Drug Repositioning for Cancer Therapy Based on Large-Scale Drug-Induced Transcriptional Signatures

Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning

Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in other, seemingly unrelated, assays. Applications of such models include predicting mechanisms-of-action (MoA) for phenotypic hits, identifying off-target activities, and identifying polypharmacologies. Here, we introduce transcriptomics-to-activity transformer (TAT) models that leverage gene expression profiles observed over compound treatment at multiple concentrations to predict the compound activity in other biochemical or cellular assays. We built TAT models based on gene expression data from a RASL-seq assay to predict the activity of 2692 compounds in 262 dose-response assays. We obtained useful models for 51% of the assays, as determined through a realistic held-out set. Prospectively, we experimentally validated the activity predictions of a TAT model in a malaria inhibition assay. With a 63% hit rate, TAT successfully identified several submicromolar malaria inhibitors. Our results thus demonstrate the potential of transcriptomic responses over compound concentration and the TAT modeling framework as a cost-efficient way to identify the bioactivities of promising compounds across many assays.

IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder

Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.

Network neighborhood operates as a drug repositioning method for cancer treatment

Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.

Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer's disease patients

BACKGROUND

Despite numerous clinical trials and decades of endeavour, there is still no effective cure for Alzheimer's disease. Computational drug repositioning approaches may be employed for the development of new treatment strategies for Alzheimer's patients since an extensive amount of omics data has been generated during pre-clinical and clinical studies. However, targeting the most critical pathophysiological mechanisms and determining drugs with proper pharmacodynamics and good efficacy are equally crucial in drug repurposing and often imbalanced in Alzheimer's studies.

METHODS

Here, we investigated central co-expressed genes upregulated in Alzheimer's disease to determine a proper therapeutic target. We backed our reasoning by checking the target gene's estimated non-essentiality for survival in multiple human tissues. We screened transcriptome profiles of various human cell lines perturbed by drug induction (for 6798 compounds) and gene knockout using data available in the Connectivity Map database. Then, we applied a profile-based drug repositioning approach to discover drugs targeting the target gene based on the correlations between these transcriptome profiles. We evaluated the bioavailability, functional enrichment profiles and drug-protein interactions of these repurposed agents and evidenced their cellular viability and efficacy in glial cell culture by experimental assays and Western blotting. Finally, we evaluated their pharmacokinetics to anticipate to which degree their efficacy can be improved.

RESULTS

We identified glutaminase as a promising drug target. Glutaminase overexpression may fuel the glutamate excitotoxicity in neurons, leading to mitochondrial dysfunction and other neurodegeneration hallmark processes. The computational drug repurposing revealed eight drugs: mitoxantrone, bortezomib, parbendazole, crizotinib, withaferin-a, SA-25547 and two unstudied compounds. We demonstrated that the proposed drugs could effectively suppress glutaminase and reduce glutamate production in the diseased brain through multiple neurodegeneration-associated mechanisms, including cytoskeleton and proteostasis. We also estimated the human blood-brain barrier permeability of parbendazole and SA-25547 using the SwissADME tool.

CONCLUSIONS

This study method effectively identified an Alzheimer's disease marker and compounds targeting the marker and interconnected biological processes by use of multiple computational approaches. Our results highlight the importance of synaptic glutamate signalling in Alzheimer's disease progression. We suggest repurposable drugs (like parbendazole) with well-evidenced activities that we linked to glutamate synthesis hereby and novel molecules (SA-25547) with estimated mechanisms for the treatment of Alzheimer's patients.

Identification of significant gene expression changes in multiple perturbation experiments using knockoffs

Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.

NMSDR: Drug repurposing approach based on transcriptome data and network module similarity

Computational drug repurposing aims to discover new treatment regimens by analyzing approved drugs on the market. This study proposes previously approved compounds that can change the expression profile of disease-causing proteins by developing a network theory-based drug repurposing approach. The novelty of the proposed approach is an exploration of module similarity between a disease-causing network and a compound-specific interaction network; thus, such an association leads to more realistic modeling of molecular cell responses at a system biology level. The overlap of the disease network and each compound-specific network is calculated based on a shortest-path similarity of networks by accounting for all protein pairs between networks. A higher similarity score indicates a significant potential of a compound. The approach was validated for breast and lung cancers. When all compounds are sorted by their normalized-similarity scores, 36 and 16 drugs are proposed as new candidates for breast and lung cancer treatment, respectively. A literature survey on candidate compounds revealed that some of our predictions have been clinically investigated in phase II/III trials for the treatment of two cancer types. As a summary, the proposed approach has provided promising initial results by modeling biochemical cell responses in a network-level data representation.

