Current Drug Metabolism - Volume 15, Issue 4, 2014

The central axiom of science purports the explanation of every natural phenomenon using all possible logics coming from pure as well as mixed scientific background. The quantitative structure-activity relationship (QSAR) analysis is a study correlating the behavioral manifestation of compounds with their structures employing the interdisciplinary knowledge of chemistry, mathematics, biology as well as physics. Several studies have attempted to mathematically correlate the chemistry and property (physicochemical/ biological/toxicological) of molecules using various computationally or experimentally derived quantitative parameters termed as descriptors. The dimensionality of the descriptors depends on the type of algorithm employed and defines the nature of QSAR analysis. The most interesting feature of predictive QSAR models is that the behavior of any new or even hypothesized molecule can be predicted by the use of the mathematical equations. The phrase “2D-QSAR” signifies development of QSAR models using 2D-descriptors. Such predictor variables are the most widely practised ones because of their simple and direct mathematical algorithmic nature involving no time consuming energy computations and having reproducible operability. 2D-descriptors have a deluge of contributions in extracting chemical attributes and they are also capable of representing the 3D molecular features to some extent; although in no case they should be considered as the ultimate one, since they often suffer from the problems of intercorrelation, insufficient chemical information as well as lack of interpretation. However, by following rational approaches, novel 2D-descriptors may be developed to obviate various existing problems giving potential 2D-QSAR equations, thereby solving the innumerable chemical mysteries still unexplored.

Human Intestinal Absorption (HIA) has been modeled many times by using classification models. However, regression models are scarce. Here, Artificial Neural Networks (ANNs) are implemented for this purpose. A dataset of structurally diverse chemicals with their respective experimental HIA were used to design robust, true predictive and widespread applicable ANN models. An input variables pool was made up of structural invariants calculated by using either Dragon or our software Desmol 1. The selection of best variables was performed following three steps using the entire dataset of molecules. Firstly, variables poorly correlated with the experimental data were eliminated. Secondly, input variable selection was performed by stepwise multilinear regression. Thirdly, correlation matrix in the set of selected variables was then obtained to eliminate those variables strongly intercorrelated. Backpropagation ANNs were trained for these variables finally selected as inputs, and HIA as output. The training and selection procedure to find robust models consisted of randomly partitioning the dataset into three sets: training set, with 50% of the population, test set with 25%, and validation set with the other 25%. With each partitioning, diverse numbers of hidden nodes were assayed to optimize the performance in the prediction for the three sets. Models with r2 greater than 0.6 for the three sets were considered as robust. A randomization test following all these steps was performed, and the poor results obtained confirm the validity of the method presented in this paper to predict HIA for datasets of structurally diverse organic compounds.

The aligned manuscript reports synthesis, screening and QSAR analysis of twenty six 1, 2, 4-triazole analogues from their respective aromatic carboxylic acids. The structures of synthesized analogues were characterized using physical and spectral analysis. 1, 2, 4-Triazole analogs antioxidant capacity was determined using DPPH radical scavenging assay. Results revealed that out of L, T & VRT series, VRT series of 1, 2, 4-triazoles have significant antioxidant activities when compared with standard ascorbic acid. To obtain structural insights for development of new antioxidants a 2D-QSAR analysis of this dataset of 26 molecules was performed. The 2D-QSAR models correlate with the in vitro results and explain the salient structural features predominant in the molecules responsible for antioxidant activity.

The role of ghrelin in regulating metabolism and energy balance has been a subject of intense focus ever since its discovery. Ghrelin regulates energy balance in the short term by induction of appetite and in the longer term by increasing body weight and adiposity. It is the only known peripheral orexigenic hormone and one of the most potent endogenous orexigenic factors discovered to date. However, whilst extensively studied, the mechanism of ghrelin secretion is not well understood. A better understanding of the pathways controlling ghrelin secretion could be useful in the development of new therapeutic approaches to appetite-related disorders. Here, we discuss current knowledge of the processes that control ghrelin secretion, focusing on neural, chemical and hormonal stimuli. In addition, we share our view on the potential of targeting ghrelin for the treatment of eating disorders such as obesity, anorexia nervosa and cachexia.

The immune system helps to halt the infections caused by pathogenic microbial and parasitic agents. The ChEMBL database lists very large datasets of cytotoxicity of organic compounds but notably, a large number of compounds have unknown effects over molecular and cellular targets in the immune system. Flow Cytometry Analysis (FCA) is a very important technique to determine the effect of organic compounds over these molecular and cellular targets in the immune system. In addition, multi-target Quantitative Structure- Property Relationship (mt-QSPR) models can predict drug-target interactions, networks. The objectives of this paper are the following. Firstly, we carried out a review of general aspects and some examples of applications of FCA to study the effect of drugs over different cellular targets. However, we focused more on methods, materials, and experimental results obtained in previous works reported by our group in the study of the drug Dermofural. We also reviewed different mt-QSPR models useful to predict the immunotoxicity and/or the effects of drugs over immune system targets including immune cell lineages or proteins. Secondly, we included new results not published before. Initially, we used ChEMBL data to train and validate a new model but with emphasis in the effect of drugs over lymphocytes. Lastly, we report unpublished results of the computational and FCA study of a new nitro-vinyl-furan compound over thymic lymphocytes T helpers (CD4+) and T cytotoxic (CD8+) population.

