Data analysis has become a critical tool in the pharmaceutical industry. In fact, pharmaceutical makers are constantly looking for ways to improve the odds of getting new drugs through the discovery pipeline to market as quickly and cost effectively as possible. The application of big data – including data governance, data mining and predictive analysis – is helping pharmaceutical companies and researchers make smarter decisions about which drugs to pursue.
Applications of Big Data Analysis
Using the Data
The pharmaceutical industry can only use the vast amount of molecular and clinical data stored in proprietary networks (as well as the boundless amount of consumer information on the Internet) to its advantage if it can translate it into actionable business intelligence. This is where data science professionals can make a huge difference.
According to pharmaceutical data scientist Aditya Joshi, several drug researchers and manufacturers are partnering with data management companies to create a globally accessible private cloud where the pharmaceutical industry can securely collaborate around anonymous clinical trial information.
Bringing a new drug to market is one of the riskiest and most expensive endeavors a company can undertake. According to an article in Forbes Magazine, the industry average for the cost per successful drug is around $4 billion, and it can be as much as $11 billion.
When you consider that only 10 to 12 percent of new drugs make it from the early phases of the drug discovery process to the consumer market, the modern pharmaceutical industry has every reason to incorporate data analysis to gain a competitive edge.
To speed up the process, they are using predictive models to search enormous virtual databases of molecular and clinical data. Analysts can identify likely drug candidates with the help of criteria based on chemical structure, diseases/targets and other characteristics.
When it comes to avoiding negative outcomes, data analysis can also be used in clinical drug trials to rapidly identify safety or operational signals requiring action to avoid significant and potentially costly issues such as adverse events and unnecessary delays. By using multiple data sources, including social media and public health databases, companies can also identify which populations would work best in trials.
Targeted Sales and Marketing
In a recent survey by Accenture respondents noted that around 25 percent of their pharmaceutical marketing is delivered over a digital platform, and 87 percent intended to increase their use of analytics to target spending and improve return on investment. Some of the money will be used to collect data that has a direct relevance to the sales cycle, which in turn will feed into predictive methods and sophisticated sales tactics.
In recent years, pharmaceutical companies have even sponsored crowd-sourced competitions to predict patient and clinical outcomes, sales patterns, molecule activity and other actions involving big data. Recently, pharmaceutical giant Boehringer Ingelheim teamed up with Kaggle (the world’s largest community of data scientists) to sponsor a competition to assess the likelihood of mutagenicity (the tendency to cause DNA damage), a key side effect to avoid in new drug development.
Join the Bright Future
Data science has become a highly complex and advanced discipline. Being able to analyze large amounts of data and identify trends is a skill set that is highly coveted in many industries. In the pharmaceutical sector, the need continues to grow for experts who are able to mine vast troves of clinical, market, and legal data to identify which drugs have the best chance of ending up on pharmacy shelves, mitigate risk, and deliver maximum benefit to patients. If you are interested in entering this field, learn more about degrees in data science.