Aporphine and isoquinoline derivatives block glioblastoma cell stemness and enhance temozolomide cytotoxicity

Glioblastoma (GBM) is the most aggressive and common primary malignant brain tumor with limited available therapeutic approaches. Despite improvements in therapeutic options for GBM patients, efforts to develop new successful strategies remain as major unmet medical needs. Based on the cytotoxic properties of aporphine compounds, we evaluated the biological effect of 12 compounds obtained through total synthesis of ( ±)-apomorphine hydrochloride (APO) against GBM cells. The compounds 2,2,2-trifluoro-1-(1-methylene-3,4-dihydroisoquinolin-2(1H)-yl)ethenone (A5) and ( ±)-1-(10,11-dimethoxy-6a,7-dihydro-4H-dibenzo[de,g]quinolin-6(5H)-yl)ethenone (C1) reduced the viability of GBM cells, with 50% inhibitory concentration ranging from 18 to 48 μM in patient-derived GBM cultures. Our data show that APO, A5 or C1 modulate the expression of DNA damage and apoptotic markers, impair 3D-gliomasphere growth and reduce the expression of stemness markers. Potential activity and protein targets of A5, C1 or APO were predicted in silico based on PASS and SEA software. Dopamine receptors (DRD1 and 5), CYP2B6, CYP2C9 and ABCB1, whose transcripts were differentially expressed in the GBM cells, were among the potential A5 or C1 target proteins. Docking analyses (HQSAR and 3D-QSAR) were performed to characterize possible interactions of ABCB1 and CYP2C9 with the compounds. Notably, A5 or C1 treatment, but not temozolomide (TMZ), reduced significantly the levels of extracellular ATP, suggesting ABCB1 negative regulation, which was correlated with stronger cytotoxicity induced by the combination of TMZ with A5 or C1 on GBM cells. Hence, our data reveal a potential therapeutic application of A5 and C1 as cytotoxic agents against GBM cells and predicted molecular networks that can be further exploited to characterize the pharmacological effects of these isoquinoline-containing substances.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

Drug Repurposing to Enhance Antitumor Response to PD-1/PD-L1 Immune Checkpoint Inhibitors

Monoclonal antibodies targeting the PD-1/PD-L1 immune checkpoint have considerably improved the treatment of some cancers, but novel drugs, new combinations, and treatment modalities are needed to reinvigorate immunosurveillance in immune-refractory tumors. An option to elicit antitumor immunity against cancer consists of using approved and marketed drugs known for their capacity to modulate the expression and functioning of the PD-1/PD-L1 checkpoint. Here, we have reviewed several types of drugs known to alter the checkpoint, either directly via the blockade of PD-L1 or indirectly via an action on upstream effectors (such as STAT3) to suppress PD-L1 transcription or to induce its proteasomal degradation. Specifically, the repositioning of the approved drugs liothyronine, azelnidipine (and related dihydropyridine calcium channel blockers), niclosamide, albendazole/flubendazole, and a few other modulators of the PD-1/PD-L1 checkpoint (repaglinide, pimozide, fenofibrate, lonazolac, propranolol) is presented. Their capacity to bind to PD-L1 or to repress its expression and function offer novel perspectives for combination with PD-1 targeted biotherapeutics. These known and affordable drugs could be useful to improve the therapy of cancer.

The steroid mometasone alters protein containing lung surfactant monolayers in a concentration-dependent manner

Mometasone is an investigational anti-inflammatory steroidal drug to treat inflammation via pulmonary administration. For steroid drugs to be effective they need to be adsorbed by lung surfactants, a thin monolayer at the air-water interface in alveoli that reduces surface tension. Information on the molecular-level interactions of the drug with lung surfactants is useful to understand the mechanism of adsorption. In this study, we use coarse-grained molecular dynamics simulation to understand the concentration-dependent effect of mometasone on a lung surfactant monolayer (LSM) composed of lipids and surfactant proteins, under two different breathing conditions (exhalation, at surface tension 0 mNm^-1 and inhalation, surface tension 20-25 mNm^-1). A series of fixed-APL and fixed-surface tension simulations were used to demonstrate that in the absence of drugs, the model LSM reproduces the surface tensions for the compressed and expanded states, as well as compressibility at different surface tensions. In-depth analysis of simulations of a LSM in the presence of five different drug concentrations shows that mometasone alters the structure and dynamics of the LSM in a concentration-dependent manner. Mometasone induces a collapse in the monolayer that is affected by the surfactant protein and surface tension. Overall, these findings suggest that the surfactant proteins, surface tension and drug concentration are all critical components affecting monolayer stability and drug adsorption. The outcomes of this study may be beneficial for a more in-depth understanding of how mometasone is adsorbed by lung surfactants.

Comprehensive Survey of Recent Drug Discovery Using Deep Learning

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug-target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

Drug Repurposing in Oncology, an Attractive Opportunity for Novel Combinatorial Regimens

The costs of developing, validating and buying new drugs are dramatically increasing. On the other hand, sobering economies have difficulties in sustaining their healthcare systems, particularly in countries with an elderly population requiring increasing welfare. This conundrum requires immediate action, and a possible option is to study the large, already present arsenal of drugs approved and to use them for innovative therapies. This possibility is particularly interesting in oncology, where the complexity of the cancer genome dictates in most patients a multistep therapeutic approach. In this review, we discuss a) Computational approaches; b) preclinical models; c) currently ongoing or already published clinical trials in the drug repurposing field in oncology; and d) drug repurposing to overcome resistance to previous therapies.

An Alternative Pipeline for Glioblastoma Therapeutics: A Systematic Review of Drug Repurposing in Glioblastoma

Simple Summary

Glioblastoma is a devastating malignancy that has continued to prove resistant to a variety of therapeutics. No new systemic therapy has been approved for use against glioblastoma in almost two decades. This observation is particularly disturbing given the amount of money invested in identifying novel therapies for this disease. A relatively rapid and economical pipeline for identification of novel agents is drug repurposing. Here, a comprehensive review detailing the state of drug repurposing in glioblastoma is provided. We reveal details on studies that have examined agents in vitro, in animal models and in patients. While most agents have not progressed beyond the initial stages, several drugs, from a variety of classes, have demonstrated promising results in early phase clinical trials.

