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

Adenosine receptors as promising targets for the management of ocular diseases

The ocular drug discovery arena has undergone a significant improvement in the last few years culminating in the FDA approvals of 8 new drugs. However, despite a large number of drugs, generics, and combination products available, it remains an urgent need to find breakthrough strategies and therapies for tackling ocular diseases. Targeting the adenosinergic system may represent an innovative strategy for discovering new ocular therapeutics. This review focused on the recent advance in the field and described the numerous nucleoside and non-nucleoside modulators of the four adenosine receptors (ARs) used as potential tools or clinical drug candidates.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity

Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alters ADMET characteristics. Features distinguishing between compounds with single- and multitarget activity are currently only little understood. On the basis of systematic data analysis, we have assembled large sets of promiscuous compounds with activity against related or functionally distinct targets and the corresponding compounds with single-target activity. Machine learning predicted promiscuous compounds with surprisingly high accuracy. Molecular similarity analysis combined with control calculations under varying conditions revealed that accurate predictions were largely determined by structural nearest-neighbor relationships between compounds from different classes. We also found that large proportions of promiscuous compounds with activity against related or unrelated targets and corresponding single-target compounds formed analog series with distinct chemical space coverage, which further rationalized the predictions. Moreover, compounds with activity against proteins from functionally distinct classes were often active against unique targets that were not covered by other promiscuous compounds. The results of our analysis revealed that nearest-neighbor effects determined the prediction of promiscuous compounds and that preferential partitioning of compounds with single- and multitarget activity into structurally distinct analog series was responsible for such effects, hence providing a rationale for the presence of different structure-promiscuity relationships.

Systems Biology Approaches for Therapeutics Development Against COVID-19

Understanding the systems biology approaches for promoting the development of new therapeutic drugs is attaining importance nowadays. The threat of COVID-19 outbreak needs to be vanished for global welfare, and every section of research is focusing on it. There is an opportunity for finding new, quick, and accurate tools for developing treatment options, including the vaccine against COVID-19. The review at this moment covers various aspects of pathogenesis and host factors for exploring the virus target and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers the extensive details of multiomics tools i.e., transcriptomics, proteomics, genomics, lipidomics, immunomics, and in silico computational modeling aiming towards the study of host-virus interactions in search of therapeutic targets against the COVID-19.

Multi-targeted drug design strategies for the treatment of schizophrenia

INTRODUCTION

Schizophrenia is a complex psychiatric disease (or a conglomeration of disorders) manifesting with positive, negative and cognitive symptoms. The pathophysiology of schizophrenia is not completely known; however, it involves many neurotransmitters and their receptors. In order to treat schizophrenia, drugs need to be multi-target drugs. Indeed, the action of second and third generation antipsychotics involves interactions with many receptors, belonging mainly to aminergic GPCRs.

AREAS COVERED

In this review, the authors summarize current concepts of schizophrenia with the emphasis on the modern dopaminergic, serotoninergic, and glutamatergic hypotheses. Next, they discuss treatments of the disease, stressing multi-target antipsychotics. They cover different aspects of design of multi-target ligands, including the application of molecular modeling approaches for the design and benefits and limitations of multifunctional compounds. Finally, they present successful case studies of multi-target drug design against schizophrenia.

EXPERT OPINION

Treatment of schizophrenia requires the application of multi-target drugs. While designing single target drugs is relatively easy, designing multifunctional compounds is a challenge due to the necessity to balance the affinity to many targets, while avoiding promiscuity and the problems with drug-likeness. Multi-target drugs bring many benefits: better efficiency, fewer adverse effects, and drug-drug interactions and better patient compliance to drug regime.

NetInfer: A Web Server for Prediction of Targets and Therapeutic and Adverse Effects via Network-Based Inference Methods

In this study, we developed a web server named NetInfer for prediction of targets and therapeutic and adverse effects via network-based inference methods. Compared with our previously developed standalone version of NetInfer, this web server provides a user-friendly interface. With the web server, users can easily predict potential target proteins, microRNAs, Anatomical Therapeutic Chemical (ATC) classification codes, or adverse drug events for small molecules of their interests in a few steps. Most of the prediction models were constructed on the basis of our previous studies, where those models have been evaluated systematically and demonstrated high performance. The high-quality models can generate accurate predictions. As a case study, we predicted ATC codes and target proteins for several drugs. The predicted therapeutic effects of these drugs on cardiovascular diseases and their potential molecular mechanisms were validated by the literature. This successful case study demonstrated that our web server would be a powerful tool in drug repositioning and systems pharmacology. The web server of NetInfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.

