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Information Technology Digging For Big Data Gold: Data Mining As A Route To Drug Development Success By Suzanne Elvidge, contributing editor simplest is a collection of data that's approximately bigger than one terabyte and/or is too big to handle using standard software and analytical processes. Big Data is becoming a major part of all facets of healthcare as physicians' notes on patients, test results, prescription records, and even imaging results (e.g. X-rays, MRIs) are being included in electronic medical records (EMRs). There are many electronic public databases that are part of biobanks and national healthcare studies. In addition, more and more clinical trial results and other drug development and approval documents are being stored electronically. PICKING OUT THE NUGGETS OF DATA Drug development costs are skyrocketing, and despite this, attrition rates continue to climb with drugs still failing in latestage clinical trials. Accessing this treasure trove of Big Data could help by improving compound selection and refining clinical trials. But how to find the gold among the dross? "The challenge of Big Data is the number of combinations of factors involved. For example, if you have 1,000 patients, you could have 1,000 genomes, 1,000 sets of comorbidities, 1,000 phenotypes — the list could go on," says Steve Gardner, partner at Biolauncher, a United Kingdom-based biopharma consultancy. It's situations like 54 LifeScienceLeader.com T he term Big Data is on everyone's lips, from retailers to healthcare providers. But what actually is it, and how can it help biopharma R&D;? There are many definitions of Big Data, but perhaps the this where data mining, which uses software algorithms to analyze and summarize the data, comes to the rescue. GenoKey, which provides analytics solutions for healthcare Big Data, has developed an array-based technology to solve very large combinatorial problems in case-control data, finding patterns in the data using massively parallel GPU processing. NuMedii, a start-up based in Menlo Park, CA, is using data mining to correlate disease information and drug data to predict drug efficacy. The company's database includes billions of points of disease, pharmacological, and clinical data, and it mines this using network-based algorithms. This should de-risk the development process, increasing the chance of drugs making it through to the market. The U.K. start-up MedChemica is at the core of a collaboration designed to speed drug development using data mining of precompetitive-shared data while maintaining the security of each individual partner's intellectual property. As Hans-Joachim Boehm, head of small molecule research at Roche (one of MedChemica's collaborators), explains, the driver behind the collaboration was that many companies have a lot of preclinical data, but the challenge is how to analyze it and make practical use of it. "Drug development is an iterative process, and you learn at each stage. You start with a target and a molecule that November 2013 hits the target. You then characterize the interaction and the molecule, find out what the activity and the issues are, and then make modifications, creating a new molecule. Then you start the process again," Boehm says. This is a time-consuming process and generates a lot of data. The collaboration, based on MedChemica's matched molecular pair analysis technology, aims to make it more efficient, using existing information to reduce the number of steps between hit and candidate. MedChemica's algorithms mine the partners' databases of molecules generated during the iterative process to find pairs that are very closely matched. The software then analyzes the differences between the in vitro data from the pairs of molecules and maps this to the structural changes in the molecules. The output from the analysis is then used to create rules that can be applied to virtual molecules to predict the impacts of similar structural changes. When drugs fail at a late stage of development, it's generally because of safety issues, and so toxicity data is particularly valuable to be able to "design out" issues at a much earlier stage. "We originally created the matched molecular pairs technology at AstraZeneca. However, this is a very data-hungry process, and we realized that there just wouldn't be enough data in any one individual company. MedChemica was formed as a neutral intermediary with the idea of bringing multiple companies together