Combinatorial Chemistry & High Throughput Screening - Volume 18, Issue 4, 2015

From a virtual screening campaign, a number of artificial and natural sweeteners were predicted as potential anticonvulsant agents with protective effects in the seizure animal model Maximal Electroshock Seizure (MES) test. In all cases, the predictions were experimentally confirmed in the aforementioned preclinical seizure model. The article reviews and expands previous reports from our group on anticonvulsant activity of those non-nutritive sweeteners, illustrating the potential of virtual screening approaches to propose new medical uses of food additives. This constitutes a particular case of knowledge-based drug repositioning, which may greatly shorten the development time and investment required to introduce novel medications to the pharmaceutical market. We also briefly overview evidence on possible molecular explanations on the anticonvulsant and proconvulsant effects of different non-nutritive sweeteners. Our analysis –based on Swanson’s ABC model- suggests that group I metabotropic glutamate receptors and carbonic anhydrase isoform VII (both proposed or validated molecular targets of antiepileptic drugs) might be involved in the anticonvulsant effect of artificial sweeteners. The first hypothesis is in line with recent advances on development of selective modulators of group I metabotropic glutamate receptors as potential antiepileptic agents.

GPCRs are ubiquitous in most of the organs of the human body. These receptors were found to be the important targets to attenuate inflammation, cancer, cardiac dysfunction, diabetes, etc. The advanced technologies employed on GPCRs provided an opportunity to understand the physiological process of various diseases. Recently, GPCRs were viewed as viable therapeutic targets to deliver safer and more efficacious drug. In the literature, several computational studies were reported to describe the biological mechanism, function and three-dimensional structure of GPCRs. These studies revealed the multiple conserved transmembrane domains of GPCRs which were connected by intra and extracellular loops. In this review, we provide an updated overview on the computational tools and methodologies which were conducted to explore the structural and mechanistic features of GPCRs. The study also demonstrates the most recent computeraided drug design approaches employed on GPCRs. This review provides the information that can be exploited toward the molecular understanding of GPCRs with an aim to design the novel ligands for GPCRs.

Quantitative structure-activity relationships (QSARs) were developed, for cellular uptake of nanoparticles (NPs) of the same iron oxide core but with different surface-modifying organic molecules, based on linear and non-linear (epsilon support vector regression (ε-SVR)). A linear QSAR provided high prediction accuracy of R2=0.751 (coefficient of determination) using 11 descriptors selected from an initial pool of 184 descriptors calculated for the NP surfacemodifying molecules, while a ε-SVR based QSAR with only 6 descriptors improved prediction accuracy to R2 = 0.806. The linear and ε-SVR based QSARs both demonstrated good robustness and well spanned applicability domains. It is suggested that the approach of evaluating pertinent descriptors and their significance, via QSAR analysis, to cellular NP uptake could support planning and interpretation of toxicity studies as well as provide guidance for the tailor-design NPs with respect to targeted cellular uptake for various applications.

The theoretical predictions of endpoints related to nanomaterials are attractive and more efficient alternatives for their experimental determinations. Such type of calculations for the "usual" substances (i.e. non nanomaterials) can be carried out with molecular graphs. However, in the case of nanomaterials, descriptors traditionally used for the quantitative structure - property/activity relationships (QSPRs/QSARs) do not provide reliable results since the molecular structure of nanomaterials, as a rule, cannot be expressed by the molecular graph. Innovative principles of computational prediction of endpoints related to nanomaterials extracted from available eclectic data (technological attributes, conditions of the synthesis, etc.) are suggested, applied to two different sets of data, and discussed in this work.

