Deciphering a Pharmacophore Network: A Case Study Using BCR-ABL Data.

This paper introduces a general method that can be used to create groups of pharmacophores to support their further in-depth analysis. A BCR-ABL molecular dataset was used to calculate graph edit distances between pharmacophores and led to their organization into a novel pharmacophore network. The application of a graph layout algorithm allowed us to discriminate between the pharmacophores associated with active compounds and those associated with inactive compounds. A clustering approach was used to refine the partitioning by grouping the pharmacophores based on their structures, activities, and binding modes. Analysis of a newly spatialized pharmacophore network provided us with critical insight into structure-activity relationships, most notably those that revealed distinctions between activity classes and chemical families. As shown, this method permits us to identify families of structurally homogeneous pharmacophores.

A Computational Selection of Metabolite Biomarkers Using Emerging Pattern Mining: A Case Study in Human Hepatocellular Carcinoma.

The biomarker development in metabolomics aims at discriminating diseased from normal subjects and at creating a predictive model that can be used to diagnose new subjects. From a case study on human hepatocellular carcinoma (HCC), we studied for the first time the potential usefulness of the emerging patterns (EPs) that come from the data mining domain. When applied to a metabolomics data set labeled with two classes (e.g., HCC patients vs healthy subjects), EP mining can capture differentiating combinations of metabolites between the two classes. We observed that the so-called jumping emerging patterns (JEPs), which correspond to the combinations of metabolites that occur in only one of the two classes, achieved better performance than individual biomarkers. Particularly, the implementation of the JEPs in a rules-based diagnostic tool drastically reduced the false positive rate, i.e., the rate of healthy subjects predicted as HCC patients.

Conformation and Dynamics of Human Urotensin II and Urotensin Related Peptide in Aqueous Solution.

Conformation and dynamics of the vasoconstrictive peptides human urotensin II (UII) and urotensin related peptide (URP) have been investigated by both unrestrained and enhanced-sampling molecular-dynamics (MD) simulations and NMR spectroscopy. These peptides are natural ligands of the G-protein coupled urotensin II receptor (UTR) and have been linked to mammalian pathophysiology. UII and URP cannot be characterized by a single structure but exist as an equilibrium of two main classes of ring conformations, open and folded, with rapidly interchanging subtypes. The open states are characterized by turns of various types centered at K8Y9 or F6W7 predominantly with no or only sparsely populated transannular hydrogen bonds. The folded conformations show multiple turns stabilized by highly populated transannular hydrogen bonds comprising centers F6W7K8 or W7K8Y9. Some of these conformations have not been characterized previously. The equilibrium populations that are experimentally difficult to access were estimated by replica-exchange MD simulations and validated by comparison of experimental NMR data with chemical shifts calculated with density-functional theory. UII exhibits approximately 72% open:28% folded conformations in aqueous solution. URP shows very similar ring conformations as UII but differs in an open:folded equilibrium shifted further toward open conformations (86:14) possibly arising from the absence of folded N-terminal tail-ring interaction. The results suggest that the different biological effects of UII and URP are not caused by differences in ring conformations but rather by different interactions with UTR.

Discovering structural alerts for mutagenicity using stable emerging molecular patterns.

This study is dedicated to the introduction of a novel method that automatically extracts potential structural alerts from a data set of molecules. These triggering structures can be further used for knowledge discovery and classification purposes. Computation of the structural alerts results from an implementation of a sophisticated workflow that integrates a graph mining tool guided by growth rate and stability. The growth rate is a well-established measurement of contrast between classes. Moreover, the extracted patterns correspond to formal concepts; the most robust patterns, named the stable emerging patterns (SEPs), can then be identified thanks to their stability, a new notion originating from the domain of formal concept analysis. All of these elements are explained in the paper from the point of view of computation. The method was applied to a molecular data set on mutagenicity. The experimental results demonstrate its efficiency: it automatically outputs a manageable number of structural patterns that are strongly related to mutagenicity. Moreover, a part of the resulting structures corresponds to already known structural alerts. Finally, an in-depth chemical analysis relying on these structures demonstrates how the method can initiate promising processes of chemical knowledge discovery.

