Artificial neural network for optimizing the formulation of curcumin-loaded liposomes from statistically designed experiments

Cardoso-Daodu, Ibilola M.; Ilomuanya, Margaret O.; Amenaghawon, Andrew N.; Azubuike, Chukwuemeka

doi:10.1007/s40204-022-00179-6

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…In this study, we systematically employed the EL technique to assess the impact of three independent factors, namely, compositions, sonication time, and extrusion temperature, on the particle size and PDI of nanoliposomes. Our findings strongly corroborate the established relationships between these independent factors and the parameters under assessment, in alignment with prior research endeavors 20 , 30 , 79 , 80 .…”

Section: Discussionsupporting

confidence: 90%

“…Machine learning techniques, particularly ANNs, have been successfully used to optimize the formulation of drug-loaded liposomes 30 , 32 , 93 , 94 . These models have proven to be more accurate than traditional linear regression models in predicting liposome properties 93 , 94 .…”

Section: Discussionmentioning

confidence: 99%

“…The hydrophobicity and/or hydrophilicity, biocompatibility, particle size, high loading capacity, controlled-release properties of loaded drugs and many other characteristics of liposomes make them ideal drug delivery nanoparticle for pharmaceuticals and nutrients 54 – 57 . Liposomes have been recently used for the delivery of curcumin 24 , 30 , 51 . Curcumin is the main active constituent of turmeric isolated from the plant Curcuma longa 58 .…”

Section: Introductionmentioning

confidence: 99%

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Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles

Hoseini,

Jaafari,

Golabpour

et al. 2023

Sci Rep

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Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming. This study aimed to apply a machine learning technique to assess the impact of specific factors, including sonication time, extrusion temperature, and compositions, on the size and PDI of liposomal nanoparticles. Liposomal solutions were prepared and subjected to sonication with varying values for these parameters. Two compositions: (A) HSPC:DPPG:Chol:DSPE-mPEG2000 at 55:5:35:5 molar ratio and (B) HSPC:Chol:DSPE-mPEG2000 at 55:40:5 molar ratio, were made using remote loading method. Ensemble learning (EL), a machine learning technique, was employed using the Least-squares boosting (LSBoost) algorithm to accurately model the data. The dataset was randomly split into training and testing sets, with 70% allocated for training. The LSBoost algorithm achieved mean absolute errors of 1.652 and 0.0105 for modeling the size and PDI, respectively. Under conditions where the temperature was set at approximately 60 °C, our EL model predicted a minimum particle size of 116.53 nm for composition (A) with a sonication time of approximately 30 min. Similarly, for composition (B), the model predicted a minimum particle size of 129.97 nm with sonication times of approximately 30 or 55 min. In most instances, a PDI of less than 0.2 was achieved. These results highlight the significant impact of optimizing independent factors on the characteristics of liposomal nanoparticles and demonstrate the potential of EL as a decision support system for identifying the best liposomal formulation. We recommend further studies to explore the effects of other independent factors, such as lipid composition and surfactants, on liposomal nanoparticle characteristics.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles

Hoseini,

Jaafari,

Golabpour

et al. 2023

Sci Rep

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“…Neural network models have also been used in the design and optimization of formulation Screening. Cardoso-Daodu IM’s study used an artificial neural network for the optimization of the formulation of liposomes ( 24 ). Streba CT’s study applied neural network diagnosis for the screening of contrast-enhanced ultrasonography parameters in the diagnosis of liver tumors ( 25 ), allowing for the reduction of experimental efforts significantly toward prescription screening.…”

Section: Discussionmentioning

confidence: 99%

Preparation of ultrasound contrast agents: The exploration of the structure-echogenicity relationship of contrast agents based on neural network model

Wang²,

Feng³

et al. 2022

Front. Oncol.

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There is a need to standardize the process of micro/nanobubble preparation to bring it closer to clinical translation. We explored a neural network-based model to predict the structure-echogenicity relationship for the preparation and fabrication of ultrasound-enhanced contrast agents. Seven formulations were screened, and 109 measurements were obtained. An artificial neural network-multilayer perceptron (ANN-MLP) model was used. The original data were divided into the training and testing groups, which included 73 and 36 groups of data, respectively. The hidden layer was selected from three hidden layers and included bias. The classification graph showed that the predicted values of the training and testing groups were 76.7% and 66.7%, respectively. According to the receiver operating characteristic curve, the accuracy of different imaging effects could achieve a prediction rate of 88.1–96.5%. The percentage graph showed that the data were gradually converging. The predictive analysis curves of different ultrasound effects gradually approached stable value of Gain. Normalized importance predicted contributions for the Pk1, poly-dispersity index (PDI), and intensity account were 100%, 98.5%, and 89.7%, respectively. The application of the ANN-MLP model is feasible and effective for the exploration of the synthesis process of ultrasound contrast agents. 1,2-Distearoyl-sn-glycero-3 phosphoethanolamine-N (methoxy[polyethylene glycol]-2000) (DSPE PEG-2000) correlated highly with the success rate of contrast agent synthesis.

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“…A release ratio of 1.489 which was close to the target release ratio of 1.5 was obtained indicating a functional temperature-sensitive liposome formulation with optimal release kinetics. 24,30…”

Section: Discussionmentioning

confidence: 99%

Preparation and Purification of Calcein Loaded Temperature-Sensitive Liposomes

2022

TJNPR

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Artificial neural network for optimizing the formulation of curcumin-loaded liposomes from statistically designed experiments

Cited by 10 publications

References 24 publications

Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles

Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles

Preparation of ultrasound contrast agents: The exploration of the structure-echogenicity relationship of contrast agents based on neural network model

Preparation and Purification of Calcein Loaded Temperature-Sensitive Liposomes

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