Abstract

The treatment of glioblastoma (GBM) remains a significant challenge, with outcome for most pa-tients remaining poor. Although novel therapies have been developed, several obstacles restrict the incentive of drug developers to continue these efforts including the exorbitant cost, high failure rate and relatively small patient population. Repositioning drugs that have well-characterized mechanistic and safety profiles is an attractive alternative for drug development in GBM. In ad-dition, the relative ease with which repurposed agents can be transitioned to the clinic further supports their potential for examination in patients. Here, a systematic analysis of the literature and clinical trials provides a comprehensive review of primary articles and unpublished trials that use repurposed drugs for the treatment of GBM. The findings demonstrate that numerous drug classes that have a range of initial indications have efficacy against preclinical GBM models and that certain agents have shown significant potential for clinical benefit. With examination in randomized, placebo-controlled trials and the targeting of particular GBM subgroups, it is pos-sible that repurposing can be a cost-effective approach to identify agents for use in multimodal anti-GBM strategies.

An Analytical Review of Computational Drug Repurposing

Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or failed drugs. It is cost-effective and reliable in contrast to experimental drug discovery, which is a costly, time-consuming, and risky process and limited to a relatively small number of targets. Accordingly, a plethora of computational methodologies have been propounded to repurpose drugs on a large scale by utilizing available high throughput data. The available literature, however, lacks a contemporary and comprehensive analysis of the current computational drug repurposing methodologies. In this paper, we presented a systematic analysis of computational drug repurposing which consists of three main sections: Initially, we categorize the computational drug repurposing methods based on their technical approach and artificial intelligence perspective and discuss the strengths and weaknesses of various methods. Secondly, some general criteria are recommended to analyze our proposed categorization. In the third and final section, a qualitative comparison is made between each approach which is a guide to understanding their preference to one another. Further, this systematic analysis can help in the efficient selection and improvement of drug repurposing techniques based on the nature of computational methods implemented on biological resources.

Antiparasitic antibiotics from Japan

Antibiotics are microbial secondary metabolites and they are important for the treatment of infectious diseases. Japanese researchers have made a large contribution to studies of antibiotics, and they have also been important in the discovery of antiparasitic antibiotics. Satoshi Ōmura received the Nobel Prize in 2015 for the "discoveries concerning a novel therapy against infections caused by roundworm parasites", which means discovery of a new nematocidal antibiotic, avermectin. Here, I review the many antiparasitic antibiotics and their lead compounds that have been discovered for use in human and veterinary medicine.

Drug Repositioning Screen on a New Primary Cell Line Identifies Potent Therapeutics for Glioblastoma

Glioblastoma is a malignant brain cancer with limited treatment options and high mortality rate. While established glioblastoma cell line models provide valuable information, they ultimately lose most primary characteristics of tumors under long-term serum culture conditions. Therefore, established cell lines do not necessarily recapitulate genetic and morphological characteristics of real tumors. In this study, in line with the growing interest in using primary cell line models derived from patient tissue, we generated a primary glioblastoma cell line, KUGBM8 and characterized its genetic alterations, long term growth ability, tumor formation capacity and its response to Temozolomide, the front-line chemotherapy utilized clinically. In addition, we performed a drug repurposing screen on the KUGBM8 cell line to identify FDA-approved agents that can be incorporated into glioblastoma treatment regimen and identified Topotecan as a lead drug among 1,200 drugs. We showed Topotecan can induce cell death in KUGBM8 and other primary cell lines and cooperate with Temozolomide in low dosage combinations. Together, our study provides a new primary cell line model that can be suitable for both in vitro and in vivo studies and suggests that Topotecan can offer promise as a therapeutic approach for glioblastoma.

Novel Therapeutic Approaches of Ion Channels and Transporters in Cancer

The expression and function of many ion channels and transporters in cancer cells display major differences in comparison to those from healthy cells. These differences provide the cancer cells with advantages for tumor development. Accordingly, targeting ion channels and transporters have beneficial anticancer effects including inhibition of cancer cell proliferation, migration, invasion, metastasis, tumor vascularization, and chemotherapy resistance, as well as promoting apoptosis. Some of the molecular mechanisms associating ion channels and transporters with cancer include the participation of oxidative stress, immune response, metabolic pathways, drug synergism, as well as noncanonical functions of ion channels. This diversity of mechanisms offers an exciting possibility to suggest novel and more effective therapeutic approaches to fight cancer. Here, we review and discuss most of the current knowledge suggesting novel therapeutic approaches for cancer therapy targeting ion channels and transporters. The role and regulation of ion channels and transporters in cancer provide a plethora of exceptional opportunities in drug design, as well as novel and promising therapeutic approaches that may be used for the benefit of cancer patients.

