A chemometric study of the 5-HT1A receptor affinities presented by arylpiperazine compounds

Iridium-catalyzed enantioselective synthesis of chiral γ-amino alcohols and intermediates of (S)-duloxetine, (R)-fluoxetine, and (R)-atomoxetine

Chiral γ-amino alcohols are the prevalent structural motifs and building blocks in pharmaceuticals and bioactive molecules. Enantioselective hydrogenation of β-amino ketones provides a straightforward and powerful tool for the synthesis of chiral γ-amino alcohols, but the asymmetric transformation is synthetically challenging. Here, a series of tridentate ferrocene-based phosphine ligands bearing modular and tunable unsymmetrical vicinal diamine scaffolds were designed, synthesized, and evaluated in the iridium-catalyzed asymmetric hydrogenation of β-amino ketones. The system was greatly effective to substrates with flexible structure and functionality, and diverse β-tertiary-amino ketones and β-secondary-amino ketones were hydrogenated smoothly. The excellent reactivities and enantioselectivities were achieved in the asymmetric delivery of various chiral γ-amino alcohols with up to 99% yields, >99% ee values, and turnover number (TON) of 48,500. The gram-scale reactions with low catalyst loading showed the potential application in industrial synthesis of chiral drugs, such as (S)-duloxetine, (R)-fluoxetine, and (R)-atomoxetine.

New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists

The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r2training = 0.761, q2 = 0.656, r2test = 0.746, MSEtest = 0.132 and MAEtest = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSEtest values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r2test = 0.824, MSEtest = 0.088 and MAEtest = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r2test = 0.811, MSEtest = 0.100 and MAEtest = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and μ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstract Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.

Identification of electronic and structural descriptors of adenosine analogues related to inhibition of leishmanial glyceraldehyde-3-phosphate dehydrogenase

Quantitative structure-activity relationship (QSAR) studies were performed in order to identify molecular features responsible for the antileishmanial activity of 61 adenosine analogues acting as inhibitors of the enzyme glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). Density functional theory (DFT) was employed to calculate quantum-chemical descriptors, while several structural descriptors were generated with Dragon 5.4. Variable selection was undertaken with the ordered predictor selection (OPS) algorithm, which provided a set with the most relevant descriptors to perform PLS, PCR and MLR regressions. Reliable and predictive models were obtained, as attested by their high correlation coefficients, as well as the agreement between predicted and experimental values for an external test set. Additional validation procedures were carried out, demonstrating that robust models were developed, providing helpful tools for the optimization of the antileishmanial activity of adenosine compounds.

Structure-activity differences of chlorogenic acid and its isomers on sensitization via intravenous exposure

Chlorogenic acid (CGA) is found in many plants that are used as medicinal substances in traditional Chinese medicine injectables (TCMIs). However, to date, there is controversy as to whether CGA is the major cause of TCMIs-related hypersensitivity administered intravenously. Therefore, the aim of this study was to evaluate the potential sensitization of CGA and structure-activity differences between its isomers using an intravenous exposure mouse model. The results showed that popliteal lymph nodes proliferation was significantly induced by CGA and its isomers. Both CGA and isochlorogenic acid A (iso-CGA A) significantly enhanced the secretion of trinitrophenyl (TNP) ovalbumin-specific immunoglobulin (Ig)G1; and iso-CGA B significantly induced TNP-specific IgG1, IgM, and IgG2b secreting. Furthermore, the results of quantitative structure-activity relationship analysis suggested that chemical structure factors, including atomic mass, electronegativity, atom shape and size, atom distribution, atomic weight, and atomic polarizabilities, the ionic currents, were significantly correlated with the potential sensitization of CGA and its isomers. In summary, when administered intravenously, the strength and type of sensitization may be correlated with structure differences in the CGA family.

Synthesis and pharmacological evaluation of carbamic acid 1-phenyl-3-(4-phenyl-piperazine-1-yl)-propyl ester derivatives as new analgesic agents

A series of carbamic acid 1-phenyl-3-(4-phenyl-piperazine-1-yl)-propyl ester derivatives were synthesized through discovery strategies for balancing target-based in vitro screening and phenotypic in vivo screening. All the newly synthesized compounds were screened for their analgesic activities and compared with standard drug morphine. Among them, compound 44r, a potent analgesic agent that has favorable pharmacokinetic properties in rats and most importantly, has a wide safety margin. We demonstrated with in vitro and in vivo functional assays that its analgesic activity might be through 5-HT(2A) antagonism to some extent. Hence, it is concluded that there is ample scope for further study in developing compound 44r as a good lead candidate for an analgesic agent.

3D-QSAR design of new escitalopram derivatives for the treatment of major depressive disorders

Antidepressants are psychiatric agents used for the treatment of different types of depression being at present amongst the most commonly prescribed drug, while their effectiveness and adverse effects are the subject of many studies and competing claims. Having studied five QSAR models predicting the biological activities of 18 antidepressants, already approved for clinical treatment, in interaction with the serotonin transporter (SERT), we attempted to establish the membrane ionsâ contributions (sodium, potassium, chlorine and calcium) supplied by donor/acceptor hydrogen bond character and electrostatic field to the antidepressant activity. Significant cross-validated correlation q(2) (0.5â0.6) and the fitted correlation r(2) (0.7â0.82) coefficients were obtained indicating that the models can predict the antidepressant activity of compounds. Moreover, considering the contribution of membrane ions (sodium, potassium and calcium) and hydrogen bond donor character, we have proposed a library of 24 new escitalopram structures, some of them probably with significantly improved antidepressant activity in comparison with the parent compound.

Pharmacophore-based 3D QSAR studies on a series of high affinity 5-HT1A receptor ligands

5-HT(1A) receptor antagonists have been employed to treat depression, but the lack of structural information on this receptor hampers the design of specific and selective ligands. In this study, we have performed CoMFA studies on a training set of arylpiperazines (high affinity 5-HT(1A) receptor ligands) and to produce an effective alignment of the data set, a pharmacophore model was produced using Galahad. A statistically significant model was obtained, indicating a good internal consistency and predictive ability for untested compounds. The information gathered from our receptor-independent pharmacophore hypothesis is in good agreement with results from independent studies using different approaches. Therefore, this work provides important insights on the chemical and structural basis involved in the molecular recognition of these compounds.

Modeling the antileishmanial activity screening of 5-nitro-2-heterocyclic benzylidene hydrazides using different chemometrics methods

QSAR analysis for modeling the antileishmanial activity screening of a series of 49 nitro derivatives of Hydrazides were carried out using different Chemometrics methods. First, a large number of descriptors were calculated using Hyperchem, Mopac and Dragon softwares. Then, a suitable number of these descriptors were selected using multiple linear regression (MLR) technique. Then selected descriptors were used as inputs for artificial neural networks with three different weight update functions including Levenberg-Marquardt back propagation network (LM-ANN), resilient back propagation network (RP-ANN) and variable learning rate algorithm (GDX-ANN). The best artificial neural network model was an LM-ANN with a 5-5-1 architecture. Comparison of the results indicates that the LM-ANN method has better predictive power than the other methods.

Classification of 5-HT(1A) receptor ligands on the basis of their binding affinities by using PSO-Adaboost-SVM

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT_1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies.