I Mak

Pharma’s R&D Productivity Engine

By Mak Jawadekar, Contributing Editor | May 6, 2013

Enabling technologies for leveraging Big Data

I recently attended Bio-IT World Conference & Expo (April 9-11, 2013) organized by Cambridge Health Tech Institute in Boston. The 3,000 delegates consisted of biotech/pharma representatives in R&D, academic researchers, many CIOs from various bioinformatics groups, and computing contract firms that serve global pharma R&D industry in drug discovery, e-clinical trial solutions, cloud computing, and cancer informatics, to name a few.

The keynote addresses were delivered by Andrew Hopkins, a former Pfizer researcher, and now a professor at University of Dundee. He gave a commanding presentation on Do Network Pharmacologists need Robot Chemists? He spoke about the genome mapping and its derived value, as well as the slight rise in NMEs approved by the FDA in 2012 over 2011 (39 to 35).
Another keynote address was delivered by Stanford bioinformatics professor Atul Butte, who gave an outstanding talk on the current needs of R&D and how one can work through contract labs to get access to existing data and leverage it to assist pharma’s R&D productivity engine. He also spoke about the value of existing data that one can access virtually to build hypotheses, and contracting out research work through use of online R&D labs, which would conduct research work at fraction of costs.

At the Bio-IT Expo, I got to speak to many of the contract vendors in this space. Some of the companies took the opportunity to publish whitepapers on data and how it can help Pharma R&D. There were three that captured my attention and I urge you to read these to derive further value.

The first is McKinsey & Co.’s April 2013 whitepaper by J. Cattell, S. Chilukuri & M. Levy, How big data can revolutionize Pharma R&D. According to the paper, one of the cures for declining R & D Productivity is use of the “Big Data,” and the analytics that go with it could be a key element to the cure. They discuss how predictive modeling of biological processes and drugs has become significantly more sophisticated and widespread. Using available molecular and clinical data, predictive modeling could help identify new potential-candidate molecules with higher probability of successful drug candidates with action on biological targets safely and effectively. Pharma companies can expand the data they collect and improve their approach to managing and analyzing these data by implementing eight technology-enabled measures. These measures are: 1) Integrate all data;  2) Collaborate internally and externally; 3) Employ IT-enabled portfolio-decision support; 4) Leverage new discovery technologies; 5) Deploy sensors and devices; 6) Raise clinical trial efficiency; 7) Improve safety and risk management; and 8) Sharpen focus on real-world evidence.

According to the authors, in order for pharma R&D to succeed, executives must overcome  challenges that include, organizational silos, integrating disparate data and rationalizing and connecting different systems, and improving the existing mindsets. They conclude by stating, “Pharmaceutical companies desperately need to bolster R&D innovation and efficiency. By implementing the above eight technology-enabled ways to benefit from big data, they could gradually turn the tide of declining success rates and stagnant pipelines.”

Another whitepaper that caught my attention was by Nick Clarke of Tessella Technology & Consulting, titled, Can you win the Big Data arms race?. The takeaway messages from this paper include: 1) Do smarter analytics and not bigger data; 2) Decisions first, data last; 3) Analytics virtual laboratory; 4) Use scientific approach to commercial data; and 5) Create an intelligence-led organization. According to the author, in analytics labs, optimization is the real key target, followed by predictive modeling, forecasting, and statistical analyses followed by scheduled and ad-hoc reporting. The data does not have to be big to be better. Finally, the analytics skills you need are out there: go ahead and apply those skill-sets!

The third White Paper that I enjoyed reviewing was published from Eric Newmark of IDC Health Insights, The next Phase of Industry evolution: Deriving value from Big Data in Life Sciences. The paper describes patent cliff, what has happened to R&D in pharma industry in the last five years, and new legislation on drug pedigrees and serialization adoption by pharma in commercial supply chain and in manufacturing. The paper also describes present changes in sales and marketing engines. The beneficial suggestions include the following points: 1) Create consensus on handling and data ownership to remove data access hurdles; 2) Begin consolidating silos into dynamic data storage infrastructure; 3) Seek out technologies that can search, tier and manage the data lifecycle; 4) Focus on standardizing and automating data capture, storage tiering and analysis; 5) Proactively build effective data sharing with customers, vendors and partners; and 6) Ensure that appropriate data security exists from both organizational and IT standpoint!

There was plenty to learn from these most recent whitepapers! We have a way to go on ‘Big Data Management’ and timely application of clever techniques to derive huge value to enhance efficiencies in our pharma R&D engine.

Makarand (Mak) Jawadekar most recently served as Director, Portfolio Management and Performance at Pfizer Global R&D,
until February 2010, when he opted for an early retirement after 28 years at Pfizer Inc. He currently serves on several companies’ advisory boards and also consults with bio/pharmaceutical companies for global outreach in emerging market regions. He can be reached at mjawadekar@yahoo.com.

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