Computational repositioning and preclinical validation of pentamidine for renal cell cancer

Therapeutic potential of pentamidine for glioma-initiating cells and glioma cells through multimodal antitumor effects

Glioma-initiating cells, which comprise a heterogeneous population of glioblastomas, contribute to resistance against aggressive chemoradiotherapy. Using drug reposition, we investigated a therapeutic drug for glioma-initiating cells. Drug screening was undertaken to select candidate agents that inhibit proliferation of two different glioma-initiating cells lines. The alteration of proliferation and stemness of the two glioma-initiating cell lines, and proliferation, migration, cell cycle, and survival of these two differentiated glioma-initiating cell lines and three different glioblastoma cell lines treated with the candidate agent were evaluated. We also used a xenograft glioma mouse model to evaluate anticancer effects of treated glioma cell lines. Among the 1301 agents, pentamidine-an antibiotic for Pneumocystis jirovecii-emerged as a successful antiglioma agent. Pentamidine treatment suppressed proliferation and stemness in glioma-initiating cell lines. Proliferation and migration were inhibited in all differentiated glioma-initiating cells and glioblastoma cell lines, with cell cycle arrest and caspase-dependent apoptosis induction. The in vivo study reproduced the same findings as the in vitro studies. Pentamidine showed a stronger antiproliferative effect on glioma-initiating cells than on differentiated cells. Western blot analysis revealed pentamidine inhibited phosphorylation of signal transducer and activator of transcription 3 in all cell lines, whereas Akt expression was suppressed in glioma-initiating cells but not in differentiated lines. In the present study, we identified pentamidine as a potential therapeutic drug for glioma. Pentamidine could be promising for the treatment of glioblastomas by targeting both glioma-initiating cells and differentiated cells through its multifaceted antiglioma effects.

Selective delivery of pentamidine toward cancer cells by self-assembled nanoparticles

Pentamidine (PTM) is an aromatic diamidine approved for the treatment of parasitic infections that has been recently proposed for possible repositioning as an anticancer drug. To this aim, efforts have been made to improve its therapeutic efficacy and reduce associated adverse effects through both covalent derivatization and association with nanocarriers. To efficiently encapsulate PTM into biocompatible nanoparticles and to enhance its selectivity toward cancer cells, a squalene (SQ) derivative (1,1',2-tris-norsqualenoic acid, SQ-COOH) was selected to prepare PTM-loaded nanocarriers. Indeed, SQ and its derivatives self-assemble into nanoparticles in aqueous media. Furthermore, SQ-bioconjugates strongly interact with low-density lipoproteins (LDL), thus favoring preferential accumulation in cells overexpressing the LDL receptor (LDLR). We report here the preparation of nanocarriers by ion-pairing between the negatively charged SQ-COOH and the positively charged PTM free base (PTM-B), which allowed the covalent grafting of SQ to PTM to be avoided. The nanoparticles were characterized (mean size < 200 nm and zeta potential < -20 mV for SQ-COOH/PTM-B 3:1 molar ratio) and molecular modelling studies of the SQ-COOH/PTM-B interaction confirmed the nanocarrier stability. Finally, the ability to indirectly target LDLR-overexpressing cancer cells was evaluated by in vitro cell viability assays and confirmed by LDLR silencing, serum privation and simvastatin treatment.

Transcriptomic and systems biology identifies non-antibiotic drugs for treatment of ocular bacterial infection

Increasing antibiotic resistance among ocular pathogens often results in treatment failure for blinding infections such as endophthalmitis. Hence, newer therapeutics is needed to combat multidrug-resistant infections. Here, we show a drug repurposing approach using a connectivity map based on temporal transcriptomics of Staphylococcus aureus (SA) infected mouse retina. The analysis predicted three non-antibiotic drugs, Dequalinium chloride (DC), Clofilium tosylate (CT), and Glybenclamide (Glb) which reversed the SA infection signatures. Predicted drugs exhibited anti-inflammatory properties in human retinal cells against sensitive and resistant strains of SA. Intravitreal administration of all drugs reduced intraocular inflammation in SA-infected mouse eyes while DC and CT also reduced bacterial burden. Drug treatment improved visual function coinciding with reduced Caspase-3 mediated retinal cell death. Importantly, all drugs exhibited synergy with vancomycin in improving disease outcomes. Overall, our study identified three non-antibiotic drugs and demonstrated their therapeutic and prophylactic efficacies in ameliorating intraocular bacterial infection.

