{"title":"基于神经网络的水飞蓟提取物壳聚糖颗粒优化：建模、制备和抗氧化评估","authors":"Ali Hanafi, Kazem D Safa, Shamsali Rezazadeh","doi":"10.2174/1573409918666221010101036","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Silymarin is a flavonolignan extracted from Silybum marianum with various therapeutic applications. Many studies have focused on improving the bioavailability of silymarin due to its wide range of efficacy and low bioavailability. Chitosan, a naturally occurring polymeric substance, has a strong reputation for increasing the solubility of poorly soluble compounds.</p><p><strong>Objective: </strong>This study used artificial neural networks (ANNs) to measure the effects of pH, chitosan to silymarin ratio, chitosan to tripolyphosphate ratio, and stirring time on the loading efficiency of silymarin into chitosan particles.</p><p><strong>Methods: </strong>A model was developed to investigate the interactions between input factors and silymarin loading efficiency. The DPPH method was utilized to determine the antioxidant activity of an optimized formula and pure raw materials.</p><p><strong>Results: </strong>According to the outcome of the ANN model, pH and the chitosan to silymarin ratio demonstrated significant effects on loading efficiency. In addition, increased stirring time decreased silymarin loading, whereas the chitosan-to-tripolyphosphate ratio showed a negligible effect on loading efficiency.</p><p><strong>Conclusion: </strong>Maximum loading efficiency occurred at a pH of approximately~5. Moreover, silymarin- loaded chitosan particles with a lower IC<sub>50</sub> value (36.17 ± 0.02 ppm) than pure silymarin (165.04 ± 0.07 ppm) demonstrated greater antioxidant activity.</p>","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural Network-based Optimization of <i>Silybum Marianum</i> Extract-loaded Chitosan Particles: Modeling, Preparation and Antioxidant Evaluation.\",\"authors\":\"Ali Hanafi, Kazem D Safa, Shamsali Rezazadeh\",\"doi\":\"10.2174/1573409918666221010101036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Silymarin is a flavonolignan extracted from Silybum marianum with various therapeutic applications. Many studies have focused on improving the bioavailability of silymarin due to its wide range of efficacy and low bioavailability. Chitosan, a naturally occurring polymeric substance, has a strong reputation for increasing the solubility of poorly soluble compounds.</p><p><strong>Objective: </strong>This study used artificial neural networks (ANNs) to measure the effects of pH, chitosan to silymarin ratio, chitosan to tripolyphosphate ratio, and stirring time on the loading efficiency of silymarin into chitosan particles.</p><p><strong>Methods: </strong>A model was developed to investigate the interactions between input factors and silymarin loading efficiency. The DPPH method was utilized to determine the antioxidant activity of an optimized formula and pure raw materials.</p><p><strong>Results: </strong>According to the outcome of the ANN model, pH and the chitosan to silymarin ratio demonstrated significant effects on loading efficiency. In addition, increased stirring time decreased silymarin loading, whereas the chitosan-to-tripolyphosphate ratio showed a negligible effect on loading efficiency.</p><p><strong>Conclusion: </strong>Maximum loading efficiency occurred at a pH of approximately~5. Moreover, silymarin- loaded chitosan particles with a lower IC<sub>50</sub> value (36.17 ± 0.02 ppm) than pure silymarin (165.04 ± 0.07 ppm) demonstrated greater antioxidant activity.</p>\",\"PeriodicalId\":10886,\"journal\":{\"name\":\"Current computer-aided drug design\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current computer-aided drug design\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2174/1573409918666221010101036\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CHEMISTRY, MEDICINAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current computer-aided drug design","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/1573409918666221010101036","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
Neural Network-based Optimization of Silybum Marianum Extract-loaded Chitosan Particles: Modeling, Preparation and Antioxidant Evaluation.
Background: Silymarin is a flavonolignan extracted from Silybum marianum with various therapeutic applications. Many studies have focused on improving the bioavailability of silymarin due to its wide range of efficacy and low bioavailability. Chitosan, a naturally occurring polymeric substance, has a strong reputation for increasing the solubility of poorly soluble compounds.
Objective: This study used artificial neural networks (ANNs) to measure the effects of pH, chitosan to silymarin ratio, chitosan to tripolyphosphate ratio, and stirring time on the loading efficiency of silymarin into chitosan particles.
Methods: A model was developed to investigate the interactions between input factors and silymarin loading efficiency. The DPPH method was utilized to determine the antioxidant activity of an optimized formula and pure raw materials.
Results: According to the outcome of the ANN model, pH and the chitosan to silymarin ratio demonstrated significant effects on loading efficiency. In addition, increased stirring time decreased silymarin loading, whereas the chitosan-to-tripolyphosphate ratio showed a negligible effect on loading efficiency.
Conclusion: Maximum loading efficiency occurred at a pH of approximately~5. Moreover, silymarin- loaded chitosan particles with a lower IC50 value (36.17 ± 0.02 ppm) than pure silymarin (165.04 ± 0.07 ppm) demonstrated greater antioxidant activity.
期刊介绍：
Aims & Scope
Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design.
Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews, original research articles and letter articles written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, theoretical chemistry; computational chemistry; computer and molecular graphics; molecular modeling; protein engineering; drug design; expert systems; general structure-property relationships; molecular dynamics; chemical database development and usage etc., providing excellent rationales for drug development.
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