QSAR models for predicting the activity of non-peptide luteinizing hormone-releasing hormone (LHRH) antagonists derived from erythromycin A using quantum chemical properties

Towards a non-animal risk assessment for anti-androgenic effects in humans

Toxicology testing is undergoing a transformation from a system based on high-dose studies in laboratory animals to one founded primarily on in vitro methods that evaluate changes in normal cellular signalling pathways using human-relevant cells or tissues. We review the tools and approaches that could be used to develop a non-animal safety assessment for anti-androgenic effects in humans, with a focus on the molecular initiating events (MIEs) that human disorders indicate critical for normal functioning of the hypothalamus-pituitary-testicular (HPT) axis. In vitro test systems exist which can be used to characterize the effects of test chemicals on some MIEs such as androgen receptor antagonism, inhibition of steroidogenic enzymes or 5α-reductase inhibition. When used alongside information describing the pharmacokinetics of a specific chemical exposure, these could be used to inform a pathways-based safety assessment. However, some parts of the HPT axis such as events occurring in the hypothalamus or pituitary are not well represented by accepted in vitro methods. In vitro tools to characterize perturbations in these events need to be developed before a fully integrated model of the HPT axis can be described. Knowledge gaps also exist which prevent us from using in vitro data to predict the type and severity of in vivo effect(s) that could arise from a given level of in vitro anti-androgenic activity. This means that more work is needed to reliably link an MIE with an adverse outcome. However, especially for chemicals with low anti-androgenic activity, human exposure data can be used to put in vitro mode of action data into context for risk-based safety decision-making.

Exploring the ligand recognition properties of the human vasopressin V1a receptor using QSAR and molecular modeling studies

Vaptans are compounds that act as non-peptide vasopressin receptor antagonists. These compounds have diverse chemical structures. In this study, we used a combined approach of protein folding, molecular dynamics simulations, docking, and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the vasopressin receptor V1a (V1aR) with some of its blockers (134). QSAR studies were performed using MLR analysis and were gathered into one group to perform an artificial neural network (ANN) analysis. For each molecule, 1481 molecular descriptors were calculated. Additionally, 15 quantum chemical descriptors were calculated. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. Molecular modeling enabled us to obtain a reliable tridimensional model of V1aR. The docking results indicated that the great majority of ligands reach the binding site under π-π, π-cation, and hydrophobic interactions. The QSAR studies demonstrated that the heteroatoms N and O are important for ligand recognition, which could explain the structural diversity of ligands that reach V1aR.

Exploring QSARs for inhibitory effect of a set of heterocyclic thrombin inhibitors by multilinear regression refined by artificial neural network and molecular docking simulations

Several non-peptide heterocyclic compounds reported as potent thrombin inhibitors in vitro were chosen to carry out a QSAR study upon them using MLR and ANN analysis. In order to identify the best QSAR models, the input for ANN consisted of those subsets of descriptors used in the MLR models. The best QSAR models contained the SIC₀ descriptor as the main topological descriptor. To identify the physical and chemical properties involved in the ligand-thrombin complexes, an automated ligand-flexible docking procedure was used. The docking results suggest that the thrombin inhibition by these heterocyclic compounds is driven by π-π, hydrogen bonds and salt bridge interactions. The best Gibbs free energy of ligand binding was found at the thrombin sites S1 and D. We have shown that it is possible to build MLR models with geometries taken from two different sources (semi-empirical and MD geometries) and obtain similar results.

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.

Network models in drug discovery and regenerative medicine

Network motifs and modelling paradigms are attracting increasing attention as modelling tools in drug design and development, and in regenerative medicine. There is a gradual but inexorable convergence between these hitherto disparate disciplines. This review summarizes some very recent work in these areas, leading to an understanding of the complementary roles networks play and factors driving this convergence: network paradigms can be excellent ways of modelling and understanding drug molecules and their action, an understanding of the robustness and vulnerabilities of biological targets may improve the efficacy of drug design and discovery, drug design has an increasingly large role to play in directing stem cell properties, stem cell regulatory networks can be modelled in useful ways using network models at a reasonable level of scale, and the network tools of drug design are also very useful for the design of biomaterials used in regenerative medicine.