Structure-based drug repositioning explains ibrutinib as VEGFR2 inhibitor

Many drugs are promiscuous and bind to multiple targets. On the one hand, these targets may be linked to unwanted side effects, but on the other, they may achieve a combined desired effect (polypharmacology) or represent multiple diseases (drug repositioning). With the growth of 3D structures of drug-target complexes, it is today possible to study drug promiscuity at the structural level and to screen vast amounts of drug-target interactions to predict side effects, polypharmacological potential, and repositioning opportunities. Here, we pursue such an approach to identify drugs inactivating B-cells, whose dysregulation can function as a driver of autoimmune diseases. Screening over 500 kinases, we identified 22 candidate targets, whose knock out impeded the activation of B-cells. Among these 22 is the gene KDR, whose gene product VEGFR2 is a prominent cancer target with anti-VEGFR2 drugs on the market for over a decade. The main result of this paper is that structure-based drug repositioning for the identified kinase targets identified the cancer drug ibrutinib as micromolar VEGFR2 inhibitor with a very high therapeutic index in B-cell inactivation. These findings prove that ibrutinib is not only acting on the Bruton’s tyrosine kinase BTK, against which it was designed. Instead, it may be a polypharmacological drug, which additionally targets angiogenesis via inhibition of VEGFR2. Therefore ibrutinib carries potential to treat other VEGFR2 associated disease. Structure-based drug repositioning explains ibrutinib’s anti VEGFR2 action through the conservation of a specific pattern of interactions of the drug with BTK and VEGFR2. Overall, structure-based drug repositioning was able to predict these findings at a fraction of the time and cost of a conventional screen.

Network Pharmacology-Based Approach to Investigate the Mechanisms of Mahai Capsules in the Treatment of Cardiovascular Diseases

Background

Mahai capsules (MHC) have been deemed to be an effective herb combination for treatment of cardiovascular diseases (CVD) development and improvement of the life quality of CVD patients. To systematically explore the mechanisms of MHC in CVD, a network pharmacology approach mainly comprising target prediction, network construction, biological process and pathway analysis, and related diseases was adopted in this study.

Methods

We collected the bioactive compounds and potential targets of MHC through the TCMSP servers. Candidate targets related to CVD were collected from Therapeutic Targets Database and PharmGkb database and analyzed using ClueGO plugin in Cytoscape. KEGG pathway was enriched and analyzed through the EnrichR platform, and protein-protein interaction networks were calculated by STRING platform. The compound-target, target-disease, and compound-target-disease networks were constructed using Cytoscape.

Results

A total of 303 targets of the 57 active ingredients in MHC were obtained. The network analysis showed that PTGS2, PTGS1, HSP90, Scn1a, estrogen receptor, calmodulin, and thrombin were identified as key targets of MHC in the treatment of CVD. The functional enrichment analysis indicated that MHC probably produced the therapeutic effects against CVD by synergistically regulating many biological pathways, such as PI3K-Akt, TNF, HIF-1, FoxO, apoptosis, calcium, T-cell receptor, VEGF, and NF-kappa B signaling pathway.

Conclusions

In summary, the analysis of the complete profile of the pharmacological properties, as well as the elucidation of targets, networks, and pathways, can further illuminate that the underlying mechanisms of MHC in CVD might be strongly associated with its synergic regulation of inflammation, apoptosis, and immune function, and provide new clues for its future development of therapeutic strategies and basic research.

Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope

Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.

SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules

SwissTargetPrediction is a web tool, on-line since 2014, that aims to predict the most probable protein targets of small molecules. Predictions are based on the similarity principle, through reverse screening. Here, we describe the 2019 version, which represents a major update in terms of underlying data, backend and web interface. The bioactivity data were updated, the model retrained and similarity thresholds redefined. In the new version, the predictions are performed by searching for similar molecules, in 2D and 3D, within a larger collection of 376 342 compounds known to be experimentally active on an extended set of 3068 macromolecular targets. An efficient backend implementation allows to speed up the process that returns results for a druglike molecule on human proteins in 15-20 s. The refreshed web interface enhances user experience with new features for easy input and improved analysis. Interoperability capacity enables straightforward submission of any input or output molecule to other on-line computer-aided drug design tools, developed by the SIB Swiss Institute of Bioinformatics. High levels of predictive performance were maintained despite more extended biological and chemical spaces to be explored, e.g. achieving at least one correct human target in the top 15 predictions for >70% of external compounds. The new SwissTargetPrediction is available free of charge (www.swisstargetprediction.ch).

