The exorbitant cost of bringing a new pharmaceutical product to market in the U.S. is well documented, with estimates as high as 12 years and $ 2.6 billion USD.1 There are many factors that drive this cost, including rework due to a lack of access to information from previous experiments, loss of intellectual property (IP), and data mining inefficiency. The goal in drug development is to be the “first to file” for approval of a new drug in order to maximize return on investment (ROI) and increase profit potential, all while producing a high-quality product and remaining regulatory compliant.
For a drug development program to be successful, product and process knowledge should be managed along the entire product lifecycle. Knowledge management is a systematic approach to acquiring, analyzing, storing, and disseminating information related to a product, its components, and the manufacturing processes used to develop it. Sources of knowledge include prior knowledge, innovation, pharmaceutical development studies, manufacturing experience, continual improvement, change management activities, process validation studies, and technology transfer activities.
Over the last few decades, replacement of outdated paper-based data management systems has been identified as a means to accelerate this process. While the implementation of electronic systems led to reduced cycle times and compliance risk, issues remain with systems existing in departmental silos and non-standardization of data across the drug development continuum. The result is poor data mining, inefficiencies, and hindered collaboration among the different domains. To satisfy the requirement of drug development companies for efficient data and technology transfer, standardization of data and technology transfer across the entire pharmaceutical product lifecycle is needed.