The use of big data in the pharmaceutical industry is crucial to the end-goal of providing better medicine faster to all patients. An article published by i2e Consulting offered 7 applications of big data in the pharmaceutical industry.
Reduces Research and Development Time and Costs
The cost to develop a drug is astronomical. Over a typical 10-year period, the cost to develop one drug can exceed $2.6 billion. Unfortunately, drugs for complex diseases aren’t being touched because their costs of developing outweigh their demand. Big data can be a solution for the heavy cost burden by accelerating the time and work needed for research while artificial intelligence (AI) can help reduce time that is required for clinical trials.
A big mystery among pharmaceutical researchers are complex protein structures. Researchers want to make sure that a drug does not cause reverse effects in patients, so the development of a machine-learning algorithm at Carnegie Mello University tested and analyzed the effect of drugs on protein structures. Results of the algorithm proved to be a valuable asset in saving time, and thus, speeding the rate from clinical trial to market.
Improves Clinical Trials
Machine learning algorithms offer a big opportunity for conducting clinical trials by handling the process of recruitment. Machine learning algorithms have reduced manual intervention by 85 percent thanks to techniques like association rules and decision tress which help determine trends among patient populations. Other uses for these algorithms include flowcharts for matching and recruiting more patients, predictive models for analyzing competitors of new products, and prevent negative operational outcomes.
Accelerates Discovery of New Drugs
More basic techniques within drug discovery takes a lot of time on physical testing of drugs, causing stress among patients with certain diseases and flus who require more immediate intervention. Big data gives researchers access to predictive modeling where they can analyze certain aspects of a drug, like toxicity and interactions. With the use of historical data, near accurate predictions can be quickly made and result in greater outcomes.
Better Control of Harmful Drug Reactions
Another use of predictive modeling is testing the harmful effects on drugs during clinical trials. The use of data mining on social media platforms and medical forums, as well as sentiment analysis, can access insight into adverse drug reactions.
Using Precision Medicine to Treat Patients
Once relevant data about a patient is gathered, big data analytics can step in, in the diagnosis and treatment of disease. A customized plan of treatment can then be made for patients based on symptoms they present. The detection of disease can take place much faster with predictive models that use a patient’s historical data.
Big Data Helps Pharma Sales and Marketing
For pharma companies, big data can help them predict the outcome of a drug from a business perspective using demographic factors. Big data helps companies foresee customer behavior and gives insight into how they should advertise to certain consumers.
Improved External and Internal Collaboration
With improved processes in drug discovery and development, internal collaboration will also improve and ultimately lead pharma companies to become better at drug making.