Prospects of multitarget drug designing strategies by linking molecular docking and molecular dynamics to explore the protein-ligand recognition process

The designing of drugs that can simultaneously affect different protein targets is one novel and promising way to treat complex diseases. Multitarget drugs act on multiple protein receptors each implicated in the same disease state, and may be considered to be more beneficial than conventional drug therapies. For example, these drugs can have improved therapeutic potency due to synergistic effects on multiple targets, as well as improved safety and resistance profiles due to the combined regulation of potential primary therapeutic targets and compensatory elements and lower dosage typically required. This review analyzes in-silico methods that facilitate multitarget drug design that facilitate the discovery and development of novel therapeutic agents. Here presented is a summary of the progress in structure-based drug discovery techniques that study the process of molecular recognition of targets and ligands, moving from static molecular docking to improved molecular dynamics approaches in multitarget drug design, and the advantages and limitations of each.

The kinase polypharmacology landscape of clinical PARP inhibitors

Polypharmacology plays an important role in defining response and adverse effects of drugs. For some mechanisms, experimentally mapping polypharmacology is commonplace, although this is typically done within the same protein class. Four PARP inhibitors have been approved by the FDA as cancer therapeutics, yet a precise mechanistic rationale to guide clinicians on which to choose for a particular patient is lacking. The four drugs have largely similar PARP family inhibition profiles, but several differences at the molecular and clinical level have been reported that remain poorly understood. Here, we report the first comprehensive characterization of the off-target kinase landscape of four FDA-approved PARP drugs. We demonstrate that all four PARP inhibitors have a unique polypharmacological profile across the kinome. Niraparib and rucaparib inhibit DYRK1s, CDK16 and PIM3 at clinically achievable, submicromolar concentrations. These kinases represent the most potently inhibited off-targets of PARP inhibitors identified to date and should be investigated further to clarify their potential implications for efficacy and safety in the clinic. Moreover, broad kinome profiling is recommended for the development of PARP inhibitors as PARP-kinase polypharmacology could potentially be exploited to modulate efficacy and side-effect profiles.

Biomedical data and computational models for drug repositioning: a comprehensive review

Drug repositioning can drastically decrease the cost and duration taken by traditional drug research and development while avoiding the occurrence of unforeseen adverse events. With the rapid advancement of high-throughput technologies and the explosion of various biological data and medical data, computational drug repositioning methods have been appealing and powerful techniques to systematically identify potential drug-target interactions and drug-disease interactions. In this review, we first summarize the available biomedical data and public databases related to drugs, diseases and targets. Then, we discuss existing drug repositioning approaches and group them based on their underlying computational models consisting of classical machine learning, network propagation, matrix factorization and completion, and deep learning based models. We also comprehensively analyze common standard data sets and evaluation metrics used in drug repositioning, and give a brief comparison of various prediction methods on the gold standard data sets. Finally, we conclude our review with a brief discussion on challenges in computational drug repositioning, which includes the problem of reducing the noise and incompleteness of biomedical data, the ensemble of various computation drug repositioning methods, the importance of designing reliable negative samples selection methods, new techniques dealing with the data sparseness problem, the construction of large-scale and comprehensive benchmark data sets and the analysis and explanation of the underlying mechanisms of predicted interactions.

Current computational methods for predicting protein interactions of natural products

Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.

Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications

We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.

Novel Computational Approach to Predict Off-Target Interactions for Small Molecules

Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 2,46,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of >1 million compounds (tracking >20 million compound-structure activity relationship/SAR data points) with known in vitro activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 diverse small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for >70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed in vivo effects. About 51.5% (2,025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW<200 for both categories. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of in vitro confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets. In conclusion, the OTSA process demonstrates good predictive performance characteristics and represents an additional tool with utility during the lead optimization stage of the drug discovery process. Additionally, the computed physiochemical properties such as clogP (i.e., lipophilicity), molecular weight, pKa and logS (i.e., solubility) were found to be statistically different between the approved and discontinued drugs, but the internal compounds were close to the approved drugs space in most part.

