Publications by authors named "Mahmoud Abo El Makarim Saleh"
Correlating physico-chemical properties of analytes with Hansen solubility parameters of solvents using machine learning algorithm for predicting suitable extraction solvent.
Eman A Mostafa Mohammad Abdul Azim Asmaa A ElZaher Ehab F ElKady Marwa A Fouad Mahmoud Abo El Makarim Saleh
Sci Rep
August 2024
Artificial neural networks (ANNs) are biologically inspired algorithms designed to simulate the way in which the human brain processes information. In sample preparation for bioanalysis, liquid-liquid extraction (LLE) represents an important step with the extraction solvent selection is the key laborious step. In the current work, a robust and reliable ANNs model for LLE solvent prediction was generated which could predict the suitable solvent for analyte extraction.
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- A new liquid chromatography (LC) method was developed to simultaneously analyze four drugs: metformin hydrochloride, sitagliptin phosphate, simvastatin, and ezetimibe, showcasing its versatility across different pharmacological classes.
- The chromatographic separation was achieved using a specific mobile phase and a C18 column, with UV detection set at two different wavelengths for optimal analysis of the drugs.
- The method met International Conference on Harmonization validation standards, demonstrating excellent linearity, accuracy, and precision, making it suitable for routine quality control analysis in pharmaceutical products.
Article Synopsis
- A chemometric-assisted UV-spectrophotometric method was developed to simultaneously analyze pioglitazone and glimepiride in both synthetic mixtures and pharmaceutical formulations.
- Three chemometric techniques (CRACLS, PCR, and PLS) were utilized to obtain and validate absorbance data, resulting in high recovery rates (98-100%) and strong precision metrics.
- The effectiveness of these methods was confirmed through statistical analysis, demonstrating their reliability compared to standard pharmacopeial methods for the two drugs.