Big Data Analytics – A Revolution in Drug Discovery and Pharma R&D

This column is authored by Savaram Ravindra, Content Writer at

big data drug research pharmaBig data means, the sets of data that are so complex and large that the data processing tools and technologies cannot cope with those datasets. The process of inspection of such data and uncovering the patterns which are hidden in it is termed as big data analytics. The basic which arises in our mind is, In what way is the drug discovery related to big data analytics? or how is data analytics used in the process of drug discovery?

The process of drug discovery requires the analysis, collection, and processing of unstructured and structured biomedical data which is of large volume from surveys and experiments gathered by pharmaceutical companies, laboratories, hospitals or social media. This huge amounts of data may also include data regarding sequencing and gene expression, molecular data which is included in drug data, data consisting of drug and protein interaction, data of electronic patient record and clinical trial, self-reporting and patient behaviour data in social media, data of regulatory monitoring, and literatures where protein-protein interaction and drug repurposing and trends may be found.

To examine in detail, such diversified types of data in huge volumes for purpose of discovery of the drug, we need to have algorithms that are scalable, efficient, effective and simple. We now discuss how the recent innovations in big data analytics improves the process of drug discovery. Algorithms are developed to uncover the patterns which are hidden in such data as unreported, discussions on drug side-effect in social media communications, sequencing and patient record data, drug-protein interaction and regulatory monitoring data, data regarding chemical-protein interactions etc., for the prediction of drug side-effects and how these types of predictions can be used to identify the possible drug structures with different necessary features. Big data analytics also contributes to much better drug efficiency and safety for regulators and pharmaceutical companies.

Upon implementing several measures of big data which are technology-enabled, pharmaceutical companies can enlarge the data they gather and enhance their approach to analyzing and managing this data.

1. Integration of all the data

One of the biggest challenges facing R&D organizations of the pharmaceutical companies is having data that is well-linked, consistent and reliable. Data is the foundation upon which the analytics which are value-adding is built. Integration of efficient end-to-end data establishes an authoritative source for all the bits and pieces of information and correctly links different data which cannot be compared regardless of the source. Smart algorithms which link clinical and laboratory data, for example, could create the reports that are automatic that identify the applications or compounds that are related and raise the red flags related to efficacy or safety.

2. Internal and External collaboration

R&D of pharmaceutical organizations is a secretive activity which is conducted within the R&D department with little external and internal collaboration. Pharmaceutical companies can extend their data networks and knowledge by enhancing their collaboration with external partners. Whereas end-to-end integration improves connecting the elements of data, the main aim of this collaboration is to improve the connections among all the stakeholders in delivery, commercialization, drug research, and development.

3. Make use of IT-enabled portfolio for data-driven decision making

To make sure the allocation of scarce R&D funds is appropriate, it is critical to quickly accomplish the decision making for pipeline and portfolio progression. Pharmaceutical organizations find it really challenging to make accurate decisions to about which assets to retain and which ones to kill. The financial or personal investments they have made already may affect the decisions at the expense of merit and they lack decision-support tools which are appropriate to facilitate making calls which are tough. IT-enabled portfolio management enables the decisions which are data-driven to be made seamlessly and quickly. Smart visual dashboards must be used whenever there is a possibility to facilitate effective and rapid decision making.

4. Influence the new discovery technologies

Pharmaceutical R&D must continue the use of cutting-edge tools. These include systems biology and technologies that produce huge data very quickly. One of the examples of the technologies that produce huge data quickly is next-generation sequencing. This technology will make it possible, the sequencing of an entire human genome within 18 to 24 months and at a cost of $100. The improved analytical techniques and wealth of new data will intensify the innovations of the future and feed the pipeline of drug development.

5. Deployment of devices and sensors

Advancement of instrumentation using bio-sensors which are miniaturized and evolution of latest smartphones and their applications are resulting in health-measurement devices which are increasingly sophisticated. Pharmaceutical companies are using smart devices to gather huge real-world data which was not available previously to scientists. Monitoring of patients remotely through devices and sensors constitutes an immense opportunity. This type of data can be used to analyze drug efficiency, facilitate R&D, create economic models which are new combining the provision of drugs and services and enhance the future drug sales.

6. Raise the efficiency of clinical-trial

A combination of smarter, new devices and exchange of fluid-data will enable improvements in the design of clinical-trial and outcomes as well as higher efficiency. Clinical trials will become much highly adaptable to respond to drug-safety signals which are seen only in small but subpopulations of patients which are identifiable.

The following are the challenges facing transformation of big data in pharmaceutical R&D

1. Organization

The silos in an organization result in data silos. Functions usually have responsibility for their data and systems they contain. Adopting a view which is data-centric, with a clear owner for each type of data through the data life cycle and across the functional silos, will greatly enhance the ability to share and use data.

2. Analytics and Technology

Pharmaceutical companies are following the legacy systems containing disparate and heterogeneous data. These legacy systems have become a burden for these companies. Enhancing the efficiency to share data needs connecting and rationalizing these systems. There is also a scarcity of human resources supplied with a specific task of improving the analytics and technology needed to extract maximum value from the data which is existing.

3. Mindsets

Many pharmaceutical organizations believe that unless they find a future state which is ideal, there is very less value to investing in enhancing the analytical capabilities of big data. Pharmaceutical organizations should gain knowledge from smaller, more enterprises which are entrepreneurial that see a lot of worth in the incremental improvements that get emerged from small-scale pilots.

Using Big Data in pharmaceutical companies through the implementation of these ways which are technology-enabled, could slowly turn the tide of diminishing success rates and sluggish pipelines.


Effective utilization of the bigdata opportunities can help pharmaceutical organizations better determine new candidates which have the capacity to develop into drugs and develop them into reimbursed, approved and effective medicines more quickly. Learning bigdata is of so much use today because of its wide range of applications. Many multinational organizations like Google, Microsoft, Deloitte, ZS Associates and so on are preferring the candidates with training in big data. A fresher with training in bigdata is getting almost double the salary of a normal fresher. Candidates are getting 30%-300% hikes on their usual salaries post completion of the certificate program in bigdata. So, choosing bigdata as a career option and gaining expertise in it can be an excellent career option.

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