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.

A community effort in SARS-CoV-2 drug discovery.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.

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.

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.

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.

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.

Cryptic Pockets Repository through Pocket Dynamics Tracking and Metadynamics on Essential Dynamics Space: Applications to Mcl-1.

Detection of cryptic pockets (hidden protein pockets) is a hot topic in structure-based drug discovery, especially for drugging the yet undruggable proteome. The experimental detection of cryptic pockets is still considered an expensive endeavor. Thus, computational methods, such as atomistic simulations, are used instead. These simulation methods can provide a perspective on protein dynamics that overpasses the experimental X-ray structures' static and average view. Nonetheless, unbiased molecular dynamics (MD) simulations fall short to detect transient and cryptic pockets requiring the crossing of high-energy barriers. Enhanced sampling methods, such as Metadynamics, provide a solution to overcome the time-scale problem faced by unbiased MD simulations. However, these methods are still limited by the availability of collective variable space to capture the intricate parameters, leading to the opening of cryptic pockets. Unfortunately, the design of such collective variables requires a priori knowledge of the binding site, information that is by definition lacking for cryptic pockets. In this work, we evaluated the use of the Metadynamics biasing scheme on essential coordinates space as a general method for cryptic pocket detection. This approach was applied to an antiapoptotic protein: Mcl-1 as a test model. In addition to providing a broader characterization of Mcl-1's conformational space, we show the effectiveness of this method in drawing the full repository of Mcl-1's known and novel cryptic pockets in an unsupervised manner.

Disproportionality analysis in VigiBase as a drug repositioning method for the discovery of potentially useful drugs in Alzheimer's disease.

Drug repositioning aims to propose new indications for marketed drugs. Although several methods exist, the utility of pharmacovigilance databases for this purpose is unclear. We conducted a disproportionality analysis in the World Health Organization pharmacovigilance database VigiBase to identify potential anticholinesterase drug candidates for repositioning in Alzheimer's disease (AD). METHODS: Disproportionality analysis is a validated method for detecting significant associations between drugs and adverse events (AEs) in pharmacovigilance databases. We applied this approach in VigiBase to establish the safety profile displayed by the anticholinesterase drugs used in AD and searched the database for drugs with similar safety profiles. The detected drugs with potential activity against acetylcholinesterase and butyrylcholinesterases (BuChEs) were then evaluated to confirm their anticholinesterase potential. RESULTS: We identified 22 drugs with safety profiles similar to AD medicines. Among these drugs, 4 (clozapine, aripiprazole, sertraline and S-duloxetine) showed a human BuChE inhibition rate of over 70% at 10-5  M. Their human BuChE half maximal inhibitory concentration values were compatible with clinical anticholinesterase action in humans at their normal doses. The most active human BuChE inhibitor in our study was S-duloxetine, with a half maximal inhibitory concentration of 1.2 µM. Combined with its ability to inhibit serotonin (5-HT) reuptake, the use of this drug could represent a novel multitarget directed ligand therapeutic strategy for AD. CONCLUSION: We identified 4 drugs with repositioning potential in AD using drug safety profiles derived from a pharmacovigilance database. This method could be useful for future drug repositioning efforts.

Insights into Mcl-1 Conformational States and Allosteric Inhibition Mechanism from Molecular Dynamics Simulations, Enhanced Sampling, and Pocket Crosstalk Analysis.

In this study, we explored the structural dynamics of Mcl-1, an anti-apoptotic protein. On the basis of structural ensembles, the essential dynamics was extracted and showed two major axes of variability: a breathing motion at the binding interface and a correlated motion through the internal loops. A free energy surface characterizing the breathing motion at the binding interface was generated and suggested an equilibrium between a closed conformation and a "ready to bind" conformation as the predominant states of Mcl-1 in solution. Moreover, the analysis of the dynamics along the internal loops revealed a hidden communication network of transient and cryptic pockets controlling the allosteric inhibition of Mcl-1. A detailed model joining the pocket crosstalk and salt bridge networks along the internal loops was proposed and allowed us to shed light on the key interactions governing Mcl-1's allosteric inhibition.

Drug repositioning: a brief overview.

