Guest Commentary: Generating more knowledge from data and more value from studies
The nature of drug discovery and development remains an information-rich endeavor, and in order to increase success rates, we must make better use of our data—and our information science experts.
he ability to make meaningful correlations betweenpreclinical observations and clinical outcomes can mean the difference betweensuccessfully moving a candidate drug through development and pipelineattrition. Getting an integrated perspective on the entire project—the studies,results, research citations, therapeutic and business potential—is a majorchallenge in translational medicine. Efforts to find relevant data, leverageinformation and generate new knowledge can be hampered by the exponentialgrowth in the volume of data available.
Until recently, the role of the information scientist haslargely focused on creating better, more efficient tools and ways to search,access, manage, manipulate and retrieve relevant data. These tools have evolvedover time to become more intuitive and integrated, with expert systems thatenable the end user to conduct more meaningful analysis, visualization and datamining.
But who is the end user? More often than not, it's thebiologist, the chemist and other researchers on the project team. In principle,this makes sense. The problem is that data sources are growing in volume andcomplexity, with a diversity of formats that extends beyond the written word toinclude pictures, videos and podcasts. The diversity, complexity and volume ofdata also go beyond the technical expertise of the project scientist who rarelyhas the technical, computational or quantitative skills to extract the mostmeaning from the universe of available and relevant data.
The change that's needed is not just in software or systems,but in who drives those systems. Information scientists often have both thescientific training and the necessary computational skills to assist projectteams with complex analysis and interpretation, yet even they often overlookhow critical their function is in the excitement of using a new technology ordeveloping a better tool. So, culture and convention work against them, andthey are rarely considered for this more involved and expert user role.
Translational research, by its very nature, incorporatesdiverse data from multiple areas: disease information, target sequence, leadcompounds, animal models, drug toxicity, medical records and drug metabolism, amongmany others. To further complicate the integration and standardization of dataare the various types of data, parameters, formats, sources and standards—orlack of standards. The more sources there are to consider, the harder it is tomake comparisons and evaluate content across all dimensions.
The tools of informatics have continued to improve, enablingbetter access, integration and mining of data. However, even the best toolsfall short if drug project teams lack the involvement of those who know besthow to use them, know how to interpret their content and know enough tounderstand the questions that need to be asked and answered.
This speaks to the cultural and skill-based challengesinvolved in getting more value from data. The nature of drug discovery anddevelopment remains an information-rich endeavor, and in order to increasesuccess rates, we must make better use of our data—and our information scienceexperts.
Consider the composition of drug development project teams.They likely include biologists who understand the disease area and work withassays; medicinal chemists who design and synthesize compounds;pharmacokineticists to study the bioavailability of a compound and its effecton physiology; toxicologists to study the safety or any toxicity issues; andphysicians to design and lead clinical trials. What about informationscientists? They're the experts in the tools, systems and the content (the dataand information in those systems) to turn data and information into knowledge.They're the ones who can tap specific information and technical expertise asneeded. And they're the ones who are often left off teams, except as ad hoc members, brought in to answerspecific predefined questions instead of being problem-solvers, expert users ofdata and information and enablers of best practice.
A cultural shift needs to occur: one where informationscientists are brought on board as full members of project teams, where theycan be the expert users of data and help get the most out of availableinformation, trying new tools and approaches as appropriate. They can be aunifying influence in the fragmented environment of multiple scientificdisciplines, as they work across silos and clear bottlenecks in informationflow.
The role of the information scientist can be likened to anadvisor from a data perspective, much like the role of a financial advisor at abrokerage firm. Financial advisors listen to your goals, find out what you'retrying to achieve with your investments, pull together a number of options foryou to consider and then execute your agreed-upon plan. While it's possible toplan your financial future without the help of a financial advisor, having oneadds the expert knowledge of someone who knows how to find and assessappropriate alternatives. Similarly, the information scientist can be a "one-stopshop" for the project team, looking at the totality of information, helpingwith interpretation and suggesting different research questions to pursue oreven identifying questions or sub-questions that existing data can answer.
Information scientists play an evolving role in projectteams. Once purely a data resource, with a narrow focus on technology, now theyadd more value by tapping their broad understanding of the science and of businessneeds. Instead of asking, "what question do you want me to answer?" theinformation scientists needs a different approach: "Tell me the problem you'relooking to solve, so together we can formulate the most meaningful question andI can help you use the information available to answer it."
Two real-life examples come to mind that demonstrate thevalue of having this kind of information expert on project teams. In one case,the expert information scientist was able to circumvent the running of assaysby recognizing that the answer to a question already existed. The team membershad been looking at slices of the picture and didn't see the totality of inputsor that the answer to one of their questions was already available in existingdata if analyzed differently, across studies. Having the answer to one questionmeant that a six-month study did not need to be run or repeated; instead, adifferent one could be run to test the hypothesis and glean new knowledge.
A second example illustrates the value of being connectedwithin an information network and contributing to project memory. Byunderstanding the competitive landscape and the value of learning from others'experience, the information scientist discovered a conference that would beaddressing research relevant to a specific project. The conference would beheld in another part of the world, without any attendance by project teammembers, so the information scientist worked to gain access to the proceedingsand important new knowledge.
For information scientists to be fully accepted into projectteams, they need to sharpen their quantitative and analytical skills to handlethe complex analysis and interpretation of data—and to trust, or refresh, theirscientific training. They need to be more actively engaged as core members,using their scientific and technical knowledge to determine what informationcan best answer a particular therapeutic or business question. Theirinvolvement should lead the team to a greater level of confidence in makingdecisions, and the ability to make them sooner, in crucial aspects of drugprojects.
The value of expert information skills is having both thescientific understanding to help define the questions and the technicalknow-how to bring all the relevant data together and use the most appropriatetools and analysis methods. Just as important as finding the sought-afterinformation is playing a key role in interpreting the data and findingrelevancy and meaning. Information scientists should not just be shepherds ofdata, but skilled users of data and technology systems that can help transformdata into information and information into knowledge.
It takes more than expert systems to get better answers; ittakes systems experts and data experts to extract meaning and generate morevalue from studies and assays.
Dr. AnastasiaChristianson is senior director of R&D Information at AstraZeneca inWilmington, Del. She is responsible for delivering the information needs of theCentral Nervous System & Pain Innovative Medicines unit and Global Productteams, Personalized Healthcare and Biomarkers and R&D Strategy, Portfolioand Performance. Anastasia obtained herPh.D. in Biological Chemistry from the University of Pennsylvania in 1989,followed by postdoctoral training at Harvard University in Cellular andDevelopmental Biology.