Dual histamine H3R/serotonin 5-HT4R ligands with antiamnesic properties: pharmacophore-based virtual screening and polypharmacology.

In recent years, preclinical and clinical studies have generated considerable interest in the development of histamine H3 receptor (H3R) antagonists as novel treatment for degenerative disorders associated with impaired cholinergic function. To identify novel scaffolds for H3R antagonism, a common feature-based pharmacophore model was developed and used to screen the 17,194 compounds of the CERMN (Centre d'Etudes et de Recherche sur le Médicament de Normandie) chemical library. Out of 268 virtual hits which have been gathered in 34 clusters, we were particularly interested in tricyclic derivatives also exhibiting a potent 5HT4R affinity. Benzo[h][1,6]naphthyridine derivatives showed the highest H3R affinity, and compound 17 (H3R Ki = 41.6 nM; 5-HT4R Ki = 208 nM) completely reversed the amnesiant effect of scopolamine at 3 mg/kg in a spatial working memory experiment. For the first time we demonstrated the feasibility to combine H3R and 5-HT4R activities in a single molecule, raising the exciting possibility that dual H3R antagonist/5HT4R agonist have potential for the treatment of neurodegenerative diseases such as Alzheimer's disease.

Assessment of the genotoxic and carcinogenic potentials of 3-aminothiophene derivatives using in vitro and in silico methodologies.

Thiophene derivatives, a class of compounds widely used in products such as pharmaceuticals, agrochemicals or dyestuffs, represent chemicals of concern. Indeed, the thiophene ring is often considered as a structural moiety that may be involved in toxic effects in humans. We primarily focus on the genotoxic/mutagenic and carcinogenic potentials of the methyl 3-amino-4-methylthiophene-2-carboxylate (1), a precursor of the articaine local anesthetic (4) which falls within the scope of the European REACH (Registration, Evaluation, Authorisation and restriction of CHemicals) legislation. To discern some structure-toxicity relationships, we also studied two related compounds, namely the 3-amino 4-methylthiophene (2) and the 2-acetyl 4-chlorothiophene (3). Techniques employed to assess mutagenic and DNA-damaging effects involved the Salmonella mutagenicity assay (or Ames test) and the single-cell gel electrophoresis assay (or Comet assay). In the range of tested doses, none of these derivatives led to a positive response in the Ames tests and DNA damage was only observed in the Comet assay after high concentration exposure of 2. The study of their carcinogenic potential using the in vitro SHE (Syrian Hamster Embryo) cell transformation assay (CTA) highlighted the activity of compound 2. A combination of experimental data with in silico predictions of the reactivity of thiophene derivatives towards cytochrome P450 (CYP450), enabled us to hypothesize possible pathways leading to these toxicological profiles.

Navigating pharmacophore space to identify activity discontinuities: a case study with BCR-ABL.

The exploration of chemical space is a fundamental aspect of chemoinformatics, particularly when one explores a large compound data set to relate chemical structures with molecular properties. In this study, we extend our previous work on chemical space visualization at the pharmacophoric level. Instead of using conventional binary classification of affinity (active vs inactive), we introduce a refined approach that categorizes compounds into four distinct classes based on their activity levels: super active, very active, active, and inactive. This classification enriches the color scheme applied to pharmacophore space, where the color representation of a pharmacophore hypothesis is driven by the associated compounds. Using the BCR-ABL tyrosine kinase as a case study, we identified intriguing regions corresponding to pharmacophore activity discontinuities, providing valuable insights for structure-activity relationships analysis.

Dynamic microfluidic single-cell screening identifies pheno-tuning compounds to potentiate tuberculosis therapy.

