In Silico Prediction of Potential Drug Combinations for Type 2 Diabetes Mellitus by an Integrated Network and Transcriptome Analysis

Type 2 diabetes mellitus (T2DM) is a heterogeneous disorder, so achieving the desired therapeutic efficacy through monotherapy is tricky. Drug combinations play a vital role in treating multiple complex diseases by providing increased efficacy and reduced toxicity. Here, we adopted a computational framework to discover potential drugs and drug pairs for T2DM. Firstly, we collected T2DM-associated genes and constructed the disease module for T2DM. Then, by quantifying the proximity between drugs and the disease module, we found out potential drugs. Based on the drug-induced gene expression profiles, we further performed Gene Set Enrichment Analysis (GSEA) on these drugs and identified several potential candidates. In addition, through network-based separation, potential drug combinations for T2DM were predicted. Results from this study could provide insights for anti-T2DM drug discovery and rational drug use of existing agents. As a useful computational framework, our approach could also be applied in drug research for other complex diseases.     

Repurposing Histaminergic Drugs in Multiple Sclerosis

Multiple sclerosis is an autoimmune disease with a strong neuroinflammatory component that contributes to severe demyelination, neurodegeneration and lesions formation in white and grey matter of the spinal cord and brain. Increasing attention is being paid to the signaling of the biogenic amine histamine in the context of several pathological conditions. In multiple sclerosis, histamine regulates the differentiation of oligodendrocyte precursors, reduces demyelination, and improves the remyelination process. However, the concomitant activation of histamine H1–H4 receptors can sustain either damaging or favorable effects, depending on the specifically activated receptor subtype/s, the timing of receptor engagement, and the central versus peripheral target district. Conventional drug development has failed so far to identify curative drugs for multiple sclerosis, thus causing a severe delay in therapeutic options available to patients. In this perspective, drug repurposing offers an exciting and complementary alternative for rapidly approving some medicines already approved for other indications. In the present work, we have adopted a new network-medicine-based algorithm for drug repurposing called SAveRUNNER, for quantifying the interplay between multiple sclerosis-associated genes and drug targets in the human interactome. We have identified new histamine drug-disease associations and predicted off-label novel use of the histaminergic drugs amodiaquine, rupatadine, and diphenhydramine among others, for multiple sclerosis. Our work suggests that selected histamine-related molecules might get to the root causes of multiple sclerosis and emerge as new potential therapeutic strategies for the disease.     

Combination of Drugs and Cell Transplantation: More Beneficial Stem Cell-Based Regenerative Therapies Targeting Neurological Disorders

Cell transplantation therapy using pluripotent/multipotent stem cells has gained attention as a novel therapeutic strategy for treating neurodegenerative diseases, including Parkinson’s disease, Alzheimer’s disease, Huntington’s disease, ischemic stroke, and spinal cord injury. To fully realize the potential of cell transplantation therapy, new therapeutic options that increase cell engraftments must be developed, either through modifications to the grafted cells themselves or through changes in the microenvironment surrounding the grafted region. Together these developments could potentially restore lost neuronal function by better supporting grafted cells. In addition, drug administration can improve the outcome of cell transplantation therapy through better accessibility and delivery to the target region following cell transplantation. Here we introduce examples of drug repurposing approaches for more successful transplantation therapies based on preclinical experiments with clinically approved drugs. Drug repurposing is an advantageous drug development strategy because drugs that have already been clinically approved can be repurposed to treat other diseases faster and at lower cost. Therefore, drug repurposing is a reasonable approach to enhance the outcomes of cell transplantation therapies for neurological diseases. Ideal repurposing candidates would result in more efficient cell transplantation therapies and provide a new and beneficial therapeutic combination.     

