Generation of Predictive Pharmacophore Models for CCR5 Antagonists:  Study with Piperidine- and Piperazine-Based Compounds as a New Class of HIV-1 Entry Inhibitors

Abstract

Predictive pharmacophore models were developed for a large series of piperidine- and piperazine-based CCR5 antagonists as anti-HIV-1 agents reported by Schering-Plough Research Institute in recent years. The pharmacophore models were generated using a training set consisting of 25 carefully selected antagonists based on well documented criteria. The activity spread, expressed in K(i), of training set molecules was from 0.1 to 1300 nM. The most predictive pharmacophore model (hypothesis 1), consisting of five features, namely, two hydrogen bond acceptors and three hydrophobic, had a correlation (r) of 0.920 and a root mean square of 0.879, and the cost difference between null cost and fixed cost was 44.46 bits. The model was cross-validated by randomizing the data using the CatScramble technique. The results confirmed that the pharmacophore models generated from the test set were not due to chance correlation. The best model (hypothesis 1) was validated using test set molecules (total of 78) and performed well in classifying active and inactive molecules correctly. The model was further validated by mapping onto it a diverse set of six CCR5 antagonists identified by five different pharmaceutical companies. The best model correctly predicted these compounds as being highly active. These multiple validation approaches provide confidence in the utility of the predictive pharmacophore model developed in this study as a 3D query tool in virtual screening to retrieve new chemical entities as potent CCR5 antagonists. The model can also be used in predicting biological activities of compounds prior to undertaking their costly synthesis.

Exploration of the structural requirements of HIV-protease inhibitors using pharmacophore, virtual screening and molecular docking approaches for lead identification

“…The pharmacophore model was developed based on training set (n tr = 30) compounds by the was confirmed by Debnath's method [31,57]. A valid hypothesis should have the overall cost of the hypothesis far from the null cost and close to the fixed cost.…”

“…The pharmacophore features: hydrogen bond acceptor (HBA) and donor (HBD), hydrophobic (H) and aromatic ring (R) were found to be the key features associated with the selectivity and potency of HIV protease inhibitors. The pharmacophore model can be used in virtual screening to identify potential molecules, predict the activity of the newly synthesized compound before animal experiment; or understand the possible mechanism of action [31,32]. In this study, an attempt was made to identify the pharmacophore hypothesis using the HypoGen algorithm [33] based on key chemical features of HIV-protease inhibitors with inhibition constant covering a satisfactory wide range of magnitude.…”

Ninhydrin in synthesis of heterocyclic compounds

“…[392][393][394] Additionally, the piperazinone ring has proven to be a valuable scaffold for the construction of biologically active molecules. [395][396] In 1978 The reaction of 1,2-bishydroxylamines 358 with ninhydrin 1 to synthesize functional derivatives of condensed dihydroindeno [1,2-b]pyrazine N,N'-dioxides 360 was investigated by Volodarsky and co-workers (Scheme 116).…”

Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods and Applications

“…The underlying methodology of pharmacophore model was defined by different researchers [33]. Recently, this model was successfully applied in mesangial cell proliferation inhibitor discovery and virtual screening of potential ligands for many targets such as HIV integrase and CCR5 antagonist [34][35][36][37]. In 3D pharmacophore model, the molecular spatial features and volume constraints represent the intrinsic interactions of small bioactive ligands with protein receptors.…”