Large-scale detection of drug off-targets: hypotheses for drug repurposing and understanding side-effects

Integrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery

BACKGROUND

Large-scale pharmacogenomic resources, such as the Connectivity Map (CMap), have greatly assisted computational drug discovery. However, despite their widespread use, CMap-based methods have thus far been agnostic to the biological activity of drugs as well as to the genomic effects of drugs in multiple disease contexts. Here, we present a network-based statistical approach, Pathopticon, that uses CMap to build cell type-specific gene-drug perturbation networks and integrates these networks with cheminformatic data and diverse disease phenotypes to prioritize drugs in a cell type-dependent manner.

METHODS

We build cell type-specific gene-drug perturbation networks from CMap data using a statistical procedure we call Quantile-based Instance Z-score Consensus (QUIZ-C). Using these networks and a large-scale disease-gene network consisting of 569 disease signatures from the Enrichr database, we calculate Pathophenotypic Congruity Scores (PACOS) between input gene signatures and drug perturbation signatures and combine these scores with cheminformatic data from ChEMBL to prioritize drugs. We benchmark our approach by calculating area under the receiver operating characteristic curves (AUROC) for 73 gene sets from the Molecular Signatures Database (MSigDB) using target gene expression profiles from the Comparative Toxicogenomics Database (CTD). We validate the drugs predicted in our proofs-of-concept using real-time polymerase chain reaction (qPCR) experiments.

RESULTS

Cell type-specific gene-drug perturbation networks built using QUIZ-C are topologically distinct, reflecting the biological uniqueness of the cell lines in CMap, and are enriched in known drug targets. Pathopticon demonstrates a better prediction performance than solely cheminformatic measures as well as state-of-the-art network and deep learning-based methods. Top predictions made by Pathopticon have high chemical structural diversity, suggesting their potential for building compound libraries. In proof-of-concept applications on vascular diseases, we demonstrate that Pathopticon helps guide in vitro experiments by identifying pathways that are potentially regulated by the predicted therapeutic candidates.

CONCLUSIONS

Our network-based analytical framework integrating pharmacogenomics and cheminformatics (available at https://github.com/r-duh/Pathopticon ) provides a feasible blueprint for a cell type-specific drug discovery and repositioning platform with broad implications for the efficiency and success of drug development.

ZA-II-05, a novel NMDA-receptor antagonist reverses vanadium-induced neurotoxicity in Caenorhabditis elegans (C. elegans)

INTRODUCTION

Vanadium is a widely used transition metal in industrial applications, but it also poses significant neurotoxic and environmental risks. Previous studies have shown that exposure to vanadium may lead to neurodegenerative diseases and neuropathic pain, raising concerns about its impact on human health and the ecosystem. To address vanadium neurotoxicity, through targeting NMDA glutamate and dopamine signaling, both involved in neurodegenerative disorders, shows promise. Using Caenorhabditis elegans as a model, we evaluated a novel compound with a mixed NMDA glutamate receptor-dopamine transporter pharmacology, ZA-II-05 and found it effectively ameliorated vanadium-induced neurotoxicity, suggesting a potential neuroprotective role.

METHODS

Synchronized young adult worms were assigned to four different experimental groups; Controls; 100 mM of Vanadium; Vanadium and 1 mg/ml ZA-II-05; and ZA-II-05 alone. These were examined with different markers, including DAPI, MitoTracker Green and MitoSox stains for assessment of nuclei and mitochondrial density and oxidative stress, respectively.

RESULTS

Exposure to vanadium in C. elegans resulted in decreased nuclear presence and reduction in mitochondrial content were also analyzed based on fluorescence in the pharyngeal region, signifying an increase in the production of reactive oxygen species, while vanadium co-treatment with ZA-II-05 caused a significant increase in nuclear presence and mitochondrial content.

DISCUSSION

Treatment with ZA-II-05 significantly preserved cellular integrity, exhibiting a reversal of the detrimental effects induced by vanadium by modulating and preserving the normal function of chemosensory neurons and downstream signaling pathways. This study provides valuable insights into the mechanisms of vanadium-induced neurotoxicity and offers perspectives for developing therapeutic interventions for neurodegenerative diseases related to environmental toxins.

Bridging the gap between target-based and phenotypic-based drug discovery

INTRODUCTION

The unparalleled progress in science of the last decades has brought a better understanding of the molecular mechanisms of diseases. This promoted drug discovery processes based on a target approach. However, despite the high promises associated, a critical decrease in the number of first-in-class drugs has been observed.

AREAS COVERED

This review analyses the challenges, advances, and opportunities associated with the main strategies of the drug discovery process, i.e. based on a rational target approach and on an empirical phenotypic approach. This review also evaluates how the gap between these two crossroads can be bridged toward a more efficient drug discovery process.

