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

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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


Abstract: Aims and ObjectivesSolid 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, biodistribution 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.

Materials and methodsSLNs were prepared by high-speed homogenizaton. PS, size distribution and zeta potential measurements were performed on freshly prepared samples. In order to model the formulation of the particles in terms of mixing time and formulation ingredients and evaluate the predictability of PS depending on these parameters, different machine learning algorithms were applied on the prepared dataset and the performances of the algorithms were also evaluated.

ResultsPS of SLNs obtained was in the range of 263-498nm. The results present that PS of SLNs can be best estimated by decision tree based methods, among which Random Forest has the least mean absolute error value with 0.028. As a result, the estimation of machine learning algorithms demonstrates that particle size can be estimated by both decision rule-based machine learning methods and function fitting machine learning methods.

ConclusionOur findings present that machine learning methods can be highly useful for determining formulation parameters for further research.