Application of lignin in controlled release: development of predictive model based on artificial neural network for API release

Pishnamazi, Mahboubeh; Ismail, Hamza Y.; Shirazian, Saeed; Iqbal, Javed; Walker, Gavin; Collins, Maurice N.

doi:10.1007/s10570-019-02522-w

Cited by 76 publications

(10 citation statements)

References 35 publications

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“…Likewise, in another study, an infill in the form of honeycomb was modified, thereby also changing the drug release profile and predicting the resulting release profiles [ 43 ]. Artificial neural networks (ANN) are also being implemented to identify the influence of formulation and process parameters on the release behaviour and to improve predictions [ 28 , 34 , 44 , 45 , 46 , 47 ]. For example, Novák investigated the influence of varying infills and resulting tablet porosities on drug release using an ANN.…”

Section: Introductionmentioning

confidence: 99%

Predicting Drug Release from 3D Printed Oral Medicines Based on the Surface Area to Volume Ratio of Tablet Geometry

2021

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3D printing offers the advantage of being able to modify dosage form geometry, which can be exploited to modify release characteristics. In this study, we investigated the influence of the surface area to volume ratio (SA/V) to change and predict release profiles of 3D printed dosage forms. Geometries with varying SA/V and dosages were designed and printed, and drug dissolution was investigated. Three drug substances were used: pramipexole, levodopa (both BCS I) and praziquantel (BCS II). Two polymers were chosen as matrix formers: polyvinyl alcohol (water-soluble) and ethylene vinyl acetate (inert). Drug release was characterized using the mean dissolution time (MDT) and established equations that describe complete dissolution curves were applied. Predictions were validated with previously un-printed dosage forms. Based on an identified MDT-SA/V correlation, the MDT can be predicted with a deviation of ≤5 min for a given SA/V. Using correlations of fit parameters and SA/V, RMSEP values of 0.6–2.8% and 1.6–3.4% were obtained for the BCS I formulations and RMSEP values of 1.0–3.8% were obtained for the BCS II formulation, indicating accurate prediction over a wide range of dissolution profiles. With this approach, MDT and release profiles of dosage forms with a given SA/V can be precisely predicted without performing dissolution tests and vice versa, the required SA/V can be predicted for a desired release profile.

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Section: Introductionmentioning

confidence: 99%

Predicting Drug Release from 3D Printed Oral Medicines Based on the Surface Area to Volume Ratio of Tablet Geometry

2021

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“…For instance, lignin can work as a natural broad-spectrum UV blocker due to its functional groups [10]. Lignin versatility is also patent in its use as, for instance, an antioxidant and antibacterial agent [11], excipient for controlled drug release [12,13] or even as building block in the development of different value added materials, such as carbon fibres [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Dissolution of kraft lignin in alkaline solutions

Melro

Filipe

Sousa

et al. 2020

International Journal of Biological Macromolecules

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Lignins are among the most abundant renewable resources on the planet. However, their application is limited by the lack of efficient dissolution and extraction methodologies. In this work, a systematic and quantitative analysis of the dissolution efficiency of different alkaline-based aqueous systems (i.e. lithium hydroxide, LiOH; sodium hydroxide, NaOH; potassium hydroxide, KOH; cuprammonium hydroxide, CuAOH; tetrapropylammonium hydroxide, TPAOH and tetrabutylammonium hydroxide, TBAOH) is reported, for the first time, for kraft lignin. Phase maps were determined for all systems and lignin solubility was found to decrease in the following order: LiOH N NaOH N KOH N CuAOH N TPAOH N TBAOH, thus suggesting that the size of the cation plays an important role on its solubility. The π * parameter has an opposite trend to the solubility, supporting the idea that cations of smaller size favor lignin solubility. Dissolution was observed to increase exponentially above pH 9-10 being the LiOH system the most efficient. The soluble and insoluble fractions of lignin in 0.1 M NaOH were collected and analyzed by several techniques. Overall, data suggests a greater amount of simple aromatic compounds, preferentially containing sulfur, in the soluble fraction while the insoluble fraction is very similar to the native kraft lignin.

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“…Therefore, tools used to assist the prediction of the release profiles of drugs that are highspeed and effective are very feasible. For instance, developing an ANN model that has the capability to predict drug release would reduce the workload greatly in the pre-prescription stage, and the adequacy of the predictions has been confirmed [21,35,45,66,74,79,82,[86][87][88][89][90][91][92][93][94].…”

Section: Prediction Of Drug Release Behavior In Vitromentioning

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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Application of lignin in controlled release: development of predictive model based on artificial neural network for API release

Cited by 76 publications

References 35 publications

Predicting Drug Release from 3D Printed Oral Medicines Based on the Surface Area to Volume Ratio of Tablet Geometry

Predicting Drug Release from 3D Printed Oral Medicines Based on the Surface Area to Volume Ratio of Tablet Geometry

Dissolution of kraft lignin in alkaline solutions

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

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