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dc.contributor.authorAghazadeh Shabestari, Farnaz
dc.date.accessioned2022-12-19T09:49:24Z
dc.date.available2022-12-19T09:49:24Z
dc.date.issued2022en_US
dc.identifier.urihttps://dspace.tbzmed.ac.ir:443/xmlui/handle/123456789/67908
dc.description.abstractPharmacokinetics and physicochemical properties have main role in drug discovery and development. Biopharmaceutics Classification System (BCS) and Biopharmaceutics Drug Disposition Classification System (BDDCS) are four-class systems based on solubility and permeability/metabolism. Drugs are classified into high or low soluble drugs. A drug is considered highly soluble when the highest dose strength is soluble in 250 ml or less of aqueous media over the pH range of 1 to 7.5. Therefore, ionization parameters can be significant factors in estimating solubility class. Aims: In this study, a large data set (577 compounds) is used to develop computational models based on structural parameters and descriptors of drugs to predict their solubility class in the BCS/BDDCS system. Methods: Solubility class of drugs, maximum dose and molecular weight were collected from literature and structural parameters were calculated by ACD/LAB software. Three different methods were used for solubility class prediction include binary logistic regression, decision tree model and estimating class by previously developed QSPR model for determination of aqueous solubility. We used receiver operating characteristic (ROC) curve to validate the developed logistic regression models. Results: Dividing drugs into two groups based on maximum dose parameter (drugs with max dose ≥10 mg, drugs with max dose <10 mg) and developing models for drugs with max dose ≥10 mg revealed better outcome in all used methods. Regression logistic model based on logP, regression logistic model based on logD, decision tree model and combination Dave et al. method and prediction solubility class with a previously established QSPR model could predict with 70.7%, 77.6%, 76.1% and 69.8% accuracy, respectively. Discussion and Conclusion:Structural properties of drugs and Abraham solvation parameters can be used to develop various computational models to predict solubility class of drugs in the BCS/BDDCS system.en_US
dc.language.isoenen_US
dc.publisherTabriz University of Medical Sciences , School of Pharmacyen_US
dc.subjectBCSen_US
dc.subjectBDDCSen_US
dc.subjectSolubilityen_US
dc.subjectPredictionen_US
dc.subjectComputational modelsen_US
dc.titlePrediction of solubility class based on structural parameters in biopharmaceutics classification systemen_US
dc.typeThesisen_US
dc.contributor.supervisorShayanfar, Ali
dc.identifier.callno4241en_US
dc.description.disciplinePharmacyen_US
dc.description.degreePharm Den_US


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