Six reasons why automating digital data management in biopharmaceutical is important

Introduction

Automating digital data management in biopharmaceutical manufacturing is a process of using digital technologies such as cloud, artificial intelligence, data lakes, and wearables to collect, store, analyze, and share data across the entire development lifecycle. This can help biopharma companies improve operational performance, quality, yield, supply chain, and technology transfer.

In the rapidly evolving world of biopharmaceutical manufacturing, the importance of automating digital data management cannot be overstated. Here are some reasons why it’s crucial:

1. Efficiency and Speed

Automation and data management across biomanufacturing workflows can lead to greater efficiency and speed at all stages of development and manufacturing. Traditional methods that rely on manual data entry and transfer across the workflow increase the likelihood of errors, potentially delaying the delivery of the final product to the market.

2. Quality Control

Data integrity is expected through the control of digital/electronic systems, data, and audit review processes [1]. Automated processes have enabled a number of biopharma companies to reduce their efforts in data collection and analysis, a key requirement in maintaining production quality and FDA compliance. Digital and analytics improve then the quality and yield of the products by enabling real-time control of the manufacturing process such as Process Analytical Technologies, reducing variability, and improving product quality.

3. Error Reduction

Any errors or delays during the transition from small-scale to large-scale production can result in costly production delays or deviations, or worse, may require full redevelopment of the process itself. Automation reduces the risk of such errors. Automation and digitization can ensure better quality and compliance by reducing manual errors and variability [2].

4. Improved Productivity

Digital and analytics can drive the next wave of business optimization by transforming operational performance [3]. They enable faster and more effective problem resolution and a risk-based approach to optimizing testing volume, tools, and methods [2]. By using technology and automation to drive quality and efficiency in R&D, process controls can be automated and managed, allowing improved productivity while maintaining high-quality standards during process optimization and scale-up.

5. Data Analysis

Digital biomanufacturing provides an integral connection and real-time access to divergent information sources by enabling:

• Real-time control of the manufacturing process, reducing variability, and improving product quality [4].
• Real-time monitoring and control of the bioprocessing workflow, allowing for precise control of process parameters [4].
• online quality testing and review-by-exception, which can enable real-time release [5].

Thus, it can enable deep analysis and predictions leading to advanced process control.

6. Supply Chain and Logistics

The enhancement of the manufacturing process chain by digitalization affects the supply chain, logistics, and predictive rather than preventive maintenance. So Adopting automation and digital data management will help in reducing supply chain volatility, uncertainty, complexity and ambiguity.

Conclusion

Automating digital data management in biopharmaceutical manufacturing can lead to improved efficiency, quality control, error reduction, productivity, and data analysis, all of which are crucial for the success of biopharmaceutical manufacturing. In some cases, digitization and automation have resulted in a more than 65% reduction in overall deviations and over 90% faster closure times. They can also prevent major compliance issues, which can in themselves be worth millions in cost savings [2].

References

• [1] Implementing Data Control Strategy to ensure Data Integrity. https://mivado.com/mgp/implementing-data-control-strategy-to-ensure-data-integrity/
• [2] Smart quality control in pharmaceuticals | McKinsey. https://www.mckinsey.com/industries/life-sciences/our-insights/digitization-automation-and-online-testing-embracing-smart-quality-control.
• [3] Six principles biopharma companies can follow to leverage digital and …. https://www.mckinsey.com/capabilities/operations/our-insights/leveraging-digital-and-analytics-in-biopharma-operations-six-principles.
• [4] Automated bioprocessing – 7 advantages of automation. https://www.susupport.com/knowledge/bioconjugates/automated-bioprocessing-advantages-automation.
• [5] Automating Biopharma Manufacturing – PharmTech. https://www.pharmtech.com/view/automating-biopharma-manufacturing.

About the author: Kossi Molley, PMP., LSSBB., Chemist