Organophosphate toxicity patterns: A new approach for assessing organophosphate neurotoxicity.

Organophosphorus compounds or organophosphates (OPs) are widely used as flame retardants, plasticizers, lubricants and pesticides. This contributes to their ubiquitous presence in the environment and to the risk of human exposure. The persistence of OPs and their bioaccumulative characteristics raise serious concerns regarding environmental and human health impacts. To address the need for safer OPs, this study uses a New Approach Method (NAM) to analyze the neurotoxicity pattern of 42 OPs. The NAM consists of a 4-step process that combines computational modeling with in vitro and in vivo experimental studies. Using spherical harmonic-based cluster analysis, the OPs were grouped into four main clusters. Experimental data and quantitative structure-activity relationships (QSARs) analysis were used in conjunction to provide information on the neurotoxicity profile of each group. Results showed that one of the identified clusters had a favorable safety profile, which may help identify safer OPs for industrial applications. In addition, the 3D-computational analysis of each cluster was used to identify meta-molecules with specific 3D features. Toxicity was found to correspond to the level of phosphate surface accessibility. Substances with conformations that minimize phosphate surface accessibility caused less neurotoxic effect. This multi-assay NAM could be used as a guide for the classification of OP toxicity, helping to minimize the health and environmental impacts of OPs, and providing rapid support to the chemical regulators, whilst reducing reliance on animal testing.

GESSE: Predicting Drug Side Effects from Drug-Target Relationships.

The in silico prediction of unwanted side effects (SEs) caused by the promiscuous behavior of drugs and their targets is highly relevant to the pharmaceutical industry. Considerable effort is now being put into computational and experimental screening of several suspected off-target proteins in the hope that SEs might be identified early, before the cost associated with developing a drug candidate rises steeply. Following this need, we present a new method called GESSE to predict potential SEs of drugs from their physicochemical properties (three-dimensional shape plus chemistry) and to target protein data extracted from predicted drug-target relationships. The GESSE approach uses a canonical correlation analysis of the full drug-target and drug-SE matrices, and it then calculates a probability that each drug in the resulting drug-target matrix will have a given SE using a Bayesian discriminant analysis (DA) technique. The performance of GESSE is quantified using retrospective (external database) analysis and literature examples by means of area under the ROC curve analysis, "top hit rates", misclassification rates, and a χ(2) independence test. Overall, the robust and very promising retrospective statistics obtained and the many SE predictions that have experimental corroboration demonstrate that GESSE can successfully predict potential drug-SE profiles of candidate drug compounds from their predicted drug-target relationships.

GES polypharmacology fingerprints: a novel approach for drug repositioning.

Polypharmacology is now recognized as an increasingly important aspect of drug design. We previously introduced the Gaussian ensemble screening (GES) approach to predict relationships between drug classes rapidly without requiring thousands of bootstrap comparisons as in current promiscuity prediction approaches. Here we present the GES "computational polypharmacology fingerprint" (CPF), the first target fingerprint to encode drug promiscuity information. The similarity between the 3D shapes and chemical properties of ligands is calculated using PARAFIT and our HPCC programs to give a consensus shape-plus-chemistry ligand similarity score, and ligand promiscuity for a given set of targets is quantified using the GES fingerprints. To demonstrate our approach, we calculated the CPFs for a set of ligands from DrugBank that are related to some 800 targets. The performance of the approach was measured by comparing our CPF with an in-house "experimental polypharmacology fingerprint" (EPF) built using publicly available experimental data for the targets that comprise the fingerprint. Overall, the GES CPF gives very low fall-out while still giving high precision. We present examples of polypharmacology relationships predicted by our approach that have been experimentally validated. This demonstrates that our CPF approach can successfully describe drug-target relationships and can serve as a novel drug repurposing method for proposing new targets for preclinical compounds and clinical drug candidates.

Integrative relational machine-learning for understanding drug side-effect profiles.

BACKGROUND: Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence. RESULTS: In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site. CONCLUSIONS: Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Highly specific and sensitive pharmacophore model for identifying CXCR4 antagonists. Comparison with docking and shape-matching virtual screening performance.

