Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size

ÖZTÜRK A. A., Gunduz A. B., Ozisik O.

COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING, vol.21, no.9, pp.693-699, 2018 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 21 Issue: 9
  • Publication Date: 2018
  • Doi Number: 10.2174/1386207322666181218160704
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.693-699
  • Keywords: Solid lipid nanoparticles (SLNs), particle size, pharmaceutical formulation, high-speed homogenization, machine learning, supervised learning, estimation, DRUG-DELIVERY, SLN, FORMULATION, CARRIERS
  • Anadolu University Affiliated: Yes


Aims and Objectives: Solid Lipid Nanoparticles (SLNs) are pharmaceutical delivery systems that have advantages such as controlled drug release, long-term stability etc. Particle Size (PS) is one of the important criteria of SLNs. These factors affect drug release rate, bio-distribution etc. In this study, the formulation of SLNs using high-speed homogenization technique has been evaluated. The main emphasis of the work is to study whether the effect of mixing time and formulation ingredients on PS can be modeled. For this purpose, different machine learning algorithms have been applied and evaluated using the mean absolute error metric.