(479e) Data Management and Integration for Continuous Pharmaceutical Manufacturing

Authors: 
Singh, R., Rutgers, The State University of New Jersey
Cao, H., Rutgers, The State University of New Jersey
Mushnoori, S., Rutgers, The State University of New Jersey
Higgins, B., Johnson & Johnson
Fermier, A., Johnson & Johnson
Hausner, D., Rutgers, The State University of New Jersey
Jha, S., Rutgers University
Ierapetritou, M., Rutgers, The State University of New Jersey
Ramachandran, R., Rutgers University
As the pharmaceutical industry seeks more efficient methods for the production of higher value therapeutics, the associated data analysis, data visualization, and predictive modeling require dependable data origination, management, transfer and integration. As a result, the management and integration of data in a consistent, organized, reliable manner is a big challenge for the pharmaceutical industry.

The S88 recipe model, an international standard for describing process, has been adapted in this study to deliver a well-defined data structure that will improve the data communication inside the system architecture. This work has been divided into two parts due to the difference requirements between laboratory-based analytical measurements and the pilot-plant continuous pharmaceutical process. In the laboratory platform, recipes have been developed for a sub-set of material property tests that could be performed on analytical instrument (e.g. FT4 for flow). Drupal, an open source content management system, is implemented on an Amazon web service for data transfer between the analytical devices eventually a data management platform. A recipe module for Drupal is developed for recipe management, in which users could create, import, and modify recipes. Scientists can access recipe through Drupalâ??s webpage interface and perform experiments following recipe steps. Experiment data can be recorded by manually input or automatically parsing data file on the backend of server. This system works like a recipe based electronic laboratory notebook.

In the pilot-plant, process data is generated by unit operation equipments and implemented process analytical technology (PAT) instruments [1]. A process control system (e.g. DeltaV (Emerson)) collects the data from equipments and a PAT data management tool (e.g. synTQ (Optimal Industrial Automation)). The PAT data management collects data from inline/online measurement system [2]. The recipe for the whole continuous process is implemented in DeltaV. Data in DeltaV is collected according to the recipe and is transferred to a data storage hub (PI system (OSI Soft)) in the same structure. The event frame (EF) feature from PI system allows the possibility to create individual recipe based on continuous data feeding. From PI system, the data is sent to online data storage box and cloud system. From the box/cloud, the data can be access at different physical company sites, can be analyzed and applied for different applications. It is the first attempt to apply S88, a batch control standard, to continuous pharmaceutical manufacturing.

All the detailed information of the lab-based experiment and process manufacturing, including equipments, samples and parameters are documented in the recipe. Recipes containing data can be exported from this system to be shared and transferred. After detaching the data from recipes, a reliable and consistent data source is provided for data visualization and process modeling. Another feature is the two dimensional barcode labels that are used in this strategy. Every ingredient and equipment of the analytical experiment or manufacturing process will have a unique barcode, which can be used to identify the item and trace all the information related. This enforces material traceability which is an important requirement in the overall QbD initiative.

References:

  1. Singh, R., Sahay, A., Fernando Muzzio, Ierapetritou, M., Ramachandran, R. (2014). Systematic framework for onsite design and implementation of the control system in continuous tablet manufacturing process. Computers & Chemical Engineering Journal, 66, 186-200.
  2. Singh, R., Román-Ospino, A. D., Romañach, R. J., Ierapetritou, M., Ramachandran, R. (2015). Real time monitoring of powder blend bulk density for coupled feed-forward/feed-back control of a continuous direct compaction tablet manufacturing process. International Journal of Pharmaceutics, 495, 612-625.