Introduction Big Data in the Life Science and Pharmaceutical Industry
A key driving factor in the life science and pharmaceutical industry is the possibility to handle large volumes of data in order to access, manage and analyze the data. To be able to gain business efficiency and a good performance, the drug development needs to take place in a best time to market relationship. Therefore, more completed clinical trials are necessary in a shortened timeframe. An acceleration in the drug development leads to an improved drug pipeline, less costs and faster product releases and therefore better profitability.
Variety of Data Sources
To achieve this goal, the use of Big Data can help in wide areas. A great variety of data sources can be analyzed together. If each data source is not only analyzed individually, but data sources are put together in a context and a collection of data is created, then predictions can be improved. Consequently not only individual data with focus on partial aspects can be analyzed, but data can be combined in a global way resulting in more effective and accurate analyses.
For the purpose of clinical trials, data may consist of operating system data, real world data, clinical data, data from medical devices, wearables or sensors and research data such as biomarker identification data. Data can be retrieved via mobile apps, from wearables, from laboratories or traditional media. Once consumers have access to the drugs, in addition social media can be monitored and analyzed to better understand drug effectiveness and possible side effects.
Also other areas like marketing data from sales campaigns, company information, social media data or other information provided to or from different stakeholders can be accessed in a global way.
Benefits of Using Big Data
Pharmaceutical and life sciences companies which focus on research and clinical trials will have a great advantage of Big Data Management Systems. More effective and faster analyses are possible which decrease the duration of clinical trials and lead to earlier determined results.
The ability to predict failure as early as possible is the key to success. An early decision on whether a clinical trial will lead to a successful result or if it shall be terminated helps to focus on efficient clinical trials. This focus leads to cost savings and shortens the time frame for completing the efficient trial. Moreover, the profitability and the companies’ competiveness increase.
Also for patients, an analyses based on Big Data sources is of great advantage. For each disease several variations may occur and an individual setup for treatment is important in order to improve the efficacy of the drug. However different genetic bases and also different lifestyles have an impact on the drug efficacy. Clinical Trials are used to analyze data of many people, but they only focus on partial aspects. The outcome of a trial could be much more efficient, if researches would have bigger datasets available. Predictions on how drugs affect individuals or how they can be set up in the best way considering the conditions of the individuals could be done in a more efficient way.
Accelerating Clinical Trials
There is a great need to accelerate processes. As more and more regulations are introduced the costs for research activities grow accordingly. By improving processes and shortening development time, costs can be reduced and drug development can be speeded up – this can compensate the deceleration generated by strict regulations. Through this, a faster time to market can be achieved.
The initiation phase of clinical trials is often the most difficult and challenging one. This phase is a very labor-intensive period in which a lot of laboratory data is created. Trials often suffer from an increase of duration at the beginning, which has impact on the financial part. The recruiting of patients is difficult and time consuming and often results in a higher budget needed for the trial.
Use of Mobile Apps – Improving and Tracking Usage of Drugs
For clinical trials it is essential that patients adhere to the rules of taking the drugs. Otherwise the outcome of clinical trials cannot be interpreted correctly and it is impossible to predict the impact of drugs.
Mobile apps and wearables which send reminders to patients and to track the status are a helpful media in this case. Wearables send data to a collection of Big Data in the cloud. This makes it quite easy for the patients as they do not have to track anything actively, but the monitoring is done automatically. In this way the trial is much easier to handle for patients and more informative data can be collected.
As the data is stored globally, it is easy for CROs or pharmaceutical companies to access and use the data and it can be easily combined with other data sources.
There are lots of benefits if Big Data in the cloud is used for clinical trials and mobile apps or wearables are connected to it.
If multiple data sources are combined, better analyses with more accuracy can be done resulting in better predictions, more effective trials and an optimization of time to market. Decisions can be made faster and companies have a better chance for high percentages on the market. A cost reduction and a shortened trial period can be achieved.
The connection of mobile devices and wearables leads to better engagement and motivation of patients. Trials can be run in an easier way, data is collected automatically and tracking and adherence is much easier. Thus, recruitment and retention of patients in clinical trials will be improved. If patients are also connected for example via mobile apps throughout Internet forums, there is a better information exchange and companies are also able to gain valuable information. The compilation of data is much easier, as no manual collection from different sources, sites or investigators is needed.