Big Data Analytics: Finding & Developing New Drugs Faster

               

Big Data’s reach is stretching farther and farther. Finance, marketing, healthcare, life sciences – virtually every industry is looking to gain a competitive advantage from using data analytics and business intelligence platforms to uncover more insights from data - faster. According to advisory firm EY, research by the Economist Intelligence Unit indicates that 77 percent of companies that maximize how they use data are also ahead in financial performance.

The EY’s “Order from Chaos” report looks at Big Data from a good news/bad news perspective: Powerful data and visual analytics and the maturing of open architecture, cloud computing and predictive analytics are helping organizations get better with data, but many organizations aren’t moving fast enough to keep up. One reason: the complexity of data and its myriad sources. PerkinElmer’s partner, TIBCO, recently published a post on the promises and pitfalls of Big Data. In it, they also noted that the sheer volume of data, the speed with which it is generated, and the ‘siloing’ of multiple data sources were overwhelming companies.

Data Analytics Driving Research & Development

According to McKinsey, the pharmaceutical industry is seeing a growth in data from multiple sources, including the R&D process, retailers, patients and caregivers. Breaking it out of data silos and putting it to immediate good use is the trick. “Effectively utilizing these data will help pharmaceutical companies better identify new potential drug candidates and develop them into effective, approved and reimbursed medicines more quickly,” state the McKinsey analysts.

When it comes to effective use of data, however, the benefits of a business intelligence platform are enhanced by domain expertise. Life science and pharmaceutical R&D data generated from multiple, different, disparate sources requires effective integration as well as powerful analytical and visualization technologies to draw conclusions that give businesses leverage. Purpose-built data analytics solutions - built from the ground up by people who understand the workflows, the studies & experiments, as well as the data needs of the researchers and scientists - provide a more robust, relevant experience than one-size-fits-all solutions.

The Future of Life Science Data Analytics

In its report on Big Data in pharmaceutical R&D, McKinsey invites us to “imagine a future” where these things are possible:

• Predictive modeling - leveraging available molecular and clinical data - helping to identify new potential candidate molecules with a high probability of success as drugs that safely and effectively act on biological targets


• Clinical trial patient profiles improving with significantly more information, such as genetic factors, to enable trials that are smaller, shorter, less expensive, and more powerful. 


• Real-time monitoring of clinical trials rapidly identifying safety or operational signals, leading to action that avoids delays or potentially costly issues.


• Electronically-captured data flowing easily between functions, instead of being trapped in silos -- powering real-time and predictive analytics to generate business value.


The solutions to these challenges – or opportunities – can be addressed by using advanced analytics and applying appropriate visualizations.

EY advises that in order to harness the power of big data and advanced analytics, companies should manage data and analytics projects as a portfolio of assets – similar to a financial investment portfolio. It’s important to use an agile analytics approach to balance value. Here are some life sciences analytics from an “ideal portfolio.”

Functional AreaAnalytics
Research & DevelopmentChemistry and Lead Discovery
Genomics Data Analytics
High Content Screening
High Throughput Screening
Quantitative Pathology
Flow Cytometry
Translational Research
Clinical DevelopmentTranslational Medicine
Project and portfolio management
Clinical Trial Operations
Clinical Trial Data Management
Risk-Based Monitoring
Clinical Trial Data Review
Phamacovigilance
Health Outcomes
Real-Time Data Analytics for Biopharmaceuticals

There is evidence that pharmaceutical companies are incorporating real-time analytic solutions to effectively analyze key data without the time lags associated with previous analysis methods.

• A top 10 global pharmaceutical company sought to reduce the time, cost, and risk of running its clinical trials, while accelerating time-to- market. Deploying a data visualization and analysis platform contributed a 20-40 percent productivity improvement in its clinical data review, saving three to four days per month in a 10-week study. 

• Typical challenges in preclinical and clinical safety assessment are significantly minimized by interactive graphical data analysis. At Novartis, this approach has proved to be “efficient, powerful, and flexible” in improving both detection and systematic assessment of safety signals.


• As part of Roche’s “Fail Fast” strategy, the right analytics platform helps safety scientists and data scientists work collaboratively to solve queries from the safety science group. Data Provision Specialist Joel Allen says the ability to analyze and visualize data - to correctly answer queries in the most expeditious way - leads to better data-driven decisions. 


EY’s report predicted great benefit “if life sciences organizations are able to apply their acumen with big data and analytics to drive decisions and engage smart collaboration.” Yet Gartner cautioned that by this year, likely 85 percent of the Fortune 500 organizations would fail to effectively exploit big data for competitive advantage. 

At PerkinElmer, we’re applying our expertise in the life sciences, pharmaceutical R&D and clinical development to provide informatics solutions and services that help you take advantage of everything Big Data has to offer. You can learn more about PerkinElmer's informatics solutions powered by TIBCO Spotfire®, the leading data analytics and visualization platform at the heart of these three customer success stories above. 

How is your company using all of its data to drive its competitive advantage?

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