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CMap analysis predicted three drugs to reverse Staphylococcus aureus endophthalmitis signature

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Drugs attenuated MRSA and MSSA induced inflammatory response in retinal cells

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Drug treatment ameliorated experimental S. aureus endophthalmitis

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Predicted drugs exhibited adjunct therapeutic potential with antibiotic

Identification of anthelmintic parbendazole as a therapeutic molecule for HNSCC through connectivity map-based drug repositioning

Head and neck squamous cell carcinoma (HNSCC) is one of the most common human cancers; however, its outcome of pharmacotherapy is always very limited. Herein, we performed a batch query in the connectivity map (cMap) based on bioinformatics, queried out 35 compounds with therapeutic potential, and screened out parbendazole as a most promising compound, which had an excellent inhibitory effect on the proliferation of HNSCC cell lines. In addition, tubulin was identified as a primary target of parbendazole, and the direct binding between them was further verified. Parbendazole was further proved as an effective tubulin polymerization inhibitor, which can block the cell cycle, cause apoptosis and prevent cell migration, and it exhibited reasonable therapeutic effect and low toxicity in the in vivo and in vitro anti-tumor evaluation. Our study repositioned an anthelmintic parbendazole to treat HNSCC, which revealed a therapeutic utility and provided a new treatment option for human cancers.

Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach

BACKGROUND

The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs.

METHODS

We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line.

FINDINGS

First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability.

INTERPRETATION

These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine.

FUNDING

This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.

Nanotechnological approaches for pentamidine delivery

Pentamidine (PTM), which is a diamine that is widely known for its antimicrobial activity, is a very interesting drug whose mechanism of action is not fully understood. In recent years, PTM has been proposed as a novel potential drug candidate for the treatment of mental illnesses, myotonic dystrophy, diabetes, and tumors. Nevertheless, the systemic administration of PTM causes severe side effects, especially nephrotoxicity. In order to efficiently deliver PTM and reduce its side effects, several nanosystems that take advantage of the chemical characteristics of PTM, such as the presence of two positively charged amidine groups at physiological pH, have been proposed as useful delivery tools. Polymeric, lipidic, inorganic, and other types of nanocarriers have been reported in the literature for PTM delivery, and they are all in different development phases. The available approaches for the design of PTM nanoparticulate delivery systems are reported in this review, with a particular emphasis on formulation strategies and in vitro/in vivo applications. Furthermore, a critical view of the future developments of nanomedicine for PTM applications, based on recent repurposing studies, is provided. Created with BioRender.com.

Identification of potentially anti-COVID-19 active drugs using the connectivity MAP

Drug repurposing can be an interesting strategy for an emergency response to the severe acute respiratory syndrome-coronavirus-2, (SARS-COV-2), the causing agent of the coronavirus disease-19 (COVID-19) pandemic. For this, we applied the Connectivity Map (CMap) bioinformatic resource to identify drugs that generate, in the CMap database, gene expression profiles (GEP) that negatively correlate with a SARS-COV-2 GEP, anticipating that these drugs could antagonize the deleterious effects of the virus at cell, tissue or organism levels. We identified several anti-cancer compounds that target MDM2 in the p53 pathway or signaling proteins: Ras, PKBβ, Nitric Oxide synthase, Rho kinase, all involved in the transmission of proliferative and growth signals. We hypothesized that these drugs could interfere with the high rate of biomass synthesis in infected cells, a feature shared with cancer cells. Other compounds including etomoxir, triacsin-c, PTB1-IN-3, are known to modulate lipid metabolism or to favor catabolic reactions by activating AMPK. Four different anti-inflammatory molecules, including dexamethasone, fluorometholone and cytosporone-b, targeting the glucocorticoid receptor, cyclooxygenase, or NUR77 also came out of the analysis. These results represent a first step in the characterization of potential repositioning strategies to treat SARS-COV-2.

Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.

Drug Repositioning Based on the Reversal of Gene Expression Signatures Identifies TOP2A as a Therapeutic Target for Rectal Cancer

Rectal cancer is a common disease with high mortality rates and limited therapeutic options. Here we combined the gene expression signatures of rectal cancer patients with the reverse drug-induced gene-expression profiles to identify drug repositioning candidates for cancer therapy. Among the predicted repurposable drugs, topoisomerase II inhibitors (doxorubicin, teniposide, idarubicin, mitoxantrone, and epirubicin) presented a high potential to reverse rectal cancer gene expression signatures. We showed that these drugs effectively reduced the growth of colorectal cancer cell lines closely representing rectal cancer signatures. We also found a clear correlation between topoisomerase 2A (TOP2A) gene copy number or expression levels with the sensitivity to topoisomerase II inhibitors. Furthermore, CRISPR-Cas9 and shRNA screenings confirmed that loss-of-function of the TOP2A has the highest efficacy in reducing cellular proliferation. Finally, we observed significant TOP2A copy number gains and increased expression in independent cohorts of rectal cancer patients. These findings can be translated into clinical practice to evaluate TOP2A status for targeted and personalized therapies based on topoisomerase II inhibitors in rectal cancer patients.

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.

