compared to all the others. Tis transparency can have a profound impact on the decision-making process. Ensuring that the informatics system adds quality to the data
THE BALANCE IS SHIFTING TOWARDS IN- HOUSE RESEARCH
at the point of capture is also crucial because without the metadata, it becomes more difficult to exploit that data further down the line. Te importance of effective data management should not be underestimated, especially in research biology, and it’s important that companies invest in an informatics platform that can handle the entire life cycle.
Kabir Chaturvedi, director life science solutions portfolio marketing, Elsevier
I
nformatics tools in pharma have been keeping pace with the rising torrent of data, but there are several challenges. On average,
there is a 10-year incubation period before a drug is ready to hit the market – and, as roughly just six per cent of candidates succeed, there is a considerable amount of risk involved. Te cost implications are equally significant, as each candidate when taken through the pipeline aſter preclinical can cost several hundreds of million dollars at a minimum. Te informatics needs of pharma are complex
but one underlying point is that companies need a solution that can take the torrent of data streams and anchor them into fact. Taking next-generation sequencing as an example, there will be a time when every human will have their genome sequenced and the amount of data being generated will simply be incredible. Tose predicted features within those genomes will need to be grounded in scientific fact. Providing well indexed data and associations in that data will help inform predictive analyses, minimise
risk and ensure that there is no need to reinvent the wheel each time. Many companies have their own vast
repositories of internal data and paper records that are waiting to be mined, and it’s vital that this is integrated with external bodies of information. When faced with a proliferation of
IN THE FUTURE, I BELIEVE CLOUD WILL DEFINITELY IMPACT PHARMA
data, scientific publications, and new methods of reporting, users need access to a structure that will be able to navigate across the different standards and data formats. Interoperability and a common structure and format are priorities for pharma, and informatics solutions provide that stability. Te analytical tools do need to be scalable and in the future I believe cloud will definitely impact the pharma community.
@scwmagazine l
www.scientific-computing.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52