EXPERT OPINION

The critical lack of knowledge of the complex biological networks is leading to targets not relevant for the clinical context or to drugs that present undesired adverse effects. The phenotypic systems designed by considering available molecular mechanisms can mitigate these knowledge gaps. Associated with the expansion of the chemical space and other technologies, these designs can lead to more efficient drug discoveries. Technological and scientific knowledge should also be applied to identify, as early as possible, both drug targets and mechanisms of action, leading to a more efficient drug discovery pipeline.

Statins as anti-tumor agents: A paradigm for repurposed drugs

BACKGROUND

Statins, frequently prescribed medications, work by inhibiting the rate-limiting enzyme HMG-CoA reductase (HMGCR) in the mevalonate pathway to reduce cholesterol levels. Due to their multifaceted benefits, statins are being adapted for use as cost-efficient, safe and effective anti-cancer treatments. Several studies have shown that specific types of cancer are responsive to statin medications since they rely on the mevalonate pathway for their growth and survival.

RECENT FINDINGS

Statin are a class of drugs known for their potent inhibition of cholesterol production and are typically prescribed to treat high cholesterol levels. Nevertheless, there is growing interest in repurposing statins for the treatment of malignant neoplastic diseases, often in conjunction with chemotherapy and radiotherapy. The mechanism behind statin treatment includes targeting apoptosis through the BCL2 signaling pathway, regulating the cell cycle via the p53-YAP axis, and imparting epigenetic modulations by altering methylation patterns on CpG islands and histone acetylation by downregulating DNMTs and HDACs respectively. Notably, some studies have suggested a potential chemo-preventive effect, as decreased occurrence of tumor relapse and enhanced survival rate were reported in patients undergoing long-term statin therapy. However, the definitive endorsement of statin usage in cancer therapy hinges on population based clinical studies with larger patient cohorts and extended follow-up periods.

CONCLUSIONS

The potential of anti-cancer properties of statins seems to reach beyond their influence on cholesterol production. Further investigations are necessary to uncover their effects on cancer promoting signaling pathways. Given their distinct attributes, statins might emerge as promising contenders in the fight against tumorigenesis, as they appear to enhance the efficacy and address the limitations of conventional cancer treatments.

Novel strategies for drug repurposing

Synthetic biology, precision medicine, and nanobiotechnology are the three main emerging areas that drive translational innovation toward commercialization. There are several strategies used in precision medicine and drug repurposing is one of the key approaches as it addresses the challenges in drug discovery (high cost and time). Here, we provide a perspective on various new approaches to drug repurposing for cancer precision medicine. We report here our optimized wound healing methodology that can be used to validate drug sensitivity and drug repurposing. Using HeLa as our benchmark, we demonstrated that the assay can be applied to identify drugs that limit cell proliferation. From a future perspective, this assay can be expanded to ex vivo culturing of solid tumors in 2D culture and leukemia in 3D culture.

Direct evaluation of polarity of the ligand binding pocket in retinoid X receptor using a fluorescent solvatochromic agonist

High selectivity of small-molecule drug candidates for their target molecule is important to minimize potential side effects. One factor that contributes to the selectivity is the internal polarity of the ligand-binding pocket (LBP) in the target molecule, but this is difficult to measure. Here, we first confirmed that the retinoid X receptor (RXR) agonist 6-(ethyl(1-isobutyl-2-oxo-4-(trifluoromethyl)-1,2-dihydroquinolin-7-yl)amino)nicotinic acid (NEt-iFQ, 1) exhibits fluorescence solvatochromism, i.e., its Stokes shift depends on the polarity of the solvent, and then we utilized this property to directly measure the internal polarity of the RXRα-LBP. The Stokes shift of 1 when bound to the RXRα-LBP corresponded to that of 1 in chloroform solution. This finding is expected to be helpful for designing RXR-selective ligands. A similar approach should be appliable to evaluate the internal polarity of the LBPs of other receptors.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

Allosteric Modulation of Membrane Proteins by Small Low-Affinity Ligands

Membrane proteins may respond to a variety of ligands under an applied external stimulus. These ligands include small low-affinity molecules that account for functional effects in the mM range. Understanding the modulation of protein function by low-affinity ligands requires characterizing their atomic-level interactions under dilution, challenging the current resolution of theoretical and experimental routines. Part of the problem derives from the fact that small low-affinity ligands may interact with multiple sites of a membrane protein in a highly degenerate manner to a degree that it is better conceived as a partition phenomenon, hard to track at the molecular interface of the protein. Looking for new developments in the field, we rely on the classic two-state Boltzmann model to devise a novel theoretical description of the allosteric modulation mechanism of membrane proteins in the presence of small low-affinity ligands and external stimuli. Free energy stability of the partition process and its energetic influence on the protein coupling with the external stimulus are quantified. The outcome is a simple formulation that allows the description of the equilibrium shifts of the protein in terms of the grand-canonical partition function of the ligand at dilute concentrations. The model's predictions of the spatial distribution and response probability shift across a variety of ligand concentrations, and thermodynamic conjugates can be directly compared to macroscopic measurements, making it especially useful to interpret experimental data at the atomic level. Illustration and discussion of the theory is shown in the context of general anesthetics and voltage-gated channels for which structural data are available.

