“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”
– Geoffrey Moore, American Management Consultant, and Author
The first arenas to feel a transforming impact from the world of big data were marketing, sales, and service. The reach of data analytics then extended to industries such as automation, oil and gas, metallurgy, education, hospitality, and the military. The world of pharmaceuticals, though, lagged behind other sectors in adopting big data applications. Not until 2017 did pharma begin reaping from the innovation wrought by big data-related applications.
“However,” notes tech buff Sudipto Ghosh, “things are changing for the industry as analysts and leading research institutes see a compelling opportunity in deploying Big Data to arrest the declining success rate of R&D in pharma. In 2019, we find Big Data and Data Analytics becoming the core of pharma and driving global innovations in healthcare, pharma, and radio-medicine.”
What role can big data play in drug discovery?
Despite the tremendous advances in areas such as medical imaging and molecular sequencing that have accelerated the drug development process, the costs associated with research and development for new drugs have not decreased. Researchers Jack Scannell and Jim Bosley note, “the inflation-adjusted research and development costs for a new drug have increased 100-fold between 1950 and 2010—roughly doubling every nine years. During the same time, the likelihood of failure during development increased significantly.”
By enhancing the process of choosing the drug candidate, utilizing the vast tranches of available data to provide actionable insights, and assisting in the aggregation of information from diverse sources, big data can impact this troubling trend. Frost & Sullivan have identified four key areas where big data can play a crucial role in drug discovery by addressing issues in the planning and “choosing” phases.
- Clinical trial management and data analysis
Where conventional data management and analytical tools may prove insufficient, big data can assist, first, by ensuring sufficient population diversity. Then, to tackle the arduous task of analysis and interpretation of large, not-always-standardized amounts of data across multiple study sites.
- Omics data management
With the success and affordability of genomic sequencing techniques comes a distinct advantage for clinical diagnosis and research. Add to the available genomic information molecular data such as proteomes, metabolomes, lipidomes, glycomes, and transcriptomes to create a vast ocean of critically valuable information. Analyzing and extracting the prominent features from this mountain of data is most definitely a job for big data analytics.
- Drug candidate selection and pipeline development
A proverbial “needle in a haystack” scenario exists when attempting to identify, for testing against a specific disease, a handful of drug candidates from a library of thousands or even millions. Sophisticated algorithms can screen large databases of biological, chemical, and clinical information, to narrow down the most probable drugs for testing. Advanced analytics can also assist in determining candidate profiles to create a product development pipeline.
- Orphan drugs, rare diseases, and drug repurposing
Even though a clinical trial drug candidate does not display the statistical significance to be effective, it may still prove to be beneficial in the case of a rare or genetic disease among a specific demographic. Big data analytics can help identify such drugs and aid in the repurposing of needed medications to subpopulations, making a “failed” drug a success.
Where is big data making an impact?
One area experiencing the advantageous impact of big data is the world of neonatology where numerous novel projects are utilizing and promoting big data applications to make NICUs more reliable and friendlier.
“Even before the baby is born, Big Data would have made its impact on the new life. That’s the kind of technology we are talking about in 2019 and beyond,” states Ghosh. “Patients and doctors could both know the various NICU complications and NICU medicines available in the inventory, in advance.”
Knowledge of the patient’s history and genetics would allow pharma companies to personalize medicines, thus dealing with potential future outcomes, utilizing recommendation analytics and predictive modeling. It would afford both parents and newborn babies a better chance to deal with troublesome incidents in the future.
“Neonatal pharma procedures would scale a new height with further adoption of Big Data and analytics for early detection of clinical deterioration and congenital disorders based on historical and real-time data gathered during the gestation phase,” concludes Ghosh.
“Despite the challenges, Big Data approaches have already started to transform drug discovery,” notes Paul Workman, Albert A. Antolin, and Bissan Al-Lazikani in their article “Transforming cancer drug discovery with Big Data and AI.”
They cite how big data concerning the mechanisms of recurrence and resistance to therapy is already informing the identification of new targets and driving new drug discovery campaigns aimed to address unmet clinical needs. “Drug discovery is increasingly becoming even more rapidly and iteratively evolving, with novel discoveries continuously building on knowledge from previous successes and failures, supported by Big Data.”
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