OBJECTIVES: Drug repositioning, that is, the use of a drug in an indication other than the one for which it was initially marketed, is a growing trend. Its origins lie mainly in the attrition experienced in recent years in the field of new drug discovery. KEY FINDINGS: Despite some regulatory and economic challenges, drug repositioning offers many advantages, and a number of recent successes have confirmed both its public health benefits and its commercial value. The first examples of successful drug repositioning mainly came about through serendipity like acetylsalicylic acid, thalidomide, sildenafil or dimethylfumarate. CONCLUSION: The history of great-repositioned drugs has given some solutions to various pathologies. Serendipity is not yet useful to find repositioning drugs. Drug repositioning is of growing interest. Nowadays, a more rational approach to the identification of drug candidates for repositioning is possible, especially using data mining.

Hot-Spots of Mcl-1 Protein.

Protein-protein interactions (PPIs) control many important physiological processes within human cells. Apoptosis or programmed cell death is closely regulated by pro- and antiapoptotic signals. Dysregulation of this homeostasis is implicated in tumorigenesis and acquired resistance to treatments. The emerging importance of Mcl-1 protein in chemotherapeutic resistance makes it a high priority therapeutic target. Targeting PPIs associated with Mcl-1 presents many challenges for the design of inhibitors. This review focuses on the characterization of the Mcl-1 hot-spots which are related to four hydrophobic pockets P1-P4 and one major electrostatic interaction. Analysis of structural data highlights the high importance of the P2/P3 pockets for the binding of nonpeptide ligands. In order to guide medicinal chemists into making more selective and potent Mcl-1 inhibitors, the Mcl-1 protein is compared to other antiapoptotic proteins.

Synthesis of 3,3-Diarylazetidines by Calcium(II)-Catalyzed Friedel-Crafts Reaction of Azetidinols with Unexpected Cbz Enhanced Reactivity.

Azetidines are valuable motifs that readily access under explored chemical space for drug discovery. 3,3-Diarylazetidines are prepared in high yield from N-Cbz azetidinols in a calcium(II)-catalyzed Friedel-Crafts alkylation of (hetero)aromatics and phenols, including complex phenols such as ß-estradiol. Electron poor phenols undergo O-alkylation. The product azetidines can be derivatized to drug-like compounds through the azetidine nitrogen and the aromatic groups. The N-Cbz group is crucial to reactivity by providing stabilization of an intermediate carbocation on the four-membered ring.

Structure-guided design of pyridoclax derivatives based on Noxa / Mcl-1 interaction mode.

Protein-protein interactions are attractive targets because they control numerous cellular processes. In oncology, apoptosis regulating Bcl-2 family proteins are of particular interest. Apoptotic cell death is controlled via PPIs between the anti-apoptotic proteins hydrophobic groove and the pro-apoptotic proteins BH3 domain. In ovarian carcinoma, it has been previously demonstrated that Bcl-xL and Mcl-1 cooperate to protect tumor cells against apoptosis. Moreover, Mcl-1 is a key regulator of cancer cell survival and is a known resistance factor to Bcl-2/Bcl-xL pharmacological inhibitors making it an attractive therapeutic target. Here, using a structure-guided design from the oligopyridine lead Pyridoclax based on Noxa/Mcl-1 interaction we identified a new derivative, active at lower concentration as compared to Pyridoclax. This new derivative selectively binds to the Mcl-1 hydrophobic groove and releases Bak and Bim from Mcl-1 to induce cell death and sensitize cancer cells to Bcl-2/Bcl-xL targeting strategies.

Nontest Methods to Predict Acute Toxicity: State of the Art for Applications of In Silico Methods.

The assessment of acute toxicity of chemicals by in silico methods is actually done by two methodologies, read-across and QSAR. The two approaches are strongly based on the similarity between the chemical for which a risk assessment is required and the reference chemical(s) for which the experimental data are known. Here, we describe the two methodologies with some main publications as illustrations and the in silico data associated with acute toxicity endpoints (ECHA, REACH) accessible via eChemPortal.

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.

Joint effects of nine antidepressants on Raphidocelis subcapitata and Skeletonema marinoi: A matter of amine functional groups.