Virtual screening encompasses a wide range of computational approaches aimed at the high-throughput, cost-efficient exploration of chemical libraries or databases to discover new bioactive compounds or novel medical indications of known drugs. Here, we have performed a systematic comparison of the performance of a large number of 2D and 3D ligand-based approaches (2D and 3D similarity, QSAR models, pharmacophoric hypothesis) in a simulated virtual campaign on a chemical library containing 50 known anticonvulsant drugs and 950 decoys with no previous reports of anticonvulsant effect. To perform such comparison, we resorted to Receiver Operating Characteristic curves. We also tested the relative performance of consensus methodologies. Our results indicate that the selective combination of the individual approaches (through voting and ranking combination schemes) significantly outperforms the individual algorithms and/or models. Among the best-performing individual approaches, 2D similarity search based on circular fingerprints and 3D similarity approaches should be highlighted. Combining the results from different query molecules also led to enhanced enrichment.

Cathepsin B is a cysteine protease that belongs to the papain superfamily. Malfunctions related to cathepsin B can lead to inflammation and cancer. Via an integrated in silico approach, this study is aimed to identify novel Michael acceptors-type compounds that can irreversibly inhibit cathepsin B enzyme via covalent bond formation with the active site cysteine residue. Here, we report the first account of covalent docking approach incorporated into a hybrid ligand/structure-based virtual screening to estimate the binding affinities of various compounds from chemical databases against the cathepsin B protein. For validation, compounds with experimentally determined anti-cathepsin B activity from PubChem bioassay database were also screened and covalently docked to the enzyme target. Interestingly, four novel compounds exhibited better covalent binding affinity when compared against the experimentally determined prototypes. Molecular dynamics simulations were performed to ensure the stability of the docked complexes and to allow further analysis on the MD average structures. Perresidue interaction decomposition analysis was carried out to provide deeper insight into the interaction themes of discovered hits with the active site residues. It is found that polar and hydrophobic interactions contributed the most towards drug binding. The hybrid computational methods applied in this study should serve as a powerful tool in the drug design and development process.

Exploring molecular imaging agents against the beta amyloid (Aβ) plaques for an early detection of Alzheimer’s disease (AD) is one of the emerging research areas in medicinal chemistry. In the present in-silico study, a congeneric series of 44 imaging agents, including 17 positron emission tomography (PET) and 27 single photon emission computed tomography (SPECT) imaging agents, was utilized to understand the structural features required for having essential binding affinity against Aβ plaques. Here, 2D-quantitative structure-activity relationship (2D-QSAR) and group-based QSAR (G-QSAR) models have been developed using genetic function approximation (GFA) and validated using various statistical metrics. Both the models showed satisfactory performance signifying the reliability and robustness of the developed QSAR models. The vital information gained from both the QSAR models will be useful in developing new PET and SPECT imaging agents and also in predicting their binding affinity against Aβ plaques. The results of this study would be important in view of the widespread clinical applicability of the SPECT imaging agents, especially in the developing countries. In this study, we have also designed some imaging agents based on the information provided by the models. Some of these designed compounds were predicted to be similar to or more active than the most active imaging agents present in the original dataset.

The use of long-term animal studies for human and environmental toxicity estimation is more discouraged than ever before. Alternative models for toxicity prediction, including QSAR studies, are gaining more ground. A recent approach is to combine in vitro chemical profiling and in silico chemical descriptors with the knowledge about toxicity pathways to derive a unique signature for toxicity endpoints. In this study we investigate the ToxCast™ Phase I data regarding their ability to predict long-term animal toxicity. We investigated thousands of models constructed in an effort to predict 61 toxicity endpoints using multiple descriptor packages and hundreds of in vitro assays. We investigated the use of in vitro assays and biochemical pathways on model performance. We identified 10 toxicity endpoints where biologically derived descriptors from in vitro assays or pathway perturbations improved the model prediction ability. In vivo toxicity endpoints proved generally challenging to model. Few models were possible to readily model with a balanced accuracy (BA) above 0.7. We also constructed iin silicoi models to predict the outcome of 144 in vitro assays. This showed better statistical metrics with 79 out of 144 assays having median balanced accuracy above 0.7. This suggests that the in vitro datasets have a better modelability than in vivo animal toxicities for the given datasets. Moreover, we published an online platform (http://iprior.ochem.eu) that automates large-scale model building and analysis.