A preliminary study to identify existing drugs for potential repurposing in breast cancer based on side effect profile

Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer-related death in women after lung cancer. The present study aims to identify potential drug candidates using the PROMISCUOUS database for breast cancer based on side effect profile and then proceed with in silico and in vitro studies. PROMISCUOUS database was used to construct a group of drugs that share maximum side effects with letrozole. Based on the existing literature, ropinirole, risperidone, pregabalin, and gabapentin were selected for in silico and in vitro studies. The molecular docking was carried out using AUTODOCK 4.2.6. MCF-7 cell line was used to evaluate the anti-cancer activity of the selected drugs. PROMISCUOUS database revealed that as many as 23 existing drugs shared between 62 and 79 side-effects with letrozole. From docking result, we found that, ropinirole showed a good binding affinity (-7.7 kcal/mol) against aromatase compared to letrozole (-7.1 kcal/mol) which was followed by gabapentin (-6.4 kcal/mol), pregabalin (-5.7 kcal/mol) and risperidone (-5.1 kcal/mol). From the in vitro results, ropinirole and risperidone showed good anti-cancer activity of IC₅₀ with 40.85±11.02 μg/ml and 43.10±9.58 μg/ml cell viability. Based on this study results and existing literature we conclude that risperidone, pregabalin, and gabapentin are not ideal candidates for repurposing in breast cancer but ropinirole could be an excellent choice for repurposing in breast cancer after further studies.

Molecular interaction networks and drug development: Novel approach to drug target identification and drug repositioning

Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein-protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network

The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.

Concentration-Dependent Thermodynamic Analysis of the Partition Process of Small Ligands into Proteins

In the category of functional low-affinity interactions, small ligands may interact with multiple protein sites in a highly degenerate manner. Better conceived as a partition phenomenon at the molecular interface of proteins, such low-affinity interactions appear to be hidden to our current experimental resolution making their structural and functional characterization difficult in the low concentration regime of physiological processes. Characterization of the partition phenomenon under higher chemical forces could be a relevant strategy to tackle the problem provided the results can be scaled back to the low concentration range. Far from being trivial, such scaling demands a concentration-dependent understanding of self-interactions of the ligands, structural perturbations of the protein, among other molecular effects. Accordingly, we elaborate a novel and detailed concentration-dependent thermodynamic analysis of the partition process of small ligands aiming at characterizing the stability and structure of the dilute phenomenon from high concentrations. In analogy to an “aggregate” binding constant of a small molecule over multiple sites of a protein receptor, the model defines the stability of the process as a macroscopic equilibrium constant for the partition number of ligands that can be used to analyze biochemical and functional data of two-component systems driven by low-affinity interactions. Acquisition of such modeling-based structural information is expected to be highly welcome by revealing more traceable protein-binding spots for non-specific ligands.

Docking-Based Virtual Screening Enables Prioritizing Protein Kinase Inhibitors With In Vitro Phenotypic Activity Against Schistosoma mansoni

Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional high-throughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for “one drug-multiple targets”. Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach.

Targeting matrix metallopeptidase 2 by hydroxyurea selectively kills acute myeloid mixed-lineage leukemia

Oncogene-induced tumorigenesis results in the variation of epigenetic modifications, and in addition to promoting cell immortalization, cancer cells undergo more intense cellular stress than normal cells and depend on other support genes for survival. Chromosomal translocations of mixed-lineage leukemia (MLL) induce aggressive leukemias with an inferior prognosis. Unfortunately, most MLL-rearranged (MLL-r) leukemias are resistant to conventional chemotherapies. Here, we showed that hydroxyurea (HU) could kill MLL-r acute myeloid leukemia (AML) cells through the necroptosis process. HU target these cells by matrix metallopeptidase 2 (MMP2) deficiency rather than subordinate ribonucleotide reductase regulatory subunit M2 (RRM2) inhibition, where MLL directly regulates MMP2 expression and is decreased in most MLL-r AMLs. Moreover, iron chelation of HU is also indispensable for inducing cell stress, and MMP2 is the support factor to protect cells from death. Our preliminary study indicates that MMP2 might play a role in the nonsense-mediated mRNA decay pathway that prevents activation of unfolding protein response under innocuous endoplasmic reticulum stress. Hence, these results reveal a possible strategy of HU application in MLL-r AML treatment and shed new light upon HU repurposing.

Angiotensin II receptor 1 controls profibrotic Wnt/β-catenin signalling in experimental autoimmune myocarditis

AIMS

Angiotensin (Ang) II signalling has been suggested to promote cardiac fibrosis in inflammatory heart diseases; however, the underlying mechanisms remain obscure. Using Agtr1a-/- mice with genetic deletion of angiotensin receptor type 1 (ATR1) and the experimental autoimmune myocarditis (EAM) model, we aimed to elucidate the role of Ang II-ATR1 pathway in development of heart-specific autoimmunity and post-inflammatory fibrosis.