High Impact: The Role of Promiscuous Binding Sites in Polypharmacology

The literature focuses on drug promiscuity, which is a drug’s ability to bind to several targets, because it plays an essential role in polypharmacology. However, little work has been completed regarding binding site promiscuity, even though its properties are now recognized among the key factors that impact drug promiscuity. Here, we quantified and characterized the promiscuity of druggable binding sites from protein-ligand complexes in the high quality Mother Of All Databases while using statistical methods. Most of the sites (80%) exhibited promiscuity, irrespective of the protein class. Nearly half were highly promiscuous and able to interact with various types of ligands. The corresponding pockets were rather large and hydrophobic, with high sulfur atom and aliphatic residue frequencies, but few side chain atoms. Consequently, their interacting ligands can be large, rigid, and weakly hydrophilic. The selective sites that interacted with one ligand type presented less favorable pocket properties for establishing ligand contacts. Thus, their ligands were highly adaptable, small, and hydrophilic. In the dataset, the promiscuity of the site rather than the drug mainly explains the multiple interactions between the drug and target, as most ligand types are dedicated to one site. This underlines the essential contribution of binding site promiscuity to drug promiscuity between different protein classes.

3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns

Discovering conserved three-dimensional (3D) patterns among protein structures may provide valuable insights into protein classification, functional annotations or the rational design of multi-target drugs. Thus, several computational tools have been developed to discover and compare protein 3D-patterns. However, most of them only consider previously known 3D-patterns such as orthosteric binding sites or structural motifs. This fact makes necessary the development of new methods for the identification of all possible 3D-patterns that exist in protein structures (allosteric sites, enzyme-cofactor interaction motifs, among others). In this work, we present 3D-PP, a new free access web server for the discovery and recognition all similar 3D amino acid patterns among a set of proteins structures (independent of their sequence similarity). This new tool does not require any previous structural knowledge about ligands, and all data are organized in a high-performance graph database. The input can be a text file with the PDB access codes or a zip file of PDB coordinates regardless of the origin of the structural data: X-ray crystallographic experiments or in silico homology modeling. The results are presented as lists of sequence patterns that can be further analyzed within the web page. We tested the accuracy and suitability of 3D-PP using two sets of proteins coming from the Protein Data Bank: (a) Zinc finger containing and (b) Serotonin target proteins. We also evaluated its usefulness for the discovering of new 3D-patterns, using a set of protein structures coming from in silico homology modeling methodologies, all of which are overexpressed in different types of cancer. Results indicate that 3D-PP is a reliable, flexible and friendly-user tool to identify conserved structural motifs, which could be relevant to improve the knowledge about protein function or classification. The web server can be freely utilized at https://appsbio.utalca.cl/3d-pp/.

In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data

Chemotherapy is a routine treatment approach for early-stage cancers, but the effectiveness of such treatments is often limited by drug resistance, toxicity, and tumor heterogeneity. Combination chemotherapy, in which two or more drugs are applied simultaneously, offers one promising approach to address these concerns, since two single-target drugs may synergize with one another through interconnected biological processes. However, the identification of effective dual therapies has been particularly challenging; because the search space is large, combination success rates are low. Here, we present our method for DREAM AstraZeneca-Sanger Drug Combination Prediction Challenge to predict synergistic drug combinations. Our approach involves using biologically relevant drug and cell line features with machine learning. Our machine learning model obtained the primary metric = 0.36 and the tie-breaker metric = 0.37 in the extension round of the challenge which was ranked in top 15 out of 76 submissions. Our approach also achieves a mean primary metric of 0.39 with ten repetitions of 10-fold cross-validation. Further, we analyzed our model's predictions to better understand the molecular processes underlying synergy and discovered that key regulators of tumorigenesis such as TNFA and BRAF are often targets in synergistic interactions, while MYC is often duplicated. Through further analysis of our predictions, we were also ble to gain insight into mechanisms and potential biomarkers of synergistic drug pairs.

Computer-Aided Selective Optimization of Side Activities of Talinolol

Selective optimization of side activities is a valuable source of novel lead structures in drug discovery. In this study, a computer-aided approach was used to deorphanize the pleiotropic cholesterol-lowering effects of the beta-blocker talinolol, which result from the inhibition of the enzyme soluble epoxide hydrolase (sEH). X-ray structure analysis of the sEH in complex with talinolol enables a straightforward optimization of inhibitory potency. The resulting lead structure exhibited in vivo activity in a rat model of diabetic neuropatic pain.

