Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size
COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING, cilt.21, sa.9, ss.693-699, 2018 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 21 Sayı: 9
- Basım Tarihi: 2018
- Doi Numarası: 10.2174/1386207322666181218160704
- Dergi Adı: COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.693-699
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
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.