Apomorphine facilitates loss of respiratory chain activity in human epithelial ovarian cancer and inhibits angiogenesis in vivo

Apomorphine, a therapeutic agent for neurological diseases, is structurally similar to dopamine, and thereby holds potential in cancer therapy. However, there are no reports regarding its anti-cancer effects on human epithelial ovarian cancers (EOCs); therefore, we aimed to elucidate the mechanism underlying its action after drug repositioning. Apomorphine inhibited the proliferation of ES2 and OV90 EOC cells by inducing caspase activation and mitochondrion-associated apoptosis; it also promoted endoplasmic reticulum stress and mitochondrial dysfunction through mitochondrial membrane potential depolarization and mitochondrial calcium overload. Moreover, following apomorphine treatment, we noted the loss of respiratory chain activity by reduction of oxidative phosphorylation and energy-production shift in EOC cells. Further, we verified the anti-angiogenic capacity of apomorphine using fli:eGFP transgenic zebrafish. As a preclinical assessment, we demonstrated the synergistic anti-cancer effects of apomorphine and paclitaxel combination.

Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Motivation

Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures.

Results

The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks.

Availability and implementation

Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Connectivity map-based drug repositioning of bortezomib to reverse the metastatic effect of GALNT14 in lung cancer

Despite the continual discovery of promising new cancer targets, drug discovery is often hampered by the poor druggability of these targets. As such, repurposing FDA-approved drugs based on cancer signatures is a useful alternative to cancer precision medicine. Here, we adopted an in silico approach based on large-scale gene expression signatures to identify drug candidates for lung cancer metastasis. Our clinicogenomic analysis identified GALNT14 as a putative driver of lung cancer metastasis, leading to poor survival. To overcome the poor druggability of GALNT14 in the control of metastasis, we utilized the Connectivity Map and identified bortezomib (BTZ) as a potent metastatic inhibitor, bypassing the direct inhibition of the enzymatic activity of GALNT14. The antimetastatic effect of BTZ was verified both in vitro and in vivo. Notably, both BTZ treatment and GALNT14 knockdown attenuated TGFβ-mediated gene expression and suppressed TGFβ-dependent metastatic genes. These results demonstrate that our in silico approach is a viable strategy for the use of undruggable targets in cancer therapies and for revealing the underlying mechanisms of these targets.

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets

Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.

Receptor tyrosine kinase targeting in glioblastoma: performance, limitations and future approaches

From all central nervous system tumors, gliomas are the most common. Nowadays, researchers are looking for more efficient treatments for these tumors, as well as ways for early diagnosis. Receptor tyrosine kinases (RTKs) are major targets for oncology and the development of small-molecule RTK inhibitors has been proven successful in cancer treatment. Mutations or aberrant activation of the RTKs and their intracellular signaling pathways are linked to several malignant diseases, including glioblastoma. The progress in the understanding of malignant glioma evolution has led to RTK targeted therapies with high capacity to improve the therapeutic response while reducing toxicity. In this review, we present the most important RTKs (i.e. EGFR, IGFR, PDGFR and VEGFR) currently used for developing cancer therapeutics together with the potential of RTK-related drugs in glioblastoma treatment. Also, we focus on some therapeutic agents that are currently at different stages of research or even in clinical phases and proved to be suitable as re-purposing candidates for glioblastoma treatment.

Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work

Drug discovery and development are long and financially taxing processes. On an average it takes 12-15 years and costs 1.2 billion USD for successful drug discovery and approval for clinical use. Many lead molecules are not developed further and their potential is not tapped to the fullest due to lack of resources or time constraints. In order for a drug to be approved by FDA for clinical use, it must have excellent therapeutic potential in the desired area of target with minimal toxicities as supported by both pre-clinical and clinical studies. The targeted clinical evaluations fail to explore other potential therapeutic applications of the candidate drug. Drug repurposing or repositioning is a fast and relatively cheap alternative to the lengthy and expensive de novo drug discovery and development. Drug repositioning utilizes the already available clinical trials data for toxicity and adverse effects, at the same time explores the drug's therapeutic potential for a different disease. This review addresses recent developments and future scope of drug repositioning strategy.

Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets

Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs' modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease' biomarkers. Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.

Selective uveal melanoma inhibition with calcium channel blockade

Uveal malignant melanoma (UMM), the most common primary adult intraocular tumor with a marked metastatic potential, is genetically unique and has unfortunately had few treatment breakthroughs. In this study, we subjected a UMM cell line to high‑throughput library screening with 1,018 FDA‑approved compounds to identify potential UMM‑selective cytotoxic agents. Amlodipine, a dihydropyridine calcium channel blocker (CCB), ranked no. 2 and no. 8 of the most cytotoxic compounds. Thus, we further characterized the differential effects of calcium blockade on UMM and cutaneous malignant melanoma (CMM) lines in vitro using growth inhibition, cell cycle progression, apoptosis and senescence assays. Amlodipine had a significantly higher growth inhibitory potency in UMM (IC50=13.1 µM) than CMM (IC50=15.9 µM, P<0.05) lines. In 3D spherical cell culture, amlodipine treatment significantly impaired tissue volume growth in two UMM lines, but exerted no significant effects among all 3 CMM lines tested. Treatment with 10 and 20 µM amlodipine induced a significant impairment of cell cycle progression and the apoptosis of UMM lines, implicating both of these processes as mediators of the observed growth inhibition in UMM compared to CMM. On the whole, the findings of this study suggest that calcium channel blockade is a potentially effective strategy for selective uveal melanoma targeting.