Drug Repurposing to Identify a Synergistic High-Order Drug Combination to Treat Sunitinib-Resistant Renal Cell Carcinoma

Simple Summary

In this study, drug combination screening was used to design a multidrug combination consisting of repurposed drugs to treat sunitinib-resistant clear cell renal cell carcinoma. In the frame of this project, the multidrug combination has been optimized and validated and an insight into the mechanism of action is given. The multidrug combinations significantly altered the transcription of genes related to apoptosis and metabolic pathways. Further analysis of the metabolism revealed strong upregulation of the presence of sphingolipids after multidrug combination treatment. Final evaluation for translation of the multidrug combination in ex vivo organoid-like cultures demonstrated significant anti-cancer efficacy.

Abstract

Repurposed drugs have been evaluated for the management of clear cell renal cell carcinoma (ccRCC), but only a few have influenced the overall survival of patients with advanced disease. To combine repurposed non-oncology with oncological drugs, we applied our validated phenotypic method, which consisted of a reduced experimental part and data modeling. A synergistic optimized multidrug combination (ODC) was identified to significantly reduce the energy levels in cancer remaining inactive in non-cancerous cells. The ODC consisted of Rapta-C, erlotinib, metformin and parthenolide and low doses. Molecular and functional analysis of ODC revealed a loss of adhesiveness and induction of apoptosis. Gene-expression network analysis displayed significant alterations in the cellular metabolism, confirmed by LC-MS based metabolomic analysis, highlighting significant changes in the lipid classes. We used heterotypic in vitro 3D co-cultures and ex vivo organoids to validate the activity of the ODC, maintaining an efficacy of over 70%. Our results show that repurposed drugs can be combined to target cancer cells selectively with prominent activity. The strong impact on cell adherence and metabolism indicates a favorable mechanism of action of the ODC to treat ccRCC.

One Century of Study: What We Learned about Paracoccidioides and How This Pathogen Contributed to Advances in Antifungal Therapy

Paracoccidioidomycosis (PCM) is a notable fungal infection restricted to Latin America. Since the first description of the disease by Lutz up to the present day, Brazilian researchers have contributed to the understanding of the life cycle of this pathogen and provided the possibility of new targets for antifungal therapy based on the structural and functional genomics of Paracoccidioides. In this context, in silico approaches have selected molecules that act on specific targets, such as the thioredoxin system, with promising antifungal activity against Paracoccidioides. Some of these are already in advanced development stages. In addition, the application of nanostructured systems has addressed issues related to the high toxicity of conventional PCM therapy. Thus, the contribution of molecular biology and biotechnology to the advances achieved is unquestionable. However, it is still necessary to transcend the boundaries of synthetic chemistry, pharmaco-technics, and pharmacodynamics, aiming to turn promising molecules into newly available drugs for the treatment of fungal diseases.

Harmine and Piperlongumine Revert TRIB2-Mediated Drug Resistance

Simple Summary

Poor survival and treatment failure of patients with cancer are mainly due to resistance to therapy. Tribbles homologue 2 (TRIB2) has recently been identified as a protein that promotes resistance to several anti-cancer drugs. In this study, RNA sequencing and bioinformatics analysis were used with the aim of characterizing the impact of TRIB2 on the expression of genes and developing pharmacological strategies to revert these TRIB2-mediated changes, thereby overcoming therapy resistance. We show that two naturally occurring alkaloids, harmine and piperlongumine, inverse the gene expression profile produced by TRIB2 and sensitize cancer cells to anti-cancer drugs. Our data suggest that harmine and piperlongumine or similar compounds might have the potential to overcome TRIB2-mediated therapy resistance in cancer patients.

Abstract

Therapy resistance is responsible for most relapses in patients with cancer and is the major challenge to improving the clinical outcome. The pseudokinase Tribbles homologue 2 (TRIB2) has been characterized as an important driver of resistance to several anti-cancer drugs, including the dual ATP-competitive PI3K and mTOR inhibitor dactolisib (BEZ235). TRIB2 promotes AKT activity, leading to the inactivation of FOXO transcription factors, which are known to mediate the cell response to antitumor drugs. To characterize the downstream events of TRIB2 activity, we analyzed the gene expression profiles of isogenic cell lines with different TRIB2 statuses by RNA sequencing. Using a connectivity map-based computational approach, we identified drug-induced gene-expression profiles that invert the TRIB2-associated expression profile. In particular, the natural alkaloids harmine and piperlongumine not only produced inverse gene expression profiles but also synergistically increased BEZ235-induced cell toxicity. Importantly, both agents promote FOXO nuclear translocation without interfering with the nuclear export machinery and induce the transcription of FOXO target genes. Our results highlight the great potential of this approach for drug repurposing and suggest that harmine and piperlongumine or similar compounds might be useful in the clinic to overcome TRIB2-mediated therapy resistance in cancer patients.

iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding

Computational drug repositioning and drug-target prediction have become essential tasks in the early stage of drug discovery. In previous studies, these two tasks have often been considered separately. However, the entities studied in these two tasks (i.e., drugs, targets, and diseases) are inherently related. On one hand, drugs interact with targets in cells to modulate target activities, which in turn alter biological pathways to promote healthy functions and to treat diseases. On the other hand, both drug repositioning and drug-target prediction involve the same drug feature space, which naturally connects these two problems and the two domains (diseases and targets). By using the wisdom of the crowds, it is possible to transfer knowledge from one of the domains to the other. The existence of relationships among drug-target-disease motivates us to jointly consider drug repositioning and drug-target prediction in drug discovery. In this paper, we present a novel approach called iDrug, which seamlessly integrates drug repositioning and drug-target prediction into one coherent model via cross-network embedding. In particular, we provide a principled way to transfer knowledge from these two domains and to enhance prediction performance for both tasks. Using real-world datasets, we demonstrate that iDrug achieves superior performance on both learning tasks compared to several state-of-the-art approaches. Our code and datasets are available at: https://github.com/Case-esaC/iDrug.

A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease

Numerous clinical trials of drug candidates for Alzheimer’s disease (AD) have failed, and computational drug repositioning approaches using omics data have been proposed as effective alternative approaches to the discovery of drug candidates. However, little multi-omics data is available for AD, due to limited availability of brain tissues. Even if omics data exist, systematic drug repurposing study for AD has suffered from lack of big data, insufficient clinical information, and difficulty in data integration on account of sample heterogeneity derived from poor diagnosis or shortage of qualified post-mortem tissue. In this study, we developed a proteotranscriptomic-based computational drug repositioning method named Drug Repositioning Perturbation Score/Class (DRPS/C) based on inverse associations between disease- and drug-induced gene and protein perturbation patterns, incorporating pharmacogenomic knowledge. We constructed a Drug-induced Gene Perturbation Signature Database (DGPSD) comprised of 61,019 gene signatures perturbed by 1,520 drugs from the Connectivity Map (CMap) and the L1000 CMap. Drugs were classified into three DRPCs (High, Intermediate, and Low) according to DRPSs that were calculated using drug- and disease-induced gene perturbation signatures from DGPSD and The Cancer Genome Atlas (TCGA), respectively. The DRPS/C method was evaluated using the area under the ROC curve, with a prescribed drug list from TCGA as the gold standard. Glioblastoma had the highest AUC. To predict anti-AD drugs, DRPS were calculated using DGPSD and AD-induced gene/protein perturbation signatures generated from RNA-seq, microarray and proteomic datasets in the Synapse database, and the drugs were classified into DRPCs. We predicted 31 potential anti-AD drug candidates commonly belonged to high DRPCs of transcriptomic and proteomic signatures. Of these, four drugs classified into the nervous system group of Anatomical Therapeutic Chemical (ATC) system are voltage-gated sodium channel blockers (bupivacaine, topiramate) and monamine oxidase inhibitors (selegiline, iproniazid), and their mechanism of action was inferred from a potential anti-AD drug perspective. Our approach suggests a shortcut to discover new efficacy of drugs for AD.

Pentamidine-Loaded Lipid and Polymer Nanocarriers as Tunable Anticancer Drug Delivery Systems

Initially developed as a synthetic analogue of insulin, pentamidine (PTM) is an antimicrobial drug that has recently shown in vitro and in vivo anticancer activity. Nevertheless, systemic administration of PTM causes severe side effects, especially nephrotoxicity. Here we propose the association of PTM to different biocompatible nanosystems in order to compare the physicochemical characteristics of the loaded nanocarriers and their influence on the drug cytotoxicity toward cancer cells. In particular, PTM (as free base or with different counterions) was encapsulated into liposomes and poly(lactide-co-glycolide) (PLGA) nanoparticles and all the formulations have been deeply characterized concerning mean diameter, polydispersity index, zeta potential, stability, morphology, PTM loading, and drug release profile. The anticancer activity was evaluated on a human ovarian cancer cell line over 72 h. Results showed that PTM is efficiently loaded into liposomes with a transmembrane citrate or sulfate gradient; concerning PLGA nanoparticles, important association occurred, thanks to ionic interactions between the drug and the polymer. The in vitro studies confirmed the anticancer activity of PTM, which was gradually released with different profiles depending on the drug form and the nanocarrier structure.

Pentamidine inhibits prostate cancer progression via selectively inducing mitochondrial DNA depletion and dysfunction

Objectives

We investigated the anti‐cancer activity of pentamidine, an anti‐protozoal cationic aromatic diamidine drug, in prostate cancer cells and aimed to provide valuable insights for improving the efficacy of prostate cancer treatment.

Materials and methods

Prostate cancer cell lines and epithelial RWPE‐1 cells were used in the study. Cell viability, wound‐healing, transwell and apoptosis assays were examined to evaluate the influences of pentamidine in vitro. RNA‐seq and qPCR were performed to analyse changes in gene transcription levels upon pentamidine treatment. Mitochondrial changes were assessed by measuring mitochondrial DNA content, morphology, membrane potential, cellular glucose uptake, ATP production and ROS generation. Nude mouse xenograft models were used to test anti‐tumour effects of pentamidine in vivo.

