Comparative analyses of response surface methodology and artificial neural networks on incorporating tetracaine into liposomes

Pereira, Ana Karina Vidal; Barbosa, Raquel de Melo; Fernandes, Marcelo A. C.; Finkler, Leandro; Finkler, Christine Lamenha Luna

doi:10.1590/s2175-97902019000317808

Cited by 8 publications

(9 citation statements)

References 37 publications

(48 reference statements)

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“…Moreover, inclusion of tetracaine in the CP-TRANS nanoparticles induced a variation in the observed lamellar structure, since the 1 0-plane spacing (d10) diminished from 259 Å in CP-TRANS, to 230 Å in CP-TRANS/TTC. Such reduction in the lamellar interplanar distances (d10) suggests that tetracaine interacts with the CP molecules, decreasing the thickness or the lamellae by promoting lateral expansion, a phenomenon already observed for TTC in monolayers and bilayers 55 , 56 . TTC really causes dynamic rearrangements in lamellar phases, as recently demonstrated by Hu et al in dioleylphosphatidylcholine supported bilayers, increasing the lipid chain mobility and even inducing the formation of curved tubular structures prior to membrane disruption, at high TTC:lipid ratios 57 .…”

Section: Resultsmentioning

confidence: 66%

A pre-formulation study of tetracaine loaded in optimized nanostructured lipid carriers

Castro

Ribeiro

Breitkreitz

et al. 2021

Sci Rep

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Tetracaine (TTC) is a local anesthetic broadly used for topical and spinal blockade, despite its systemic toxicity. Encapsulation in nanostructured lipid carriers (NLC) may prolong TTC delivery at the site of injection, reducing such toxicity. This work reports the development of NLC loading 4% TTC. Structural properties and encapsulation efficiency (%EE > 63%) guided the selection of three pre-formulations of different lipid composition, through a 23 factorial design of experiments (DOE). DLS and TEM analyses revealed average sizes (193–220 nm), polydispersity (< 0.2), zeta potential |− 21.8 to − 30.1 mV| and spherical shape of the nanoparticles, while FTIR-ATR, NTA, DSC, XRD and SANS provided details on their structure and physicochemical stability over time. Interestingly, one optimized pre-formulation (CP-TRANS/TTC) showed phase-separation after 4 months, as predicted by Raman imaging that detected lack of miscibility between its solid (cetyl palmitate) and liquid (Transcutol) lipids. SANS analyses identified lamellar arrangements inside such nanoparticles, the thickness of the lamellae been decreased by TTC. As a result of this combined approach (DOE and biophysical techniques) two optimized pre-formulations were rationally selected, both with great potential as drug delivery systems, extending the release of the anesthetic (> 48 h) and reducing TTC cytotoxicity against Balb/c 3T3 cells.

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Section: Resultsmentioning

confidence: 66%

A pre-formulation study of tetracaine loaded in optimized nanostructured lipid carriers

Castro

Ribeiro

Breitkreitz

et al. 2021

Sci Rep

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“…ANN is an alternative better than the methods based on RSM in the case of performance. Furthermore, ANN can increase the level of certainty associated with the results and simultaneously can be used to validate new technological strategies [35]. Therefore, using RSM-ANN modeling, the shortcomings of RSM can be resolved and the actual relationship between independent and response parameters can be studied through experimental data [40].…”

Section: Comparative Evaluation Of Rsm and Annmentioning

confidence: 99%

“…There have been many works in the literature about the catalytic combustion, however, there have been fewer studies about the different mathematical modeling approaches and comparative analyzes. The mathematical modeling studies World Journal of Engineering and Technology are necessary to understand the process and show the optimization alternatives to the process [35].…”

Section: Introductionmentioning

confidence: 99%

Response Surface Methodology and Artificial Neural Network Methods Comparative Assessment for Fuel Rich and Fuel Lean Catalytic Combustion

Gendy¹,

Zakhary²,

Ghoneim³

2021

WJET

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“…Artificial intelligence (AI) based on machine learning (ML) has been increasingly used in different areas of knowledge. For example, ML techniques and algorithms allow a new analysis alternative and have accelerated discoveries of materials and formulations in the pharmaceutical field [37][38][39][40][41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Comparative analyses of response surface methodology and artificial neural networks on incorporating tetracaine into liposomes

Cited by 8 publications

References 37 publications

A pre-formulation study of tetracaine loaded in optimized nanostructured lipid carriers

A pre-formulation study of tetracaine loaded in optimized nanostructured lipid carriers

Response Surface Methodology and Artificial Neural Network Methods Comparative Assessment for Fuel Rich and Fuel Lean Catalytic Combustion

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

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