Julio Caballero

Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)

Many articles in “in silico” drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure–activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand–target interactions.

3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds

Inhibitory activities of flavonoid derivatives against aldose reductase (AR) enzyme were modelled by using CoMFA, CoMSIA and GALAHAD methods. CoMFA and CoMSIA methods were used for deriving quantitative structure-activity relationship (QSAR) models. All QSAR models were trained with 55 compounds, after which they were evaluated for predictive ability with additional 14 compounds. The best CoMFA model included both steric and electrostatic fields, meanwhile, the best CoMSIA model included steric, hydrophobic and H-bond acceptor fields. These models had a good predictive quality according to both internal and external validation criteria. On the other hand, GALAHAD was used for deriving a 3D pharmacophore model. Twelve active compounds were used for deriving this model. The obtained model included hydrophobe, hydrogen bond acceptor and hydrogen bond donor features; it was able to identify the active AR inhibitors from the remaining compounds. These in silico tools might be useful in the rational design of new AR inhibitors.

Minimizing the risk of reporting false aromaticity and antiaromaticity in inorganic heterocycles following magnetic criteria

Although aromaticity is a concept in chemistry, in the last years, special efforts have been carried out in order to propose theoretical strategies to quantify it as a property of molecular rings. Among them, perhaps the computation of nucleus independent chemical shifts (NICSs) is the most commonly used, since it is possible to calculate it in an easy and fast way with most used quantum chemistry software. However, contradicting assignments of aromaticity by NICS and other methods have been reported in the literature, especially in studies concerning inorganic chemistry. In this Article is proposed a new and simple strategy to use the NICS information to assess aromaticity, identifying the point along the axis perpendicular to the molecular plane where the in-plane component of NICS becomes zero; it is called free of in-plane component NICS (FiPC-NICS). This spatial point is proposed as secure to consider NICS as an aromaticity descriptor; this simple proposal is evaluated in borazine and cyclotriphosphazenes. The results are compared with carefully examined aromatic stabilization energies and magnetically induced current-density analysis.

Identification of a potent and selective σ1 receptor agonist potentiating NGF-induced neurite outgrowth in PC12 cells

Herein we report the synthesis, drug-likeness evaluation, and in vitro studies of new sigma (σ) ligands based on arylalkenylaminic scaffold. For the most active olefin the corresponding arylalkylamine was studied. Novel arylalkenylamines generally possess high σ(1) receptor affinity (K(i) values <25 nM) and good σ(1)/σ(2) selectivity (K(i)σ(2) >100). Particularly, the piperidine derivative (E)-17 and its arylalkylamine analog (R,S)-33 were observed to be excellent σ(1) receptor ligands (K(i)=0.70 and 0.86 nM, respectively) and to display significantly high selectivity over σ(2), μ-, and κ-opioid receptors and phencyclidine (PCP) binding site of the N-methyl-d-aspartate (NMDA) receptors. Moreover in PC12 cells (R,S)-33 promoted the nerve growth factor (NGF)-induced neurite outgrowth and elongation. Co-administration of the selective σ(1) receptor antagonist BD-1063 totally counteracted this effect, confirming that σ(1) receptors are involved in the (R,S)-33 modulation of the NGF effect in PC12 cells and suggesting a σ(1) agonist profile. As a part of our work, a threedimensional σ(1) pharmacophore model was also developed employing GALAHAD methodology. Only active compounds were used for deriving this model. The model included two hydrophobes and a positive nitrogen as relevant features and it was able to discriminate between molecules with and without affinity toward σ(1) receptor subtype.

Structural requirements of pyrido[2,3-d]pyrimidin-7-one as CDK4/D inhibitors : 2D autocorrelation, CoMFA and CoMSIA analyses

2D autocorrelation, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of pyrido[2,3-d]pyrimidin-7-ones to correlate cyclin-dependent kinase (CDK) cyclin D/CDK4 inhibition with 2D and 3D structural properties of 60 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling CDK4/D inhibitory activity were selected by linear and nonlinear genetic algorithms (GAs) using multiple linear regression (MLR) and Bayesian-regularized genetic neural network (BRGNN) approaches, respectively. Both models showed good predictive statistics; but BRGNN model enables better external predictions. A weight-based input ranking scheme and Kohonen self-organized maps (SOMs) were carried out to interpret the final net weights. The 2D autocorrelation space brings different descriptors for CDK4/D inhibition, and suggests the atomic properties relevant for the inhibitors to interact with CDK4/D active site. CoMFA and CoMSIA analyses were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the electrostatic and hydrophobic fields around them.

Docking and quantitative structure-activity relationship studies for 3-fluoro-4-(pyrrolo(2,1-f)(1,2,4)triazin-4-yloxy)aniline, 3-fluoro-4-(1H-pyrrolo(2,3-b)pyridin-4-yloxy)aniline, and 4-(4- amino-2-fluorophenoxy)-2-pyridinylamine derivatives as c-Met kinase inhibitors

We have performed docking of 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline (FPTA), 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline (FPPA), and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine (AFPP) derivatives complexed with c-Met kinase to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 103 compounds with variations both in structure and activity. Docking helped to analyze the molecular features which contribute to a high inhibitory activity for the studied compounds. In addition, the predicted biological activities of the c-Met kinase inhibitors, measured as IC50 values were obtained by using quantitative structure–activity relationship (QSAR) methods: Comparative molecular similarity analysis (CoMSIA) and multiple linear regression (MLR) with topological vectors. The best CoMSIA model included steric, electrostatic, hydrophobic, and hydrogen bond-donor fields; furthermore, we found a predictive model containing 2D-autocorrelation descriptors, GETAWAY descriptors (GETAWAY: Geometry, Topology and Atom-Weight AssemblY), fragment-based polar surface area (PSA), and MlogP. The statistical parameters: cross-validate correlation coefficient and the fitted correlation coefficient, validated the quality of the obtained predictive models for 76 compounds. Additionally, these models predicted adequately 25 compounds that were not included in the training set.

