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A Random Forest Approach for Counting Silicone Oil Droplets and Protein Particles in Antibody Formulations Using Flow Microscopy

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Abstract

Purpose

To evaluate a random forest model that counts silicone oil droplets and non-silicone oil particles in protein formulations with large class imbalance.

Methods

In this work, we present a novel approach for automated image analysis of flow microscopy data based on random forest classification enabling rapid analysis of large data sets. The random forest approach overcomes many of the limitations of traditional classification schemes derived from simple filters or regression models. In particular, the approach does not require a priori selection of important morphology parameters.

Results

We analyzed silicone oil droplets and non-silicone oil particles observed in four model systems with protein concentrations of 20, 50 and 125 mg/mL. Filters based on random forests achieve higher classification accuracies when compared to regression based filters. Additionally, we showcase a procedure that allows for accurate counting of particles ≥1 μm.

Conclusions

Our method is generally applicable for classification and counting of different classes of particles as long as class morphologies are differentially expressed.

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Notes

  1. The density histogram is a smoothed version of the relative histogram such that the entire area of the histogram equals 1.

Abbreviations

CART:

Classification and regression tree

ECD:

Equivalent cirlcular diameter

ESD:

Equivalent spherical diameter

mAb:

Monoclonal antibody

NSO:

Non-silicone oil

PFS:

Pre-filled syringe

SO:

Silicone oil

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ACKNOWLEDGMENTS AND DISCLOSURES

The authors would like to acknowledge Greg Downing, Mark Hu and Thomas Scherer for providing samples and valuable discussions. Daniel Coleman and Barthelemy Demeule are acknowledged for helpful discussions and reviewing the manuscript.

Author information

Authors and Affiliations

  1. Late Stage Pharmaceutical Development, Genentech Inc., South San Francisco, California, 94080, USA

    Miguel Saggu & Ankit R. Patel

  2. Nonclinical Biostatistics, Genentech Inc., South San Francisco, California, 94080, USA

    Theodoro Koulis

Authors
  1. Miguel Saggu
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  2. Ankit R. Patel
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  3. Theodoro Koulis
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Corresponding authors

Correspondence to Miguel Saggu or Theodoro Koulis.

Electronic supplementary material

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ESM 1

Details about the used training sets and size distributions for all model systems. Parameter importance and counting accuracy of the FlowCam (color) data. (DOC 2104 kb)

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Saggu, M., Patel, A.R. & Koulis, T. A Random Forest Approach for Counting Silicone Oil Droplets and Protein Particles in Antibody Formulations Using Flow Microscopy. Pharm Res 34, 479–491 (2017). https://doi.org/10.1007/s11095-016-2079-x

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  • DOI: https://doi.org/10.1007/s11095-016-2079-x

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