2024 : 11 : 22
Mohammad Reza Hadjmohammadi

Mohammad Reza Hadjmohammadi

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId:
HIndex:
Faculty: Faculty of Chemistry
Address: Babolsar
Phone: 01135302350

Research

Title
Green mixed micellar liquid chromatography as a toxicity screening method of psychotropic drugs
Type
JournalPaper
Keywords
Lethal dose (LD50) · Sodium dodecyl sulfate (SDS) · Polyoxyethylene (23) lauryl ether (Brij-35) · Biopartitioning micellar chromatography (BMC)
Year
2015
Journal Journal of the Iranian Chemical Society
DOI
Researchers Mina Salary ، Mohammad Reza Hadjmohammadi

Abstract

The knowledge of drug toxicity is necessary for risk assessment and ranking of drugs according to their hazard potential. Also, it is essential to determine potential toxicity problems in the early stage of drug discovery scheme to minimize expensive drug failures due to toxicity being found in the late development process. In this study, the capability of biopartitioning micellar chromatography (BMC) using the pure polyoxyethylene (23) lauryl ether (Brij-35) solution and the mixed micellar system of Brij- 35/sodium dodecyl sulfate (Brij-35/SDS, 85:15 molar ratio) as a green mobile phase, respectively, was studied to predict the acute toxicity (lethal dose, LD50) of psychotropic drugs. The relationships between the BMC retention data of 13 basic psychotropic drugs and their LD50 parameter were studied in two different mobile phases and the predictive ability of models was evaluated. A better statistical model was obtained using retention data in Brij-35/SDS, 85:15 (molar ratio), mobile phase (R2 = 0.936, F = 73.715, SE = 277.33, RCV 2 = 0.913). The superiority of mixed micellar mobile phase in BMC is due to the fact that this kind of phase can simulate the resting membrane potential, and the intermolecular interaction among the Brij-35 molecules gradually weakens as the ratio of Brij-35/SDS decreases. The application of the developed model to a prediction set demonstrated that the model was also reliable with good predictive accuracy.