A Generative Adversarial Network Approach to Predict Nanoparticle Size in Microfluidics

ACS Biomaterials Science & Engineering, vol. 11, pp. 268–279

Abstract

To achieve precise control over the properties and performance of nanoparticles (NPs) in a microfluidic setting, a profound understanding of the influential parameters governing the NP size is crucial. This study specifically delves into poly(lactic- co -glycolic acid) (PLGA)-based NPs synthesized through microfluidics that have been extensively explored as drug delivery systems (DDS). A comprehensive database, containing more than 11 hundred data points, is curated through an extensive literature review, identifying potential effective features. Initially, we employed a tabular generative adversarial network (TGAN) to enhance data sets, increasing the reliability of the obtained results and elevating prediction accuracy. Subsequently, NP size prediction was performed using different machine learning (ML) techniques including decision tree (DT), random forest (RF), deep neural networks (DNN), linear regression (LR), support vector regression (SVR), and gradient boosting (GB). Among these ensembles, DT emerges as the most accurate algorithm, yielding an average prediction error of 8%. Further simulations underscore the pivotal role of the synthesis method, poly(vinyl alcohol) (PVA) concentration, and lactide-to-glycolide (LA/GA) ratio of PLGA copolymers as the primary determinants influencing NP size.

Authors 5

1. Sara Mihandoost

   Urmia University of Technology

   Affiliation as printed

   Electrical Engineering Department, Urmia University of Technology, Urmia 57166-419, Iran

2. Sima Rezvantalab

   Urmia University of Technology

   Affiliation as printed

   Chemical Engineering Department, Urmia University of Technology, Urmia 57166-419, Iran

3. Roger M. Pallares Aachen Institute for Experimental Molecular Imaging

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, 52074 Aachen, Germany

4. Volkmar Schulz Aachen Department of Physics of Molecular Imaging Systems Institute for Experimental Molecular Imaging Institute of Imaging & Computer Vision

   Fraunhofer Institute for Digital Medicine · RWTH Aachen University

   Affiliation as printed

   Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, 52074 Aachen, Germany

   Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, 52074 Aachen, Germany

5. Fabian M. Kiessling corresponding Aachen Institute for Experimental Molecular Imaging

   Universitätsklinikum Aachen · Fraunhofer Institute for Digital Medicine · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany

   Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, 52074 Aachen, Germany