HIV infection is initiated by fusion of the virus with the target cell through binding of the viral gp120 protein with the CD4 cell surface receptor protein and the CXCR4 or CCR5 coreceptors. There is currently considerable interest in developing novel ligands that can modulate the conformations of these coreceptors and, hence, ultimately block virus-cell fusion. Herein, we present a highly specific and sensitive pharmacophore model for identifying CXCR4 antagonists that could potentially serve as HIV entry inhibitors. Its performance was compared with docking and shape-matching virtual screening approaches using 3OE6 CXCR4 crystal structure and high-affinity ligands as query molecules, respectively. The performance of these methods was compared by virtually screening a library assembled by us, consisting of 228 high affinity known CXCR4 inhibitors from 20 different chemotype families and 4696 similar presumed inactive molecules. The area under the ROC plot (AUC), enrichment factors, and diversity of the resulting virtual hit lists was analyzed. Results show that our pharmacophore model achieves the highest VS performance among all the docking and shape-based scoring functions used. Its high selectivity and sensitivity makes our pharmacophore a very good filter for identifying CXCR4 antagonists.

Benchmarking of HPCC: A novel 3D molecular representation combining shape and pharmacophoric descriptors for efficient molecular similarity assessments.

Since 3D molecular shape is an important determinant of biological activity, designing accurate 3D molecular representations is still of high interest. Several chemoinformatic approaches have been developed to try to describe accurate molecular shapes. Here, we present a novel 3D molecular description, namely harmonic pharma chemistry coefficient (HPCC), combining a ligand-centric pharmacophoric description projected onto a spherical harmonic based shape of a ligand. The performance of HPCC was evaluated by comparison to the standard ROCS software in a ligand-based virtual screening (VS) approach using the publicly available directory of useful decoys (DUD) data set comprising over 100,000 compounds distributed across 40 protein targets. Our results were analyzed using commonly reported statistics such as the area under the curve (AUC) and normalized sum of logarithms of ranks (NSLR) metrics. Overall, our HPCC 3D method is globally as efficient as the state-of-the-art ROCS software in terms of enrichment and slightly better for more than half of the DUD targets. Since it is largely admitted that VS results depend strongly on the nature of the protein families, we believe that the present HPCC solution is of interest over the current ligand-based VS methods.

Recent trends and applications in 3D virtual screening.

Virtual screening (VS) is becoming an increasingly important approach for identifying and selecting biologically active molecules against specific pharmaceutically relevant targets. Compared to conventional high throughput screening techniques, in silico screening is fast and inexpensive, and is increasing in popularity in early-stage drug discovery endeavours. This paper reviews and discusses recent trends and developments in three-dimensional (3D) receptor-based and ligand-based VS methodologies. First, we describe the concept of accessible chemical space and its exploration. We then describe 3D structural ligand-based VS techniques, hybrid approaches, and new approaches to exploit additional knowledge that can now be found in large chemogenomic databases. We also briefly discuss some potential issues relating to pharmacokinetics, toxicity profiling, target identification and validation, inverse docking, scaffold-hopping and drug re-purposing. We propose that the best way to advance the state of the art in 3D VS is to integrate complementary strategies in a single drug discovery pipeline, rather than to focus only on theoretical or computational improvements of individual techniques. Two recent 3D VS case studies concerning the LXR-ß receptor and the CCR5/CXCR4 HIV co-receptors are presented as examples which implement some of the complementary methods and strategies that are reviewed here.

Comparison of three preprocessing filters efficiency in virtual screening: identification of new putative LXRbeta regulators as a test case.

In silico screening methodologies are widely recognized as efficient approaches in early steps of drug discovery. However, in the virtual high-throughput screening (VHTS) context, where hit compounds are searched among millions of candidates, three-dimensional comparison techniques and knowledge discovery from databases should offer a better efficiency to finding novel drug leads than those of computationally expensive molecular dockings. Therefore, the present study aims at developing a filtering methodology to efficiently eliminate unsuitable compounds in VHTS process. Several filters are evaluated in this paper. The first two are structure-based and rely on either geometrical docking or pharmacophore depiction. The third filter is ligand-based and uses knowledge-based and fingerprint similarity techniques. These filtering methods were tested with the Liver X Receptor (LXR) as a target of therapeutic interest, as LXR is a key regulator in maintaining cholesterol homeostasis. The results show that the three considered filters are complementary so that their combination should generate consistent compound lists of potential hits.

