Shape-based reprofiling of FDA-approved drugs for the H₁ histamine receptor

G protein-coupled receptors in neurodegenerative diseases and psychiatric disorders

Neuropsychiatric disorders are multifactorial disorders with diverse aetiological factors. Identifying treatment targets is challenging because the diseases are resulting from heterogeneous biological, genetic, and environmental factors. Nevertheless, the increasing understanding of G protein-coupled receptor (GPCR) opens a new possibility in drug discovery. Harnessing our knowledge of molecular mechanisms and structural information of GPCRs will be advantageous for developing effective drugs. This review provides an overview of the role of GPCRs in various neurodegenerative and psychiatric diseases. Besides, we highlight the emerging opportunities of novel GPCR targets and address recent progress in GPCR drug development.

Dopamine and Dopamine-Related Ligands Can Bind Not Only to Dopamine Receptors

The dopaminergic system is one of the most important neurotransmitter systems in the central nervous system (CNS). It acts mainly by activation of the D₁-like receptor family at the target cell. Additionally, fine-tuning of the signal is achieved via pre-synaptic modulation by the D₂-like receptor family. Some dopamine drugs (both agonists and antagonists) bind in addition to DRs also to α₂-ARs and 5-HT receptors. Unfortunately, these compounds are often considered subtype(s) specific. Thus, it is important to consider the presence of these receptor subtypes in specific CNS areas as the function virtually elicited by one receptor type could be an effect of other—or the co-effect of multiple receptors. However, there are enough molecules with adequate specificity. In this review, we want to give an overview of the most common off-targets for established dopamine receptor ligands. To give an overall picture, we included a discussion on subtype selectivity. Molecules used as antipsychotic drugs are reviewed too. Therefore, we will summarize reported affinities and give an outline of molecules sufficiently specific for one or more subtypes (i.e., for subfamily), the presence of DR, α₂-ARs, and 5-HT receptors in CNS areas, which could help avoid ambiguous results.

NegStacking: Drug-Target Interaction Prediction Based on Ensemble Learning and Logistic Regression

Drug-target interactions (DTIs) identification is an important issue of drug research, and many methods proposed to predict potential DTIs based on machine learning treat it as a binary classification problem. However, the number of known interacting drug-target pairs (positive samples) is far less than that of non-interacting pairs (negative samples). Most methods do not utilize these large numbers of negative samples sufficiently, which limits their prediction performance. To address this problem, we proposed a stacking framework named NegStacking. First, it uses sampling to obtain multiple completely different negative sample sets. Then, each weak learner is trained with a different negative sample set and the same positive sample set, and the logistic regression (LR) is used as a meta-learner to adaptively combine these weak learners. Moreover, in the training process, feature subspacing and hyperparameter perturbation are applied to increase ensemble diversity. Finally, the trained model could be used to predict new samples. We compared NegStacking with other methods, and the experimental results show that our model is superior. NegStacking can improve the performance of predictive DTIs, and it has broad application prospects for improving the drug discovery process. The source code and datasets are available at https://github.com/Open-ss/NegStacking.

Antifungal Activity and DNA Topoisomerase Inhibition of Hydrolysable Tannins from Punica granatum L

Punica granatum L. (pomegranate) fruit is known to be an important source of bioactive phenolic compounds belonging to hydrolysable tannins. Pomegranate extracts have shown antifungal activity, but the compounds responsible for this activity and their mechanism/s of action have not been completely elucidated up to now. The aim of the present study was the investigation of the inhibition ability of a selection of pomegranate phenolic compounds (i.e., punicalagin, punicalin, ellagic acid, gallic acid) on both plant and human fungal pathogens. In addition, the biological target of punicalagin was identified here for the first time. The antifungal activity of pomegranate phenolics was evaluated by means of Agar Disk Diffusion Assay and minimum inhibitory concentration (MIC) evaluation. A chemoinformatic analysis predicted for the first time topoisomerases I and II as potential biological targets of punicalagin, and this prediction was confirmed by in vitro inhibition assays. Concerning phytopathogens, all the tested compounds were effective, often similarly to the fungicide imazalil at the label dose. Particularly, punicalagin showed the lowest MIC for Alternaria alternata and Botrytis cinerea, whereas punicalin was the most active compound in terms of growth control extent. As for human pathogens, punicalagin was the most active compound among the tested ones against Candida albicans reference strains, as well as against the clinically isolates. UHPLC coupled with HRMS indicated that C. albicans, similarly to the phytopathogen Coniella granati, is able to hydrolyze both punicalagin and punicalin as a response to the fungal attack. Punicalagin showed a strong inhibitory activity, with IC₅₀ values of 9.0 and 4.6 µM against C. albicans topoisomerases I and II, respectively. Altogether, the results provide evidence that punicalagin is a valuable candidate to be further exploited as an antifungal agent in particular against human fungal infections.