METHODS AND RESULTS

EAM was induced in wild-type (WT) and Agtr1a-/- mice by subcutaneous injections with alpha myosin heavy chain peptide emulsified in complete Freund's adjuvant. Agtr1a-/- mice developed myocarditis to a similar extent as WT controls at day 21 but showed reduced fibrosis and better systolic function at day 40. Crisscross bone marrow chimaera experiments proved that ATR1 signalling in the bone marrow compartment was critical for cardiac fibrosis. Heart infiltrating, bone-marrow-derived cells produced Ang II, but lack of ATR1 in these cells reduced transforming growth factor beta (TGF-β)-mediated fibrotic responses. At the molecular level, Agtr1a-/- heart-inflammatory cells showed impaired TGF-β-mediated phosphorylation of Smad2 and TAK1. In WT cells, TGF-β induced formation of RhoA-GTP and RhoA-A-kinase anchoring protein-Lbc (AKAP-Lbc) complex. In Agtr1a-/- cells, stabilization of RhoA-GTP and interaction of RhoA with AKAP-Lbc were largely impaired. Furthermore, in contrast to WT cells, Agtr1a-/- cells stimulated with TGF-β failed to activate canonical Wnt pathway indicated by suppressed activity of glycogen synthase kinase-3 (GSK-3)β and nuclear β-catenin translocation and showed reduced expression of Wnts. In line with these in vitro findings, β-catenin was detected in inflammatory regions of hearts of WT, but not Agtr1a-/- mice and expression of canonical Wnt1 and Wnt10b were lower in Agtr1a-/- hearts.

CONCLUSION

Ang II-ATR1 signalling is critical for development of post-inflammatory fibrotic remodelling and dilated cardiomyopathy. Our data underpin the importance of Ang II-ATR1 in effective TGF-β downstream signalling response including activation of profibrotic Wnt/β-catenin pathway.

Yeast Double Transporter Gene Deletion Library for Identification of Xenobiotic Carriers in Low or High Throughput

The routes of uptake and efflux should be considered when developing new drugs so that they can effectively address their intracellular targets. As a general rule, drugs appear to enter cells via protein carriers that normally carry nutrients or metabolites. A previously developed pipeline that searched for drug transporters using Saccharomyces cerevisiae mutants carrying single-gene deletions identified import routes for most compounds tested. However, due to the redundancy of transporter functions, we propose that this methodology can be improved by utilizing double mutant strains in both low- and high-throughput screens. We constructed a library of over 14,000 strains harboring double deletions of genes encoding 122 nonessential plasma membrane transporters and performed low- and high-throughput screens identifying possible drug import routes for 23 compounds. In addition, the high-throughput assay enabled the identification of putative efflux routes for 21 compounds. Focusing on azole antifungals, we were able to identify the involvement of the myo-inositol transporter, Itr1p, in the uptake of these molecules and to confirm the role of Pdr5p in their export.

Network pharmacology: curing causal mechanisms instead of treating symptoms

For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease-one target-one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation.

Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients

Lung imaging and autopsy reports among COVID-19 patients show elevated lung scarring (fibrosis). Early data from COVID-19 patients as well as previous studies from severe acute respiratory syndrome, Middle East respiratory syndrome, and other respiratory disorders show that the extent of lung fibrosis is associated with a higher mortality, prolonged ventilator dependence, and poorer long-term health prognosis. Current treatments to halt or reverse lung fibrosis are limited; thus, the rapid development of effective antifibrotic therapies is a major global medical need that will continue far beyond the current COVID-19 pandemic. Reproducible fibrosis screening assays with high signal-to-noise ratios and disease-relevant readouts such as extracellular matrix (ECM) deposition (the hallmark of fibrosis) are integral to any antifibrotic therapeutic development. Therefore, we have established an automated high-throughput and high-content primary screening assay measuring transforming growth factor-β (TGFβ)-induced ECM deposition from primary human lung fibroblasts in a 384-well format. This assay combines longitudinal live cell imaging with multiparametric high-content analysis of ECM deposition. Using this assay, we have screened a library of 2743 small molecules representing approved drugs and late-stage clinical candidates. Confirmed hits were subsequently profiled through a suite of secondary lung fibroblast phenotypic screening assays quantifying cell differentiation, proliferation, migration, and apoptosis. In silico target prediction and pathway network analysis were applied to the confirmed hits. We anticipate this suite of assays and data analysis tools will aid the identification of new treatments to mitigate against lung fibrosis associated with COVID-19 and other fibrotic diseases.

