Till now, only 0.1% of drugs tested across the globe get accepted as medicine, let alone become successful medicines. The cost of testing and miscellaneous operations have doubled and the returns have halved. Harnessing the possibilities of Big Data analytics can increase the efficiency and sustainability of pharmaceutical industries by efficiently utilising under-leveraged and unleveraged data.
The lifestyle and pharmaceutical industry is also using Big Data from retailers, patients and caregivers to map potential candidates for a particular drug. Engineers and pharmacists across the globe are getting Big Data Certification to stay relevant in the rapidly changing pharmaceutical industry.
Professions like Data Visualiser, Data Architect, Data Scientist, Financial Analyst, and Domain analyst are vital requirements of a pharmaceutical enterprise who are expected to be trained in technologies like Flume, Sqoop, SAP BODS, R, and Oracle Data Integrator.
Understanding relationship between Big Data and Pharmaceutical industry:
Declining productivity of the research and development wing, globalisation of the pharmaceutical market, region-specific policies and guidelines related to drugs, and rising alternatives to conventional medicines are pressing challenges of the pharmaceutical industry in India.
Understanding types of data and the need for the algorithm(s)
Thorough analysis, procurement and processing of the structured and unstructured biomedical data is at the core of any drug discovery. Pharmaceutical companies, laboratories and hospitals gather the data in large volumes through large-scale experiments and surveys. The surveys are often also conducted digitally through emails, in the form of social media advertisements, or through subscribed digital newsletters.
The internal data of pharmaceutical companies, laboratories and hospitals additionally give insights into gene sequencing, gene functionality, molecular behaviour regarding how a particular type of drug already in place has reacted to protein in the human body, and how the biochemistry of a specific complex drug molecule behaves with the biochemistry of the human body. This type of data is collected through clinical trials conducted over months, primary staff reportage, data collected digitally through machinery used (like ECG machines, electronic thermometers, etc.) and finally from previously found medical literature.
Various roles of a data professional in a pharma industry
A professional trained in developing algorithms that are simplistic, scalable and efficient to mine and simplify into understandable, applicable and scalable observations find a popular requirement in pharmaceutical industries. The data scientist recruited will also be able to identify trending keywords on digital media, use them to collect data from social media conversations (by designing various types of web scrapers), email conversations and patient’s web interactions with physicians, psychologists and lifestyle bloggers. The professional will also be able to sequence the patient’s drug-protein record from hospitals.
The roles go beyond just putting the data on a significant platform; a data manager would also be able to devise a secure framework for the personal data of patients, the creation of a data governance plan that is company-wide, as well as make sure the data is consistently generated from credible sources and is in compliance with company standard protocols.
Regardless of the data source, a relationship needs to be formed within the vast pool of objective and factual raw data. Data scientists devise algorithms that can map relatively smaller data like the total cost required in the creation of the drug, its feasibility in the market, existing substitutes of the medicine and their dire need in the market. The data analytics professional will also be able to automatically generated reports from clinics and laboratories to identify the applications or compounds that are prone to cause ill-effects in the patient’s body.
The data manager can expand his data pool by forming a link with fellow laboratories and clinics. This way, the companies mutually benefit from each other’s data in the race of coming up with a successful medicine. Externalisation of data sources can also take into account collaborating with retailers, advertisers, in-house hospital chemists, NGOs and policy-making organisations. Understanding the dynamics of the market, consumer behaviour and marketability of a drug help pharmaceutical companies make more intelligent investments.
A data manager skilled in data architecture tools is a boon in the industry. Issues like financial investment in the infrastructure of the pharmaceutical company, prioritising on machinery procurement etc. can be objectively studied by someone who understands the overall objective and vision of the industry alongside data sciences.
Big data finds application in predicting molecular development in laboratories to help identify potential candidate of drugs that can best suit their biological hosts. Studying previous and present data of clinical trials can help data managers prevent costly events like unnecessary delays and adverse effects on patients. Once the company identifies the drug which has the maximum possibility to be used by masses, it can invest time and money into holistically developing it into an effective and approved medicine. Upcoming technologies like CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) – that is theorised to cure autoimmune diseases like cancer, leprosy, Alzheimer’s by removing the disease-causing trait from the gene sequence in the patient’s body are challenging the pharmaceutical industry to come up with drugs that cater to post-surgical recovery.
Pharmaceutical industries do not lack technology, but a complete and robust data management framework. The digital machinery used in internal operations at hospitals, clinics and laboratories store within themselves a plethora of data. The complexity of data from clinical trials, lab tests, online surveys and clinical claims overwhelm the capabilities of an IT engineer and demands intensive data management, data structuring and data visualisation that can be performed only by a professional exclusively skilled in these subjects.