Artificial Neural Network and Response Surface Methodology as Tools of Quality by Design Approach for the Enhancement of the Solubility a Poorly Soluble Drug Using Albumin Nanoparticles

dc.AffiliationOctober University for modern sciences and Arts (MSA)  
dc.contributor.authorTaha, Huda
dc.contributor.authorMohamed Hossam, Mahmoud
dc.contributor.authorMohamed Kamaly, Mahmoud
dc.contributor.authorKhaled, Merna
dc.date.accessioned2020-12-20T11:01:51Z
dc.date.available2020-12-20T11:01:51Z
dc.date.issued2020
dc.descriptionA project submitted for the partial fulfillment of BSc degree in Pharmaceutical Sciences for October University for Modern Sciences and Artsen_US
dc.description.abstractThe aim of the current work is the enhancement of the solubility of a poorly soluble drug using albumin nanoparticles by applying quality by design approach. using several tools as artificial neural network and response surface methodology for the optimization of albumin nanoparticles. Silymarin is an extract of dried milk thistle seed with many uses such as anticancer drug, anti-inflammatory, antioxidant and hepatoprotective agent. One of the main problems in the Silymarin that it has poor water solubility and poor bioavailability. This problem could be solved by its formulation in albumin nanoparticles. A complete quality target product profile has been constructed, and Ishikawa diagrams were very beneficial in the risk assessment study. Fractional factorial design was used in the screening, where time of stirring, albumin concentration, pH, drug amount, amount of ethanol and the type of solvent were the critical process parameters/ material attributes (CPP/MA), and were tested on the particle size, polydispersity index and the encapsulation efficiency, which were considered as the critical quality attributes (CQA). Whereas, D-optimal design the response surface design (RSD) and was used for the optimization step, where the drug amount and the albumin concentration were only tested on the same previously measured CQA. Artificial neural network (ANN) was applied, by taking the CPP/MA of the optimization design as the inputs and the measured CQA as the outputs, where the obtained correlation coefficients were compared with that obtained from the RSD,, and was found to be higher than the RSD. Design space and control strategies were generated, where an optimized formula as suggested from software was prepared and its results were compared with the expected ones to calculate the % bias, where the results reasonable agreement indicating the validity of the design. Thus quality by design was found to be a successful approach in the formulation and optimization of albumin nanoparticles loaded with silymarin. ANN showed better results than RSD, which could be used in many applications in industrial formulations.en_US
dc.description.sponsorshipDr. Marwa Hamdy A.L. Lamis Helmyen_US
dc.identifier.citationCopyright © 2020 MSA University. All Rights Reserved.en_US
dc.identifier.urihttp://repository.msa.edu.eg/xmlui/handle/123456789/4263
dc.language.isoenen_US
dc.publisherMSA university Faculty of pharmacyen_US
dc.subjectجامعة أكتوبر للعلوم الحديثة والآدابen_US
dc.subjectDSpace Egypten_US
dc.subjectUniversity of Modern Sciences and Artsen_US
dc.subjectMSA Universityen_US
dc.subjectPharmaceuticsen_US
dc.titleArtificial Neural Network and Response Surface Methodology as Tools of Quality by Design Approach for the Enhancement of the Solubility a Poorly Soluble Drug Using Albumin Nanoparticlesen_US
dc.title.alternative(RSPT2.15)en_US
dc.typeOtheren_US

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