A Structure-Based Drug Discovery Paradigm

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

The Alexipharmic Mechanisms of Five Licorice Ingredients Involved in CYP450 and Nrf2 Pathways in Paraquat-Induced Mice Acute Lung Injury

Oxidative stress is an important mechanism in acute lung injury (ALI) induced by paraquat (PQ), one of the most widely used herbicides in developing countries. In clinical prophylaxis and treatment, licorice is a widely used herbal medicine in China due to its strong alexipharmic characteristics. However, the corresponding biochemical mechanism of antioxidation and detoxification enzymes induced by licorice's ingredients is still not fully demonstrated. In this study, the detoxification effect of licorice was evaluated in vivo and in vitro. The detoxification and antioxidation effect of its active ingredients involved in the treatment was screened systematically according to Absorption, Distribution, Metabolism, and Excretion (ADME): predictions and evidence-based literature mining methods in silico approach. Data shows that licorice alleviate pulmonary edema and fibrosis, decrease Malondialdehyde (MDA) contents and increase Superoxide Dismutase (SOD) activity in PQ-induced ALI mice, protect the morphologic appearance of lung tissues, induce cytochrome 3A4 (CYA3A4) and Nuclear factor erythroid 2-related factor 2 (Nrf2) expression to active detoxification pathways, reduce the accumulation of PQ in vivo, protect or improve the liver and renal function of mice, and increase the survival rate. The 104 genes of PPI network contained all targets of licorice ingredients and PQ, which displayed the two redox regulatory enzymatic group modules cytochrome P450 (CYP450) and Nrf2 via a score-related graphic theoretic clustering algorithm in silico. According to ADME properties, glycyrol, isolicoflavonol, licochalcone A, 18beta-glycyrrhetinic acid, and licoisoflavone A were employed due to their oral bioavailability (OB) ≥ 30%, drug-likeness (DL) ≥ 0.1, and being highly associated with CYP450 and Nrf2 pathways, as potential activators to halt PQ-induced cells death in vitro. Both 3A4 inhibitor and silenced Nrf2 gene decreased the alexipharmic effects of those ingredients significantly. All these disclosed the detoxification and antioxidation effects of licorice on acute lung injury induced by PQ, and glycyrol, isolicoflavonol, licochalcone A, 18beta-glycyrrhetinic acid, and licoisoflavone A upregulated CYP450 and Nrf2 pathways underlying the alexipharmic mechanisms of licorice.

MolTarPred: A web tool for comprehensive target prediction with reliability estimation

Molecular target prediction can provide a starting point to understand the efficacy and side effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target prediction methods are not available as web tools. Furthermore, these are limited in the number of targets that can be predicted, do not estimate which target predictions are more reliable and/or lack comprehensive retrospective validations. We present MolTarPred ( http://moltarpred.marseille.inserm.fr/), a user-friendly web tool for predicting protein targets of small organic compounds. It is powered by a large knowledge base comprising 607,659 compounds and 4,553 macromolecular targets collected from the ChEMBL database. In about 1 min, the predicted targets for the supplied molecule will be listed in a table. The chemical structures of the query molecule and the most similar compounds annotated with the predicted target will also be shown to permit visual inspection and comparison. Practical examples of the use of MolTarPred are showcased. MolTarPred is a new resource for scientists that require a more complete knowledge of the polypharmacology of a molecule. The introduction of a reliability score constitutes an attractive functionality of MolTarPred, as it permits focusing experimental confirmatory tests on the most reliable predictions, which leads to higher prospective hit rates.

Genie in a bottle: controlled release helps tame natural polypharmacology?

Ability to faithfully report drug-target interactions constitutes a major critical parameter in preclinical/clinical settings. Yet the assessment of target engagement remains challenging, particularly for promiscuous and/or polypharmacologic ligands. Drawing from our improved insights into native electrophile signaling and emerging technologies that profile and interrogate these non-enzyme-assisted signaling subsystems, we posit that 'trained' polypharmocologic covalent inhibitors can be designed. Accumulating evidence indicates that electrophile-modified states at fractional occupancy can alter cell fate. Thus, by understanding sensing preferences and ligandable regions favored by the natural electrophilic signals at individual protein-ligand resolution, we can better evaluate target engagement and develop a function-guided understanding of polypharmacology.