Tackling Alzheimer's Disease with Existing Drugs: A Promising Strategy for Bypassing Obstacles

The unmet need for the development of effective drugs to treat Alzheimer 's disease has been steadily growing, representing a major challenge in drug discovery. In this context, drug repurposing, namely the identification of novel therapeutic indications for approved or investigational compounds, can be seen as an attractive attempt to obtain new medications reducing both the time and the economic burden usually required for research and development programs. In the last years, several classes of drugs have evidenced promising beneficial effects in neurodegenerative diseases, and for some of them, preliminary clinical trials have been started. This review aims to illustrate some of the most recent examples of drugs reprofiled for Alzheimer's disease, considering not only the finding of new uses for existing drugs but also the new hypotheses on disease pathogenesis that could promote previously unconsidered therapeutic regimens. Moreover, some examples of structural modifications performed on existing drugs in order to obtain multifunctional compounds will also be described.

Towards the routine use of in silico screenings for drug discovery using metabolic modelling

Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.

PROMISCUOUS 2.0: a resource for drug-repositioning

The development of new drugs for diseases is a time-consuming, costly and risky process. In recent years, many drugs could be approved for other indications. This repurposing process allows to effectively reduce development costs, time and, ultimately, save patients’ lives. During the ongoing COVID-19 pandemic, drug repositioning has gained widespread attention as a fast opportunity to find potential treatments against the newly emerging disease. In order to expand this field to researchers with varying levels of experience, we made an effort to open it to all users (meaning novices as well as experts in cheminformatics) by significantly improving the entry-level user experience. The browsing functionality can be used as a global entry point to collect further information with regards to small molecules (∼1 million), side-effects (∼110 000) or drug-target interactions (∼3 million). The drug-repositioning tab for small molecules will also suggest possible drug-repositioning opportunities to the user by using structural similarity measurements for small molecules using two different approaches. Additionally, using information from the Promiscuous 2.0 Database, lists of candidate drugs for given indications were precomputed, including a section dedicated to potential treatments for COVID-19. All the information is interconnected by a dynamic network-based visualization to identify new indications for available compounds. Promiscuous 2.0 is unique in its functionality and is publicly available at http://bioinformatics.charite.de/promiscuous2.

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.

Side chain similarity comparisons for integrated drug repositioning and potential toxicity assessments in epidemic response scenarios: The case for COVID-19

Structures of protein-drug-complexes provide an atomic level profile of drug-target interactions. In this work, the three-dimensional arrangements of amino acid side chains in known drug binding sites (substructures) were used to search for similarly arranged sites in SARS-CoV-2 protein structures in the Protein Data Bank for the potential repositioning of approved compounds. We were able to identify 22 target sites for the repositioning of 16 approved drug compounds as potential therapeutics for COVID-19. Using the same approach, we were also able to investigate the potentially promiscuous binding of the 16 compounds to off-target sites that could be implicated in toxicity and side effects that had not been provided by any previous studies. The investigations of binding properties in disease-related proteins derived from the comparison of amino acid substructure arrangements allows for effective mechanism driven decision making to rank and select only the compounds with the highest potential for success and safety to be prioritized for clinical trials or treatments. The intention of this work is not to explicitly identify candidate compounds but to present how an integrated drug repositioning and potential toxicity pipeline using side chain similarity searching algorithms are of great utility in epidemic scenarios involving novel pathogens. In the case of the COVID-19 pandemic caused by the SARS-CoV-2 virus, we demonstrate that the pipeline can identify candidate compounds quickly and sustainably in combination with associated risk factors derived from the analysis of potential off-target site binding by the compounds to be repurposed.

Drugs, host proteins and viral proteins: how their promiscuities shape antiviral design

The reciprocal nature of drug specificity and target specificity implies that the same is true for their respective promiscuities. Protein promiscuity has two broadly different types of footprint in drug design. The first is relaxed specificity of binding sites for substrates, inhibitors, effectors or cofactors. The second involves protein-protein interactions of regulatory processes such as signal transduction and transcription, and here protein intrinsic disorder plays an important role. Both viruses and host cells exploit intrinsic disorder for their survival, as do the design and discovery programs for antivirals. Drug action, strictly speaking, always relies upon promiscuous activity, with drug promiscuity enlarging its scope. Drug repurposing searches for additional promiscuity on the part of both the drug and the target in the host. Understanding the subtle nuances of these promiscuities is critical in the design of novel and more effective antivirals.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Utilizing drug-target-event relationships to unveil safety patterns in pharmacovigilance

INTRODUCTION

Signal detection is the most pivotal activity of signal management to guarantee that drugs maintain a positive risk-benefit ratio during their lifetime on the market. Signal detection is based on the systematic evaluation of available data sources, which have recently been extended in order to improve timely and comprehensive signal detection of drug safety problems.

AREAS COVERED

In recent years, attempts have been made to incorporate pharmacological data for the prediction of safety signals. Previous studies have shown that data on the pharmacological targets of drugs are predictive of post-marketing adverse events. However, current approaches limit such predictions to adverse events expected from the interaction of a drug with the main pharmacological target and do not take off-target interactions into consideration.