In silico drug repositioning: from large-scale transcriptome data to therapeutics

Drug repositioning is an attractive alternative to conventional drug development when new beneficial effects of old drugs are clinically validated because pharmacokinetic and safety profiles are generally already available. Since ~ 30% of drugs newly approved by the US food and drug administration (FDA) are developed through drug repositioning, identifying novel usage for existing drugs is an emerging strategy for developing disease treatments. With advances in next-generation sequencing technologies, available transcriptome data related to diseases have expanded rapidly. Harnessing these resources enables a better understanding of disease mechanisms and drug mode of action (MOA), and moves toward personalized pharmacotherapy. In this review, we briefly outline publicly available large-scale transcriptome databases and tools for drug repositioning. We also highlight recent approaches leading to the discovery of novel drug targets, drug response biomarkers, drug indications, and drug MOA.

Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression.

Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses

The ability to predict responsiveness to drugs in individual patients is limited. We hypothesized that integrating molecular information from databases would yield predictions that could be experimentally tested to develop transcriptomic signatures for specific drugs. We analyzed lung adenocarcinoma patient data from The Cancer Genome Atlas and identified a subset of patients in which xanthine dehydrogenase (XDH) expression correlated with decreased survival. We tested allopurinol, an FDA‐approved drug that inhibits XDH, on human non‐small‐cell lung cancer (NSCLC) cell lines obtained from the Broad Institute Cancer Cell Line Encyclopedia and identified sensitive and resistant cell lines. We utilized the transcriptomic profiles of these cell lines to identify six‐gene signatures for allopurinol‐sensitive and allopurinol‐resistant cell lines. Transcriptomic networks identified JAK2 as an additional target in allopurinol‐resistant lines. Treatment of resistant cell lines with allopurinol and CEP‐33779 (a JAK2 inhibitor) resulted in cell death. The effectiveness of allopurinol alone or allopurinol and CEP‐33779 was verified in vivo using tumor formation in NCR‐nude mice. We utilized the six‐gene signatures to predict five additional allopurinol‐sensitive NSCLC cell lines and four allopurinol‐resistant cell lines susceptible to combination therapy. We searched the transcriptomic data from a library of patient‐derived NSCLC tumors from the Jackson Laboratory to identify tumors that would be predicted to be sensitive to allopurinol or allopurinol + CEP‐33779 treatment. Patient‐derived tumors showed the predicted drug sensitivity in vivo. These data indicate that we can use integrated molecular information from cancer databases to predict drug responsiveness in individual patients and thus enable precision medicine.

Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer

Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.

A review of network-based approaches to drug repositioning

Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.

Effectiveness of Plasmocure™ in Elimination of Mycoplasma Species from Contaminated Cell Cultures: A Comparative Study versus other Antibiotics

Objective

Mycoplasmas spp. is among major contaminants of eukaryotic cell cultures. They cause a wide range of problems associated with cell culture in biology research centers or biotechnological companies. Mycoplasmas are also resistant to several antibiotics. Plasmocin™ has been used to treat cell lines but Plasmocin™-resistant strains have been reported. InvivoGen has developed a new anti-Mycoplasma agent called Plasmocure™ in order to eliminate resistant Mycoplasma contamination. The aim of this study was the selection of the best antibiotics for treatment of mycoplasma in cell cultures.

Materials and Methods

In this experimental study, a total of 100 different mammalian cell lines contaminated with different Mycoplasma species were evaluated by microbiological culture (as the gold standard method), indirect DNA fluorochrome staining, enzymatic (MycoAlert™), and universal or species-specific polymerase chain reaction (PCR) detection methods. In this study, animal and human cell lines available in National Cell Bank of Iran, were treated with Plasmocure™. The treatment efficacy and cytotoxicity of Plasmocure™ were compared with those of commonly used antibiotics such as BM- cyclin, Plasmocin™, MycoRAZOR™, sparfloxacin and enrofloxacin.

Results

Plasmocure™ is comprised of two antibiotics that act through various mechanisms of action than those in Plasmocin™. Two-week treatment with Plasmocure™ was enough to completely eliminate Mycoplasma spp. A moderate toxicity was observed during Mycoplasma treatment with plasmocure™; But, after elimination of Mycoplasma, cells were fully recovered. Mycoplasma infections were eliminated by Plasmocure™, BM-cyclin, Plasmocin™, MycoRAZOR™, sparfloxacin and enrofloxacin. However, the outcome of the treatment process (i.e. the frequency of complete cure, regrowth or cell death) varied among different antibiotics.

Conclusion

The highest number of cured cell lines was achieved by using Plasmocure™ which also had the lowest regrowth rate after a period of four months. As a conclusion; Plasmocure™ might be considered an effective antibiotic to treat Mycoplasma infections in mammalian cell cultures especially for precious or vulnerable cells.

A Comparative Study of Cluster Detection Algorithms in Protein-Protein Interaction for Drug Target Discovery and Drug Repurposing

The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein–protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.

Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data

Background

Although in silico drug discovery is necessary for drug development, two major strategies, a structure-based and ligand-based approach, have not been completely successful. Currently, the third approach, inference of drug candidates from gene expression profiles obtained from the cells treated with the compounds under study requires the use of a training dataset. Here, the purpose was to develop a new approach that does not require any pre-existing knowledge about the drug–protein interactions, but these interactions can be inferred by means of an integrated approach using gene expression profiles obtained from the cells treated with the analysed compounds and the existing data describing gene–gene interactions.