Web-Based Tools for Polypharmacology Prediction

Drug promiscuity or polypharmacology is the ability of small molecules to interact with multiple protein targets simultaneously. In drug discovery, understanding the polypharmacology of potential drug molecules is crucial to improve their efficacy and safety, and to discover the new therapeutic potentials of existing drugs. Over the past decade, several computational methods have been developed to study the polypharmacology of small molecules, many of which are available as Web services. In this chapter, we review some of these Web tools focusing on ligand based approaches. We highlight in particular our recently developed polypharmacology browser (PPB) and its application for finding the side targets of a new inhibitor of the TRPV6 calcium channel.

In Silico Oncology Drug Repositioning and Polypharmacology

Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.

Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and substructures (ECfp4) and features an unprecedented combination of nearest neighbor (NN) searches and Naı̈ve Bayes (NB) machine learning, together with simple NN searches, NB and Deep Neural Network (DNN) machine learning models as further options. Although NN(ECfp4) gives the best results in terms of recall in a 10-fold cross-validation study, combining NN searches with NB machine learning provides superior precision statistics, as well as better results in a case study predicting off-targets of a recently reported TRPV6 calcium channel inhibitor, illustrating the value of this combined approach. PPB2 is available to assess possible off-targets of small molecule drug-like compounds by public access at http://gdb.unibe.ch .

A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL)

We present MuSSeL, a multifingerprint similarity search algorithm, able to predict putative drug targets for a given query small molecule as well as to return a quantitative assessment of its bioactivity in terms of K_i or IC₅₀ values. Predictions are automatically made exploiting a large collection of high quality experimental bioactivity data available from ChEMBL (version 22.1) combining, in a consensus-like approach, predictions resulting from a similarity search performed using 13 different fingerprint definitions. Importantly, the herein proposed algorithm is also effective in detecting and handling activity cliffs. A calibration set including small molecules present in the last updated version of ChEMBL (version 23) was employed to properly tune the algorithm parameters. Three randomly built external sets were instead challenged for model performances. The potential use of MuSSeL was also challenged by a prospective exercise for the prediction of five bioactive compounds taken from articles published in the Journal of Medicinal Chemistry just few months ago. The paper emphasizes the importance of implementing multifingerprint consensus strategies to increase the confidence in prediction of similarity search algorithms and provides a fast and easy-to-run tool for drug target and bioactivity prediction.

Identifying Lysophosphatidic Acid Acyltransferase β (LPAAT-β) as the Target of a Nanomolar Angiogenesis Inhibitor from a Phenotypic Screen Using the Polypharmacology Browser PPB2

By screening a focused library of kinase inhibitor analogues in a phenotypic co-culture assay for angiogenesis inhibition, we identified an aminotriazine that acts as a cytostatic nanomolar inhibitor. However, this aminotriazine was found to be completely inactive in a whole-kinome profiling assay. To decipher its mechanism of action, we used the online target prediction tool PPB2 (http://ppb2.gdb.tools), which suggested lysophosphatidic acid acyltransferase β (LPAAT-β) as a possible target for this aminotriazine as well as several analogues identified by structure-activity relationship profiling. LPAAT-β inhibition (IC₅₀ ≈15 nm) was confirmed in a biochemical assay and by its effects on cell proliferation in comparison with a known LPAAT-β inhibitor. These experiments illustrate the value of target-prediction tools to guide target identification for phenotypic screening hits and significantly expand the rather limited pharmacology of LPAAT-β inhibitors.

Repurposing approach identifies new treatment options for invasive fungal disease

Drug repositioning is the process of discovery, validation and marketing of previously approved drugs for new indications. Our aim was drug repositioning, using ligand-based and structure-based computational methods, of compounds that are similar to two hit compounds previously selected by our group that show promising antifungal activity. Through the ligand-based method, 100 compounds from each of three databases (MDDR, DrugBank and TargetMol) were selected by the Tanimoto coefficient, as similar to LMM5 or LMM11. These compounds were analyzed by the scaffold trees, and up to 10 compounds from each database were selected. The structure-based method (molecular docking) using thioredoxin reductase as the target drug was performed as a complementary approach, resulting in six compounds that were tested in an in vitro assay. All compounds, particularly raltegravir, showed antifungal activity against the genus Paracoccidioides. Raltegravir, an antiviral drug, showed promising antifungal activity against the experimental murine paracoccidioidomycosis, with significant reduction of the fungal burden and decreased alterations in the lung structure of mice treated with 1 mg/kg of raltegravir. In conclusion, the combination of two in silico methods for drug repositioning was able to select an antiviral drug with promising antifungal activity for treatment of paracoccidioidomycosis.

