The global pharmaceutical industry is experiencing a massive revamp by adopting several latest technologies, including big data. It is pretty easy to analyze the large volumes of data that form the core of drug discovery and development with today’s technology. This requires high-performance systems that turn the real-time data generated in the companies into valuable information for predictive, diagnostic, and prescriptive analytics.
Big data analytics has become a mainstream area in the pharma industry because of its ability to identify patterns and uncover the efficacy of the treatments by solely relying on empirical data. Doing so lowers the time of the clinical trials and decreases the amount of research work that must be carried out.
The incorporation of data analytics in the pharma industry has resulted in several cutting-edge innovations that have helped them rapidly advance in the market in recent years. This snowballing is mainly attributed to the several benefits offered by big data analytics and its supporting infrastructures such as cloud computing and machine learning. 1
Some of the core areas in which big data analytics is widely exploited in the pharma industry are :
a) Big Data Analytics in Drug discovery and development :
Data analytics has tremendously accelerated the process of drug discovery and development. Traditionally, this was done via an iterative process that required a lot of time and financial resources.
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The biggest setback of this approach was that it physically tested several compounds to discover the new lead molecules. This skyrocketed the cost of introducing the drug into the market and led to the waste of many resources.
But this problem is now tackled by the use of predictive modeling. This technique utilizes mathematical and computational models to predict the drug affinity with the receptors, its interactions with the targets, toxicity profile, and other essential information.
Generally, the required data are generated by running predictive algorithms through the datasets obtained from the control groups,
academic research papers, and scientific publications. Besides providing the above-stated information, it also predicts the druggability of the lead compound. These data ultimately help the companies to make better decisions regarding the market outlook of the drug under investigation.
b) Big Data Analytics and Clinical trials :
Clinical trials bridge the gap between clinical research and clinical practice. This crucial area has been revolutionized using electronic databases in recent years. With the intervention of big data analytics solutions, the efficiency of clinical trials has been tremendously improved.
Big data in clinical research comprises an electronic medical record system used to identify and analyze various data points in the study. This includes data regarding the patients’ demographics, medical history, remote patient monitoring, and past clinical trial event, if any.
The database software merges the data from multiple sources and identifies trends and patterns observed in the clinical trial. Following the optimization of these data, clinical researchers can come up with more targeted medications to treat patients who share certain common features.
Besides this, big data can also recruit patients for clinical trials by identifying the test sites with high patient availability. It also helps design more efficient control groups by using genetic information, personality traits, and disease status.
Subsequently, it increases the success rate of the drug, lowers the clinical trial dropout rates, and reduces the cost of the study. In addition to this, big data analytics also help monitor the patients on a real-time basis to detect any adverse effects at the earliest.
c) Big Data Analytics and Precision medicine :
Advances in data collection technology have enabled the development of personalized medicines for everyone. Data analytics has paved the way to combine genomic sequencing data with the data obtained from the patient’s medical sensor and electronic medical records. Read: Important Terms used in Pharmacology
The resulting dataset of each patient can be used to describe drugs and therapies that enhance the treatment’s effectiveness.
This database is a valuable asset in the pharma industry that offers comprehensive information on the patient’s genetic makeup and current lifestyle. By extrapolating the datasets of different databases, the companies spot patterns to design a more precise and effective treatment regimen for the patients.
d) Increasing revenue and reducing costs :
It becomes easier for pharma companies to measure their business performance with big data. Analysis of the key metrics in the manufacturing area, such as the average cost of ingredient per prescription and percentage of total cost spent on the drug, can be accurately listed using these tools.
Besides this, big data can also be employed to better understand the drug utilization savings of each member per year. Furthermore, by leveraging prescription data and other vital datasets, the company can optimize its supply chain and lower the expenditure of the pharma company.
Analyzing these data will undoubtedly help the pharma company make more intelligent decisions that boost the company’s revenue and cater to the market’s needs.
e) Data Analytics in Sales and marketing operations
Big data offers better insights into pharma companies’ sales and marketing performance. The companies can identify a newer, niche, and underserved markets to boost sales operations by capturing the critical data points. This is generally done by analyzing the data obtained from social media, demographics, electronic medical records, and other sources.
Besides this, it can also be used to analyze patient trends to identify new markets, which will offer a competitive gain to the company. The efficiency of the different marketing channels used by the company can also be probed via data analytics to prioritize the best marketing route and gain a competitive edge.
Yet another application of big data in this area is to make adequate capital and resource allocation decisions and understand the performance of sales reps.
f) Analytics assist in legal decisions :
Drugmakers are usually working in complex and dynamic environments across multiple geographies. This inevitably results in them facing complex legal issues, which must be resolved amicably to avoid harming their reputation.
Failure to adhere to the government’s strict regulations can result in criminal and civil lawsuits, often with huge payouts. By using data analytics, the gaps in the safety of the current drugs can also be predicted to reduce the risk of any compliance failures that might arise in the future.
g) Optimization of the manufacturing process :
Pharma companies can understand the existing operations and lower the cost of the business by predictive analytics and big data analytics. With the help of advanced analytics, the company can gain deeper insights into the variation of the output quality with different machine settings, operator training levels, and raw material inputs.
By extensively analyzing the external factors and process parameters, this technology helps companies predict risks. The common risks identified are machine failure, quality issues, and significant changes in the market demand. The pharma companies often use this data to optimize the whole process to lower the chances of encountering these risks.
The bottom line :
Integration of data analytics with the different areas of pharmaceutical companies has definitely given rise to innovative applications. This technology has primarily impacted the sales, operations, and strategy to boost the company’s performance on many fronts. The advent of big data in clinical trials and drug discovery is rapidly gaining momentum. The benefits of cost-efficiency and reduced clinical trial dropout rates are the significant advantages of using big data in this area.
Big data analytics is clearly revolutionizing the pharmaceutical industry, and within the next few decades, it will undoubtedly transform the pharmaceutical sector that we know today.