Drug-recalcitrant infections are a leading global-health concern. Bacterial cells benefit from phenotypic variation, which can suggest effective antimicrobial strategies. However, probing phenotypic variation entails spatiotemporal analysis of individual cells that is technically challenging, and hard to integrate into drug discovery. In this work, we develop a multi-condition microfluidic platform suitable for imaging two-dimensional growth of bacterial cells during transitions between separate environmental conditions. With this platform, we implement a dynamic single-cell screening for pheno-tuning compounds, which induce a phenotypic change and decrease cell-to-cell variation, aiming to undermine the entire bacterial population and make it more vulnerable to other drugs. We apply this strategy to mycobacteria, as tuberculosis poses a major public-health threat. Our lead compound impairs Mycobacterium tuberculosis via a peculiar mode of action and enhances other anti-tubercular drugs. This work proves that harnessing phenotypic variation represents a successful approach to tackle pathogens that are increasingly difficult to treat.

New Pharmacophore Fingerprints and Weight-matrix Learning for Virtual Screening. Application to Bcr-Abl Data.

In this work, we propose to analyze the potential of a new type of pharmacophoric descriptors coupled to a novel feature transformation technique, called Weight-Matrix Learning (WML, based on a feed-forward neural network). The application concerns virtual screening on a tyrosine kinase named BCR-ABL. First, the compounds were described using three different families of descriptors: our new pharmacophoric descriptors, and two circular fingerprints, ECFP4 and FCFP4. Afterwards, each of these original molecular representations were transformed using either an unsupervised WML method or a supervised one. Finally, using these transformed representations, K-Means clustering algorithm was applied to automatically partition the molecules. Combining our pharmacophoric descriptors with supervised Weight-Matrix Learning (SWMLR ) leads to clearly superior results in terms of several quality measures.

Automated detection of toxicophores and prediction of mutagenicity using PMCSFG algorithm.

Maximum common substructures (MCS) have received a lot of attention in the chemoinformatics community. They are typically used as a similarity measure between molecules, showing high predictive performance when used in classification tasks, while being easily explainable substructures. In the present work, we applied the Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm to automatically detect toxicophores (structural alerts) and to compute fingerprints based on MCS. We present a comparison between our MCS-based fingerprints and 12 well-known chemical fingerprints when used as features in machine learning models. We provide an experimental evaluation and discuss the usefulness of the different methods on mutagenicity data. The features generated by the MCS method have a state-of-the-art performance when predicting mutagenicity, while they are more interpretable than the traditional chemical fingerprints.

Towards a partial order graph for interactive pharmacophore exploration: extraction of pharmacophores activity delta.

This paper presents a novel approach called Pharmacophore Activity Delta for extracting outstanding pharmacophores from a chemogenomic dataset, with a specific focus on a kinase target known as BCR-ABL. The method involves constructing a Hasse diagram, referred to as the pharmacophore network, by utilizing the subgraph partial order as an initial step, leading to the identification of pharmacophores for further evaluation. A pharmacophore is classified as a 'Pharmacophore Activity Delta' if its capability to effectively discriminate between active vs inactive molecules significantly deviates (by at least δ standard deviations) from the mean capability of its related pharmacophores. Among the 1479 molecules associated to BCR-ABL binding data, 130 Pharmacophore Activity Delta were identified. The pharmacophore network reveals distinct regions associated with active and inactive molecules. The study includes a discussion on representative key areas linked to different pharmacophores, emphasizing structure-activity relationships.

Interpretation of honeybees contact toxicity associated to acetylcholinesterase inhibitors.

The widespread use of different pesticides generates adverse effects on non target organisms like honeybees. Organophosphorous and carbamates kill honeybees through the inactivation of acetylcholinesterase (AChE), thereby interfering with nerve signaling and function. For this class of pesticides, it is fundamental to understand the relationship between their structures and the contact toxicity for honeybees. A Quantitative Structure-Activity Relationship (QSAR) study was carried out on 45 derivatives by a genetic algorithm approach starting from more than 2500 descriptors. In parallel, a new 3D model of AChE associated to honeybees was defined. Physicochemical properties of the receptor and docking studies of the derivatives allow understanding the meaningful of three descriptors and the implication of several amino acids in the overall toxicity of the pesticides.