In Silico Studies on Compounds Derived from Calceolaria: Phenylethanoid Glycosides as Potential Multitarget Inhibitors for the Development of Pesticides

An increasing occurrence of resistance in insect pests and high mammal toxicity exhibited by common pesticides increase the need for new alternative molecules. Among these alternatives, bioinsecticides are considered to be environmentally friendly and safer than synthetic insecticides. Particularly, plant extracts have shown great potential in laboratory conditions. However, the lack of studies that confirm their mechanisms of action diminishes their potential applications on a large scale. Previously, we have reported the insect growth regulator and insecticidal activities of secondary metabolites isolated from plants of the Calceolaria genus. Herein, we report an in silico study of compounds isolated from Calceolaria against acetylcholinesterase, prophenoloxidase, and ecdysone receptor. The molecular docking results are consistent with the previously reported experimental results, which were obtained during the bioevaluation of Calceolaria extracts. Among the compounds, phenylethanoid glycosides, such as verbascoside, exhibited good theoretical affinity to all the analyzed targets. In light of these results, we developed an index to evaluate potential multitarget insecticides based on docking scores.

Multi-Target Approach for Drug Discovery against Schizophrenia

Polypharmacology is nowadays considered an increasingly crucial aspect in discovering new drugs as a number of original single-target drugs have been performing far behind expectations during the last ten years. In this scenario, multi-target drugs are a promising approach against polygenic diseases with complex pathomechanisms such as schizophrenia. Indeed, second generation or atypical antipsychotics target a number of aminergic G protein-coupled receptors (GPCRs) simultaneously. Novel strategies in drug design and discovery against schizophrenia focus on targets beyond the dopaminergic hypothesis of the disease and even beyond the monoamine GPCRs. In particular these approaches concern proteins involved in glutamatergic and cholinergic neurotransmission, challenging the concept of antipsychotic activity without dopamine D₂ receptor involvement. Potentially interesting compounds include ligands interacting with glycine modulatory binding pocket on N-methyl-d-aspartate (NMDA) receptors, positive allosteric modulators of α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, positive allosteric modulators of metabotropic glutamatergic receptors, agonists and positive allosteric modulators of α7 nicotinic receptors, as well as muscarinic receptor agonists. In this review we discuss classical and novel drug targets for schizophrenia, cover benefits and limitations of current strategies to design multi-target drugs and show examples of multi-target ligands as antipsychotics, including marketed drugs, substances in clinical trials, and other investigational compounds.

Network-Based Methods for Prediction of Drug-Target Interactions

Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.

Orthologue chemical space and its influence on target prediction

Motivation

In silico approaches often fail to utilize bioactivity data available for orthologous targets due to insufficient evidence highlighting the benefit for such an approach. Deeper investigation into orthologue chemical space and its influence toward expanding compound and target coverage is necessary to improve the confidence in this practice.

Results

Here we present analysis of the orthologue chemical space in ChEMBL and PubChem and its impact on target prediction. We highlight the number of conflicting bioactivities between human and orthologues is low and annotations are overall compatible. Chemical space analysis shows orthologues are chemically dissimilar to human with high intra-group similarity, suggesting they could effectively extend the chemical space modelled. Based on these observations, we show the benefit of orthologue inclusion in terms of novel target coverage. We also benchmarked predictive models using a time-series split and also using bioactivities from Chemistry Connect and HTS data available at AstraZeneca, showing that orthologue bioactivity inclusion statistically improved performance.

Availability and implementation

Orthologue-based bioactivity prediction and the compound training set are available at www.github.com/lhm30/PIDGINv2.

Contact

ab454@cam.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome

Drug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.

Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds

Multitargeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation, or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multitarget compound development with valuable tools. In this Perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite some characteristic challenges remaining unresolved, designed polypharmacology holds enormous potential to secure future therapeutic innovation.

