Assessment of subvisible particles in biopharmaceuticals with image feature extraction and machine learning

Ravi Maharjan*, Jae Chul Lee*, Johan Peter Bøtker*, Ki Hyun Kim*, Nam Ah Kim*, Seong Hoon Jeong*, Jukka Rantanen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

4 Citations (Scopus)

Abstract

An image classification tool was developed to classify subvisible particles, namely silicone oil (SO) and non-silicone oil (NSO; protein aggregate, rubber closure, and air bubble) particles, present in biopharmaceuticals using feature extraction in FlowCam® images, and the outcomes were validated with six machine learning (ML) classifiers. The image classification tool set at specific configuration: condition 1 − CDEP {(compactness, diameter, elongation, perimeter) = (8.06, 10.44, 13.30, 27.20)} identified SO, while the configuration set at condition 2 – CDEL {(compactness, diameter, elongation, length) = (3.14, 24.48, 1.97, 4.28)} detected NSO. The classification tool was particularly useful in detecting the release of SO after exposure to the stress sources. Additionally, the morphological features-based classification tool (p < 0.05) enhanced the predictive accuracy of the ML classification tools (≥97.2 %). Specifically, CNN (100 %) outperformed naïve Bayes (99.3 %), linear discriminant analysis (98.4 %), artificial neural network (98.1 %), support vector machine (SVM 97.2 %). Bootstrap forest was excluded because it failed to classify SO in a large dataset. The developed classification tool could be an alternative in classifying the image datasets without the burden of complex ML tools. Such image-based classification tool can be computationally economical solution in quality control of the protein formulations.

Original languageEnglish
Article number105061
JournalChemometrics and Intelligent Laboratory Systems
Volume245
Number of pages10
ISSN0169-7439
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 Elsevier B.V.

Keywords

  • Biopharmaceuticals
  • Classification
  • Feature extraction
  • Image classification tool
  • Machine learning
  • Subvisible particle

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