Antidepressants are among the most prescribed pharmaceuticals throughout the world. Their presence has already been detected in several aquatic ecosystems worldwide and their effects on non-target organisms justify the growing concern of both the public and regulatory authorities. These emerging pollutants do not occur as isolated compounds but rather as multi-component mixtures, which may lead to increased adverse effects compared to individual compounds. Freshwater and marine algae seem particularly sensitive to pharmaceuticals, including antidepressants. Studies assessing the toxicity of antidepressant mixture to algae focused mainly on binary mixtures of selective serotonin reuptake inhibitors. In the present experiment, the freshwater algae Raphidocelis subcapitata (formerly known as Pseudokirchneriella subcapitata) and the marine diatom Skeletonema marinoi were exposed to equitoxic mixtures of 9 antidepressants (fluvoxamine, fluoxetine, sertraline, duloxetine, venlafaxine, clomipramine, amitriptyline, and citalopram) at different concentrations. The growth inhibition was measured. Results showed that the toxicity of this mixture was higher than the effects of each individual component, highlighting simple additivity or synergistic effects, whereas tested concentrations were below the 10% inhibition concentration (IC10) of each compound. Moreover, the QSAR analysis highlighted that antidepressants would act through narcosis (non-specific mode of action) towards the two species of algae. However, more specific effects can be observed by differentiating compounds with a primary/secondary amine from those with a tertiary amine. These mixture effects on algal species have to be assessed, especially since any impacts on phytoplankton could ultimately impact higher trophic levels (less food, secondary poisoning).

Toward Understanding Mcl-1 Promiscuous and Specific Binding Mode.

Mcl-1, which is an anti-apoptotic member of the Bcl-2 protein family, is overexpressed in various cancers and promotes the aberrant survival of tumor cells. To inhibit Mcl-1, and initiate apoptosis, an interaction between BH3-only proteins and Mcl-1 anti-apoptotic protein is necessary. These protein-protein interactions exhibit some selectivity: Mcl-1 binds specifically to Noxa, whereas Bim and Puma bind strongly to all anti-apoptotic proteins. Even if the three-dimensional (3D) structures of several Mcl-1/BH3-only complexes have been solved, the BH3-only binding specificity to Mcl-1 is still not completely understood. In this study, molecular dynamics simulations were used to elucidate the molecular basis of the interactions with Mcl-1. Our results corroborate the importance of four conserved hydrophobic residues and a conserved aspartic acid on BH3-only as a common binding pattern. Furthermore, our results highlight the contribution of the fifth hydrophobic residue in the C-terminal part and a negatively charged patch in the N-terminal of BH3-only peptides as important for their fixation to Mcl-1. We hypothesize that this negatively charged patch will be an Mcl-1 specific binding pattern.

The Arg-Phe-amide peptide 26RFa/glutamine RF-amide peptide and its receptor: IUPHAR Review 24.

The RFamide neuropeptide 26RFa was first isolated from the brain of the European green frog on the basis of cross-reactivity with antibodies raised against bovine neuropeptide FF (NPFF). 26RFa and its N-terminally extended form glutamine RF-amide peptide (QRFP) have been identified as cognate ligands of the former orphan receptor GPR103, now renamed glutamine RF-amide peptide receptor (QRFP receptor). The 26RFa/QRFP precursor has been characterized in various mammalian and non-mammalian species. In the brain of mammals, including humans, 26RFa/QRFP mRNA is almost exclusively expressed in hypothalamic nuclei. The 26RFa/QRFP transcript is also present in various organs especially in endocrine glands. While humans express only one QRFP receptor, two isoforms are present in rodents. The QRFP receptor genes are widely expressed in the CNS and in peripheral tissues, notably in bone, heart, kidney, pancreas and testis. Structure-activity relationship studies have led to the identification of low MW peptidergic agonists and antagonists of QRFP receptor. Concurrently, several selective non-peptidic antagonists have been designed from high-throughput screening hit optimization. Consistent with the widespread distribution of QRFP receptor mRNA and 26RFa binding sites, 26RFa/QRFP exerts a large range of biological activities, notably in the control of energy homeostasis, bone formation and nociception that are mediated by QRFP receptor or NPFF2. The present report reviews the current knowledge concerning the 26RFa/QRFP-QRFP receptor system and discusses the potential use of selective QRFP receptor ligands for therapeutic applications.

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.

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.