Receptor- and ligand-based study on novel 2,2'-bithienyl derivatives as non-peptidic AANAT inhibitors.

Arylalkylamine N-acetyl transferase (serotonin N-acetyl transferase, AANAT) is a critical enzyme in the light-mediated regulation of melatonin production and circadian rythm. With the objective of discovering new chemical entities with inhibitory potencies against AANAT, a medium-throughput screening campaign was performed on a chemolibrary. We found a class of molecules based on a 2,2'-bithienyl scaffold, and compound 1 emerged as a first hit. Herein, we describe our progress from hit discovery and to optimization of this new class of compounds. To complete the study, computational approaches were carried out: a docking study which provided insights into the plausible binding modes of these new AANAT inhibitors and a three-dimensional quantitative structure-activity relationship study that applied comparative molecular field analysis (CoMFA) methodology. Several CoMFA models were developed (variable alignments and options), and the best predictive one yields good statistical results (q(2) = 0.744, r(2) = 0.891, and s = 0.273). The resulting CoMFA contour maps were used to illustrate the pharmacomodulations relevant to the biological activities in this series of analogs and to design new active inhibitors. This novel series of 2,2'-bithienyl derivatives gives new insights into the design of AANAT inhibitors.

Introduction of jumping fragments in combination with QSARs for the assessment of classification in ecotoxicology.

Starting from a random set of structures taken from the European Chemical Bureau (ECB) Web site, an estimation of the classification by acute category in ecotoxicology was carried out. This estimation was based on two approaches. One approach consists in starting with global quantitative structure-activity relationship (QSAR) equations, analyzing the results and defining an interpretation in terms of overall results and mode of action. The other starts with the notion of emerging fragments and more specifically with the introduction of a particular concept: the jumping fragments. This publication studies the scopes and limitations of each approach for the classification of the derivatives. A promising combination of the two methods is proposed for the classification and also for bringing new information about the importance, for the ecotoxicity, of specific chemical fragments considered alone or in association with others.

Prediction of acute toxicity in fish by using QSAR methods and chemical modes of action.

Three quantitative structure-activity relationship (QSAR) models were evaluated for their power to predict the toxicity of chemicals in two datasets: (1) EPAFHM (US Environmental Protection Agency-Fathead Minnow) and (2) derivatives having a high production volume (HPV), as compiled by the European Chemical Bureau. For all three QSAR models, the quality of the predictions was found to be highly dependent on the mode of action of the chemicals. An analysis of outliers from the three models gives some clues for improving the QSAR models. Two classification methods, Toxtree and a Bayesian approach with fingerprints as descriptors, were also analyzed. Predictions following the Toxtree classification for narcosis were good, especially for the HPV set. The learning model (Bayesian approach) produced interesting results for the EPAFHM dataset but gave lower quality predictions for the HPV set.

hERG toxicity assessment: Useful guidelines for drug design.

All along the drug development process, one of the most frequent adverse side effects, leading to the failure of drugs, is the cardiac arrhythmias. Such failure is mostly related to the capacity of the drug to inhibit the human ether-à-go-go-related gene (hERG) cardiac potassium channel. The early identification of hERG inhibition properties of biological active compounds has focused most of attention over the years. In order to prevent the cardiac side effects, a great number of in silico, in vitro and in vivo assays have been performed. The main goal of these studies is to understand the reasons of these effects, and then to give information or instructions to scientists involved in drug development to avoid the cardiac side effects. To evaluate anticipated cardiovascular effects, early evaluation of hERG toxicity has been strongly recommended for instance by the regulatory agencies such as U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Thus, following an initial screening of a collection of compounds to find hits, a great number of pharmacomodulation studies on the novel identified chemical series need to be performed including activity evaluation towards hERG. We provide in this concise review clear guidelines, based on described examples, illustrating successful optimization process to avoid hERG interactions as cases studies and to spur scientists to develop safe drugs.

