Open AccessA review of using data mining and machine learning for predicting drug loading modeling in solid lipid nanoparticles containing curcumin
Author(s) -
R. Widyati,
Ahmad Ashari,
FNU Afiahayati
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1918/4/042015
Subject(s) - curcumin , solid lipid nanoparticle , nanoparticle , drug , drug delivery , computer science , nanotechnology , machine learning , materials science , biochemical engineering , chemistry , engineering , pharmacology , medicine , biochemistry
This article provides a comprehensive review of the use of data mining and machine learning to develop predictive models for drug loading in nanoparticles. Solid lipid nanoparticle technology is a promising new approach to lipophile drug delivery. Solid lipid nanoparticles (SLNs) are an important advance in this area. The bio-acceptable and biodegradable properties of SLN make it less toxic than polymer nanoparticles. This review article contains a series that applies computer-oriented processes and tools to extract information, analyze data and finally extract the correlation and meaning of the results obtained regarding solid lipid nanoparticles especially those containing curcumin. The purpose of this review is to describe the development of several research results that have been published over a period that is useful for new insights on drug loading modeling.