Results

Pentamidine exerted profound inhibitory effects on proliferation, colony formation, migration and invasion of prostate cancer cells. In addition, the drug suppressed growth of xenograft tumours without exhibiting any obvious toxicity in nude mice. Mechanistically, pentamidine caused mitochondrial DNA content reduction and induced mitochondrial morphological changes, mitochondrial membrane potential dissipation, ATP level reduction, ROS production elevation and apoptosis in prostate cancer cells.

Conclusions

Pentamidine can efficiently suppress prostate cancer progression and may serve as a novel mitochondria‐targeted therapeutic agent for prostate cancer.

Influence of batch effect correction methods on drug induced differential gene expression profiles

Background

Batch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods.

Results

Differences in average signature size were observed with different methods applied. The gene signatures identified by the Latent Effect Adjustment after Primary Projection (LEAPP) method and the methods fitted with Linear Models for Microarray Data (limma) software demonstrated little agreement. The external validity of the gene signatures was evaluated by connectivity mapping between the CMAP database and the Library of Integrated Network-based Cellular Signatures (LINCS) database. The results of connectivity mapping indicate that the genes identified were not reliable for drugs with total sample size (drug + control samples) smaller than 40, irrespective of the batch effect correction method applied. With total sample size larger than 40, the methods correcting for batch effects produced significantly better results than the method with no batch effect correction. In a simulation study, the power was generally low for simulated data with sample size smaller than 40. We observed best performance when using the limma method correcting for two principal components.

Conclusion

Batch effect correction methods strongly impact differential gene expression analysis when the sample size is large enough to contain sufficient information and thus the downstream drug repositioning. We recommend including two or three principal components as covariates in fitting models with limma when sample size is sufficient (larger than 40 drug and controls combined).

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3028-6) contains supplementary material, which is available to authorized users.

Connectivity Mapping: Methods and Applications

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

Identification of Pyrvinium, an Anthelmintic Drug, as a Novel Anti-Adipogenic Compound Based on the Gene Expression Microarray and Connectivity Map

Obesity is a serious health problem, while the current anti-obesity drugs are not very effective. The Connectivity Map (C-Map), an in-silico drug screening approach based on gene expression profiles, has recently been indicated as a promising strategy for drug repositioning. In this study, we performed mRNA expression profile analysis using microarray technology and identified 435 differentially expressed genes (DEG) during adipogenesis in both C3H10T1/2 and 3T3-L1 cells. Then, DEG signature was uploaded into C-Map, and using pattern-matching methods we discovered that pyrvinium, a classical anthelminthic, is a novel anti-adipogenic differentiation agent. Pyrvinium suppressed adipogenic differentiation in a dose-dependent manner, as evidenced by Oil Red O staining and the mRNA levels of adipogenic markers. Furthermore, we identified that the inhibitory effect of pyrvinium was resulted primarily from the early stage of adipogenesis. Molecular studies showed that pyrvinium downregulated the expression of key transcription factors C/EBPa and PPARγ. The mRNA levels of notch target genes Hes1 and Hey1 were obviously reduced after pyrvinium treatment. Taken together, this study identified many differentially expressed genes involved in adipogenesis and demonstrated for the first time that pyrvinium is a novel anti-adipogenic compound for obesity therapy. Meanwhile, we provided a new strategy to explore potential anti-obesity drugs.

Drug repositioning in head and neck squamous cell carcinoma: An integrated pathway analysis based on connectivity map and differential gene expression

The severe damage to health and social burden caused by head and neck squamous cell carcinoma (HNSCC) generated an urgent need to develop novel anti-cancer therapy. Currently, drug repositioning has risen in responses to the proper time as an efficient approach to invention of new anti-cancer therapies. In the present study, we aimed to screen candidate drugs for HNSCC by integrating HNSCC-related pathways from differentially expressed genes (DEGs) and drug-affected pathways from connectivity map (CMAP). We also endeavored to unveil the molecular mechanism of HNSCC through creating drug-target network and protein-to-protein (PPI) network of component DEGs in key overlapping pathways. As a result, a total of 401 DEGs were obtained from TCGA and GTEx mRNA-seq data. Taking the intersection part of 27 HNSCC-related Kyoto Encyclopedia of Genes and Genomes pathways and 33 drug-affected pathways, we retained 22 candidate drugs corresponding to two key pathways (cell cycle and p53 signaling pathways) of the five overlapping pathways. Two of the hub genes (PCNA and CCND1) identified from the PPI network of component DEGs in cell cycle and p53 signaling pathways were defined as the critical targets of candidate drugs with increased protein expression in HNSCC tissues, which was reported by the human protein atlas (HPA) database and cBioPortal. Finally, we validated via molecular docking analysis that two drugs with unknown effects in HNSCC: MG-262 and bepridil might perturb the development of HNSCC through targeting PCNA. These candidate drugs possessed broad application prospect as medication for HNSCC.

