🔬 These companies are focusing on systems that capture and store data generated by high-throughput analytics and automation.
🌐 The goal is to simplify data analysis and make it easier to apply analytical tools like machine learning.
⚙️ Genedata has designed a structured data management system that allows for standardized data collection and automated analysis.
🧪 This system can handle complex data from various workflows, including gene and cell therapies, antibodies, and next generation sequencing.
💡 Innovation is happening in the field of AI and analytical instruments to enable better data processing and analysis in the pharmaceutical industry.
Introduction:
The article discusses how large pharmaceutical companies are adopting structured and centralized data management systems for bioprocess development. This shift is aimed at simplifying data analysis by capturing and organizing data in a standardized format, which can then be easily analyzed using tools such as machine learning. The article highlights the importance of structured data management in enabling automation, improving data analysis capabilities, and meeting regulatory requirements.
- Traditionally, the focus in bioprocess development has been on high-throughput analytics, machine learning, and automation, with less emphasis on the systems that capture and manage the generated data.
- Pharmaceutical companies are recognizing the need for structured data management systems that can streamline data analysis by collecting data in a standardized format for the entire workflow.
- Genedata has developed a structured data management system that allows for automated data analysis and can handle various workflows, including gene and cell therapies, antibodies, and next-generation sequencing data.
- The adoption of structured data management systems enables easier data analysis using tools like machine learning, and it facilitates compliance with regulatory requirements for structured data exchange.
- The integration of laboratory information management systems (LIMS) and electronic lab notebooks (ELNs) with centralized data management systems further enhances data organization and analysis capabilities.
Conclusion:
The adoption of structured and centralized data management systems by pharmaceutical companies is revolutionizing bioprocess development. By collecting and organizing data in a standardized format, these systems enable automated data analysis, enhance data quality, and facilitate compliance with regulatory requirements. The integration of LIMS and ELNs further improves data organization and analysis capabilities, making it easier to apply tools like machine learning. This shift towards structured data management systems is expected to drive further innovation at the intersection of artificial intelligence and bioprocessing.
