Bio Beers 9 09
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Bio Beers 9 09



Presentation by BioBeers Sept 09 by FIT

Presentation by BioBeers Sept 09 by FIT



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Bio Beers 9 09 Presentation Transcript

  • 1. Rapid Particle Characterization of Therapeutic Protein Formulations Using Continuous Digital Imaging Lew Brown Fluid Imaging Technologies, Inc. Yarmouth, ME USA
  • 2. Outline:
    • Historical Usage of Microscope Images
    • Limitations
    • New Technologies for Analysis of Particulates (Benefits/Limitations)
    • Fluid Imaging FlowCAM ® : Architecture and Features
    • Demonstration
  • 3. Limitations of Traditional Microscopy
    • Static Sample
      • Only a small amount of sample can be processed at a time
      • Small sample = decreased reliability/repeatability
    • Time to Prepare and Analyze Sample
      • Time required to prepare sample onto microscope slide
      • Each particle counted and measured one at a time
    • Human Error
      • “ Tired eyes”
      • Interruptions
  • 4. New Technologies for Analysis of Particulates (Benefits/Limitations)
    • “ Particle Analyzers”
      • Operate on “Particle Volume” Principle
      • Benefits:
        • Non-static sampling
        • Very rapid counting
      • Drawbacks:
        • “ Particle Volume” only
        • No shape measurements
        • Limited information (“Relative Measurements”)
        • No picture!
  • 5. New Technologies for Analysis of Particulates (Benefits/Limitations)
    • Optical Automated Micrography
      • Originally photographic, now (mostly) digital
      • Benefits:
        • “ A picture is worth a thousand words!”
        • Automates measurements
        • Allows subjective review by scientist
      • Drawbacks:
        • Static sample
        • Still time consuming
  • 6. The Big Question:
  • 7. FlowCAM Architecture
  • 8. Continuous Imaging Particle Analysis: How it Works
  • 9. Continuous Imaging Particle Analyzer: Features
    • Continuous imaging
    • Fluorescence/scatter triggered imaging
    • Can collect upwards of 25,000 images/min
    • Intuitive software (including pattern recognition) allows users to quickly navigate tens of thousands of images
      • Qualify, isolate and quantify sub-populations based on any combination of the 23 measured parameters
      • Remove unwanted particles from data sets
  • 10. Analysis of Two Drug Formulations
    • Goal
      • Provide a concentration of particles >10um and >25um w/o counting the Si oil droplets that are common to these samples
      • Provide images of these particles
    • Sample analysis
      • 10X Objective ( ~100X Overall Magnification), and a 90um deep flow cell
      • Post-processed to remove Si oil droplets and air bubbles
  • 11. Typical Results
  • 12. Removing Unwanted Particles
    • Display all particles >10um
    • Use sorting or pattern matching to isolate unwanted images
    • Delete unwanted images
    • Display summary statistics for particles >10um and >25um
  • 13. Effects of Processing
    • Before Droplet Removal
    • After Droplet Removal
  • 14. Summary of Results
    • Sample A:
  • 15. Summary of Results
    • Sample B:
  • 16.
    • Formulations that contain high concentrations of particles larger than 10um have a higher chance of causing complications in patients than those that do not
    • Sample B has a many more outsized particles (492 p/ml) than sample A (95 p/ml)
    • Sample B is more apt to cause complications than than A
  • 17. Flow Microscopy offers the Speed of Rapid P.A., With the Differentiation of Microscopy
      • Speed enables the collection of statistically significant data populations
      • Instant visual feedback from particle images; great for outlier quantification and qualification
      • More data/particle provides an enhanced ability to identify and quantify different particle types
  • 18. Flow Microscopy Offers the Speed of Rapid P.A., With the Differentiation of Microscopy
      • All particle images saved; can be re-analyzed at any time, and can serve as concrete documentation
      • Morphological pattern matching allows for rapid data processing
      • Image libraries can be compiled and used as references for pattern matching (normalize out human error)
  • 19. Thank You!