Process Variability Source Analysis for a Multi-step Bio-process

Yuan JIN, S. Joe QIN, Victor SAUCEDO, Zheng LI, Angela MEIER, Siddhartha KUNDA, Salim CHARANIYA

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


Typical manufacturing bio-processes operate as multi-step batch operations. Abnormal variations are often experienced in commercial manufacturing which affects the ability to maintain the product supply to the patients. The low titer is usually highly correlated with high lactate in recombinant Chinese hamster ovary (CHO) cells producing immunoglobulin G, but there are limited means to analyze the vast amount of process data archived from manufacturing sites and understand the causes of the multiple sources of process variability. In this study, we investigate diverse data from a cell culture process, from historical runs of a multi-step manufacturing process for a recombinant antibody. Data includes intra-batch measurements, batch conditions and media preparation measurements. Cell culture process stages from inoculum train to production runs.
To analyze manufacturing variability in multiple stages with various sources of data sets, we first adopt principal component analysis (PCA) based monitoring to find in which space high lactate and low lactate batches can be separated, and apply clustering to identify fault directions for intra-batch data from production runs. We then predict final lactate using data from inoculum stages with a nonlinear support vector machine (SVM) classifier. These models are used to uncover potential causes of high lactate. This data-driven modelling can provide new insights on process characteristics to understand the sources of process variability and thus mitigation procedures can be implemented.
Original languageEnglish
Title of host publicationProceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018, July 1-5, 2018, San Diego, California, USA
EditorsMario R. EDEN, Marianthi G. IERAPETRITOU, Gavin P. TOWLER
Number of pages6
ISBN (Print)9780444642455
Publication statusPublished - 2018
Externally publishedYes
Event13th International Symposium on Process Systems Engineering (PSE 2018) - San Diego, United States
Duration: 1 Jul 20185 Jul 2018

Publication series

NameComputer Aided Chemical Engineering
ISSN (Print)1570-7946


Conference13th International Symposium on Process Systems Engineering (PSE 2018)
Country/TerritoryUnited States
CitySan Diego


  • cell culture process
  • PCA monitoring
  • process variability
  • SVM


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