EXPERT OPINION

The authors propose the application of predictive modeling techniques utilizing pharmacological data from public databases for predicting drug-target-event relationships deriving from main- and off-target binding and from which potential safety signals can be deduced. Additionally, they provide an operative procedure for the identification of clinically relevant subgroups for predicted safety signals.

Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning

A common drug repositioning strategy is the re-application of an existing drug to address alternative targets. A crucial aspect to enable such repurposing is that the drug's binding site on the original target is similar to that on the alternative target. Based on the assumption that proteins with similar binding sites may bind to similar drugs, the 3D substructure similarity data can be used to identify similar sites in other proteins that are not known targets. The Drug ReposER (DRug REPOSitioning Exploration Resource) web server is designed to identify potential targets for drug repurposing based on sub-structural similarity to the binding interfaces of known drug binding sites. The application has pre-computed amino acid arrangements from protein structures in the Protein Data Bank that are similar to the 3D arrangements of known drug binding sites thus allowing users to explore them as alternative targets. Users can annotate new structures for sites that are similarly arranged to the residues found in known drug binding interfaces. The search results are presented as mappings of matched sidechain superpositions. The results of the searches can be visualized using an integrated NGL viewer. The Drug ReposER server has no access restrictions and is available at http://mfrlab.org/drugreposer/.

Drug rechanneling: A novel paradigm for cancer treatment

Cancer continues to be one of the leading contributors towards global disease burden. According to NIH, cancer incidence rate per year will increase to 23.6 million by 2030. Even though cancer continues to be a major proportion of the disease burden worldwide, it has the lowest clinical trial success rate amongst other diseases. Hence, there is an unmet need for novel, affordable and effective anti-neoplastic medications. As a result, a growing interest has sparkled amongst researchers towards drug repurposing. Drug repurposing follows the principle of polypharmacology, which states, "any drug with multiple targets or off targets can present several modes of action". Drug repurposing also known as drug rechanneling, or drug repositioning is an economic and reliable approach that identifies new disease treatment of already approved drugs. Repurposing guarantees expedited access of drugs to the patients as these drugs are already FDA approved and their safety and toxicity profile is completely established. Epidemiological studies have identified the decreased occurrence of oncological or non-oncological conditions in patients undergoing treatment with FDA approved drugs. Data from multiple experimental studies and clinical observations have depicted that several non-neoplastic drugs have potential anticancer activity. In this review, we have summarized the potential anti-cancer effects of anti-psychotic, anti-malarial, anti-viral and anti-emetic drugs with a brief overview on their mechanism and pathways in different cancer types. This review highlights promising evidences for the repurposing of drugs in oncology.

Targeting the C-Terminal Domain Small Phosphatase 1

The human C-terminal domain small phosphatase 1 (CTDSP1/SCP1) is a protein phosphatase with a conserved catalytic site of DXDXT/V. CTDSP1’s major activity has been identified as dephosphorylation of the 5th Ser residue of the tandem heptad repeat of the RNA polymerase II C-terminal domain (RNAP II CTD). It is also implicated in various pivotal biological activities, such as acting as a driving factor in repressor element 1 (RE-1)-silencing transcription factor (REST) complex, which silences the neuronal genes in non-neuronal cells, G1/S phase transition, and osteoblast differentiation. Recent findings have denoted that negative regulation of CTDSP1 results in suppression of cancer invasion in neuroglioma cells. Several researchers have focused on the development of regulating materials of CTDSP1, due to the significant roles it has in various biological activities. In this review, we focused on this emerging target and explored the biological significance, challenges, and opportunities in targeting CTDSP1 from a drug designing perspective.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

Human Copper-Containing Amine Oxidases in Drug Design and Development

Two members of the copper-containing amine oxidase family are physiologically important proteins: (1) Diamine oxidase (hDAO; AOC1) with a preference for diamines is involved in degradation of histamine and (2) Vascular adhesion protein-1 (hVAP-1; AOC3) with a preference for monoamines is a multifunctional cell-surface receptor and an enzyme. hVAP-1-targeted inhibitors are designed to treat inflammatory diseases and cancer, whereas the off-target binding of the designed inhibitors to hDAO might result in adverse drug reactions. The X-ray structures for both human enzymes are solved and provide the basis for computer-aided inhibitor design, which has been reported by several research groups. Although the putative off-target effect of hDAO is less studied, computational methods could be easily utilized to avoid the binding of VAP-1-targeted inhibitors to hDAO. The choice of the model organism for preclinical testing of hVAP-1 inhibitors is not either trivial due to species-specific binding properties of designed inhibitors and different repertoire of copper-containing amine oxidase family members in mammalian species. Thus, the facts that should be considered in hVAP-1-targeted inhibitor design are discussed in light of the applied structural bioinformatics and structural biology approaches.