Novel antagonists of serotonin-4 receptors: synthesis and biological evaluation of pyrrolothienopyrazines.

Based on the definition of a 5-HT(4) receptor antagonist pharmacophore, a series of pyrrolo[1,2-a]thieno[3,2-e] and pyrrolo[1,2-a]thieno[2,3-e] pyrazine derivatives were designed, prepared, and evaluated to determine the properties necessary for high-affinity binding to 5-HT(4) receptors. The compounds were synthesized by substituting the chlorine atom of the pyrazine ring with various N-alkyl-4-piperidinylmethanolates. They were evaluated in binding assays with [(3)H]GR113808 (1) as the 5-HT(4) receptor radioligand. The affinity values (K(i) or inhibition percentages) were affected by both the substituent on the aromatic ring and the substituent on the lateral piperidine chain. A methyl group on the tricyclic ring produced a marked increase in affinity while an N-propyl or N-butyl group gave compounds with nanomolar affinities. Among the most potent ligands, 34d was selected for further pharmacological studies and evaluated in vivo. This compound acts as an antagonist/weak partial agonist in COS-7 cells stably expressing the 5-HT(4(a)) receptor and is of great interest as a peripheral antinociceptive agent.

Structure-activity relationships of serotonin 5-HT7 receptors ligands: A review.

5-HT7 receptors are the most recently discovered serotonergic receptors, for which numerous physiological implications in the central and the peripheral nervous systems as well as the endocrine system are described. A current public health challenge is to propose new and more efficient treatments against neuropsychiatric disorders such as epilepsy or Alzheimer's disease. In this context, 5-HT7 receptors represent an interesting target for the treatment and prevention of those pathologies, as an alternative or in association with other medicines. Thus, numerous chemical series of agonists and antagonists have been developed. Some of these molecules have shown a therapeutic potential in various in vivo studies. This review aims to present an overview of 5-HT7 receptors and of the medicinal chemistry programs that led to the identification of new, potent and selective 5-HT7 receptors ligands. Structure-activity relationships studies based on molecular docking and pharmacophoric approaches are also described.

The Pharmacophore Network: A Computational Method for Exploring Structure-Activity Relationships from a Large Chemical Data Set.

Historically, structure-activity relationship (SAR) analysis has focused on small sets of molecules, but in recent years, there has been increasing efforts to analyze the growing amount of data stored in public databases like ChEMBL. The pharmacophore network introduced herein is dedicated to the organization of a set of pharmacophores automatically discovered from a large data set of molecules. The network navigation allows to derive essential tasks of a drug discovery process, including the study of the relations between different chemical series, the analysis of the influence of additional chemical features on the compounds' activity, and the identification of diverse binding modes. This paper describes the method used to construct the pharmacophore network, and a case study dealing with BCR-ABL exemplifies its usage for large-scale SAR analysis. Thanks to a benchmarking study, we also demonstrate that the selection of a subset of representative pharmacophores can be used to conduct classification tasks.

Non a Priori Automatic Discovery of 3D Chemical Patterns: Application to Mutagenicity.

This article introduces a new type of structural fragment called a geometrical pattern. Such geometrical patterns are defined as molecular graphs that include a labelling of atoms together with constraints on interatomic distances. The discovery of geometrical patterns in a chemical dataset relies on the induction of multiple decision trees combined in random forests. Each computational step corresponds to a refinement of a preceding set of constraints, extending a previous geometrical pattern. This paper focuses on the mutagenicity of chemicals via the definition of structural alerts in relation with these geometrical patterns. It follows an experimental assessment of the main geometrical patterns to show how they can efficiently originate the definition of a chemical feature related to a chemical function or a chemical property. Geometrical patterns have provided a valuable and innovative approach to bring new pieces of information for discovering and assessing structural characteristics in relation to a particular biological phenotype.