Protein Kinases and Parkinson’s Disease

Currently, the lack of new drug candidates for the treatment of major neurological disorders such as Parkinson’s disease has intensified the search for drugs that can be repurposed or repositioned for such treatment. Typically, the search focuses on drugs that have been approved and are used clinically for other indications. Kinase inhibitors represent a family of popular molecules for the treatment and prevention of various cancers, and have emerged as strong candidates for such repurposing because numerous serine/threonine and tyrosine kinases have been implicated in the pathobiology of Parkinson’s disease. This review focuses on various kinase-dependent pathways associated with the expression of Parkinson’s disease pathology, and evaluates how inhibitors of these pathways might play a major role as effective therapeutic molecules.     

Drug Repurposing in Rare Diseases: An Integrative Study of Drug Screening and Transcriptomic Analysis in Nephropathic Cystinosis

Diagnosis and cure for rare diseases represent a great challenge for the scientific community who often comes up against the complexity and heterogeneity of clinical picture associated to a high cost and time-consuming drug development processes. Here we show a drug repurposing strategy applied to nephropathic cystinosis, a rare inherited disorder belonging to the lysosomal storage diseases. This approach consists in combining mechanism-based and cell-based screenings, coupled with an affordable computational analysis, which could result very useful to predict therapeutic responses at both molecular and system levels. Then, we identified potential drugs and metabolic pathways relevant for the pathophysiology of nephropathic cystinosis by comparing gene-expression signature of drugs that share common mechanisms of action or that involve similar pathways with the disease gene-expression signature achieved with RNA-seq.     

Entrectinib—A SARS-CoV-2 Inhibitor in Human Lung Tissue (HLT) Cells

Since the start of the COVID-19 outbreak, pharmaceutical companies and research groups have focused on the development of vaccines and antiviral drugs against SARS-CoV-2. Here, we apply a drug repurposing strategy to identify drug candidates that are able to block the entrance of the virus into human cells. By combining virtual screening with in vitro pseudovirus assays and antiviral assays in Human Lung Tissue (HLT) cells, we identify entrectinib as a potential antiviral drug.     

Protein Folding Interdiction Strategy for Therapeutic Drug Development in Viral Diseases: Ebola VP40 and Influenza A M1

In a recent paper, we proposed the folding interdiction target region (FITR) strategy for therapeutic drug design in SARS-CoV-2. This paper expands the application of the FITR strategy by proposing therapeutic drug design approaches against Ebola virus disease and influenza A. We predict target regions for folding interdicting drugs on correspondingly relevant structural proteins of both pathogenic viruses: VP40 of Ebola, and matrix protein M1 of influenza A. Identification of the protein targets employs the sequential collapse model (SCM) for protein folding. It is explained that the model predicts natural peptide candidates in each case from which to start the search for therapeutic drugs. The paper also discusses how these predictions could be tested, as well as some challenges likely to be found when designing effective therapeutic drugs from the proposed peptide candidates. The FITR strategy opens a potential new avenue for the design of therapeutic drugs that promises to be effective against infectious diseases.     

Exploring Aβ Proteotoxicity and Therapeutic Candidates Using Drosophila melanogaster

Alzheimer’s disease is a widespread and devastating neurological disorder associated with proteotoxic events caused by the misfolding and aggregation of the amyloid-β peptide. To find therapeutic strategies to combat this disease, Drosophila melanogaster has proved to be an excellent model organism that is able to uncover anti-proteotoxic candidates due to its outstanding genetic toolbox and resemblance to human disease genes. In this review, we highlight the use of Drosophila melanogaster to both study the proteotoxicity of the amyloid-β peptide and to screen for drug candidates. Expanding the knowledge of how the etiology of Alzheimer’s disease is related to proteotoxicity and how drugs can be used to block disease progression will hopefully shed further light on the field in the search for disease-modifying treatments.     

Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases

Identifying new disease indications for existing drugs can help facilitate drug development and reduce development cost. The previous drug–disease association prediction methods focused on data about drugs and diseases from multiple sources. However, they did not deeply integrate the neighbor topological information of drug and disease nodes from various meta-path perspectives. We propose a prediction method called NAPred to encode and integrate meta-path-level neighbor topologies, multiple kinds of drug attributes, and drug-related and disease-related similarities and associations. The multiple kinds of similarities between drugs reflect the degrees of similarity between two drugs from different perspectives. Therefore, we constructed three drug–disease heterogeneous networks according to these drug similarities, respectively. A learning framework based on fully connected neural networks and a convolutional neural network with an attention mechanism is proposed to learn information of the neighbor nodes of a pair of drug and disease nodes. The multiple neighbor sets composed of different kinds of nodes were formed respectively based on meta-paths with different semantics and different scales. We established the attention mechanisms at the neighbor-scale level and at the neighbor topology level to learn enhanced neighbor feature representations and enhanced neighbor topological representations. A convolutional-autoencoder-based module is proposed to encode the attributes of the drug–disease pair in three heterogeneous networks. Extensive experimental results indicated that NAPred outperformed several state-of-the-art methods for drug–disease association prediction, and the improved recall rates demonstrated that NAPred was able to retrieve more actual drug–disease associations from the top-ranked candidates. Case studies on five drugs further demonstrated the ability of NAPred to identify potential drug-related disease candidates.     

Synergistic Growth Inhibition of HT-29 Colon and MCF-7 Breast Cancer Cells with Simultaneous and Sequential Combinations of Antineoplastics and CNS Drugs

Several central nervous system (CNS) drugs exhibit potent anti-cancer activities. This study aimed to design a novel model of combination that combines different CNS agents and antineoplastic drugs (5-fluorouracil (5-FU) and paclitaxel (PTX)) for colorectal and breast cancer therapy, respectively. Cytotoxic effects of 5-FU and PTX alone and in combination with different CNS agents were evaluated on HT-29 colon and MCF-7 breast cancer cells, respectively. Three antimalarials alone and in combination with 5-FU were also evaluated in HT-29 cells. Different schedules and concentrations in a fixed ratio were added to the cultured cells and incubated for 48 h. Cell viability was evaluated using MTT and SRB assays. Synergism was evaluated using the Chou-Talalay, Bliss Independence and HSA methods. Our results demonstrate that fluphenazine, fluoxetine and benztropine have enhanced anticancer activity when used alone as compared to being used in combination, making them ideal candidates for drug repurposing in colorectal cancer (CRC). Regarding MCF-7 cells, sertraline was the most promising candidate alone for drug repurposing, with the lowest IC₅₀ value. For HT-29 cells, the CNS drugs sertraline and thioridazine in simultaneous combination with 5-FU demonstrated the strongest synergism among all combinations. In MCF-7 breast cancer cells, the combination of fluoxetine, fluphenazine and benztropine with PTX resulted in synergism for all concentrations below IC₅₀. We also found that the antimalarial artesunate administration prior to 5-FU produces better results in reducing HT-29 cell viability than the inverse drug schedule or the simultaneous combination. These results demonstrate that CNS drugs activity differs between the two selected cell lines, both alone and in combination, and support that some CNS agents may be promising candidates for drug repurposing in these types of cancers. Additionally, these results demonstrate that 5-FU or a combination of PTX with CNS drugs should be further evaluated. These results also demonstrate that antimalarial drugs may also be used as antitumor agents in colorectal cancer, besides breast cancer.     

Novel Aptamer-Based Small-Molecule Drug Screening Assay to Identify Potential Sclerostin Inhibitors against Osteoporosis

