Understanding the structural requirements of hybrid (S)-6-((2-(4-phenylpiperazin-1-yl)ethyl)(propyl)amino)-5,6,7,8-tetrahydronaphthalen-1-ol and its analogs as D2/D3 receptor ligands: a 3D QSAR investigation

Abstract

To gain insights into the structural requirements for dopamine D2 and D3 agonists in the treatment of Parkinson’s disease (PD) and to elucidate the basis of selectivity for D3 over D2 (D2/D3), 3D quantitative structure-activity relationship (3D QSAR) investigations using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) methods were performed on a series of 45 structurally related D2 and D3 dopaminergic ligands. Two alignment methods (atom-based and flexible) and two charge calculation methods (Gasteiger-Hückel and AM1) were used in the present study. Overall, D2 affinity and selectivity (D2/D3) models performed better with r2cv values of 0.71 and 0.63 for CoMFA and 0.71 and 0.79 for CoMSIA, respectively. The corresponding predictive r2 values for the CoMFA and CoMSIA models were 0.92 and 0.86 and 0.91 and 0.78, respectively. The CoMFA models generated using flexible alignment outperformed the models with the atom-based alignment in terms of relevant statistics and interpretability of the generated contour maps while CoMSIA models obtained using atom-based alignment showed superiority in terms of internal and external predictive abilities. The presence of carbonyl group (C=O) attached to the piperazine ring and the hydrophobic biphenyl ring were found to be the most important features responsible for the D3 selectivity over D2. This study can be further utilized to design and develop selective and potent dopamine agonists to treat PD.

Predicting subtype selectivity of dopamine receptor ligands with three‐dimensional biologically relevant spectrum

“…The former models were usually built with a small set of analogous compounds and concentrated with mechanistic interpretation. The latter kind of QSAR models focused on modeling with large and heterogeneous data sets for the purpose of reliable prediction . Prediction models reported in this study belong to the second type of QSAR models, in which chemical diversity of data sets is very important.…”

“…[14] In the past years, several QSAR models were developed to predict the activity or selectivity of DR ligands ( Table 1). [2,[15][16][17][18][19][20][21][22][23] Most of the reported models were developed with limited number of compounds, which belong to the same series and then can be superimposed for modeling. However, these methods are not suitable for automatically screening large and diverse chemical libraries.…”

“…Ligand‐based methods such as quantitative structure–activity relationship (QSAR) could be applied in subtype‐selective compounds screening when the target structure is not available . In the past years, several QSAR models were developed to predict the activity or selectivity of DR ligands (Table ) . Most of the reported models were developed with limited number of compounds, which belong to the same series and then can be superimposed for modeling.…”

Computer-Aided Drug Design Approaches to Study Key Therapeutic Targets in Alzheimer’s Disease

“…The presence of bulky substituents at 6-, 7-, and 8positions of quinoline ring, electropositive groups on 3-position, and hydrophilic substituents on quinoline ring is favorable for activity. The introduction of hydrophilic substituents around the piperazine ring and hydrogen bond donor groups on the nitrogen atom of piperazine ring enhances the binding affinity [490]. 3 Pharmacophore modeling using Discovery Studio software [398] Arylpiperazines Pharmacophore model including key features for ligand activity: (i) salt bridge formation between the basic nitrogen atom of piperazine ring and the receptor; (ii) one or more aromatic interactions involving arylpiperazine substructure; (iii) hydrogen bond interaction between the oxygen atom of methoxy group and the receptor; (iv) possibility of hydrogen bond interaction involving the linker part [399].…”

“…Regarding the aminotetralin head group, the introduction of bulky substituents around 7-and 8-positions, hydrophobic and hydrophilic substituents on phenyl and cyclohexyl rings of aminoteralin moiety, respectively, is favorable for activity. The presence of bulky groups near the N-propyl group of aminotetraline moiety is expected to reduce the binding affinity to D 3 R. The introduction of both hydrogen bond donor and acceptor substituents near piperazine ring is predicted to be favorable for agonist activity [490]. 9…”