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Big Medicine – Big Data at Work in Pharma & Life Sciences Space

Big Data has successfully established its importance within business organizations as a successful contributor to organizational success. Almost all the industries have realized the value of big data insights in making informed business decisions and having a positive impact on the bottom line. Pharma and Life Sciences too have recognized the importance of big data and are increasingly depending on it to improve research outcomes. These sectors generate large and complex data sets as a result of various scientific experiments, network logs, manufacturing processes etc. However, in order to derive value from it, along with generating and hoarding petabytes and zettabytes of data, pharma and life sciences companies have to comb through these large volumes of data and identify patterns that are relevant for them to make progress in their line of business.

McKinsey Global Institute estimates that big data application in the pharma and life sciences sector can optimize innovation and improve research and clinical trial efficiencies and generate approximately USD$100 billion in value across the healthcare sector in U.S alone.

In this blog, we take a look at how the use of Big Data is ushering in the age of Big Medicine.

Big Data and Drug Discovery

Big Data is being used increasingly in research to aid drug discovery. Data analysts are processing large volumes of structured and unstructured data collected from numerous surveys and experiments from laboratories, hospitals, pharma companies, and even social media for the purpose of drug discovery. Considering the sheer volume of the data mined from these sources, it becomes essential to use algorithms to summarize this data and correlate it with disease information and find the patterns that will make drug discovery easier. As drug discovery is an iterative process, big data algorithms help in identifying activities and issues that arise during molecular interactions and map them to predict the impacts of the structural changes. With the use of advanced algorithms and statistical models using specific data points, bio-researchers are able to identify the target molecules required for drug discovery in less time and early detection of disorder and disease.

Big Data and Clinical Trials

For pharma and life science companies, clinical trials eat into a sizable chunk of the R&D budget. Considering that the large clinical trials are focused on data mining, research organizations need to ensure that they get the right data sets for trials. Big data can help in this by helping researchers by identify right demographic profiles through analysis of demographic information and historical data and, thus, completely doing away with randomization of the trial. It also helps review of results of the previous clinical trials, evaluate drug readiness, and enable remote patient monitoring. With the help of big data, researchers can also detect any undesirable effects of a drug during clinical trials even before it is reported.

Big Data and Large-Scale Genome Sequencing

Pharma and life sciences companies see a lot of value in utilizing big data for large-scale genome sequencing. Considering that the cost of generating raw data has reduced considerably, bio researchers are looking towards big data to successfully sequence an individual’s healthy and diseased genomes and be able to quantify differently expressed genes. This can help considerably in cancer research and has the capability to transform oncology. While researchers are presently battling the problem of processing sequence data as this needs a large number of computations using research-intensive algorithms, they are also aware that the need of such samples will only increase over the next couple of years. However, by using big data the processing time for genome sequencing can be reduced considerably.

Big Data and Patient Adherence

With the help of big data, pharma companies can track their own patient programs to improve adherence rates and consequently improve health outcomes. Researchers can evaluate the quantity of drugs manufactured in the quantity prescribed to a patient and the amount that was consumed to assess the efficacy of a drug. Pharma companies can leverage data generated from wearables and sensors to not just overcome communication barriers but also ensure better patient adherence. With the help of big data, pharma companies can efficiently determine the cause behind treatment ineffectiveness and identify other factors that are contributing to this outcome.

Big Data and Predictive Medicine

Big Data is also the enabler of predictive medicine. With the advances in information technology today, predictive tools can utilize big data and, as a consequence, anticipate medical issues and problems with greater clarity. With the help of predictive analytics, hospitals and researchers are able to identify at-risk patients and create individualized treatment regimens by analyzing the disease history against patient history to gain new insights.

Along with all this, pharma companies are also looking towards big data to understand market conditions and determine the life span of a particular drug. Clearly, big data is giving pharma and life science companies insights that can help them develop better medicines and also identify who will benefit the most from those medicines by combining genomic, clinical and real-world data – the final aim being to improve the quality of care and better costs.

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