Thanks to Industry 4.0, we now live in a data-driven economy. There are 2.5 quintillion bytes of data generated each day through different digital and smart devices[i]. Big Data refers to the humongous volume of data, which can be both structured as well as unstructured. To make sense of this data is the latest interest of any data scientist. It can help with various predictive models, analyzing the trend, helping businesses make better decisions, make the operations more efficient to result in higher profitability.
With the introduction of big data analytics in the pharmaceutical world and life sciences industry, the complex business processes were streamlined, and the efficiency of the process was improved. Thus, various investors from the healthcare and pharma domain have invested around $4.7 billion in big data analytics[ii].
Big data analytics enables businesses to dig deep into their data and gain insights from them. These data can be historical or real-time and can come from various sources like social media, Sensors, log files, and patient enrolment. With the help of big data analytics, you can identify hidden data patterns to make data-driven decisions for your business.
According to the McKinsey Global Institute, the application of big data strategies would lead to better decision-making. This will lead to a value generation of $100 billion across the US Health-care system[iii]. It will serve to efficient research work, advanced clinical trials, and innovation of new tools. Effective utilization of these data will help the pharma companies to identify new candidates for drug trials and develop them into effective medicines.
Big data can be beneficial in the pharmaceutical industry due to the following applications
Reduces Research and development cost
Do you know that developing a single drug could cross over $2.6 billion over a period that usually lasts for over 10 years [iv]? According to Joseph A. Dimasi, director of economic analysis at Tufts CSDD, drug development and research are costly undertakings across the pharmaceutical industry. Medicines to fight diseases like ALS (Amyotrophic Lateral Sclerosis) are not being developed because the cost of developing the medicines outweighs the demand. Big data can help in fast-tracking the research work with the help of artificial intelligence to minimize the time needed for clinical trials. This will reduce the required research, thus lowering the cost of medicine in the long run.
Solving complex protein structure is another mystery for the pharma researchers. The researches need to ensure that the drug does not have any reverse effect on the patients. To ensure this, a machine-learning algorithm was developed at Carnegie Mellon University to test and analyze the interaction of different drugs with protein structure. The accuracy of the results obtained through the machine learning algorithm has saved valuable time, thus getting the drug from the clinical to the market at a faster rate.
Better Clinical trials
There can be a lot of application of big data analytics in conducting clinical trials. The process of matching or recruiting a patient can be done using various Machine-learning algorithms. These algorithms have reduced manual intervention by 85% thus leading to cost and timesaving during large trials. Machine learning techniques like association rules and decision tree helps in determining trends relating to patient acceptance, adherence, and various other metrics. Big data can help in designing flowcharts to match and recruit more patients in clinical trials, which will, in turn, increase the success rate of the drug. A different predictive model can help in analyzing the competitors of the new product based on several clinical and commercial scenarios. Big data models can also save the company from undergoing any adverse situations, which can be caused due to operational inefficiencies or other unsafe measures.
Escalated Drug Discovery
With primitive techniques, drug discovery took much time owing to the physical testing of these drugs on plants and animals, which was an iterative process. This caused inconvenience with patients requiring immediate attention like the ones suffering from Ebola, or swine flu. With the help of big data analytics, researchers use predictive modeling to analyze the toxicity, interactions, and inhibition of the drug. These models use historical data collected from various sources like clinical studies, drug trials, etc. for near accurate predictions.
Controlling Drug Reaction
Real-world scenarios are replicated to test the harmful effects of drugs in their clinical trials, with the help of predictive modeling. Data mining on social media platforms and medical forums are performed along with sentiment analysis to gain insight into adverse drug reactions (ADRs).
Diagnosis and treatments of various diseases are carried out with the help of big data analytics after gathering relevant data about the patient’s genetics, environment, and behavior patterns. A combination of customized medicine can be created for individual patients who show different symptoms. The predictive model developed from the patient’s historical data can also help in detecting diseases much in advance.
Focus on Sales and marketing
Big data can help the pharma companies predict the sale of a particular medicine owing to the various demographic factors. This will help companies predict customer behavior and build advertisements accordingly to reach out to these consumers. Accurate industry trends can be predicted and analyzed with the help of big data.
External and Internal Collaboration
Streamlining of drug discovery, clinical trials, and medical affairs will help in improving internal collaboration. Whereas, the insights provided by the external researchers, contract research organizations(CROs) can help the pharma company in better drug making.
Big data can help the pharmaceutical representatives to identify the appropriate medicines for every patient. This will help in creating customizable medicine plans for each patient owing to their unique blend of diseases.
Whether it is the application of big data in precision medicines, or to decrease the rate of drug failures or to lower the cost of research and drug discovery, there is a bright future for big data analytics in the pharma world. With data being the new oil, harnessing this resource is a must for any pharma company to provide better and quicker medicine to humankind.