A novel aptamer-based competitive drug screening platform for osteoporosis was devised in which fluorescence-labeled, sclerostin-specific aptamers compete with compounds from selected chemical libraries for the binding of immobilized recombinant human sclerostin to achieve high-throughput screening for potential small-molecule sclerostin inhibitors and to facilitate drug repurposing and drug discovery. Of the 96 selected inhibitors and FDA-approved drugs, six were shown to result in a significant decrease in the fluorescence intensity of the aptamer, suggesting a higher affinity toward sclerostin compared with that of the aptamer. The targets of these potential sclerostin inhibitors were correlated to lipid or bone metabolism, and several of the compounds have already been shown to be potential osteogenic activators, indicating that the aptamer-based competitive drug screening assay offered a potentially reliable strategy for the discovery of target-specific new drugs. The six potential sclerostin inhibitors suppressed the level of both intracellular and/or extracellular sclerostin in mouse osteocyte IDG-SW3 and increased alkaline phosphatase activity in IDG-SW3 cells, human bone marrow-derived mesenchymal stem cells and human fetal osteoblasts hFOB1.19. Potential small-molecule drug candidates obtained in this study are expected to provide new therapeutics for osteoporosis as well as insights into the structure–activity relationship of sclerostin inhibitors for rational drug design.     

An Integrative Transcriptomic Analysis of Systemic Juvenile Idiopathic Arthritis for Identifying Potential Genetic Markers and Drug Candidates

Systemic juvenile idiopathic arthritis (sJIA) is a rare subtype of juvenile idiopathic arthritis, whose clinical features are systemic fever and rash accompanied by painful joints and inflammation. Even though sJIA has been reported to be an autoinflammatory disorder, its exact pathogenesis remains unclear. In this study, we integrated a meta-analysis with a weighted gene co-expression network analysis (WGCNA) using 5 microarray datasets and an RNA sequencing dataset to understand the interconnection of susceptibility genes for sJIA. Using the integrative analysis, we identified a robust sJIA signature that consisted of 2 co-expressed gene sets comprising 103 up-regulated genes and 25 down-regulated genes in sJIA patients compared with healthy controls. Among the 128 sJIA signature genes, we identified an up-regulated cluster of 11 genes and a down-regulated cluster of 4 genes, which may play key roles in the pathogenesis of sJIA. We then detected 10 bioactive molecules targeting the significant gene clusters as potential novel drug candidates for sJIA using an in silico drug repositioning analysis. These findings suggest that the gene clusters may be potential genetic markers of sJIA and 10 drug candidates can contribute to the development of new therapeutic options for sJIA.     

Chimeric Structures in Mental Illnesses—“Magic” Molecules Specified for Complex Disorders

Mental health problems cover a wide spectrum of diseases, including mild to moderate anxiety, depression, alcohol/drug use disorders, as well as bipolar disorder and schizophrenia. Pharmacological treatment seems to be one of the most effective opportunities to recover function efficiently and satisfactorily. However, such disorders are complex as several target points are involved. This results in a necessity to combine different types of drugs to obtain the necessary therapeutic goals. There is a need to develop safer and more effective drugs. Considering that mental illnesses share multifactorial processes, the paradigm of one treatment with multiple modes of action rather than single-target strategies would be more effective for successful therapies. Therefore, hybrid molecules that combine two pharmacophores in one entity show promise, as they possess the desired therapeutic index with a small off-target risk. This review aims to provide information on chimeric structures designed for mental disorder therapy (i.e., schizophrenia and depression), and new types of drug candidates currently being tested. In addition, a discussion on some benefits and limitations of multifunctional, bivalent drug candidates is also given.     

Towards Novel 3-Aminopyrazinamide-Based Prolyl-tRNA Synthetase Inhibitors: In Silico Modelling,Thermal Shift Assay and Structural Studies