Strategies to Obtain Encapsulation and Controlled Release of Pentamidine in Mesoporous Silica Nanoparticles

Pentamidine (PTM), an antiprotozoal agent used in clinics as pentamidine isethionate salt (PTM-S), recently showed high potential also for the treatment of cancer and myotonic dystrophy type I. However, a severe limit to the systemic administration of PTM is represented by its nephrotoxicity, leading to the need for a system able to achieve a controlled release of the drug. In this study, mesoporous silica nanoparticles (MSNs) were employed for the first time to encapsulate PTM. PTM-S was first used for loading experiments into bare (MSN-OH) and aminopropyl, cyanopropyl and carboxypropyl-functionalized MSNs (MSN-NH₂, MSN-CN and MSN-COOH respectively) but it was not adequately loaded in any MSNs. The free base of PTM (PTM-B) was then obtained from PTM-S and successfully loaded into MSNs. Specifically, MSN-COOH exhibited the highest loading capacity. In vitro evaluation of PTM-B kinetic release from the different MSNs was carried out. An influence of the functional groups in slowing the release of the drug, when compared to bare MSNs was observed. Altogether, these results demonstrate that MSN-COOH could be a promising system to achieve a controlled release of PTM.

LINCS L1000 dataset-based repositioning of CGP-60474 as a highly potent anti-endotoxemic agent

Sepsis is one of the most common clinical syndromes that causes death and disability. Although many studies have developed drugs for sepsis treatment, none have decreased the mortality rate. The aim of this study was to identify a novel treatment option for sepsis using the library of integrated network-based cellular signatures (LINCS) L1000 perturbation dataset based on an in vitro and in vivo sepsis model. Sepsis-related microarray studies of early-stage inflammatory processes in patients and innate immune cells were collected from the Gene Expression Omnibus (GEO) data repository and used for candidate drug selection based on the LINCS L1000 perturbation dataset. The anti-inflammatory effects of the selected candidate drugs were analyzed using activated macrophage cell lines. CGP-60474, an inhibitor of cyclin-dependent kinase, was the most potent drug. It alleviated tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in activated macrophages by downregulating the NF-κB activity, and it reduced the mortality rate in LPS induced endotoxemia mice. This study shows that CGP-60474 could be a potential therapeutic candidate to attenuate the endotoxemic process. Additionally, the virtual screening strategy using the LINCS L1000 perturbation dataset could be a cost and time effective tool in the early stages of drug development.

Gene Signature-Based Approach Identified MEK1/2 as a Potential Target Associated With Relapse After Anti-TNFα Treatment for Crohn's Disease

BACKGROUND

Anti-tumor necrosis factor alpha (anti-TNFα) therapy has become the mainstay of therapy for Crohn's disease (CD). However, post-therapy, the recurrence rate is still high. The aim of this study was to dissect the molecular mechanism for recurrence of CD treated with anti-TNFα therapy and investigate novel therapeutic options that could induce complete remission.

METHODS

We re-analyzed publicly available mucosal gene expression data from CD patients pre- and post-infliximab therapy to extract the transcriptional differences between responders and healthy controls. We used a systematic computational approach based on identified differences to discover novel therapies and validated this prediction through in vitro and in vivo experimentation.

RESULTS

We identified a set of 3545 anti-TNFα therapy-untreatable genes (TUGs) that are significantly regulated in intestinal epithelial cells, which remain altered during remission. Pathway enrichment analysis of these genes clearly showed excessive growth state and suppressed terminal differentiation, whereas immune components were clearly resolved. Through in silico screening strategy, we observed that MEK inhibitors were predicted to revert expression of genes dysregulated in infliximab responders. In vitro transcriptome analysis demonstrated that selective MEK1/2 inhibitor significantly normalized reference genes from TUGs. In addition, in vitro functional study proved that MEK1/2 inhibitor facilitated intestinal epithelial differentiation. Finally, using murine colitis model, administration of MEK1/2 inhibitor significantly improved diarrhea and histological score.

CONCLUSIONS

Our data revealed the abnormalities in anti-TNFα responders' CD colons that would be cause of recurrence of CD. Also, we provided evidence regarding MEK1/2 inhibitor as a potential treatment against CD to achieve sustainable remission.

Personalised drug repositioning for Clear Cell Renal Cell Carcinoma using gene expression

Reversal of cancer gene expression is predictive of therapeutic potential and can be used to find new indications for existing drugs (drug repositioning). Gene expression reversal potential is currently calculated, in almost all studies, by pre-aggregating all tumour samples into a single group signature or a limited number of molecular subtype signatures. Here, we investigate whether drug repositioning based on individual tumour sample gene expression signatures outperforms the use of tumour group and subtype signatures. The tumour signatures were created using 534 tumour samples and 72 matched normal samples from 530 clear cell renal cell carcinoma (ccRCC) patients. More than 20,000 drug signatures were extracted from the CMAP and LINCS databases. We show that negative enrichment of individual tumour samples correlated (Spearman's rho = 0.15) much better with the amount of differentially expressed genes in drug signatures than with the tumour group signature (Rho = 0.08) and the 4 tumour subtype signatures (Rho 0.036-0.11). Targeted drugs used against ccRCC, such as sirolimus and temsirolimus, which could not be identified with the pre-aggregated tumour signatures could be recovered using individual sample analysis. Thus, drug repositioning can be personalized by taking into account the gene expression profile of the individual's tumour sample.