Results

In the present study, using tensor decomposition-based unsupervised feature extraction, which represents an extension of the recently proposed principal-component analysis-based feature extraction, gene sets and compounds with a significant dose-dependent activity were screened without any training datasets. Next, after these results were combined with the data showing perturbations in single-gene expression profiles, genes targeted by the analysed compounds were inferred. The set of target genes thus identified was shown to significantly overlap with known target genes of the compounds under study.

Conclusions

The method is specifically designed for large-scale datasets (including hundreds of treatments with compounds), not for conventional small-scale datasets. The obtained results indicate that two compounds that have not been extensively studied, WZ-3105 and CGP-60474, represent promising drug candidates targeting multiple cancers, including melanoma, adenocarcinoma, liver carcinoma, and breast, colon, and prostate cancers, which were analysed in this in silico study.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2395-8) contains supplementary material, which is available to authorized users.

Elucidating the Interaction of CF Airway Epithelial Cells and Rhinovirus: Using the Host-Pathogen Relationship to Identify Future Therapeutic Strategies

Chronic lung disease remains the primary cause of mortality in cystic fibrosis (CF). Growing evidence suggests respiratory viral infections are often more severe in CF compared to healthy peers and contributes to pulmonary exacerbations (PEx) and deterioration of lung function. Rhinovirus is the most prevalent respiratory virus detected, particularly during exacerbations in children with CF <5 years old. However, even though rhinoviral infections are likely to be one of the factors initiating the onset of CF lung disease, there is no effective targeted treatment. A better understanding of the innate immune responses by CF airway epithelial cells, the primary site of infection for viruses, is needed to identify why viral infections are more severe in CF. The aim of this review is to present the clinical impact of virus infection in both young children and adults with CF, focusing on rhinovirus infection. Previous in vitro and in vivo investigations looking at the mechanisms behind virus infection will also be summarized. The review will finish on the potential of transcriptomics to elucidate the host-pathogen responses by CF airway cells to viral infection and identify novel therapeutic targets.

New tricks for an old dog: A repurposing approach of apomorphine

Apomorphine is a 150-year old nonspecific dopaminergic agonist, currently indicated for treating motor fluctuations in Parkinson's disease. At the era of drug repurposing, its pleiotropic biological functions suggest other possible uses. To further explore new therapeutic and diagnostic applications, the available literature up to July 2018 was reviewed using the PubMed and Google Scholar databases. As many of the retrieved articles consisted of case reports and preclinical studies, we adopted a descriptive approach, tackling each area of research in turn, to give a broad overview of the potential of apomorphine. Apomorphine may play a role in neurological diseases like restless legs syndrome, Huntington's chorea, amyotrophic lateral sclerosis, Alzheimer's disease and disorders of consciousness, but also in sexual disorders, neuroleptic malignant(-like) syndrome and cancer. Further work is needed in both basic and clinical research; current developments in novel delivery strategies and apomorphine derivatives are expected to open the way.

The circadian gene Nr1d1 in the mouse nucleus accumbens modulates sociability and anxiety-related behaviour

Nuclear receptor subfamily 1, group D, member 1 (Nr1d1) (also known as Rev-erb alpha) has been linked to circadian rhythm regulation, mood-related behaviour and disorders associated with social deficits. Recent work from our laboratory found striking decreases in Nr1d1 in the nucleus accumbens (NAc) in the maternal condition and indirect evidence that Nr1d1 was interacting with numerous addiction and reward-related genes to modulate social reward. In this study, we applied our insights from the maternal state to nonparental adult mice to determine whether decreases in Nr1d1 expression in the NAc via adeno-associated viral (AAV) vectors and short hairpin RNA (shRNA)-mediated gene knockdown were sufficient to modulate social behaviours and mood-related behaviours. Knockdown of Nr1d1 in the NAc enhanced sociability and reduced anxiety, but did not affect depressive-like traits in female mice. In male mice, Nr1d1 knockdown had no significant behavioural effects. Microarray analysis of Nr1d1 knockdown in females identified changes in circadian rhythm and histone deacetylase genes and suggested possible drugs, including histone deacetylase inhibitors, that could mimic actions of Nr1d1 knockdown. Quantitative real-time PCR (qPCR) analysis confirmed expression upregulation of gene period circadian clock 1 (Per1) and period circadian clock 2 (Per2) with Nr1d1 knockdown. The evidence for roles for opioid-related genes opioid receptor, delta 1 (Oprd1) and preproenkephalin (Penk) was also found. Together, these results suggest that Nr1d1 in the NAc modulates sociability and anxiety-related behaviour in a sex-specific manner, and circadian, histone deacetylase and opioid-related genes may be involved in the expression of these behavioural phenotypes.

Review of Drug Repositioning Approaches and Resources

Drug discovery is a time-consuming, high-investment, and high-risk process in traditional drug development. Drug repositioning has become a popular strategy in recent years. Different from traditional drug development strategies, the strategy is efficient, economical and riskless. There are usually three kinds of approaches: computational approaches, biological experimental approaches, and mixed approaches, all of which are widely used in drug repositioning. In this paper, we reviewed computational approaches and highlighted their characteristics to provide references for researchers to develop more powerful approaches. At the same time, the important findings obtained using these approaches are listed. Furthermore, we summarized 76 important resources about drug repositioning. Finally, challenges and opportunities in drug repositioning are discussed from multiple perspectives, including technology, commercial models, patents and investment.

EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to improve prediction accuracy

Motivation

Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify the therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods.

Results

We first designed an expression weighted cosine (EWCos) method to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed ensemble of multiple drug repositioning approaches (EMUDRA) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs.

Availability and implementation

The EMUDRA R package is available at doi: 10.7303/syn11510888.

Supplementary information

Supplementary data are available at Bioinformatics online.

Drug Repositioning in Glioblastoma: A Pathway Perspective

Glioblastoma multiforme (GBM) is the most malignant primary adult brain tumor. The current standard of care is surgical resection, radiation, and chemotherapy treatment, which extends life in most cases. Unfortunately, tumor recurrence is nearly universal and patients with recurrent glioblastoma typically survive <1 year. Therefore, new therapies and therapeutic combinations need to be developed that can be quickly approved for use in patients. However, in order to gain approval, therapies need to be safe as well as effective. One possible means of attaining rapid approval is repurposing FDA approved compounds for GBM therapy. However, candidate compounds must be able to penetrate the blood-brain barrier (BBB) and therefore a selection process has to be implemented to identify such compounds that can eliminate GBM tumor expansion. We review here psychiatric and non-psychiatric compounds that may be effective in GBM, as well as potential drugs targeting cell death pathways. We also discuss the potential of data-driven computational approaches to identify compounds that induce cell death in GBM cells, enabled by large reference databases such as the Library of Integrated Network Cell Signatures (LINCS). Finally, we argue that identifying pathways dysregulated in GBM in a patient specific manner is essential for effective repurposing in GBM and other gliomas.

The multitargeted drug ivermectin: from an antiparasitic agent to a repositioned cancer drug

Drug repositioning is a highly studied alternative strategy to discover and develop anticancer drugs. This drug development approach identifies new indications for existing compounds. Ivermectin belongs to the group of avermectins (AVM), a series of 16-membered macrocyclic lactone compounds discovered in 1967, and FDA-approved for human use in 1987. It has been used by millions of people around the world exhibiting a wide margin of clinical safety. In this review, we summarize the in vitro and in vivo evidences demonstrating that ivermectin exerts antitumor effects in different types of cancer. Ivermectin interacts with several targets including the multidrug resistance protein (MDR), the Akt/mTOR and WNT-TCF pathways, the purinergic receptors, PAK-1 protein, certain cancer-related epigenetic deregulators such as SIN3A and SIN3B, RNA helicase, chloride channel receptors and preferentially target cancer stem-cell like population. Importantly, the in vitro and in vivo antitumor activities of ivermectin are achieved at concentrations that can be clinically reachable based on the human pharmacokinetic studies done in healthy and parasited patients. Thus, existing information on ivermectin could allow its rapid move into clinical trials for cancer patients.

The combination astemizole-gefitinib as a potential therapy for human lung cancer

Lung cancer is a major cause of cancer mortality. Thus, novel therapies are urgently needed. Repositioning of old drugs is gaining great interest in cancer treatment. Astemizole is an antihistamine proposed to be repositioned for cancer therapy. This drug targets several molecules involved in cancer including histamine receptors, ABC transporters and the potassium channels Eag1 and HERG. Astemizole inhibits the proliferation of different cancer cells including those from cervix, breast, leukemia and liver. Gefitinib is widely used to treat lung cancer; however, no response or drug resistance occurs in many cases. Here, we studied the combined effect of astemizole and gefitinib on the proliferation, survival, apoptosis and gene and protein expression of Eag1 channels in the human lung cancer cell lines A549 and NCI-H1975. Cell proliferation and survival were studied by the MTT method and the colony formation assay, respectively; apoptosis was investigated by flow cytometry. Gene expression was assessed by real-time polymerase chain reaction (RT-PCR), and protein expression was studied by Western blot analysis and immunocytochemistry. We obtained the inhibitory concentrations 20 and 50 (IC₂₀ and IC₅₀, respectively) values for each drug from the cell proliferation experiments. Drug combination at their IC₂₀ had a superior effect by reducing cell proliferation and survival in up to 80% and 100%, respectively. The drugs alone did not affect apoptosis of H1975 cells, but the drug combination at their IC₂₀ increased apoptosis roughly four times in comparison to the effect of the drugs alone. Eag1 mRNA levels and protein expression were decreased by the drug combination in A549 cells, and astemizole induced subcellular localization changes of the channel protein in these cells. Our in vitro studies strongly suggest that the combination astemizole–gefitinib may be a novel and promising therapy for lung cancer patients.

Sulfonanilide Derivatives in Identifying Novel Aromatase Inhibitors by Applying Docking, Virtual Screening, and MD Simulations Studies

Breast cancer is one of the leading causes of death noticed in women across the world. Of late the most successful treatments rendered are the use of aromatase inhibitors (AIs). In the current study, a two-way approach for the identification of novel leads has been adapted. 81 chemical compounds were assessed to understand their potentiality against aromatase along with the four known drugs. Docking was performed employing the CDOCKER protocol available on the Discovery Studio (DS v4.5). Exemestane has displayed a higher dock score among the known drug candidates and is labeled as reference. Out of 81 ligands 14 have exhibited higher dock scores than the reference. In the second approach, these 14 compounds were utilized for the generation of the pharmacophore. The validated four-featured pharmacophore was then allowed to screen Chembridge database and the potential Hits were obtained after subjecting them to Lipinski's rule of five and the ADMET properties. Subsequently, the acquired 3,050 Hits were escalated to molecular docking utilizing GOLD v5.0. Finally, the obtained Hits were consequently represented to be ideal lead candidates that were escalated to the MD simulations and binding free energy calculations. Additionally, the gene-disease association was performed to delineate the associated disease caused by CYP19A1.