The study of the metabolism of xenobiotics by the human body is an essential stage in the complex and expensive process of drug discovery, being one of the main causes of disapproval and/or withdrawal of drugs. Regarding this, enzymes known as cytochromes P450 (CYPs) play a very decisive role in the biotransformation of many chemicals. For this reason, the use of chemoinformatics to predict and /or analyze from different points of view CYPs-mediated drug metabolism, can help to reduce time and financial resources. This work is focused on the most remarkable advances in the last 5 years of the chemoinformatics tools towards the virtual analysis of CYPsmediated drug metabolism. First, a brief section is dedicated to the applicability of chemoinformatics in different areas associated with drug metabolism. Then, both the models for prediction of CYPs substrates and those allowing the assessment of sites of metabolism (SOM) are discussed. At the same time, the principal limitations of the current chemoinformatic tools are pointed out. Finally, and taking into account that metabolism is an essential step in the whole process of designing any drug, we introduce here as a case of study, the first multitasking model for quantitative-structure biological effect relationships (mtk-QSBER). The purpose of this model is to integrate different types of biological profiles such as ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles and antistaphylococci activities. The mtk-QSBER model was created by employing a heterogeneous dataset of more than 66000 cases tested in 6510 different experimental conditions. The model displayed a total accuracy higher than 94%. To the best of our knowledge, this is the first attempt to complement metabolism assays with other relevant biological data in order to speed up the discovery of efficacious antistaphylococci agents.

The present manuscript introduces, for the first time, a novel 3D-QSAR alignment free method (QuBiLS-MIDAS) based on tensor concepts through the use of the three-linear and four-linear algebraic forms as specific cases of n-linear maps. To this end, the ktj three-tuple and four-tuple spatial-(dis)similarity matrices are defined, as tensors of order 3 and 4, respectively, to represent 3Dinformation among “three and four” atoms of the molecular structures. Several measures (multi-metrics) to establish (dis)-similarity relations among “three and four” atoms are discussed, as well as, normalization schemes proposed for the n-tuple spatial-(dis)similarity matrices based on the simple-stochastic and mutual probability algebraic transformations. To consider specific interactions among atoms, both for the global and local indices, n-tuple path and length cut-off constraints are introduced. This algebraic scaffold can also be seen as a generalization of the vector-matrix-vector multiplication procedure (which is a matrix representation of the traditional linear, quadratic and bilinear forms) for the calculation of molecular descriptors and is thus a new theoretical approach with a methodological contribution. A variability analysis based on Shannon’s entropy reveals that the best distributions are achieved with the ternary and quaternary measures corresponding to the bond and dihedral angles. In addition, the proposed indices have superior entropy behavior than the descriptors calculated by other programs used in chemo-informatics studies, such as, DRAGON, PADEL, Mold2, and so on. A principal component analysis shows that the novel 3D n-tuple indices codify the same information captured by the DRAGON 3D-indices, as well as, information not codified by the latter. A QSAR study to obtain deeper criteria on the contribution of the novel molecular parameters was performed for the binding affinity to the corticosteroid-binding globulin, using Cramer’s steroid database. The achieved results reveal superior statistical parameters for the Bond Angle and Dihedral Angle approaches, consistent with the results obtained in variability analysis. Finally, the obtained QuBiLS-MIDAS models yield superior performances than all 3D-QSAR methods reported in the literature using the 31 steroids as training set, and for the popular division of Cramer’s database in training (1-21) and test (22-31) sets, comparable to superior results in the prediction of the activity of the steroids are obtained. From the results achieved, it can be suggested that the proposed QuBiLS-MIDAS N-tuples indices are a useful tool to be considered in chemo-informatics studies.

The study of quantitative structure-property relationships (QSPR) is important to study complex networks of chemical reactions in drug synthesis or metabolism or drug-target interaction networks. A difficult but possible goal is the prediction of drug absorption, distribution, metabolism, and excretion (ADME) process with a single QSPR model. For this QSPR modelers need to use flexible structural parameters useful for the description of many different systems at different structural scales (multi-scale parameters). Also they need to use powerful analytical methods able to link in a single multi-scale hypothesis structural parameters of different target systems (multi-target modeling) with different experimental properties of these systems (multi-output models). In this sense, the QSPR study of complex bio-molecular systems may benefit substantially from the combined application of spectral moments of graph representations of complex systems with perturbation theory methods. On one hand, spectral moments are almost universal parameters that can be calculated to many different matrices used to represent the structure of the states of different systems. On the other hand, perturbation methods can be used to add "small" variation terms to parameters of a known state of a given system in order to approach to a solution of another state of the same or similar system with unknown properties. Here we present one state-of-art review about the different applications of spectral moments to describe complex bio-molecular systems. Next, we give some general ideas and formulate plausible linear models for a general-purpose perturbation theory of QSPR problems of complex systems. Last, we develop three new QSPR-Perturbation theory models based on spectral moments for three different problems with multiple in-out boundary conditions that are relevant to biomolecular sciences. The three models developed correctly classify more than pairs 115,600; 48,000; 134,900 cases of the effects of in-out perturbations in intra-molecular carbolithiations, drug ADME process, or self-aggregation of micelle nanoparticles of drugs or surfactants. The Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp) of these models were >90% in all cases. The first model predicts variations in the yield or enantiomeric excess due to structural variations or changes in the solvent, temperature, temperature of addition, or time of reaction. The second model predicts changes in >18 parameters of biological effects for >3000 assays of ADME properties and/or interactions between 31,723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model predicts perturbations due to changes in temperature, solvent, salt concentration, and/or structure of anions or cations in the self-aggregation of micelle nanoparticles of drugs and surfactants.