A mini-review on the application of machine learning in polymer nanogels for drug delivery. (2022)

• Record Type: Journal Article
• Authors: Adekoya, Oluwasegun Chijioke; Yibowei, Moses Ebiowei; Adekoya, Gbolahan Joseph; Sadiku, Emmanuel Rotimi; Hamam, Yskandar; Ray, Suprakas Sinha
• Is Part Of: Materials today. Volume 62:(2022)Supplement 1
• Page Start: S141
• Page End: S144
• Publication Date: 2022
• Subjects: Machine learning -- Nanogels -- Polymers -- Hydrogel -- ANN -- Drug; ANN Artificial Neural Network -- FSC Feedback System Control -- IPRS Intelligent Pulsed-Release System -- P(NIPAAm-co-AAc)-PEG Poly (N-isopropylacrylamide-co-acrylic acid)-Poly (ethylene glycol) -- PPRS Programmed Pulsed-Release System -- SCM Set Covering Machine -- SiRNA Silencing Ribonucleic Acid -- SVM Support Vector Machine; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.02.101
• Languages: English
• ISSNs: 2214-7853

Abstract

Graphical abstract: Abstract: The advent of nanotechnology has resulted in an exponential improvement in drug delivery systems. Special attention is drawn to the use of nanogels which are nanosized hydrogels as effective drug delivery polymeric materials. Nanogels are 3-dimensional polymeric chains with sizes ranging from 100 to 200 nm. Their non-toxicity, biocompatibility, and biodegradability make them well suited for this purpose. Emerging studies have shown that the use of machine learning (ML) can optimize the drug-carrying and delivery of nanogels. This review would identify the mechanisms of nanogel drug delivery, commonly used machine learning models, areas of possible application of machine learning as it concerns nanogel drug delivery, and limitations in the application of machine learning.