Promiscuity of in Vitro Secondary Pharmacology Assays and Implications for Lead Optimization Strategies

We conducted an analysis on screening data generated from 1445 compounds against a panel of 130 enzymes, ion channels, and receptors to assess secondary pharmacological risks. Hit rates of these targets as well as physicochemical properties for those hits were evaluated. A majority of targets yielded hits with higher clogP, molecular weight, and more basic character than inactive compounds. Although most targets favored lipophilic hits, the average clogP of hits at a given target did not correlate with its hit rate. Furthermore, a matched pair analysis was completed to determine structural changes that impacted off-target activities. A correlation of binding assays used in this analysis illustrated that some pharmacologically related binding assays are highly correlative and may be substituted for a smaller set of surrogate assays.

Angiotensin II Increases Endoplasmic Reticulum Stress in Adipose Tissue and Adipocytes

The Renin Angiotensin System (RAS), a key regulator of blood pressure has been linked to metabolic disorders. We have previously reported that adipose overexpression of angiotensinogen in mice (Agt-Tg) induces obesity, in part mediated by adipose tissue inflammation, through yet unidentified mechanisms. Hence, we hypothesize that adipose tissue enrichment of angiotensinogen leads to activation of inflammatory cascades and endoplasmic reticulum (ER) stress, thereby, contributing to obesity. We used wild type (Wt), Agt-Tg and Agt-knockout (KO) mice along with 3T3-L1 and human adipocytes treated with RAS, ER stress and inflammation inhibitors. ER stress and pro-inflammation markers were significantly higher in Agt-Tg compared to Wt mice and captopril significantly reduced their expression. Furthermore, in vitro treatment with Ang II significantly induced ER stress and inflammation, whereas angiotensin II receptor inhibitor, telmisartan reduced RAS effects. Moreover, miR-30 family had significantly lower expression in Agt-Tg group. MiR-708-5p and -143-3p were upregulated when RAS was overexpressed, and RAS antagonists reduced miR-143-3p and -708-5p in both mouse adipose tissue and adipocytes. Activation of RAS by Ang II treatment, increased inflammation and ER stress in adipocytes mainly via AT1 receptor, possibly mediated by miR-30 family, -708-5p and/or -143-3p. Hence, RAS and mediating microRNAs could be used as potential targets to reduce RAS induced obesity and related comorbid diseases.

Binding site characterization - similarity, promiscuity, and druggability

The elucidation of non-obvious binding site similarities has provided useful indications for the establishment of polypharmacology, the identification of potential off-targets, or the repurposing of known drugs. The concept underlying all of these approaches is promiscuous binding which can be analyzed from a ligand-based or a binding site-based perspective. Herein, we applied methods for the automated analysis and comparison of protein binding sites to study promiscuous binding on a novel dataset of sites in complex with ligands sharing common shape and physicochemical properties. We show the suitability of this dataset for the benchmarking of novel binding site comparison methods. Our investigations also reveal promising directions for further in-depth analyses of promiscuity and druggability in a pocket-centered manner. Drawbacks concerning binding site similarity assessment and druggability prediction are outlined, enabling researchers to avoid the typical pitfalls of binding site analyses.

A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge.

Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.

Computationally-guided drug repurposing enables the discovery of kinase targets and inhibitors as new schistosomicidal agents

The development of novel therapeutics is urgently required for diseases where existing treatments are failing due to the emergence of resistance. This is particularly pertinent for parasitic infections of the tropics and sub-tropics, referred to collectively as neglected tropical diseases, where the commercial incentives to develop new drugs are weak. One such disease is schistosomiasis, a highly prevalent acute and chronic condition caused by a parasitic helminth infection, with three species of the genus Schistosoma infecting humans. Currently, a single 40-year old drug, praziquantel, is available to treat all infective species, but its use in mass drug administration is leading to signs of drug-resistance emerging. To meet the challenge of developing new therapeutics against this disease, we developed an innovative computational drug repurposing pipeline supported by phenotypic screening. The approach highlighted several protein kinases as interesting new biological targets for schistosomiasis as they play an essential role in many parasite’s biological processes. Focusing on this target class, we also report the first elucidation of the kinome of Schistosoma japonicum, as well as updated kinomes of S. mansoni and S. haematobium. In comparison with the human kinome, we explored these kinomes to identify potential targets of existing inhibitors which are unique to Schistosoma species, allowing us to identify novel targets and suggest approved drugs that might inhibit them. These include previously suggested schistosomicidal agents such as bosutinib, dasatinib, and imatinib as well as new inhibitors such as vandetanib, saracatinib, tideglusib, alvocidib, dinaciclib, and 22 newly identified targets such as CHK1, CDC2, WEE, PAKA, MEK1. Additionally, the primary and secondary targets in Schistosoma of those approved drugs are also suggested, allowing for the development of novel therapeutics against this important yet neglected disease.