Meta- and cross-species analyses of insulin resistance based on gene expression datasets in human white adipose tissues

Ample evidence indicates that insulin resistance (IR) is closely related to white adipose tissue (WAT), but the underlying mechanisms of IR pathogenesis are still unclear. Using 352 microarray datasets from seven independent studies, we identified a meta-signature which comprised of 1,413 genes. Our meta-signature was also enriched in overall WAT in in vitro and in vivo IR models. Only 12 core enrichment genes were consistently enriched across all IR models. Among the meta-signature, we identified a drug signature made up of 211 genes with expression levels that were co-regulated by thiazolidinediones and metformin using cross-species analysis. To confirm the clinical relevance of our drug signature, we found that the expression levels of 195 genes in the drug signature were significantly correlated with both homeostasis model assessment 2-IR score and body mass index. Finally, 18 genes from the drug signature were identified by protein-protein interaction network cluster. Four core enrichment genes were included in 18 genes and the expression levels of selected 8 genes were validated by quantitative PCR. These findings suggest that our signatures provide a robust set of genetic markers which can be used to provide a starting point for developing potential therapeutic targets in improving IR in WAT.

Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets

The decreasing cost of genomic technologies has enabled the molecular characterization of large-scale clinical disease samples and of molecular changes upon drug treatment in various disease models. Exploring methods to relate diseases to potentially efficacious drugs through various molecular features is critically important in the discovery of new therapeutics. Here we show that the potency of a drug to reverse cancer-associated gene expression changes positively correlates with that drug's efficacy in preclinical models of breast, liver and colon cancers. Using a systems-based approach, we predict four compounds showing high potency to reverse gene expression in liver cancer and validate that all four compounds are effective in five liver cancer cell lines. The in vivo efficacy of pyrvinium pamoate is further confirmed in a subcutaneous xenograft model. In conclusion, this systems-based approach may be complementary to the traditional target-based approach in connecting diseases to potentially efficacious drugs.

Antifungal Resistance, Metabolic Routes as Drug Targets, and New Antifungal Agents: An Overview about Endemic Dimorphic Fungi

Diseases caused by fungi can occur in healthy people, but immunocompromised patients are the major risk group for invasive fungal infections. Cases of fungal resistance and the difficulty of treatment make fungal infections a public health problem. This review explores mechanisms used by fungi to promote fungal resistance, such as the mutation or overexpression of drug targets, efflux and degradation systems, and pleiotropic drug responses. Alternative novel drug targets have been investigated; these include metabolic routes used by fungi during infection, such as trehalose and amino acid metabolism and mitochondrial proteins. An overview of new antifungal agents, including nanostructured antifungals, as well as of repositioning approaches is discussed. Studies focusing on the development of vaccines against antifungal diseases have increased in recent years, as these strategies can be applied in combination with antifungal therapy to prevent posttreatment sequelae. Studies focused on the development of a pan-fungal vaccine and antifungal drugs can improve the treatment of immunocompromised patients and reduce treatment costs.

Exploring the epigenetic drug discovery landscape

INTRODUCTION

Epigenetic modification has been implicated in a wide range of diseases and the ability to modulate such systems is a lucrative therapeutic strategy in drug discovery. Areas covered: This article focuses on the concepts and drug discovery aspects of epigenomics. This is achieved by providing a survey of the following concepts: (i) factors influencing epigenetics, (ii) diseases arising from epigenetics, (iii) epigenetic enzymes as druggable targets along with coverage of existing FDA-approved drugs and pharmacological agents, and (iv) drug repurposing/repositioning as a means for rapid discovery of pharmacological agents targeting epigenetics. Expert opinion: Despite significant interests in targeting epigenetic modifiers as a therapeutic route, certain classes of target proteins are heavily studied while some are less characterized. Thus, such orphan target proteins are not yet druggable with limited report of active modulators. Current research points towards a great future with novel drugs directed to the many complex multifactorial diseases of humans, which are still often poorly understood and difficult to treat.

Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia

High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner—treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non-restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a disease's omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.

Leveraging big data to transform target selection and drug discovery

The advances of genomics, sequencing, and high throughput technologies have led to the creation of large volumes of diverse datasets for drug discovery. Analyzing these datasets to better understand disease and discover new drugs is becoming more common. Recent open data initiatives in basic and clinical research have dramatically increased the types of data available to the public. The past few years have witnessed successful use of big data in many sectors across the whole drug discovery pipeline. In this review, we will highlight the state of the art in leveraging big data to identify new targets, drug indications, and drug response biomarkers in this era of precision medicine.