Computational Drug Repositioning: Current Progress and Challenges

Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.

Evaluation of 3-Dimensionality in Approved and Experimental Drug Space

The 3-dimensional (3D) structure of therapeutics and other bioactive molecules is an important factor in determining the strength and selectivity of their protein-ligand interactions. Previous efforts have considered the strain introduced and tolerated through conformational changes induced upon protein binding. Herein, we present an analysis of 3-dimentionality for energy-minimized structures from the DrugBank and ligands bound to proteins identified in the Protein Data Bank (PDB). This analysis reveals that the majority of molecules found in both the DrugBank and the PDB tend toward linearity and planarity, with few molecules having highly 3D conformations. Decidedly 3D geometries have been historically difficult to achieve, likely due to the synthetic challenge of making 3D organic molecules, and other considerations, such as adherence to the 'rule-of-five'. This has resulted in the dominance of planar and/or linear topologies of the molecules described here. Strategies to address the generally flat nature of these data sets are explored, including the use of 3D organic fragments and inorganic scaffolds as a means of accessing privileged 3D space. This work highlights the potential utility of libraries with greater 3D topological diversity so that the importance of molecular shape to biological behavior can be more fully understood in drug discovery campaigns.

An Integrated System Biology Approach Yields Drug Repositioning Candidates for the Treatment of Heart Failure

Identifying effective pharmacological treatments for heart failure (HF) patients remains critically important. Given that the development of drugs de novo is expensive and time consuming, drug repositioning has become an increasingly important branch. In the present study, we propose a two-step drug repositioning pipeline and investigate the novel therapeutic effects of existing drugs approved by the US Food and Drug Administration to discover potential therapeutic drugs for HF. In the first step, we compared the gene expression pattern of HF patients with drug-induced gene expression profiles to obtain preliminary candidates. In the second step, we performed a systems biology approach based on the known protein–protein interaction information and targets of drugs to narrow down preliminary candidates to obtain final candidates. Drug set enrichment analysis and literature search were applied to assess the performance of our repositioning approach. We also constructed a mode of action network for each candidate and performed pathway analysis for each candidate using genes contained in their mode of action network to uncover pathways that potentially reflect the mechanisms of candidates’ therapeutic efficacy to HF. We discovered numerous preliminary candidates, some of which are used in clinical practice and supported by the literature. The final candidates contained nearly all of the preliminary candidates supported by previous studies. Drug set enrichment analysis and literature search support the validity of our repositioning approach. The mode of action network for each candidate not only displayed the underlying mechanisms of drug efficacy but also uncovered potential biomarkers and therapeutic targets for HF. Our two-step drug repositioning approach is efficient to find candidates with potential therapeutic efficiency to HF supported by the literature and might be of particular use in the discovery of novel effective pharmacological therapies for HF.

In Silico Repositioning of Cannabigerol as a Novel Inhibitor of the Enoyl Acyl Carrier Protein (ACP) Reductase (InhA)

Cannabigerol (CBG) and cannabichromene (CBC) are non-psychoactive cannabinoids that have raised increasing interest in recent years. These compounds exhibit good tolerability and low toxicity, representing promising candidates for drug repositioning. To identify novel potential therapeutic targets for CBG and CBC, an integrated ligand-based and structure-based study was performed. The results of the analysis led to the identification of CBG as a low micromolar inhibitor of the Enoyl acyl carrier protein (ACP) reductase (InhA) enzyme.

The bio-based methyl coumalate involved Morita–Baylis–Hillman reaction

An efficient and sustainable method to prepare pharmaceutically important pyrone derivatives under very mild conditions, from bio-based methyl coumalate, is reported.

