How To Use Big Data In The Drug Development Process

Transcription

1 In Focus: The role of big data in the drug development process As big data plays an increasing role in drug development and clinical trials, The Pharma Letter s Bethany Rutter spoke to Richard Young, vice president, mid-market EMEA at Medidata, which provides clinical research organizations and companies with highly advanced technological tools for planning and managing clinical trials. Clinical trials are becoming more and more complex, with ever-greater political, ethical and economic responsibilities. Time is money: drug developers must maximize resources and time by ensuring they get the right patients on the right trials, and carrying out the right assessments, in order to give the best chance of providing a successful outcome at the end of the study. The financial pressure of doing more with less means that companies are continually reviewing their processes and practices. It is no longer good enough that a medicine works in 95% of the population; developers need to be able to give 100% certainty that their product is up to scratch. Data capture and tracking in the clinical trial process has historically been a challenge, as it was generally only readable by humans and carried out on paper. Now, in a digitally-focused industry, everything is electronic, which means it is accessible and available for reporting much earlier in the process. From the days when it would take 10 weeks between a patient seeing the investigator and the data becoming available, that has now been shortened to a matter of hours and minutes. This disparate and paperbased way that reporting was done in the past meant it was often difficult to 1

2 gain a comprehensive view of how the trial is going at any one time. An easily accessible overview that allows data to flow freely between all platforms ensures that any decision-making is based on the most up-todate data. HOW BIG DATA CAN HELP Richard Young, vice president, mid-market EMEA at Medidata, said: The global spend on clinical trials, including personnel, technology, all of the parts of the project, is in the region of $100 billion. To bring a new compound to market is around $4 billion from start to finish. One of the big drivers in clinical trials is the failure rate. As an industry we see a lot of compounds that look very positive in Phase I, but then fail in Phase II or III, due to competition posed by another company getting there faster, or an issue with its efficacy or safety profile. Electronic data capture is one way that this expensive, extensive process has evolved for the better over the years. Medidata s research has shown that it was used in only 24% of trials in 2005, but by 2012, 75% of trials were likely to use electronic data capture. This not only improves accuracy in legibility but also increases the amount of oversight that companies have over the data they collect, which in turn makes it a safer, cleaner process for everyone. What happens is that very quickly, people become number-blind and that makes it easy for the best-trained eyes to miss important events or trends. Analysis of historical studies also helps us to understand that not all data is born equal, and as such we can prioritize our efforts, he added. RISK AND REWARD Safety is always a key concern when considering how information is collected in clinical trials. In a paper-driven system, or one which uses 2

3 different databases to facilitate reporting and management of serious adverse events, it is harder to gain a comprehensive view of the dataset and deal with potential problems as they arise. When trial investigators are in the field, visiting the sites of clinical studies, being able to access a complete and concurrent view of all data taken for a particular trial means they are more able to focus on patient- and safetyorientated issues onsite. Medidata proposes using the data and metadata itself to establish patterns in how data is being entered; who entered it, whether it is accurate, when and where it was entered can often tell companies running a trial which data is essentially looking after itself and which needs to be more closely monitored. "There are tremendous benefits to taking a risk-based approach to data monitoring. This doesn't mean things don't get checked, but we can allow technology to look at the data, to see if there are data points where mistakes are being made often, or where the data is most critical, or where the entries are very accurate; and adjust our efforts accordingly," said Mr Young. TOWARDS A COLLABORATIVE APPROACH The industry as a whole, while still thoroughly business-driven, is in many ways moving towards a collaborative approach in drug development, not least large-scale initiatives such as Transcelerate, established to fast-track developments in biopharma through collaboration, with founder members including UK pharma giants AstraZeneca and GlaxoSmithKline. One particularly useful function of electronic data capture, especially in collaborative projects, is to be in a position to compare the ways in which clinical trials are designed. On projects where numerous companies are 3

4 working together, it can be useful to look at historic data from both developers to establish the most appropriate way to carry out studies, learning from past successes and errors. IMPROVING TRIAL DESIGN Trials can often be an added burden on the patient, with added scans and blood tests beyond what they would usually carry out, so if companies can pool their data and compare practices, then trials can be designed in such a way that does not leave the patient undergoing unnecessary steps. One example cited by Medidata is a breast cancer therapy trial in which the planned number of mammograms notably exceeded the industry average an additional burden on the patient s time, and on the trial s resources, which can be re-evaluated before the trial begins. Having designed clinical trials myself, it s easy to blur the edge between what s necessary and what isn t. It s useful if you have a system that challenges you on what decisions you re making; it isn t designed to tell you if you re right or wrong, but it prompts you to think about the decisions you're making, and enables you to design studies that are above and beyond clinical trials in the past, and therefore hopefully faster, safer and more effective, said Mr Young. Analyzing historic data can add clarity to the steps involved in reaching a trial s endpoints, and why those endpoints were chosen in the first place. Trials always have a primary endpoint, and often have a secondary and tertiary objective, but when the data is always linked up to those objectives, it becomes easier to collect only the right data to prove those goals have been reached and no more data is being appropriately linked back to the objectives at all times. 4

5 It is particularly important in trials for therapies that manage chronic conditions that patients do not become disheartened if they do not achieve the results they would like. Developers should be able to justify every step patients are asked to undergo, and end treatment if it is not adding value to the patient. The great advances in the availability and use of electronic data capture, especially of vast quantities of data, are having an ever-increasing impact on how trials are carried out, not least in collaborative spaces. It is up to companies, then, to use such technology to their advantage, using both data and metadata to improve accuracy and give a comprehensive view of large datasets to allow the streamlining and evolution of trial design and drug development. 5