Case Studies > Oxford Biomedica and Antha: Bioprocess Informatics to Accelerate Lentiviral Vector Process Development

Oxford Biomedica and Antha: Bioprocess Informatics to Accelerate Lentiviral Vector Process Development

Company Size
1,000+
Region
  • Europe
Country
  • United Kingdom
Product
  • LentiVector® platform
  • Antha platform
  • ambr®250 HT
  • Nova BioProfile® Flex2
  • QuantStudio™
Tech Stack
  • Cloud-based data store
  • Python-based environment
  • Machine Learning
  • Artificial Intelligence
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Cost Savings
  • Customer Satisfaction
  • Digital Expertise
  • Productivity Improvements
Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Predictive Analytics
  • Application Infrastructure & Middleware - Data Exchange & Integration
  • Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
  • Life Sciences
  • Pharmaceuticals
Applicable Functions
  • Product Research & Development
  • Quality Assurance
Use Cases
  • Machine Condition Monitoring
  • Predictive Quality Analytics
  • Process Control & Optimization
Services
  • Data Science Services
  • Software Design & Engineering Services
  • System Integration
About The Customer
Oxford Biomedica is a leading company in lentiviral vector research, development, and manufacturing, with over 20 years of experience in gene and cell therapy. They were the first to treat humans with in vivo lentiviral-based vectors and have developed a valuable portfolio of gene and cell therapy product candidates for various medical indications. In 2017, they partnered with Synthace to use the Antha platform for automating and improving their bioprocess research and development.
The Challenge
Oxford Biomedica faced challenges in handling the large volume of bioprocessing data generated from their lentiviral vector development process. The traditional methods of using spreadsheets and proprietary vendor software tools were cumbersome, resource-intensive, and prone to bias. The need for a scalable, flexible, and robust software tool to automate complex experiments and handle high-volume data streams was evident.
The Solution
Oxford Biomedica implemented Synthace’s Antha platform to automate the upload, collation, organization, structuring, processing, visualization, and analysis of high-volume, high-density data streams. Antha provided a cloud-based data store and a Python-based environment for dynamic data interrogation. The platform enabled rapid organization and structuring of data from various sources, improved data integrity, and facilitated flexible analysis and visualization. This integration allowed Oxford Biomedica to handle the entire dataset, leading to better process characterization and understanding.
Operational Impact
  • Antha reduced the time from data generation to data interpretation from days to hours, achieving a ~94% resource saving for a typical process analysis.
  • The platform improved data integrity and reduced handling issues by automating data upload and structuring, eliminating manual touchpoints.
  • Antha enabled the use of the entire dataset, reducing bias and improving process characterization and understanding.
  • The platform provided a dynamic visualization environment, allowing flexible analysis and rapid access to process insights.
  • Antha facilitated collaborative development by enabling simultaneous data exploration by multiple users, enhancing efficiency and decision-making.
Quantitative Benefit
  • Antha reduced the time spent uploading data from disparate sources by 83%.
  • The time required to structure data was reduced by 92%.
  • Overall, there was a 90% saving in time spent from generating raw data to creating a structured dataset (from 2.5 to 0.25 hours).
  • The time required to visualize data was reduced by over 80% (from 1.5 to 0.25 hours for a single offline analysis and from 2.25 to 0.35 hours for two offline analyses).
  • Antha achieved a ~94% reduction in resource demand from raw data to insight (from 10.75 to 0.6 hours) for a typical vector bioprocess development run.

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