Human cytosolic prolyl-tRNA synthetase (HcProRS) catalyses the formation of the prolyl-tRNA^Pro, playing an important role in protein synthesis. Inhibition of HcProRS activity has been shown to have potential benefits in the treatment of fibrosis, autoimmune diseases and cancer. Recently, potent pyrazinamide-based inhibitors were identified by a high-throughput screening (HTS) method, but no further elaboration was reported. The pyrazinamide core is a bioactive fragment found in numerous clinically validated drugs and has been subjected to various modifications. Therefore, we applied a virtual screening protocol to our in-house library of pyrazinamide-containing small molecules, searching for potential novel HcProRS inhibitors. We identified a series of 3-benzylaminopyrazine-2-carboxamide derivatives as positive hits. Five of them were confirmed by a thermal shift assay (TSA) with the best compounds 3b and 3c showing EC₅₀ values of 3.77 and 7.34 µM, respectively, in the presence of 1 mM of proline (Pro) and 3.45 µM enzyme concentration. Co-crystal structures of HcProRS in complex with these compounds and Pro confirmed the initial docking studies and show how the Pro facilitates binding of the ligands that compete with ATP substrate. Modelling 3b into other human class II aminoacyl-tRNA synthetases (aaRSs) indicated that the subtle differences in the ATP binding site of these enzymes likely contribute to its potential selective binding of HcProRS. Taken together, this study successfully identified novel HcProRS binders from our anti-tuberculosis in-house compound library, displaying opportunities for repurposing old drug candidates for new applications such as therapeutics in HcProRS-related diseases.     

Paradoxical Pro-angiogenic Effect of Low-Dose Ellipticine Identified by In Silico Drug Repurposing

Inadequate vessel maintenance or growth causes ischemia in diseases such as myocardial infarction, stroke, and neurodegenerative disorders. Therefore, developing an effective strategy to salvage ischemic tissues using a novel compound is urgent. Drug repurposing has become a widely used method that can make drug discovery more efficient and less expensive. Additionally, computational virtual screening tools make drug discovery faster and more accurate. This study found a novel drug candidate for pro-angiogenesis by in silico virtual screening. Using Gene Expression Omnibus (GEO) microarray datasets related to angiogenesis studies, differentially expressed genes were identified and characteristic direction signatures extracted from GEO2EnrichR were used as input data on L1000CDS² to screen pro-angiogenic molecules. After a thorough review of the candidates, a list of compounds structurally similar to TWS-119 was generated using ChemMine Tools and its clustering toolbox. ChemMine Tools and ChemminR structural similarity search tools for small-molecule analysis and clustering were used for second screening. A molecular docking simulation was conducted using AutoDock v.4 to evaluate the physicochemical effect of secondary-screened chemicals. A cell viability or toxicity test was performed to determine the proper dose of the final candidate, ellipticine. As a result, we found ellipticine, which has pro-angiogenic effects, using virtual computational methods. The noncytotoxic concentration of ellipticine was 156.25 nM. The phosphorylation of glycogen synthase kinase-3β was decreased, whereas the β-catenin expression was increased in human endothelial cells treated with ellipticine. We concluded that ellipticine at sublethal dosage could be successfully repositioned as a pro-angiogenic substance by in silico virtual screening.     

Sample-Specific Perturbation of Gene Interactions Identifies Pancreatic Cancer Subtypes

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective.     

Zebrafish and Medaka: Important Animal Models for Human Neurodegenerative Diseases

Animal models of human neurodegenerative disease have been investigated for several decades. In recent years, zebrafish (Danio rerio) and medaka (Oryzias latipes) have become popular in pathogenic and therapeutic studies about human neurodegenerative diseases due to their small size, the optical clarity of embryos, their fast development, and their suitability to large-scale therapeutic screening. Following the emergence of a new generation of molecular biological technologies such as reverse and forward genetics, morpholino, transgenesis, and gene knockout, many human neurodegenerative disease models, such as Parkinson’s, Huntington’s, and Alzheimer’s, were constructed in zebrafish and medaka. These studies proved that zebrafish and medaka genes are functionally conserved in relation to their human homologues, so they exhibit similar neurodegenerative phenotypes to human beings. Therefore, fish are a suitable model for the investigation of pathologic mechanisms of neurodegenerative diseases and for the large-scale screening of drugs for potential therapy. In this review, we summarize the studies in modelling human neurodegenerative diseases in zebrafish and medaka in recent years.