Chlorogenic Acid Inhibits Human Platelet Activation and Thrombus Formation

Background Chlorogenic acid is a potent phenolic antioxidant. However, its effect on platelet aggregation, a critical factor in arterial thrombosis, remains unclear. Consequently, chlorogenic acid-action mechanisms in preventing platelet activation and thrombus formation were examined. Methods and Results Chlorogenic acid in a dose-dependent manner (0.1 to 1 mmol/L) inhibited platelet secretion and aggregation induced by ADP, collagen, arachidonic acid and TRAP-6, and diminished platelet firm adhesion/aggregation and platelet-leukocyte interactions under flow conditions. At these concentrations chlorogenic acid significantly decreased platelet inflammatory mediators (sP-selectin, sCD40L, CCL5 and IL-1β) and increased intraplatelet cAMP levels/PKA activation. Interestingly, SQ22536 (an adenylate cyclase inhibitor) and ZM241385 (a potent A2A receptor antagonist) attenuated the antiplatelet effect of chlorogenic acid. Chlorogenic acid is compatible to the active site of the adenosine A2A receptor as revealed through molecular modeling. In addition, chlorogenic acid had a significantly lower effect on mouse bleeding time when compared to the same dose of aspirin. Conclusions Antiplatelet and antithrombotic effects of chlorogenic acid are associated with the A2A receptor/adenylate cyclase/cAMP/PKA signaling pathway.

Insights into the structure of urea-like compounds as inhibitors of the juvenile hormone epoxide hydrolase (JHEH) of the tobacco hornworm Manduca sexta: Analysis of the binding modes and structure-activity relationships of the inhibitors by docking and CoMFA calculations

Substituted urea compounds are well-known as potent inhibitors of juvenile hormone epoxide hydrolase (JHEH) of the tobacco hornworm Manduca sexta. Docking simulations of 47 derivatives inside JHEH were performed to gain insight into the structural characteristics of these complexes. The obtained orientations show a strong similitude with the observed in the known X-ray crystal structures of human soluble epoxide hydrolase (sEH) complexed with dialkylurea inhibitors. In addition, the predicted inhibitor concentration (IC₅₀) of the above-mentioned compounds as JHEH inhibitors were obtained by a quantitative structure-activity relationship (QSAR) method by using comparative molecular field analysis (CoMFA) applied to aligned dataset. The best models included steric and electrostatic fields and had adequate predictive abilities. In addition, these models were used to predict the activity of an external test set of compounds that was not used for building the model. Furthermore, plots of the CoMFA fields allowed conclusions to be drawn for the choice of suitable inhibitors.

Is It Reliable to Use Common Molecular Docking Methods for Comparing the Binding Affinities of Enantiomer Pairs for Their Protein Target

Molecular docking is a computational chemistry method which has become essential for the rational drug design process. In this context, it has had great impact as a successful tool for the study of ligand–receptor interaction modes, and for the exploration of large chemical datasets through virtual screening experiments. Despite their unquestionable merits, docking methods are not reliable for predicting binding energies due to the simple scoring functions they use. However, comparisons between two or three complexes using the predicted binding energies as a criterion are commonly found in the literature. In the present work we tested how wise is it to trust the docking energies when two complexes between a target protein and enantiomer pairs are compared. For this purpose, a ligand library composed by 141 enantiomeric pairs was used, including compounds with biological activities reported against seven protein targets. Docking results using the software Glide (considering extra precision (XP), standard precision (SP), and high-throughput virtual screening (HTVS) modes) and AutoDock Vina were compared with the reported biological activities using a classification scheme. Our test failed for all modes and targets, demonstrating that an accurate prediction when binding energies of enantiomers are compared using docking may be due to chance. We also compared pairs of compounds with different molecular weights and found the same results.

Synthesis, in silico, in vitro, and in vivo investigation of 5-[11C]methoxy-substituted sunitinib, a tyrosine kinase inhibitor of VEGFR-2

Sunitinib (SU11248) is a highly potent tyrosine kinase inhibitor targeting vascular endothelial growth factor receptor (VEGFR). Radiolabeled inhibitors of receptor tyrosine kinases (RTKs) might be useful tools for monitoring RTKs levels in tumor tissue giving valuable information for anti-angiogenic therapy. Herein we report the synthesis of 5-methoxy-sunitinib 5 and its (11)C-radiolabeled analog [(11)C]-5. The non-radioactive reference compound 5 was prepared by Knoevenagel condensation of 5-methoxy-2-oxindole with the corresponding substituted 5-formyl-1H-pyrrole. A binding constant (K(d)) of 20 nM for 5 was determined by competition binding assay against VEGFR-2. In addition, the binding mode of sunitinib and its 5-methoxy substituted derivative was studied by flexible docking simulations. These studies revealed that the substitution of the fluorine at position 5 of the oxindole scaffold by a methoxy group did not affect the inhibitor orientation, but affected the electrostatic and van der Waals interactions of the ligand with residues near the DFG motif of VEGFR-2. 5-[(11)C]methoxy-sunitinib ([(11)C]-5) was synthesized by reaction of the desmethyl precursor with [(11)C]CH(3)I in the presence of DMF and NaOH in 17 ± 3% decay-corrected radiochemical yield at a specific activity of 162-205 GBq/μmol (EOS). In vivo stability studies of [(11)C]-5 in rat blood showed that more than 70% of the injected compound was in blood stream, 60 min after administration.