Synthesis and Structure−Activity Relationships of a New Model of Arylpiperazines. 6. Study of the 5-HT1A/α1-Adrenergic Receptor Affinity by Classical Hansch Analysis, Artificial Neural Networks, and Computational Simulation of Ligand Recognition

A classical quantitative structure−activity relationship (Hansch) study and artificial neural networks (ANNs) have been applied to a training set of 32 substituted phenylpiperazines with affinity for 5-HT1A and α1-adrenergic receptors, to evaluate the structural requirements that are responsible for 5-HT1A/α1 selectivity. The resulting models provide a significant correlation of electronic, steric, and hydrophobic parameters with the biological affinities. Although the derived linear Hansch correlations give good statistics and acceptable predictions, the introduction of nonlinear relationships in the analysis gives more solid models and more accurate predictions. In the ANN models on the basis of the obtained 3D plots, the 5-HT1A affinity has a nonlinear dependence on F, Vo, Vm, and πo, although the nonlinear relationship is not far from a planar one. The α1-adrenergic receptor affinity has a clear nonlinear dependence on F, Vo, Vm, πo, and πm. A comparison of both analyses gives an additional understanding for 5-HT1A/α1 selectivity: (a) high F values increase the binding affinity for 5-HT1A receptors and decrease the affinity for α1 sites; (b) the hydrophobicity at the meta-position has only influence for the α1-adrenergic receptor; (c) the meta-position seems to be implicated in the 5-HT1A/α1 selectivity. While the 5-HT1A receptor is able to accommodate bulky substituents in the region of its active site, the steric requirements of the α1-adrenergic receptor at this position are more restricted. This information was used for the design of the new ligand EF-7412 (33) (5-HT1A: Ki exptl = 27 nM, α1: Ki exptl > 1000 nM; 5-HT1A: Ki pred ANN = 36 nM, α1: Ki pred ANN = 2745 nM) which was characterized as an antagonist in vivo in pre- and postsynaptic 5-HT1AR sites. Computational simulations of the complex between EF-7412 (33) and a 3D model of the transmembrane domain of the 5-HT1A receptor allowed us to define the molecular details of the ligand−receptor interaction that includes: (i) the ionic interaction between the protonated amine of the ligand and Asp 3.32; (ii) the hydrogen bonds between the m-NHSO2Et group of the ligand and Asn 7.39; and the hydrogen bonds between the hydantoin moiety of the ligand and (iii) Thr 3.37, (iv) Ser 5.42, and (v) Thr 5.43. These QSAR and ANN results in combination with computational simulations of ligand recognition will be useful for the design of potent selective 5-HT1A ligands.