A Systematic Framework for Data Management and Integration in a Continuous Pharmaceutical Manufacturing Processing Line

Abstract

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, and reliable manner is a big challenge for the pharmaceutical industry. In this work, an ontological information infrastructure is developed to integrate data within manufacturing plants and analytical laboratories. The ANSI/ISA-88.01 batch control standard has been adapted in this study to deliver a well-defined data structure that will improve the data communication inside the system architecture for continuous processing. All the detailed information of the lab-based experiment and process manufacturing, including equipment, samples and parameters, are documented in the recipe. This recipe model is implemented into a process control system (PCS), data historian, as well as Electronic Laboratory Notebook (ELN) system. Data existing in the recipe can be eventually exported from this system to cloud storage, which could provide a reliable and consistent data source for data visualization, data analysis, or process modeling.

Technology outlook for real‐time quality attribute and process parameter monitoring in biopharmaceutical development—A review

“…Cloud‐based data lakes such as Amazon Web Services and Microsoft Azure are some of the most common solutions (Scheitz, Peck, & Groban, 2018). Cao et al (2018) reported the development of an ontological information infrastructure to integrate data within manufacturing plants and analytical laboratories with the adaptation of ANSI/ISA‐88.01 batch control standard.…”

Process analytics 4.0: A paradigm shift in rapid analytics for biologics development

“…PMS are uniquely positioned between analytical and bioprocess interface to enable not only automated data analysis and visualization but also to control analytical and process instruments for feedback or feedforward control (Figure 5). 14 Cao et al reported the development of an ontological information 71 This study demonstrated a well-defined data structure, where all the detailed information including metadata were documented in a recipe model and it was connected to PMS, DCS, ELN, LES, and cloud storage for improved data communication. 71 Most of the PMS modules contain in-built databases (Oracle or SQL servers) for handling larger data sets such as vibrational spectroscopic data which is typically used as a temporary data storage solution.…”

“…14 Cao et al reported the development of an ontological information 71 This study demonstrated a well-defined data structure, where all the detailed information including metadata were documented in a recipe model and it was connected to PMS, DCS, ELN, LES, and cloud storage for improved data communication. 71 Most of the PMS modules contain in-built databases (Oracle or SQL servers) for handling larger data sets such as vibrational spectroscopic data which is typically used as a temporary data storage solution. 72 As shown in Figure 4, we used a PMS platform to automatically shuttle Mid-IR spectra, followed by analysis and visualization in the graphical user interface.…”

“…84 Rapid analytics in seconds and minutes timescale generates an enormous amount of useful data, which requires systematic management for further use for advanced data analysis and interrogation techniques such as machine learning. 10,71,84 A robust pipeline for data management ensures analytical and metadata are captured and achieved for easy retrieval when needed. Advanced data modeling techniques allow better insight to process performance, CQA-CPP relationships, more holistic process understanding, and advanced process control.…”

Special Issue on Feature Papers for Celebrating the Fifth Anniversary of the Founding of Processes

“…Two contributions focus on challenges in pharmaceutical manufacturing. The first paper describes the development of a database of multiphase reactions for synthesizing small-molecule pharmaceutical to facilitate investigation of reaction-separation schemes [11], while the second paper provides a complementary study on developing an ontological information infrastructure for integrating data from pharmaceutical manufacturing plants and analytical laboratories [12]. The Special Issue also contains two contributions focusing on polymerization processes.…”