Title:Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size
VOLUME: 21 ISSUE: 9
Author(s):A. Alper Öztürk*, A. Bilge Gündüz and Ozan Ozisik
Affiliation:Department of Pharmaceutical Technology, Faculty of Pharmacy, Anadolu University, Eskisehir, Department of Computer Science, Electrical & Electronics Faculty, Yildiz Technical University, Istanbul, Department of Computer Science, Electrical & Electronics Faculty, Yildiz Technical University, Istanbul
Keywords:Solid lipid nanoparticles (SLNs), particle size, pharmaceutical formulation, high-speed homogenization, machine
learning, supervised learning, estimation.
Abstract: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.
Materials and methods: SLNs 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.
Results: PS 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.
Conclusion: Our findings present that machine learning methods can be highly useful for
determining formulation parameters for further research.
