Next Article in Journal
Lipid–Inorganic Hybrid Particles with Non-Lamellar Structures
Previous Article in Journal
Nano-Titanium Oxide in Polymeric Contact Lenses: Short Communication
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nanomanufacturing
Volume 2
Issue 3
10.3390/nanomanufacturing2030007
nanomanufacturing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

New Machine Learning Approach for the Optimization of Nano-Hybrid Formulations

by
Raquel de M. Barbosa
1,2,*,
Cleanne C. Lima
1,
Fabio F. de Oliveira
3,
Gabriel B. M. Câmara
1,3,
César Viseras
2,
Tulio F. A. de Lima e Moura
1,
Eliana B. Souto
4,5,*,
Patricia Severino
6,7,
Fernanda N. Raffin
1 and
Marcelo A. C. Fernandes
3,8
1
Laboratory of Drug Development, Department of Pharmacy, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
2
Department of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, Campus de Cartuja s/n, University of Granada, 18071 Granada, Spain
3
Laboratory of Machine Learning and Intelligent Instrumentation, IMD/nPITI, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
4
Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira, No. 228, 4050-313 Porto, Portugal
5
REQUIMTE/UCIBIO, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira, No. 228, 4050-313 Porto, Portugal
6
Laboratory of Nanotechnology and Nanomedicine (LNMED), Institute of Technology and Research (ITP), Av. Murilo Dantas 300, Aracaju 49010-390, Brazil
7
Industrial Biotechnology Program, University of Tiradentes (UNIT), Av. Murilo Dantas 300, Aracaju 49032-490, Brazil
8
Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
*
Authors to whom correspondence should be addressed.
Nanomanufacturing 2022, 2(3), 82-97; https://doi.org/10.3390/nanomanufacturing2030007
Submission received: 21 June 2022 / Revised: 14 July 2022 / Accepted: 15 July 2022 / Published: 18 July 2022
Browse Figures
Versions Notes

Abstract

Nano-hybrid systems are products of interactions between organic and inorganic materials designed and planned to develop drug delivery platforms that can be self-assembled. Poloxamine, commercially available as Tetronic®, is formed by blocks of copolymers consisting of poly (ethylene oxide) (PEO) and poly (propylene oxide) (PPO) units arranged in a four-armed star shape. Structurally, Tetronics are similar to Pluronics®, with an additional feature as they are also pH-dependent due to their central ethylenediamine unit. Laponite is a synthetic clay arranged in the form of discs with a diameter of approximately 25 nm and a thickness of 1 nm. Both compounds are biocompatible and considered as candidates for the formation of carrier systems. The objective is to explore associations between a Tetronic (T1304) and LAP (Laponite) at concentrations of 1–20% (w/w) and 0–3% (w/w), respectively. Response surface methodology (RMS) and two types of machine learning (multilayer perceptron (MLP) and support vector machine (SVM)) were used to evaluate the physical behavior of the systems and the β-Lapachone (β-Lap) solubility in the systems. β-Lap (model drug with low solubility in water) has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. The results show an adequate machine learning approach to predict the physical behavior of nanocarrier systems with and without the presence of LAP. Additionally, the analysis performed with SVM showed better results (R2 > 0.97) in terms of data adjustment in the evaluation of β-Lap solubility. Furthermore, this work presents a new methodology for classifying phase behavior using ML. The new methodology allows the creation of a phase behavior surface for different concentrations of T1304 and LAP at different pHs and temperatures. The machine learning strategies used were excellent in assisting in the optimized development of new nano-hybrid platforms.
Keywords: clay; polyamines; response surface methodology; machine learning; support vector machine; multilayer perceptron; thermo responsive gels; pH-responsive gels clay; polyamines; response surface methodology; machine learning; support vector machine; multilayer perceptron; thermo responsive gels; pH-responsive gels
Graphical Abstract

Share and Cite

MDPI and ACS Style

Barbosa, R.d.M.; Lima, C.C.; Oliveira, F.F.d.; Câmara, G.B.M.; Viseras, C.; Moura, T.F.A.d.L.e.; Souto, E.B.; Severino, P.; Raffin, F.N.; Fernandes, M.A.C. New Machine Learning Approach for the Optimization of Nano-Hybrid Formulations. Nanomanufacturing 2022, 2, 82-97. https://doi.org/10.3390/nanomanufacturing2030007

AMA Style

Barbosa RdM, Lima CC, Oliveira FFd, Câmara GBM, Viseras C, Moura TFAdLe, Souto EB, Severino P, Raffin FN, Fernandes MAC. New Machine Learning Approach for the Optimization of Nano-Hybrid Formulations. Nanomanufacturing. 2022; 2(3):82-97. https://doi.org/10.3390/nanomanufacturing2030007

Chicago/Turabian Style

Barbosa, Raquel de M., Cleanne C. Lima, Fabio F. de Oliveira, Gabriel B. M. Câmara, César Viseras, Tulio F. A. de Lima e Moura, Eliana B. Souto, Patricia Severino, Fernanda N. Raffin, and Marcelo A. C. Fernandes. 2022. "New Machine Learning Approach for the Optimization of Nano-Hybrid Formulations" Nanomanufacturing 2, no. 3: 82-97. https://doi.org/10.3390/nanomanufacturing2030007

APA Style

Barbosa, R. d. M., Lima, C. C., Oliveira, F. F. d., Câmara, G. B. M., Viseras, C., Moura, T. F. A. d. L. e., Souto, E. B., Severino, P., Raffin, F. N., & Fernandes, M. A. C. (2022). New Machine Learning Approach for the Optimization of Nano-Hybrid Formulations. Nanomanufacturing, 2(3), 82-97. https://doi.org/10.3390/nanomanufacturing2030007

Article Metrics

Back to TopTop