الفهرس 
Development, validation, and application of extraction solvent prediction models for different drugs from aqueous-based matrices /
Abstract EnglishOnly 14 pages are availabe for public view
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Abstract
Biological matrix represents a challenge in extraction and bioanalysis due to the variability of extraction power and efficiency of solvents, cumbersome multi-step procedures, and usage of many organic solvents which is harmful to the environment. One of the most effective techniques for isolating the desired components from the biological matrix is liquid-liquid extraction. An optimized Artificial Neural Network model was developed based on correlating the selected descriptors of the drugs and Hansen solubility parameters for the predicted extraction solvents. Besides, the prediction power of the developed algorithm has been evaluated. This model was designed on MATLAB program as an ANN linear layer network, with a set of given input drug descriptors; providing outputs of corresponding Hansen solubility parameters for predicted extraction solvents. The model was applied to ten drugs from different pharmacological classes including drugs acting on Central Nervous System, Cardiovascular System, Gastrointestinal tract, Antihistaminic, Antiviral, Antibacterial, and Anti-diabetic classes. The evaluated drugs are Chlorpheniramine maleate, Donepezil, Escitalopram, Levofloxacin, Linagliptin, Nebivolol, Omeprazole, Sertraline,Telmisartan, and Valacyclovir. HPLC-UV methods were applied for the quantitative determination of the extracted drugs. The extraction recoveries of the studied drugs using the predicted solvents are (94%, 66%, 86%, 61%, 90%, 87%, 98%, 91%, 52%, and 92%) respectively. The developed model is deemed very useful in bioanalysis in terms of saving cost and time