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Machine-guided rapid decision-making on quality of viruses as products and for viral clearance determination

Process control
Project status
99% Completed

Industry Need

  • Develop a model that can better predict functional titer from lower-cost methods of analysis compared to using transmission electron microscopy (TEM) images of viruses


Carnegie Mellon University and Sartorius aim to develop a computational workflow to integrate results of rapid viral titer assays to best match the outcomes of high precision determination of functional titer.  

The two organizations will incorporate and validate their methods by using machine learning (ML) approaches.  

Experiments will include: 

  • Analysis of physical and functional virus titers at various concentrations, from bioreactor supernatant in the presence of cell lysate or other confounding matrices 
  • Determination of physical characteristics of infected cells 

This project will determine the workflow that optimizes analytical assay reproducibility and repeatability and will predict gaps in knowledge that may limit product analysis.  

Outcomes and Impacts

Decrease decision-making time and provides reduced use of costly assays

Create computational assessment of rapid viral titer assays (e.g., qPCR, ddPCR, optical imaging, probe-capture methods) to best predict functional titer of viruses

Provide evaluation methods that are applicable during viral production (e.g., bioreactor stage) to facilitate early decision making

Updates, Related Publications, and Deliverables

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Project Lead

Carnegie Mellon University

Carnegie Mellon University

Participating Organizations

Sartorius Stedim

Sartorius Stedim