Big Data Is Revolutionizing The Way We Develop Life-Saving Medicine
Big data sets are so complex and large that common data processing tools and technologies cannot cope with them. The process of inspection of such data and uncovering patterns is called big data analytics. The basic question which arises in our mind is, “In what way is the drug discovery related to big data analytics?” or “how is data analytics useful 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 even social media. These 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 to be able to discover new drugs, we need to have algorithms that are scalable, efficient, effective and simple. We now discuss how recent innovations in big data analytics improve the process of drug discovery. Algorithms are developed to uncover the patterns which are hidden in such data as unreported, discussions on drug side-effects 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 analysing and managing this data.
One of the biggest challenges facing the R&D organizations of pharmaceutical companies is having well-linked, consistent and reliable data. Data is the foundation upon which the value-adding analytics are 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 automatic reports that identify applications or compounds that are related and raise red flags related to efficacy or safety.
R&D in 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.
To make sure the allocation of scarce R&D funds is appropriate, it is critical to speed up decision making for pipeline and portfolio progression.
