The term is “Big Data” is everywhere and being talked by everyone. But what is big data?
Big data can be defined as the usage of huge amounts of data for getting insights and understanding patterns, to take informed decisions. But do you know the term is not all novel but has been existing for a long time period of time.
It is used by banks for detecting fraud on credit cards and whereas marketing departments connect demographic and socio-economic data to help customers. And now it has even entered the Pharma industry as well.
It’s no hidden fact that the pharmaceutical R&D many tools are required which can reduce the discovery and development cost. According to studies, introducing a drug within the market costs more than an application of a mobile.
So how Pharma companies can cut-down the costs? All the companies need to do is identify the area where is the majority of revenue being spent. It is always better to invest in the initial stages of R&D rather than in the final stage.
Secondly, the companies can predict the future of the New Molecular Entities (NCE’S or NBE’s) through a method called pharmokinetic modeling. In this, scientists can how the compound is going to act in the body by using advanced mathematical modeling and simulations. Or they can go the previous data and then use it for foreseeing the result.
Instead of rigid data silos which are difficult to use, data is captured electronically & can flow easily between functions. The easy flow is necessary for powering the immediate & predictive analytics that generate business value.
As pharmaceutical data are silo-ed, is a so valuable, pharmaceutical companies are recognizing the significance of acquiring analytic services from outside resources to drive their drug discovery processes. For example, in the year 2014, a company called two XAR started offering analytic answers to pharmaceutical companies, intrinsically recognizing possible drug candidates through the analysis of large biomedical datasets. (Two XAR is a Silicon Valley-based company.)
Additionally, a software company named Schrodinger presents predictive insights on compounds by utilizing the ability of computational models. Even companies such as Cyclica supply predictive analytics software. The software uses large datasets of proteomic target ligand information to foretell how compounds will take action in the body.
So, in this the Big Data is utilized in the pharmaceutical industry.