S,S-Chiral Linker Induced U Shape with a Syn-facial Sensitizer and Photocleavable Ethene Group

There is a major need for light-activated materials for the release of sensitizers and drugs. Considering the success of chiral columns for the separation of enantiomer drugs, we synthesized an S,S-chiral linker system covalently attached to silica with a sensitizer ethene near the silica surface. First, the silica surface was modified to be aromatic rich, by replacing 70% of the surface groups with (3-phenoxypropyl)silane. We then synthesized a 3-component conjugate [chlorin sensitizer, S,S-chiral cyclohexane and ethene building blocks] in 5 steps with a 13% yield, and covalently bound the conjugate to the (3-phenoxypropyl)silane-coated silica surface. We hypothesized that the chiral linker would increase exposure of the ethene site for enhanced ¹ O₂ -based sensitizer release. However, the chiral linker caused the sensitizer conjugate to adopt a U shape due to favored 1,2-diaxial substituent orientation; resulting in a reduced efficiency of surface loading. Further accentuating the U shape was π-π stacking between the (3-phenoxypropyl)silane and sensitizer. Semiempirical calculations and singlet oxygen luminescence data provided deeper insight into the sensitizer's orientation and release. This study has lead to insight on modifications of surfaces for drug photorelease and can help lead to the development of miniaturized photodynamic devices.

Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3

To extend the utility of ligand 3D shape similarity into pose prediction and virtual screening, we have previously developed CDVS and PoPSS methods. Both of them utilize ligand 3D shape similarity with the crystallographic ligands to improve pose prediction. While CDVS utilizes shape similarity to select suitable receptor structures for molecular docking, PoPSS places a ligand conformation of the highest shape similarity with crystal ligands into the target protein binding pocket which is then refined by side-chain repacking and Monte Carlo energy minimization. Analyses of PoPSS revealed some drawbacks in ligand conformation generation and the scoring scheme used. Moreover, as PoPSS does not sample the ligand conformation after placing it in the binding pocket, it relies solely on conformation generation methods to produce native like conformations. To address these limitations of PoPSS method, we report here a modified approach named as PoPSS-Lite, where side-chain repacking was replaced by a simple grid-based energy minimization. This modification also allowed the sampling of terminal functional groups while keeping the core scaffold fixed. Furthermore, shape similarity calculations were improved by increasing the number of ligand conformations and using a different similarity metric. The performance of PoPSS-Lite was prospectively evaluated in D3R GC3. Comparison of PoPSS-Lite demonstrated superior performance over PoPSS and CDVS with lower mean and median RMSDs. Furthermore, comparison with other D3R GC3 pose prediction submissions revealed top performance for PoPSS-Lite. Our D3R GC3 result extends our perspective that ligand 3D shape similarity with known crystallographic information can be successfully used to predict the binding pose of ligands with unknown binding modes. Our D3R GC3 results further highlight the necessity for improvement in conformer generation methods in order to improve shape similarity guided pose prediction.

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

Ligand-Based Methods in GPCR Computer-Aided Drug Design

This chapter describes two powerful 3D ligand-based shape similarity and scoring methods called ROCS and EON, their basic operation and selected validation data. The steps required to prepare a database of molecules for successful use with ROCS and EON are described and selected examples of their application in prospective lead discovery experiments are summarized.

Unveiling some FDA-approved drugs as inhibitors of the store-operated Ca2+ entry pathway

The store-operated calcium entry (SOCE) pathway is an important route for generating cytosolic Ca²⁺ signals that regulate a diverse array of biological processes. Abnormal SOCE seem to underlie several diseases that notably include allergy, inflammation and cancer. Therefore, any modulator of this pathway is likely to have significant impact in cell biology under both normal and abnormal conditions. In this study, we screened the FDA-approved drug library for agents that share significant similarity in 3D shape and surface electrostatics with few, hitherto best known inhibitors of SOCE. This has led to the identification of five drugs that showed dose-dependent inhibition of SOCE in cell-based assay, probably through interacting with the Orai1 protein which effectively mediates SOCE. Of these drugs, leflunomide and teriflunomide could suppress SOCE significantly at clinically-relevant doses and this provides for an additional mechanism towards the therapeutic utility of these drugs as immunosuppressants. The other three drugs namely lansoprazole, tolvaptan and roflumilast, were less potent in suppressing SOCE but were more selective and thus they may serve as novel scaffolds for future development of new, more efficacious SOCE inhibitors.

On the Integration of In Silico Drug Design Methods for Drug Repurposing

Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug–target, target–disease, and ultimately drug–disease associations.

