Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing.

Journal: International journal of pharmaceutics
Published Date: Jun 15, 2021

Abstract

The current study utilized an artificial neural network (ANN) to generate computational models to achieve process optimization for a previously developed continuous liposome manufacturing system. The liposome formation was based on a continuous manufacturing system with a co-axial turbulent jet in a co-flow technology. The ethanol phase with lipids and aqueous phase resulted in liposomes of homogeneous sizes. The input features of the ANN included critical material attributes (CMAs) (e.g., hydrocarbon tail length, cholesterol percent, and buffer type) and critical process parameters (CPPs) (e.g., solvent temperature and flow rate), while the ANN outputs included critical quality attributes (CQAs) of liposomes (i.e., particle size and polydispersity index (PDI)). Two common ANN architectures, multiple-input-multiple-output (MIMO) models and multiple-input-single-output (MISO) models, were evaluated in this study, where the MISO outperformed MIMO with improved accuracy. Molecular descriptors, obtained from PaDEL-Descriptor software, were used to capture the physicochemical properties of the lipids and used in training of the ANN. The combination of CMAs, CPPs, and molecular descriptors as inputs to the MISO ANN model reduced the training and testing mean relative error. Additionally, a graphic user interface (GUI) was successfully developed to assist the end-user in performing interactive simulated risk analysis and visualizing model predictions.

Authors

  • Sameera Sansare
    Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA.
  • Tibo Duran
    Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA.
  • Hossein Mohammadiarani
    Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA.
  • Manish Goyal
    Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.
  • Gowtham Yenduri
    Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA.
  • Antonio Costa
    Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA.
  • Xiaoming Xu
    Division of Product Quality Research, Office of Testing and Research, Office of Pharmaceutical Quality/CDER/FDA, Silver Spring, MD 20993, USA.
  • Thomas O'Connor
    Division of Product Quality Research, Office of Testing and Research, Office of Pharmaceutical Quality/CDER/FDA, Silver Spring, MD 20993, USA.
  • Diane Burgess
    Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA.
  • Bodhisattwa Chaudhuri
    Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA; Institute of Material Sciences, University of Connecticut, Storrs, CT 06269, USA; Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USA. Electronic address: bodhi.chaudhuri@uconn.edu.

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