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.
Keywords: Solid lipid nanoparticles (SLNs), particle size, pharmaceutical formulation, high-speed homogenization, machine learning, supervised learning, estimation.
Combinatorial Chemistry & High Throughput Screening
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,Turkey
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.
Export Options
About this article
Cite this article as:
Öztürk Alper A. *, Gündüz Bilge A. and Ozisik Ozan , Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size, Combinatorial Chemistry & High Throughput Screening 2018; 21 (9) . https://dx.doi.org/10.2174/1386207322666181218160704
DOI https://dx.doi.org/10.2174/1386207322666181218160704 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
Call for Papers in Thematic Issues
Artificial Intelligence Methods for Biomedical, Biochemical and Bioinformatics Problems
Recently, a large number of technologies based on artificial intelligence have been developed and applied to solve a diverse range of problems in the areas of biomedical, biochemical and bioinformatics problems. By utilizing powerful computing resources and massive amounts of data, methods based on artificial intelligence can significantly improve the ...read more
Eco-friendly Agents for Biological Control of Pathogenic Diseases
The discovery of an alternative biological approach to disease management includes work on medicinal products derived from natural sources as a starting point for the development of eco-friendly agents for these diseases and the injuries they cause, as well as reducing human contact with hazardous chemicals and their residues. We ...read more
Emerging trends in diseases mechanisms, noble drug targets and therapeutic strategies: focus on immunological and inflammatory disorders
Recently infectious and inflammatory diseases have been a key concern worldwide due to tremendous morbidity and mortality world Wide. Recent, nCOVID-9 pandemic is a good example for the emerging infectious disease outbreak. The world is facing many emerging and re-emerging diseases out breaks at present however, there is huge lack ...read more
Exploring Spectral Graph Theory in Combinatorial Chemistry
Scope of the Thematic Issue: Combinatorial chemistry involves the synthesis and analysis of a large number of diverse compounds simultaneously. Traditional methods rely on brute force experimentation, which can be time-consuming and resource-intensive. Spectral Graph Theory, a branch of mathematics dealing with the properties of graphs in relation to the ...read more
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
Related Articles
-
Therapeutic Approaches Targeting Pathological Tau Aggregates
Current Pharmaceutical Design Revealing Changes in Curcumin Bioavailability using Vitamin C as an Enhancer by HPLC-MS/MS
Current Pharmaceutical Analysis Sleep Related Disorders in the Elderly: An Overview
Current Respiratory Medicine Reviews Nanosuspensions as a Versatile Carrier based Drug Delivery System - An Overview
Current Drug Delivery New Insights on the Antitumoral Properties of Prodiginines
Current Medicinal Chemistry The Genetic Basis of Graves Disease
Current Genomics Functions of Fukutin, a Gene Responsible for Fukuyama Type Congenital Muscular Dystrophy, in Neuromuscular System and Other Somatic Organs
Central Nervous System Agents in Medicinal Chemistry Anti-VEGF Compounds in the Treatment of Neovascular Age Related Macular Degeneration
Current Drug Targets HCV-Related Transformation and New Therapeutic Strategies: An Update
Current Cancer Therapy Reviews Central Nervous System Involvement in Pediatric Rheumatic Diseases: Current Concepts in Treatment
Current Pharmaceutical Design Nanoplatforms for Delivery of siRNA to the Eye
Current Pharmaceutical Design Crosstalk Between Covid-19 and Associated Neurological Disorders: A Review
Current Neuropharmacology Dendritic Cell Immunotherapy for Malignant Gliomas
Reviews on Recent Clinical Trials Targeted Liposomal Drug Delivery in Cancer
Current Pharmaceutical Design Combination of Photodynamic Therapy with Anti-Cancer Agents
Current Medicinal Chemistry Targeted Delivery of siRNA Therapeutics using Ligand Mediated Biodegradable Polymeric Nanocarriers
Current Pharmaceutical Design Pigment Epithelium-derived Factor (PEDF) and Cardiometabolic Disorders
Current Pharmaceutical Design Flavonoids in Neurodegeneration: Limitations and Strategies to Cross CNS Barriers
Current Medicinal Chemistry Bacteriophage and Peptidoglycan Degrading Enzymes with Antimicrobial Applications
Recent Patents on Biotechnology Clinical Uses of Melatonin in Neurological Diseases and Mental and Behavioural Disorders
Current Medicinal Chemistry