Exploring the enantiorecognition mechanism of Cinchona alkaloid‐based zwitterionic chiral stationary phases and the basic trans‐paroxetine enantiomers

Abstract

The enantiomers of trans-paroxetine (the selectand) were separated on four chiral stationary phases incorporating either quinine [ZWIX(+), ZWIX(+A)] or quinidine [ZWIX(-), ZWIX(-A)] and (R,R)-aminocyclohexanesulfonic acid [in ZWIX(-), and ZWIX(+A)] or (S,S)-aminocyclohexanesulfonic acid [in ZWIX(+), and ZWIX(-A)] chiral selectors. The zwitterion nature of the phases is due to the presence of either (R,R)- or (S,S)-aminocyclohexanesulfonic acid in the selector structure bearing the quinuclidine moiety. ZWIX(+) and ZWIX(-) phases are available on the market with the commercial names CHIRALPAK ZWIX(+) and CHIRALPAK ZWIX(-), respectively. With the aim of rationalizing the enantiomer elution order with the above chiral stationary phases, a molecular dynamic protocol was applied and two energetic parameters were initially measured: selectand conformational energy and selectand interaction energy. In the search for other descriptors allowing a better fitting with the experimental evidences, in the present work we consider an energetic parameter, defined as the selector conformational energy, which resulted to be relevant in the explanation of the experimental elution order in most of the cases. Very importantly, the computational data produced by the present study strongly support the outstanding role of the conformational energy of the chiral selector as it interacts with the analytes.

Binding modes identification through molecular dynamic simulations: A case study with carnosine enantiomers and the Teicoplanin A2‐2‐based chiral stationary phase

“…In order to evaluate the ability of the simulation system to reproduce the thermodynamic of retention, which means, inter alia, the EEO, a well‐established analysis [16,28,30] was applied, calculating three descriptors: the interaction energy between SO and SA (INTER), the SA conformational energy (SELF_SA), and the SO conformational energy (SELF_SO). More details on these three variables are reported in the Experimental Section.…”

“…In the last decade, we have developed conceptually different molecular modeling methods [13–17] aimed at understanding the main interactions and perturbations governing the enantiorecognition process in several sub‐domains of chiral chromatography.…”

Recent studies of docking and molecular dynamics simulation for liquid‐phase enantioseparations

“…In this field, Natalini, Sardella and co‐workers developed a MD simulation protocol (Desmond Molecular Dynamics System 4.0/4.4/5.2 program, 300 ns of simulation time) , which recently has been extensively applied, in collaboration with other groups, to investigate chiral recognition mechanisms and rationalize experimental EEO observed with different Cinchona alkaloid‐based CSPs under PO elution conditions (Table ). In these studies, the experimental MP composition has been accounted for by adding the proper custom solvent (ACN/MeOH/water (0.6%), MeOH/THF, ACN) to the simulation systems.…”

Modeling and predicting chiral stationary phase enantioselectivity: An efficient random forest classifier using an optimally balanced training dataset and an aggregation strategy

“…Chemoinformatic approaches have emerged as one of the most powerful tools for predicting enantioselectivity of chiral HPLC separations . Among them, machine learning approaches represent the latest evolution for computational chemistry .…”

“…However, identifying the most promising chiral columns given a particular chemical structure remains a challenge. Nowadays, the column choice is mostly based on the trial and error method, and thus it may sometimes be necessary to screen a great variety of chiral Chemoinformatic approaches have emerged as one of the most powerful tools for predicting enantioselectivity of chiral HPLC separations [12][13][14]. Among them, machine learning approaches represent the latest evolution for computational chemistry [15].…”