Validation of a compartmental model to predict drug release from porous structures produced by ScCO2 techniques

González-Garcinuño, Álvaro; Baldino, Lucia; Guastaferro, Mariangela; Cardea, Stefano; Reverchon, Ernesto; Valle, Eva Martín del

doi:10.1016/j.ejps.2022.106325

Cited by 4 publications

(2 citation statements)

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“…Supporting Information for this article can be found under DOI: https://doi.org/10.1002/cben.202300027. This section includes additional references to primary literature relevant for this research [98,112,122,124,134,137,145,146,[297][298][299][300][301][302][303][304][305][306][307][308][309][310][311][312][313].…”

Section: Supporting Informationmentioning

confidence: 99%

Kinetic Modeling to Explain the Release of Medicine from Drug Delivery Systems

Askarizadeh,

Esfandiari,

Honarvar

et al. 2023

ChemBioEng Reviews

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Proper medication dissolution must be ensured when developing or manufacturing a new solid dosage form. Quantitative analyses performed in dissolution or release tests become simpler when applying mathematical formulae which represent dissolution outcomes as a function of several dosage form properties. Methodologies utilized to examine the kinetics of drug release from controlled‐release formulations are reviewed. The analysis of variance was conducted using statistical, model‐independent, and ‐dependent techniques for the dissolution profile comparison and fitting, respectively. Model equations, including zero‐ and first‐order, Hixson‐Crowell, Weibull, Higuchi, Korsmeyer‐Peppas, Baker‐Lonsdale, Hopfenberg, etc., were employed to match the experimental data. Additional release parameters were taken to illustrate the drug release patterns. Using correlation factors and the Akaike information criterion (AIC), the best‐fitting model was discovered, as were the transport phenomena affecting the behavior of the recognized formulations.

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Section: Supporting Informationmentioning

confidence: 99%

Kinetic Modeling to Explain the Release of Medicine from Drug Delivery Systems

Askarizadeh,

Esfandiari,

Honarvar

et al. 2023

ChemBioEng Reviews

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“…Modeling and computation of drug release from carriers can be utilized for design and optimization of drug delivery systems based on polymeric carriers. Some mathematical models have been developed to simulate mass transfer in polymeric-based drug release (González-Garcinuño et al, 2023;Kubinski et al, 2023;Carr and Pontrelli, 2024). Usually, molecular diffusion is the main mechanism that happens in polymeric-based drug delivery systems where the drug molecules diffuse through the porous structure of polymeric carrier.…”

Section: Introductionmentioning

confidence: 99%

Modeling and validation of drug release kinetics using hybrid method for prediction of drug efficiency and novel formulations

Alshahrani,

Alotaibi,

Alqarni

2024

Front. Chem.

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This paper presents a thorough examination for drug release from a polymeric matrix to improve understanding of drug release behavior for tissue regeneration. A comprehensive model was developed utilizing mass transfer and machine learning (ML). In the machine learning section, three distinct regression models, namely, Decision Tree Regression (DTR), Passive Aggressive Regression (PAR), and Quadratic Polynomial Regression (QPR) applied to a comprehensive dataset of drug release. The dataset includes r(m) and z(m) inputs, with corresponding concentration of solute in the matrix (C) as response. The primary objective is to assess and compare the predictive performance of these models in finding the correlation between input parameters and chemical concentrations. The hyper-parameter optimization process is executed using Sequential Model-Based Optimization (SMBO), ensuring the robustness of the models in handling the complexity of the controlled drug release. The Decision Tree Regression model exhibits outstanding predictive accuracy, with an R2 score of 0.99887, RMSE of 9.0092E-06, MAE of 3.51486E-06, and a Max Error of 6.87000E-05. This exceptional performance underscores the model’s capability to discern intricate patterns within the drug release dataset. The Passive Aggressive Regression model, while displaying a slightly lower R2 score of 0.94652, demonstrates commendable predictive capabilities with an RMSE of 6.0438E-05, MAE of 4.82782E-05, and a Max Error of 2.36600E-04. The model’s effectiveness in capturing non-linear relationships within the dataset is evident. The Quadratic Polynomial Regression model, designed to accommodate quadratic relationships, yields a noteworthy R2 score of 0.95382, along with an RMSE of 5.6655E-05, MAE of 4.49198E-05, and a Max Error of 1.86375E-04. These results affirm the model’s proficiency in capturing the inherent complexities of the drug release system.

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