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Maximizing Bioprocess Yield by Reducing Fermentation Time

Impact Delivered

  • 45% Reduction in fermentation cycle time
  • 25% Increase in batch yield
  • 30% Lower batch-failure risk
  • 35% Faster insight generation
  • The Customer

    The Customer

    A Fortune 500 pharma company optimizing fermentation used in drug development; they sought to reduce total process time while maximizing yield by predicting correct manual input parameters throughout the fermentation process.

  • The Challenge

    The Challenge

    3D-Priniting-Challenges-Img.jpg
    • High process times with tight time constraints for maximizing output
    • Need high accuracy with low odds of defective batches
    • Minimize risk of batch failure while achieving desired quantity and quality
  • The Solution

    The Solution

    the-solution-img_nextiva.jpg

    Stand up an end-to-end ML pipeline for bioprocess optimization:

    • Data cleaning and prep (easy read from Redshift)
    • Enable ML with Spark, scikit-learn, Keras, Prophet, ARIMA (100+ processors)
    • Process evaluation and workflow-driven testing for manual input parameters
    • External integration: downstream apps; workflows versioned in GitHub
  • Solution Components

    Solution Components

    Redshift; Spark; scikit-learn; Keras; Prophet; ARIMA; GitHub

  • The Results

    The Results

    • Optimized parameter selection across fermentation stages
    • Faster, more reliable batch outcomes
    • Continuous improvements via new features/algorithms
    • Global collaboration and seamless execution

Impact Delivered

  • 45% Reduction in fermentation cycle time
  • 25% Increase in batch yield
  • 30% Lower batch-failure risk
  • 35% Faster insight generation

The Customer

A Fortune 500 pharma company optimizing fermentation used in drug development; they sought to reduce total process time while maximizing yield by predicting correct manual input parameters throughout the fermentation process.

The Challenge

3D-Priniting-Challenges-Img.jpg
  • High process times with tight time constraints for maximizing output
  • Need high accuracy with low odds of defective batches
  • Minimize risk of batch failure while achieving desired quantity and quality

The Solution

the-solution-img_nextiva.jpg

Stand up an end-to-end ML pipeline for bioprocess optimization:

  • Data cleaning and prep (easy read from Redshift)
  • Enable ML with Spark, scikit-learn, Keras, Prophet, ARIMA (100+ processors)
  • Process evaluation and workflow-driven testing for manual input parameters
  • External integration: downstream apps; workflows versioned in GitHub

Solution Components

Redshift; Spark; scikit-learn; Keras; Prophet; ARIMA; GitHub

The Results

  • Optimized parameter selection across fermentation stages
  • Faster, more reliable batch outcomes
  • Continuous improvements via new features/algorithms
  • Global collaboration and seamless execution
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