Formulation optimization of smart thermosetting lamotrigine loaded hydrogels using response surface methodology, box benhken design and artificial neural networks

Melamane, Siyabonga; Walker, Roderick B.; Khamanga, Sandile M.

doi:10.1080/03639045.2020.1791163

Cited by 7 publications

(4 citation statements)

References 51 publications

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“…Though ANN may be considered to be the most suited technique for handling complex multivariate nonlinear relationships (Agatonovic-Kustrin & Beresford, 2000) and some studies that used both these techniques reported ANN to be superior (Geyikçi et al, 2012;Lal et al, 2021;Malekjani & Jafari, 2022;Shandilya et al, 2013;Srikanth et al, 2020), in this study BBD had the higher R 2 values and hence it may be considered as the suitable technique for design of multiple fiber extractor. Similar opinion was voiced by some researchers like de Luna et al, 2020, Melamane et al, 2020and Latif et al, 2022 However, this may be because ANN requires a large quantity of experimental data for training to give accurate results, and may give better outcomes by increasing the number of nodes in the hidden layer (Agatonovic-Kustrin & Beresford, 2000). Still, BBD has the advantage of being able to study the interactions in comparison with ANN.…”

Section: Optimization Of Machine Operating Parametersmentioning

confidence: 75%

Design consideration and optimization of process parameters in fiber extraction unit via modelling studies

Vishnu

Harikrishnan

Warrier

et al. 2023

J Food Process Engineering

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Demand for natural fibers as an alternative to synthetic fibers is on the rise. Constraints of current fiber extraction units are limited to single raw material and has low energy efficiency. Hence, this study was taken up for the design and development of a multiple fiber extractor, modelling and optimization of the machine operating parameters, and economic analysis of the developed prototype. The designed machine operates based on the raspador principle. Box Behnken design (BBD) and artificial neural network (ANN) were used to evaluate the performance and optimize the variables of the designed multiple fiber extractor. The input variables included roller speed (550, 735, and 920 rpm), gap/clearance of the roller (2, 3, and 4 mm) and the pitch/distance between the spline slots (0.5, 1.5, and 2.5 mm), while the output variables were yield (kg), capacity (kg/h), energy consumption (kJ), and efficiency (%).The prediction accuracy of quadratic models (R 2 = 0.962-0.985) developed in BBD were better than the ANN models (R 2 = 0.831-0.946). The optimized combination was found to be 714.23 rpm roller speed, 3.12 mm gap, and 1.49 mm pitch of the blade, which yielded (1.48 kg) a desirability value of 0.95. The XRD results indicated the highest crystallinity index was recorded for Pineapple leaf fiber compared to banana fiber. SEM results revealed that extracted BF having typical network structure (cellulose, hemicellulose, lignin, and waxes) compared to smooth PLF (lignin and hemicellulose). The fibers extracted using the developed machine was utilized for making biodegradable cutlery. Practical ApplicationsThis study was taken up to design a multifiber extractor, to extract fibers from agricultural residues after their useful parts are taken for consumption or processing.Thus, a single machine capable of handling multiple byproducts can utilize those residues, which were previously discarded as waste. Moreover, the extracted fibers are used for the manufacture of biodegradable cutlery. With a small investment, this machine can provide an additional income to the farmers, along with taking away the concern regarding waste disposal.

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Section: Optimization Of Machine Operating Parametersmentioning

confidence: 75%

Design consideration and optimization of process parameters in fiber extraction unit via modelling studies

Vishnu

Harikrishnan

Warrier

et al. 2023

J Food Process Engineering

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“…In a DoE experiment, each dependent variable requires a separate model [109] and ANNs are capable of ranking the contributions of the materials and processing factors (input layer parameters) that affect the key properties of the formulations. Therefore, many ANN models can be used to conduct fundamental research of formulation and preparation techniques, which demonstrates the superiority of ANNs [51,60,63,64,68,70,100,[112][113][114][115][116][117][118].…”

Section: Selection and Optimization Of Formulationsmentioning

confidence: 99%

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Wang¹,

Di²,

Wang

et al. 2022

Pharmaceutics

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During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

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“…As such, polymers such as chitosan and alginate have been impregnated with drugs to produce sustained drug delivery while also preventing efflux of said drugs by BBB efflux transporters such as Pgp as well as preventing enzymatic degradation of the active pharmaceutical ingredients outside the CNS [ 190 ]. Melamane and colleagues developed a thermosetting hydrogel loaded with lamotrigine for intranasal administration to manage and treat generalized epilepsy [ 192 ]. The hydrogel was prepared by physically cross-linking Pluronics ® 407 (L127), Pluronics ® 188 (L68), and Carbopol ® 974P NF(C974) [ 192 ].…”

Section: Opportunities For Cross-linked Gels In Cns Drug Deliverymentioning

confidence: 99%

“…Melamane and colleagues developed a thermosetting hydrogel loaded with lamotrigine for intranasal administration to manage and treat generalized epilepsy [ 192 ]. The hydrogel was prepared by physically cross-linking Pluronics ® 407 (L127), Pluronics ® 188 (L68), and Carbopol ® 974P NF(C974) [ 192 ]. Liu et al took it a step further by using carboxymethyl chitosan nanoparticles as carriers to successfully deliver carbamazepine intranasally to bypass the blood-brain barrier [ 191 ].…”

Section: Opportunities For Cross-linked Gels In Cns Drug Deliverymentioning

confidence: 99%

A Comprehensive Review of Cross-Linked Gels as Vehicles for Drug Delivery to Treat Central Nervous System Disorders

Mashabela

Maboa

Miya

et al. 2022

Gels

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Gels are attractive candidates for drug delivery because they are easily producible while offering sustained and/or controlled drug release through various mechanisms by releasing the therapeutic agent at the site of action or absorption. Gels can be classified based on various characteristics including the nature of solvents used during preparation and the method of cross-linking. The development of novel gel systems for local or systemic drug delivery in a sustained, controlled, and targetable manner has been at the epitome of recent advances in drug delivery systems. Cross-linked gels can be modified by altering their polymer composition and content for pharmaceutical and biomedical applications. These modifications have resulted in the development of stimuli-responsive and functionalized dosage forms that offer many advantages for effective dosing of drugs for Central Nervous System (CNS) conditions. In this review, the literature concerning recent advances in cross-linked gels for drug delivery to the CNS are explored. Injectable and non-injectable formulations intended for the treatment of diseases of the CNS together with the impact of recent advances in cross-linked gels on studies involving CNS drug delivery are discussed.

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Formulation optimization of smart thermosetting lamotrigine loaded hydrogels using response surface methodology, box benhken design and artificial neural networks

Cited by 7 publications

References 51 publications

Design consideration and optimization of process parameters in fiber extraction unit via modelling studies

Design consideration and optimization of process parameters in fiber extraction unit via modelling studies

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

A Comprehensive Review of Cross-Linked Gels as Vehicles for Drug Delivery to Treat Central Nervous System Disorders

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