Drug repositioning: computational approaches and research examples classified according to the evidence level

Increasing need for novel drugs and their application for treating diseases are the main reasons for the development of bioinformatics platforms for drug repositioning. The use of existing approved drugs for treating other diseases reduces cost and time needed for a drug to come to clinical use. Different strategies for drug repositioning have been reported. The use of several omics types is becoming increasingly important in drug repositioning. Although there are several public databases intended for drug repositioning, not many successful cases of novel use of drugs have been reported in the literature and transferred to clinical use. Additionally, the study approaches in published literature are very heterogeneous. A classification scheme - Drug Repositioning Evidence Level (DREL) - for drug repositioning projects, according to the level of scientific evidence has been proposed previously. In the present study, we have reviewed main databases and bioinformatics approaches enabling drug repositioning studies. We also reviewed six published studies and evaluated them according to the DREL classification. The evaluated cases used drug repositioning approach for therapy of rheumatoid arthritis, cancer, coronary artery disease, diabetes, and gulf war illness. The drug repositioning study field could benefit from clearer definition in published articles therefore including drug repositioning DREL classification scheme could be included in published original and review studies. Novel bioinformatics approaches to improve prediction of drug-target interactions, continuous updating of the databases, and development of novel validation techniques are needed to facilitate the development of the drug repositioning field. Although there are still many challenges in drug repositioning and personalized medicine, stratification of patients based on their molecular signatures and testing of signature-targeting drugs should improve drug efficacy in clinical trials.

Apomorphine suppresses TNF-α-induced MMP-9 expression and cell invasion through inhibition of ERK/AP-1 signaling pathway in MCF-7 cells

Recent studies have shown that dopamine plays an important role in several types of cancer by inhibiting cell growth and invasion via dopamine receptors (DRs), such as dopamine receptor D2. However, the roles of DR agonists in cancer cell growth and invasion remain unclear. In our study, we found that apomorphine (APO), one of the most commonly prescribed DR agonists, inhibited TNF-α-induced matrix metalloprotease-9 (MMP-9) expression and cell invasion in MCF-7 human breast carcinoma cells through DR-independent pathways. Further mechanistic studies demonstrated that APO suppresses TNF-α-induced transcription of MMP-9 by inhibiting activator protein-1 (AP-1), a well-described transcription factor. This is achieved via extracellular signal-regulated kinases 1 and 2 (ERK1/2). Our study has demonstrated that APO targets human MMP-9 in a DR-independent fashion in MCF-7 cells, suggesting that APO is a potential anticancer agent that can suppress the metastatic progression of cancer cells.

Ivermectin: enigmatic multifaceted 'wonder' drug continues to surprise and exceed expectations

Over the past decade, the global scientific community have begun to recognize the unmatched value of an extraordinary drug, ivermectin, that originates from a single microbe unearthed from soil in Japan. Work on ivermectin has seen its discoverer, Satoshi Ōmura, of Tokyo's prestigious Kitasato Institute, receive the 2014 Gairdner Global Health Award and the 2015 Nobel Prize in Physiology or Medicine, which he shared with a collaborating partner in the discovery and development of the drug, William Campbell of Merck & Co. Incorporated. Today, ivermectin is continuing to surprise and excite scientists, offering more and more promise to help improve global public health by treating a diverse range of diseases, with its unexpected potential as an antibacterial, antiviral and anti-cancer agent being particularly extraordinary.

Molecular and Cellular Effects of Hydrogen Peroxide on Human Lung Cancer Cells: Potential Therapeutic Implications

Lung cancer has a very high mortality-to-incidence ratio, representing one of the main causes of cancer mortality worldwide. Therefore, new treatment strategies are urgently needed. Several diseases including lung cancer have been associated with the action of reactive oxygen species (ROS) from which hydrogen peroxide (H₂O₂) is one of the most studied. Despite the fact that H₂O₂ may have opposite effects on cell proliferation depending on the concentration and cell type, it triggers several antiproliferative responses. H₂O₂ produces both nuclear and mitochondrial DNA lesions, increases the expression of cell adhesion molecules, and increases p53 activity and other transcription factors orchestrating cancer cell death. In addition, H₂O₂ facilitates the endocytosis of oligonucleotides, affects membrane proteins, induces calcium release, and decreases cancer cell migration and invasion. Furthermore, the MAPK pathway and the expression of genes related to inflammation including interleukins, TNF-α, and NF-κB are also affected by H₂O₂. Herein, we will summarize the main effects of hydrogen peroxide on human lung cancer leading to suggesting it as a potential therapeutic tool to fight this disease. Because of the multimechanistic nature of this molecule, novel therapeutic approaches for lung cancer based on the use of H₂O₂ may help to decrease the mortality from this malignancy.