DrugResearcher two-part special feature
Drug Discovery in profile: Dr Tim Jaeger
took time out to talk to Dr. Tim Jaeger, director of business
development at iAS interactive Systems, Germany. Speaking at the
recent Drug Discovery Technology Europe conference in London,
Jaeger spoke about his thoughts and concerns knowledge management
would have on the future of drug discovery.
Drug research and development is currently facing a major problem with data overload, which threatens to slow down productivity, if its management and interpretation is not efficiently dealt with.
Present day methods of interpreting this data have proved inadequate and there are real concerns, which were highlighted at the conference, that pharmaceutical companies are not fully extracting and understanding relationships between genes and diseases - the fundamental essence in drug discovery.
iAS interactive systems is a company that designs, develops, installs and maintains systems for knowledge management in the field of pharmaceutical R&D. Since August 2003 Jaeger has been responsible for strategy and business development of research and knowledge services.
Jaeger received his M.D. and Ph.D. in 1997 and his MBA in 2004. Before joining iAS in 2002 he worked at the University of Heidelberg. He was heading the Research Services of iAS between 2001 and 2003.
His activities also include work in various committees and task forces including CDISC, DART, EHTEL and AFGIS.
Thanks for talking to us Dr Jaeger. Firstly, to start off, what do you think are the fundamental core issues facing the pharmaceutical and biotech industry in the face of this overload of data?
"The issue of knowledge management can be looked on at four different levels - technology, people, results and regulation."
"In today's climate large pharmaceutical companies have an assortment of tools that they need to be integrated into a landscape that fits into that specific organisation. Each lab must assemble its own tool landscape to make it work. The selection of tools will reflect the ideology and culture of that company."
"There is a socio-technical aspect to people that will have an impact as to the what tool you give them and the way they use them to achieve results."
"What we are seeing is scientists are willing to use new tools in drug discovery as long as there is a significant impact on the way the work is done. What I mean by that is if you calculate the return on investment figures, the scientist will not be interested in that. However if you tell him the tool allows you to find specific papers or research in less time, it will mean more to him than the financial aspect."
To extend the idea of a return on investment figures, how then would you measure results in drug discovery management?
"We envisage knowledge management tools will need to come with key performance indicators. That is something that IT vendors, pharma companies and scientists will have to collaborate on."
"Measurement has become an issue with the regulators and regulation. In looking at legislation that requires, even in the very early stages, documentation and going through numerous established processes, these need to be applied to what you do."
Obviously the quality of data management tools such, as databases are dependent on the quality of the data out there. How reliant are you on public research such as the Human Genome project?
"The European commission is the single biggest entity, which spends the most money on biomedical R&D in Europe. The commission for the past 15-20 years has been putting up framework programs. These have large chunks of money directed towards biomedical research."
"We have been very fortunate to work in some of the projects at the forefront of these initiatives working on the architecture of these public projects so we can vouch for their quality."
"I see two issues here that need looking at - processing and intellectual property. Academic research, which is largely funded within these projects, sometimes filters through in the processes when compared to research done by pharma."
"This means they do deliver great data. But how do you interpret this data considering there is no audit trail and no document control? That is a problem. As we see these projects gradually opening up to pharma participation, processes will be aligned between the industry and academic research."
"Secondly, intellectual property. Pharma has been extremely good perfecting intellectual property. Academia, historically, has been extremely bad at protecting intellectual property. Pharma has at this point the opportunity to go in there and participate in the projects and benefit from the intellectual property that has been made but has not been secured in these projects."
But would you be concerned about the quality and the validity of this data, specifically the sources?
"No IT vendor will ever be able to qualify content data or to judge on content quality when it's needed. This will never happen. However how can you quantify information that has been published in more prestigious journals compared to research that is not? To deal with the quality problem means that researcher using information and weighing up the validity within his/her own mind asking themselves, "Do I believe this source?"
"There is a lot of contradiction in medical research. You can be sure a study will have a corresponding study, which concludes the opposite. It boils down to how much supporting evidence you can present which backs up a certain side of the argument. You want to make your own call on what you believe is true."
So then how will you keep the validity process constant?
"That is the fundamental essence of science. People will always be debating the facts. It is important for information to be annotated, to be able to say, "I believe this is wrong because…", or "I believe this is right because…," and you sign that, creating an audit trail."
"I believe that a good piece of data can be annotated easily. Annotating is one part of the cycle of knowledge and its management allowing people to share the information. It allows people to accomplish results."
"Knowledge sharing is a huge challenge as we have seen from many programs. People don't like to share, because it leads them to believe that they are expendable, which is not the case. However if the information can be attributed to you, that's credit to you, the company and a good performance indicator."