Extending in Silico Protein Target Prediction Models to Include Functional Effects

In silico protein target deconvolution is frequently used for mechanism-of-action investigations; however existing protocols usually do not predict compound functional effects, such as activation or inhibition, upon binding to their protein counterparts. This study is hence concerned with including functional effects in target prediction. To this end, we assimilated a bioactivity training set for 332 targets, comprising 817,239 active data points with unknown functional effect (binding data) and 20,761,260 inactive compounds, along with 226,045 activating and 1,032,439 inhibiting data points from functional screens. Chemical space analysis of the data first showed some separation between compound sets (binding and inhibiting compounds were more similar to each other than both binding and activating or activating and inhibiting compounds), providing a rationale for implementing functional prediction models. We employed three different architectures to predict functional response, ranging from simplistic random forest models ('Arch1') to cascaded models which use separate binding and functional effect classification steps ('Arch2' and 'Arch3'), differing in the way training sets were generated. Fivefold stratified cross-validation outlined cascading predictions provides superior precision and recall based on an internal test set. We next prospectively validated the architectures using a temporal set of 153,467 of in-house data points (after a 4-month interim from initial data extraction). Results outlined Arch3 performed with the highest target class averaged precision and recall scores of 71% and 53%, which we attribute to the use of inactive background sets. Distance-based applicability domain (AD) analysis outlined that Arch3 provides superior extrapolation into novel areas of chemical space, and thus based on the results presented here, propose as the most suitable architecture for the functional effect prediction of small molecules. We finally conclude including functional effects could provide vital insight in future studies, to annotate cases of unanticipated functional changeover, as outlined by our CHRM1 case study.

What Contributes to Serotonin-Norepinephrine Reuptake Inhibitors' Dual-Targeting Mechanism? The Key Role of Transmembrane Domain 6 in Human Serotonin and Norepinephrine Transporters Revealed by Molecular Dynamics Simulation

Dual inhibition of serotonin and norepinephrine transporters (hSERT and hNET) gives greatly improved efficacy and tolerability for treating major depressive disorder (MDD) compared with selective reuptake inhibitors. Pioneer studies provided valuable information on structure, function, and pharmacology of drugs targeting both hSERT and hNET (serotonin-norepinephrine reuptake inhibitors, SNRIs), and the differential binding mechanism between SNRIs and selective inhibitors of 5-HT (SSRIs) or NE (sNRIs) to their corresponding targets was expected to be able to facilitate the discovery of a privileged drug-like scaffold with improved efficacy. However, the dual-target mechanism of SNRIs was still elusive, and the binding mode distinguishing SNRIs from SSRIs and sNRIs was also unclear. Herein, an integrated computational strategy was adopted to discover the binding mode shared by all FDA approved SNRIs. The comparative analysis of binding free energy at the per-residue level discovered that residues Phe335, Leu337, Gly338, and Val343 located at the transmembrane domain 6 (TM6) of hSERT (the corresponding residues Phe317, Leu319, Gly320, and Val325 in hNET) were the determinants accounting for SNRIs' dual-acting inhibition, while residues lining TM3 and 8 (Ile172, Ser438, Thr439, and Leu443 in hSERT; Val148, Ser419, Ser420, and Met424 in hNET) contributed less to the binding of SNRIs than that of SSRIs and sNRIs. Based on these results, the distances between an SNRI's centroid and the centroids of its two aromatic rings (measuring the depth of rings stretching into hydrophobic pockets) were discovered as the key to the SNRIs' dual-targeting mechanism. This finding revealed SNRIs' binding mechanism at an atomistic level, which could be further utilized as structural blueprints for the rational design of privileged drug-like scaffolds treating MDD.

Deep learning improves prediction of drug-drug and drug-food interactions

Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. Several computational methods have been developed to better understand drug interactions, especially for DDIs. However, these methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction. Here, we report development of a computational framework DeepDDI that uses names of drug-drug or drug-food constituent pairs and their structural information as inputs to accurately generate 86 important DDI types as outputs of human-readable sentences. DeepDDI uses deep neural network with its optimized prediction performance and predicts 86 DDI types with a mean accuracy of 92.4% using the DrugBank gold standard DDI dataset covering 192,284 DDIs contributed by 191,878 drug pairs. DeepDDI is used to suggest potential causal mechanisms for the reported ADEs of 9,284 drug pairs, and also predict alternative drug candidates for 62,707 drug pairs having negative health effects. Furthermore, DeepDDI is applied to 3,288,157 drug-food constituent pairs (2,159 approved drugs and 1,523 well-characterized food constituents) to predict DFIs. The effects of 256 food constituents on pharmacological effects of interacting drugs and bioactivities of 149 food constituents are predicted. These results suggest that DeepDDI can provide important information on drug prescription and even dietary suggestions while taking certain drugs and also guidelines during drug development.