Drug repurposing in oncology--patient and health systems opportunities

In most countries, healthcare service budgets are not likely to support the current explosion in the cost of new oncology drugs. Repurposing the large arsenal of approved, non-anticancer drugs is an attractive strategy to offer more-effective options to patients with cancer, and has the substantial advantages of cheaper, faster and safer preclinical and clinical validation protocols. The potential benefits are so relevant that funding of academically and/or independently driven preclinical and clinical research programmes should be considered at both national and international levels. To date, successes in oncology drug repurposing have been limited, despite strong evidence supporting the use of many different drugs. A lack of financial incentives for drug developers and limited drug development experience within the non-profit sector are key reasons for this lack of success. We discuss these issues and offer solutions to finally seize this opportunity in the interest of patients and societies, globally.

Expression Profiling Identifies Bezafibrate as Potential Therapeutic Drug for Lung Adenocarcinoma

Drug-induced gene expression patterns that invert disease profiles have recently been illustrated to be a new strategy for drug-repositioning. In the present study, we validated this approach and focused on prediction of novel drugs for lung adenocarcinoma (AC), for which there is a pressing need to find novel therapeutic compounds. Firstly, connectivity map (CMap) analysis computationally predicted bezafibrate as a putative compound against lung AC. Then this hypothesis was verified by in vitro assays of anti-proliferation and cell cycle arrest. In silico docking evidence indicated that bezafibrate could target cyclin dependent kinase 2(CDK2), which regulates progression through the cell cycle. Furthermore, we found that bezafibrate can significantly down-regulate the expression of CDK2 mRNA and p-CDK2. Using a nude mice xenograft model, we also found that bezafibrate could inhibit tumor growth of lung AC in vivo. In conclusion, this study proposed bezafibrate as a potential therapeutic option for lung AC patients, illustrating the potential of in silico drug screening.

Relating hepatocellular carcinoma tumor samples and cell lines using gene expression data in translational research

Cancer cell lines are used extensively to study cancer biology and to test hypotheses in translational research. The relevance of cell lines is dependent on how closely they resemble the tumors being studied. Relating tumors and cell lines, and recognizing their similarities and differences are thus very important for translational research. Rapid advances in genomics have led to the generation of large volumes of genomic and transcriptomic data for a diverse set of primary cancer samples, normal tissue samples and cancer cell lines. Hepatocellular Carcinoma (HCC) is one of the most common tumors worldwide, with high occurrence in Asia and sub-Saharan regions. The current effective treatments of HCC remain limited. In this work, we compared the gene expression measurements of 200 HCC tumor samples from The Cancer Genome Atlas and over 1000 cancer cell lines including 25 HCC cancer cell lines from Cancer Cell Line Encyclopedia. We showed that the HCC tumor samples correlate closely with HCC cell lines in comparison to cell lines derived from other tumor types. We further demonstrated that the most commonly used HCC cell lines resemble HCC tumors, while we identified nearly half of the cell lines that do not resemble primary tumors. Interestingly, a substantial number of genes that are critical for disease development or drug response are either expressed at low levels or absent among highly correlated cell lines; additional attention should be paid to these genes in translational research. Our study will be used to guide the selection of HCC cell lines and pinpoint the specific genes that are differentially expressed in either tumors or cell lines.

Development of a liposome formulation for improved biodistribution and tumor accumulation of pentamidine for oncology applications

Pentamidine isethionate, widely used for the treatment of parasitic infections, has shown strong anticancer activity in cancer cells and models of melanoma and lung cancer. Systemic administration of pentamidine is associated with serious toxicities, particularly renal, affecting as many as 95% of patients (O'Brien et al., 1997). This work presents the development of a liposome pentamidine formulation for greater tumor accumulation and lower drug exposure to vulnerable tissues. Liposomes formulated with saturated/unsaturated phospholipids of different chain lengths, varying cholesterol content, and surface PEG were explored to understand the effects of such variations on drug release, encapsulation efficiency, stability and in vivo performance. Saturated phospholipids with longer chain lengths, higher cholesterol content and PEG resulted in greater stability. The optimal formulation obtained showed significantly lower clearance rate (3.6 ± 1.2 mL/h/Kg) and higher AUC0-inf (348 ± 31 μmol/L × h) in vivo when compared to free drug (414 ± 138 mL/h/Kg and 2.58 ± 0.74 μmol/L × h, respectively). In tumor-bearing mice, liposomal delivery decreased kidney drug levels by up to 5-fold at 6 and 24h post-administration. Tumor drug exposure was up to 12.7-fold greater with liposomal administration compared to free drug. Overall, the liposomal pentamidine formulation developed has significant potential for the treatment of solid tumors.

A survey of current trends in computational drug repositioning

Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.