Your browser doesn't support javascript.
loading
Correlating physico-chemical properties of analytes with Hansen solubility parameters of solvents using machine learning algorithm for predicting suitable extraction solvent.
Mostafa, Eman A; Azim, Mohammad Abdul; ElZaher, Asmaa A; ElKady, Ehab F; Fouad, Marwa A; Ghazy, Fatma H; Radi, Esraa A; El Makarim Saleh, Mahmoud Abo; El Kerdawy, Ahmed M.
Afiliación
  • Mostafa EA; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt. eman.saleh@pharma.cu.edu.eg.
  • Azim MA; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.
  • ElZaher AA; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.
  • ElKady EF; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.
  • Fouad MA; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.
  • Ghazy FH; Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Newgiza University (NGU), Newgiza, km 22 Cairo-Alexandria Desert Road, Cairo, Egypt.
  • Radi EA; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.
  • El Makarim Saleh MA; Health Minister's Technical Office, Ministry of Health, Cairo, Egypt.
  • El Kerdawy AM; Egyptian Drug Authority, Giza, Egypt.
Sci Rep ; 14(1): 18741, 2024 Aug 13.
Article en En | MEDLINE | ID: mdl-39138274
ABSTRACT
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. The developed ANNs model takes a set of chosen descriptors for the cited analyte as an input and predicts the corresponding Hansen solubility parameters of the suitable extraction solvent as a model output. Then, from the solvent combination's appendix, the analyst can identify the proposed extraction solvents' combination for the cited analyte easily and efficiently. For the experimental validation of the model prediction capabilities, twenty structurally diverse drugs belonging to different pharmacological classes were extracted from human plasma. The extraction process was performed using the predicted extraction solvent combination for each drug and quantitively estimated by HPLC/UV methods to assess their extraction recovery. The developed LLE solvent prediction model is in- line with the global trend towards green chemistry since it limits the consumption of organic solvents.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Egipto Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Egipto Pais de publicación: Reino Unido