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BioProcess R&D

Home > Solutions > BioProcess R&D
A Consistent and Reliable Approach to BioProcess R&D
 
The role of BioProcess R&D in pharmaceutical development is twofold: to supply drug substance for use in preclinical and clinical evaluation, and to develop economical, scalable processes for manufacturing biologically-synthesized drug substances or drug intermediate.   Multidisciplinary teams are tasked with developing biologically-synthesized drug substances and intermediates including vaccines, therapeutic proteins, natural products, gene therapies, and key intermediates for chemical synthesis. 
 
Along with these typical products, BioProcess R&D develops biological assays for product characterization and release, fermentations and cell culture processes, downstream processing methods for product isolation and purification, methods to characterize the biological product, and regulatory packages required for product approval by the FDA. 
 
Inference for QbD provides BioProcess R&D teams with the following benefits (categorized by team member role):

BioProcess Scientists and Engineers
  • enjoy a consistent approach to identification of bioprocess parameters that are critical to product quality using the standardized and validated Inference for QbD function library
  • employ a reliable approach to identifications, visualization, documentation and verification of bioprocess design space using the Inference for QbD template library
  • gain valuable bioprocess understanding from low-value bioprocess material and process attributes data using machine learning methods
  • save time by automating repetitive QbD tasks, e.g. creating standard biological assay reports and capturing them in an electronic lab notebook
  • save time by rapidly prototyping, testing and deploying new data-analysis methods for QbD applications
  • shorten turn-around time for delivery of custom bioprocess reports by enabling scientists to perform data pipelining and documentation directly in Inference Word
  • easily find and reuse prior QbD information from a global searchable database of QbD bioprocess methods for assembly into SOPs

BioProcess Managers

  • deploy a consistent approach to implementing QbD goals across BioProcess R&D
  • ensure consistent, accurate and traceable BioProcess QbD records and reports
  • shorten time-to-decision and improve efficiency by providing streamlined, regulatory compliant BioProcess workflows
  • improve BioProcess R&D’s capability to share and collaborate on QbD projects
  • leverage and exploit an underutilized asset: QbD information from prior BioProcess projects 
  • collect a broadly accessible, enterprise wide BioProcess R&D knowledge base for training new employees and initiating future QbD projects
  • improve decision quality and reduce time-to-decision by supplying facts-based predictive modeling of BioProcess methods to entire R&D team
  • shorten QbD training by using templates and leveraging familiarity with Microsoft Office

Adjoining Development Functions  

  • clearly communicate QbD project status, uncertainties and development strategy to senior stakeholders and the entire development team
  • enable adjoining development functions involved in QbD project to have real-time access to relevant BioProcess R&D information
  • support regulatory by maintaining QbD study data in a searchable 21 CFR Part 11 compliant centralized repository with full audit trails
  • enable regulatory and CMC project teams to easily assemble standardized QbD results for CMC filing
  • support process engineering during scale-up and validation by enabling direct access to bioprocess QbD information
  • support IT by providing: a single platform that spans the full development organization; a standards-based architecture that is scalable to thousands of users; and a set of core applications that requires minimal training and support built on familiar Microsoft Office
 
 
Learn More
Documents
ResultsDoc R CPP of PPO Enzyme Catalysis.pdfResultsDoc R CPP of PPO Enzyme Catalysis
INF03+ R CPP of PPO Enzyme Catalysis.pdfINF03+ R CPP of PPO Enzyme Catalysis
Screencasts
  Overview of Inference for Office
  Perform Data Analysis Using an Inference Template
  Build and Deploy an Inference Template
  Test-Driven Data-Analysis Method Development
  Verify New Data Analysis Method using Inference
Screenshots
Histograms of enzyme activity and heatmap of experiments
Critical process parameters measure by impact on accuracy of prediction and partial dependence plots of enzyme activity
Quality of predictive model and surface plot of predicted enzyme activity
 
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