In silico methods to address polypharmacology: current status, applications and future perspectives

Polypharmacology, a new paradigm in drug discovery that focuses on multi-target drugs (MTDs), has potential application for drug repurposing, the process of finding new uses for existing approved drugs, prediction of off-target toxicities and rational design of MTDs. In this scenario, computational strategies have demonstrated great potential in predicting polypharmacology and in facilitating drug repurposing. Here, we provide a comprehensive overview of various computational approaches that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes and outline their potential for drug discovery. We give an outlook on the latest advances in rational design of MTDs and discuss possible future directions of algorithm development in this field.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Scaffold hopping with virtual screening from IP3 to a drug-like partial agonist of the inositol trisphosphate receptor

Inositol 1,4,5-trisphosphate (IP3 ) is a universal signalling molecule that releases calcium from stores within cells by activating the IP3 receptor. Although chemical tools that modulate the IP3 receptor exist, none is ideal due to trade offs between potency, selectivity and cell permeability, and their chemical properties make them challenging starting points for optimisation. Therefore, to find new leads, we used virtual screening to scaffold hop from IP3 by using the program ROCS to perform a 3D ligand-based screen of the ZINC database of purchasable compounds. We then used the program FRED to dock the top-ranking hits into the IP3 binding pocket of the receptor. We tested the 12 highest-scoring hits in a calcium-release bioassay and identified SI-9 as a partial agonist. SI-9 competed with [(3) H]IP3 binding, and reduced histamine-induced calcium signalling in HeLa cells. SI-9 has a novel 2D scaffold that represents a tractable lead for designing improved IP3 receptor modulators.

Cheminformatics at the interface of medicinal chemistry and proteomics

Multiple factors have to be optimized in the course of a drug discovery project. Traditionally this includes potency on a single target, eventually specificity as well as the pharmacokinetic, physicochemical and the safety profile. Recently an additional dimension has been added by realizing that the therapeutic outcome of a drug is often determined not only by its activity on a single target but also by its activity profile across a variety of biological targets. To address the polypharmacology of drug candidates many compounds are tested on a set of targets or in phenotypic screens generating a tremendous amount of data. To extract useful information computational methods at the interface of proteomics and cheminformatics are indispensable. This review will focus on some recent developments in this field. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Chemical informatics uncovers a new role for moexipril as a novel inhibitor of cAMP phosphodiesterase-4 (PDE4)

PDE4 is one of eleven known cyclic nucleotide phosphodiesterase families and plays a pivotal role in mediating hydrolytic degradation of the important cyclic nucleotide second messenger, cyclic 3′5′ adenosine monophosphate (cAMP). PDE4 inhibitors are known to have anti-inflammatory properties, but their use in the clinic has been hampered by mechanism-associated side effects that limit maximally tolerated doses. In an attempt to initiate the development of better-tolerated PDE4 inhibitors we have surveyed existing approved drugs for PDE4-inhibitory activity. With this objective, we utilised a high-throughput computational approach that identified moexipril, a well tolerated and safe angiotensin-converting enzyme (ACE) inhibitor, as a PDE4 inhibitor. Experimentally we showed that moexipril and two structurally related analogues acted in the micro molar range to inhibit PDE4 activity. Employing a FRET-based biosensor constructed from the nucleotide binding domain of the type 1 exchange protein activated by cAMP, EPAC1, we demonstrated that moexipril markedly potentiated the ability of forskolin to increase intracellular cAMP levels. Finally, we demonstrated that the PDE4 inhibitory effect of moexipril is functionally able to induce phosphorylation of the small heat shock protein, Hsp20, by cAMP dependent protein kinase A. Our data suggest that moexipril is a bona fide PDE4 inhibitor that may provide the starting point for development of novel PDE4 inhibitors with an improved therapeutic window.

Drug repositioning by structure-based virtual screening

Approved drugs have favourable or validated pharmacokinetic properties and toxicological profiles, and the repositioning of existing drugs for new indications can potentially avoid expensive costs associated with early-stage testing of the hit compounds. In recent years, technological advances in virtual screening methodologies have allowed medicinal chemists to rapidly screen drug libraries for therapeutic activity against new biomolecular targets in a cost-effective manner. This review article outlines the basic principles and recent advances in structure-based virtual screening and highlights the powerful synergy of in silico techniques in drug repositioning as demonstrated in several recent reports.