Thesis (Ph.D.) - Cairo University - Faculty of Pharmacy - Department of Chemistry

In this thesis, antibacterial agents were assessed regarding their chromatographic behavior in order to build reliable QSRR models. Such models facilitate the analysis of this class of pharmaceutical compounds in their pharmaceutical, biological and environmental applications. The studied class was divided into three groups: Ý-lactam antibiotics, quinolones and sulfonamides. For Ý-lactam antibiotics, two validated QSRR models were built in order to predict the retention factors of several Ý-lactams. These models are: firefly algorithm and forward selection multiple linear regression models.Then, statistical analysis methods were conducted to compare between the two models. For quinolones, we planned to relate their retention behavior with their different ionization state through QSRR model based on advanced firefly algorithm and support vector learning machine. For sulfonamides, the point of view was to study the effect of using different ratios of organic modifier (acetonitrile) on their retention behavior through building validated QSRR model using advanced firefly as a variable selection algorithm and support vector as a modeling machine

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