Pharmaceutical companies undergo large investments of money and time to bring a product to market.
According to The Journal of the American Medical Association (JAMA), the financial investment required to create a drug and bring it to market is around 985 million dollars (including expenditures on failed trials), and the time investment is around 12 years according to Drug Approvals – From Invention to Market . . 12 Years!
Drug research is a scientific process that involves the collection, sharing and processing of vast amounts of data (easily tens of terabytes of data every day through scientific experiments).
The massive amount of data creates challenges of how to reduce the cost of storing and managing it and how to help researchers and collaborators work more efficiently.
Data management can help reduce the economic investment in drug development by improving clinical trial outcomes, for example, by identifying the right patients to participate in a trial (by analysing demographic and historical data), remotely monitoring patients, reviewing events from previous clinical trials, and even helping to identify potential side effects before they actually occur. All these will lead to lower costs as fewer trials will be performed.
In addition, expenses can also be reduced by having a more optimised storage and data infrastructure, avoiding unnecessary investments in the cloud.
The key to reducing time in getting a drug to market is to manage data centrally and provide a consistent version of the data to all teams and systems, which will improve the operational efficiency of research teams. Without a data management programme, these teams run the risk of ending up using inconsistent versions of the same reference data, creating duplicate studies in different systems and wasting time harmonising, mapping and stitching this data together.
In addition to generating large amounts of data, the pharmaceutical sector exchanges a lot of data between research teams and universities, laboratories and clinical research organisations and receives the results back. The data they receive may be in a different format, contain invalid values or use different (or non-existent) units of measurement.
Once again, it becomes clear that a data management solution is needed to prepare this data so that it can be efficiently visualised and interpreted..
In conclusion, intelligent data management and a more centralised way of working that enables teams to have access to the same data at the right time and in a way that they are able to visualise it, will result in a reduction of the cost and time to bring a drug to market.