Structure-activity relationship study of betulinic acid, a novel and selective TGR5 agonist, and its synthetic derivatives: potential impact in diabetes.

We describe here the biological screening of a collection of natural occurring triterpenoids against the G protein-coupled receptor TGR5, known to be activated by bile acids and which mediates some important cell functions. This work revealed that betulinic (1), oleanolic (2), and ursolic acid (3) exhibited TGR5 agonist activity in a selective manner compared to bile acids, which also activated FXR, the nuclear bile acid receptor. The most potent natural triterpenoid betulinic acid was chosen as a reference compound for an SAR study. Hemisyntheses were performed on the betulinic acid scaffold, and we focused on structural modifications of the C-3 alcohol, the C-17 carboxylic acid, and the C-20 alkene. In particular, structural variations around the C-3 position gave rise to major improvements of potency exemplified with derivatives 18 dia 2 (RG-239) and 19 dia 2. The best derivative was tested in vitro and in vivo, and its biological profile is discussed.

Induced fit in liver X receptor beta: a molecular dynamics-based investigation.

Ligand induced fit phenomenon occurring at the ligand binding domain of the liver X receptor beta (LXRbeta) was investigated by means of molecular dynamics. Reliability of a 4-ns trajectory was tested from two distinct LXRbeta crystal complexes 1PQ6B/GW and 1PQ9B/T09 characterized by an open and a closed state of the pocket, respectively. Crossed complexes 1PQ6B/T09 and 1PQ9B/GW were then submitted to the same molecular dynamic conditions, which were able to recover LXRbeta conformations similar to the original crystallography data. Analysis of "open to closed" and "closed to open" conformational transitions pointed out the dynamic role of critical residues lining the ligand binding pocket involved in the local remodeling upon ligand binding (e.g., Phe271, Phe329, Phe340, Arg319, Glu281). Altogether, the present study indicates that the molecular dynamic protocol is a consistent approach for managing LXRbeta-related induced fit process. This protocol could therefore be used for refining ligand docking solutions of a structure-based design strategy.

EMI domains are widespread and reveal the probable orthologs of the Caenorhabditis elegans CED-1 protein.

The EMI domain, first named after its presence in proteins of the EMILIN family, was identified here in several metazoan proteins with various domain architectures, among which the mammalian NEU1/NG3 proteins and Caenorhabditis elegans CED-1, identified as a transmembrane receptor that mediates cell corpse engulfment. Functional data available for EMILIN proteins suggest that the EMI domain could be a protein-protein interaction module. Sequence profiles specific of the EMI family of domains led to identify the probable orthologs of the C. elegans CED-1 protein in mammals and insects, which were yet uncovered.

Human p63RhoGEF, a novel RhoA-specific guanine nucleotide exchange factor, is localized in cardiac sarcomere.

The Rho small GTPases are crucial proteins involved in regulation of signal transduction cascades from extracellular stimuli to cell nucleus and cytoskeleton. It has been reported that these GTPases are directly associated with cardiovascular disorders. In this context, we have searched for novel modulators of Rho GTPases, and here we describe p63RhoGEF a new Db1-like guanine nucleotide exchange factor (GEF). P63RhoGEF encodes a 63 kDa protein containing a Db1 homology domain in tandem with a pleckstrin homology domain and is most closely related to the second Rho GEF domain of Trio. Northern blot and in situ analysis have shown that p63RhoGEF is mainly expressed in heart and brain. In vitro guanine nucleotide exchange assays have shown that p63RhoGEF specifically acts on RhoA. Accordingly, p63RhoGEF expression induces RhoA-dependent stress fiber formation in fibroblasts and in H9C2 cardiac myoblasts. Moreover, we show that p63RhoGEF activation of RhoA in intact cells is dependent on the presence of the PH domain. Using a specific anti-p63RhoGEF antibody, we have detected the p63RhoGEF protein by immunocytochemistry in human heart and brain tissue sections. Confocal microscopy shows that p63RhoGEF is located in the sarcomeric I-band mainly constituted of cardiac sarcomeric actin. Together, these results show that p63RhoGEF is a RhoA-specific GEF that may play a key role in actin cytoskeleton reorganization in different tissues, especially in heart cellular morphology.