The biopharmaceutical industry, as an important part of China's strategic emerging industries, has achieved world-renowned results in recent years. With the rapid development of biotechnology, biopharmaceutical companies have higher and higher requirements for production support systems. Data management, as an important part of the production support system, is of great significance to ensure the stability of the production process and improve product quality. In this paper, we will discuss the application and key points of data management in the biopharmaceutical production support system from the characteristics of the system.

Characteristics of biopharmaceutical production support system

1. Complexity: The biopharmaceutical production process involves genetic engineering, cell culture, protein purification and many other aspects of the process, with a complex process and numerous parameters.

2. Continuity: In the production process of biopharmaceuticals, the links are closely connected, and any failure in any link may lead to the failure of the whole production process.

3. High risk: Biopharmaceutical products are directly related to human health, and quality control during the production process is crucial.

4. Dynamic: During the production process of biopharmaceuticals, various parameters will fluctuate with the changes of time, environment and other factors, which need to be monitored and adjusted in real time.

5. Data-intensive: The production process of biopharmaceuticals generates a large amount of data, including process parameters, equipment status, product quality and so on.

Application of data management in biopharmaceutical production support system

1. Data acquisition and Storage

Data acquisition is the first step of data management, which mainly includes process parameters, equipment status, and environmental parameters. Data storage adopts database management system to ensure the safety, integrity and traceability of data.

2. Data processing and analysis

Data processing and analysis mainly includes data cleaning, data mining, trend analysis and so on. Through the processing and analysis of production data, potential problems are found, providing a basis for production optimization.

3. Data display and application

Data display and application mainly includes real-time monitoring, historical data query, report generation and so on. By means of visualization, the data is transformed into intuitive information, which is convenient for production managers to grasp the production status.

4. Data security and compliance

Biopharmaceutical production data involves the core secrets of the enterprise, and stringent security measures need to be taken to ensure that the data is not leaked. At the same time, follow relevant regulatory requirements to ensure data compliance.

Key points for data management of biopharmaceutical production support systems

​1. Establish a perfect data management system

Enterprises should establish a set of perfect data management system, and clearly define the responsibilities, processes, and systems of data management to ensure the standardization of data management.

2. Select appropriate data management tools

According to the actual needs of the enterprise, select appropriate data management tools to improve the efficiency of data management. For example, the SCADA system is used to realize real-time monitoring of the production process, and the MES system is used to realize the integration and management of production data.

3. Strengthen data quality management

Data quality management is a key aspect of data management. Enterprises should strengthen data quality management to ensure the authenticity, accuracy and completeness of data.

4. Cultivate data management talents

Biopharmaceutical companies should pay attention to the cultivation of data management talents, improve the data literacy of the staff, and provide talent guarantee for data management work.

5. Continuous optimization of data management process

Enterprises should continuously summarize their data management experience, optimize their data management process, and improve their data management level.

Data management has a pivotal position in the biopharmaceutical production support system. Enterprises should fully recognize the importance of data management, and start from system construction, tool selection, quality management, talent training, etc., to continuously improve the level of data management and provide strong support for biopharmaceutical production. With the continuous development of biotechnology, the role of data management in the biopharmaceutical production support system will become more and more prominent, creating greater value for the enterprise.