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Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics.
Damiati, Safa A; Rossi, Damiano; Joensson, Haakan N; Damiati, Samar.
Afiliación
  • Damiati SA; Department of Pharmaceutics, Faculty of Pharmacy, King Abdulaziz University (KAU), Jeddah, 21589, Saudi Arabia. smdamiati@kau.edu.sa.
  • Rossi D; Blacktrace Holdings Ltd (Dolomite Microfluidics), Royston, SG8 5TW, UK.
  • Joensson HN; Division of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Damiati S; Novo Nordisk Foundation Center for Biosustainability at KTH, Stockholm, Sweden.
Sci Rep ; 10(1): 19517, 2020 11 11.
Article en En | MEDLINE | ID: mdl-33177577
In this study, synthetic polymeric particles were effectively fabricated by combining modern technologies of artificial intelligence (AI) and microfluidics. Because size uniformity is a key factor that significantly influences the stability of polymeric particles, therefore, this work aimed to establish a new AI application using machine learning technology for prediction of the size of poly(D,L-lactide-co-glycolide) (PLGA) microparticles produced by diverse microfluidic systems either in the form of single or multiple particles. Experimentally, the most effective factors for tuning droplet/particle sizes are PLGA concentrations and the flow rates of dispersed and aqueous phases in microfluidics. These factors were utilized to develop five different and simple in structure artificial neural network (ANN) models that are capable of predicting PLGA particle sizes produced by different microfluidic systems either individually or jointly merged. The systematic development of ANN models allowed ultimate construction of a single in silico model which consists of data for three different microfluidic systems. This ANN model eventually allowed rapid prediction of particle sizes produced using various microfluidic systems. This AI application offers a new platform for further rapid and economical exploration of polymer particles production in defined sizes for various applications including biomimetic studies, biomedicine, and pharmaceutics.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Reino Unido