The rise of resistance through the intensive use of drugs targeted to treat specific infectious diseases means that new therapeutics are continually required. Diseases common in the tropics and sub-tropics, classified as neglected tropical diseases, suffer from a lack of new drug treatments due to the difficulty in developing new drugs and the lack of market incentive. One such disease is schistosomiasis, a major human helminth disease caused by worms from the genus Schistosoma. It is currently treated by a 40-year old drug, praziquantel, but its widespread use has led to signs of drug-resistance emerging, with no alternative effective treatments available. To meet this challenge, we have developed an innovative computational drug repurposing pipeline supported by experimental phenotypic screening. Protein kinases emerged from our pipeline as interesting new biological targets. Given that many human kinase inhibitors have been successfully applied specially in cancer therapy and kinases have conserved structures and functions, we also undertook a detailed analysis of the kinases present in all infective Schistosoma species and human host. This allowed identification of new Schistosoma-specific kinase targets and suggest approved drugs to be used for treating schistosomiasis as well as opening new avenues to treat this neglected disease.

Rational application of drug promiscuity in medicinal chemistry

‘Drug promiscuity’ refers to a drug that can act on multiple molecular targets, exhibiting similar or different pharmacological effects. Drugs may interact with unwanted targets, leading to off-target effects (one of the main reasons for side effects). Thus, intervention to prevent off-target effects in the early stages of drug discovery could reduce the risk of failure. The conversion between target and off-target effects is important for drug repurposing. Drug repurposing strategies could reduce research and development costs. This review details the research progress in the rational application of drug promiscuity for the discovery of multi-target drugs, drug repurposing and improving druggability in medicinal chemistry over the last 5 years.

In Silico Screening of Isocitrate Lyase for Novel Anti-Buruli Ulcer Natural Products Originating from Africa

Buruli ulcer (BU) is caused by Mycobacterium ulcerans and is predominant in both tropical and subtropical regions. The neglected debilitating disease is characterized by chronic necrotizing skin lesions attributed to a mycolactone, which is a macrolide toxin secreted by M. ulcerans. The preferred treatment is surgical excision of the lesions followed by a prolonged combination antibiotic therapy using existing drugs such as rifampicin and streptomycin or clarithromycin. These antibiotics appear not to be adequately potent and efficacious against persistent and late stage ulcers. In addition, emerging drug resistance to treatment poses great challenges. There is a need to identify novel natural product-derived lead compounds, which are potent and efficacious for the treatment of Buruli ulcer. Natural products present a rich diversity of chemical compounds with proven activity against various infectious diseases, and therefore, are considered in this study. This study sought to computationally predict natural product-derived lead compounds with the potential to be developed further into potent drugs with better therapeutic efficacy than the existing anti-buruli ulcer compounds. The three-dimensional (3D) structure of Isocitrate lyase (ICL) of Mycobacterium ulcerans was generated using homology modeling and was further scrutinized with molecular dynamics simulations. A library consisting of 885 compounds retrieved from the AfroDb database was virtually screened against the validated ICL model using AutoDock Vina. AfroDb is a compendium of “drug-like” and structurally diverse 3D structures of natural products originating from different geographical regions in Africa. The molecular docking with the ICL model was validated by computing a Receiver Operating Characteristic (ROC) curve with a reasonably good Area Under the Curve (AUC) value of 0.89375. Twenty hit compounds, which docked firmly within the active site pocket of the ICL receptor, were assessed via in silico bioactivity and pharmacological profiling. The three compounds, which emerged as potential novel leads, comprise ZINC38143792 (Euscaphic acid), ZINC95485880, and ZINC95486305 with reasonable binding energies (high affinity) of −8.6, −8.6, and −8.8 kcal/mol, respectively. Euscaphic acid has been reported to show minimal inhibition against a drug-sensitive strain of M. tuberculosis. The other two leads were both predicted to possess dermatological activity while one was antibacterial. The leads have shown promising results pertaining to efficacy, toxicity, pharmacokinetic, and safety. These leads can be experimentally characterized to assess their anti-mycobacterial activity and their scaffolds may serve as rich skeletons for developing anti-buruli ulcer drugs.

Exploration of the Anti-Inflammatory Drug Space Through Network Pharmacology: Applications for Drug Repurposing

The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space.

Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design

Docking simulations help us understand molecular interactions. Here we present a hands-on tutorial to utilize FlexAID (Flexible Artificial Intelligence Docking), an open source molecular docking software between ligands such as small molecules or peptides and macromolecules such as proteins and nucleic acids. The tutorial uses the NRGsuite PyMOL plugin graphical user interface to set up and visualize docking simulations in real time as well as detect and refine target cavities. The ease of use of FlexAID and the NRGsuite combined with its superior performance relative to widely used docking software provides nonexperts with an important tool to understand molecular interactions with direct applications in structure-based drug design and virtual high-throughput screening.