Review of Drug Repositioning Approaches and Resources

Next-generation drug repurposing using human genetics and network biology

Drug repurposing has attracted increased attention, especially in the context of drug discovery rates that remain too low despite a recent wave of approvals for biological therapeutics (e.g. gene therapy). These new biological entities-based treatments have high costs that are difficult to justify for small markets that include rare diseases. Drug repurposing, involving the identification of single or combinations of existing drugs based on human genetics data and network biology approaches represents a next-generation approach that has the potential to increase the speed of drug discovery at a lower cost. This Pharmacological Perspective reviews progress and perspectives in combining human genetics, especially genome-wide association studies, with network biology to drive drug repurposing for rare and common diseases with monogenic or polygenic etiologies. Also, highlighted here are important features of this next generation approach to drug repurposing, which can be combined with machine learning methods to meet the challenges of personalized medicine.

Critical View on the Usage of Ribavirin in Already Existing Psychostimulant-Use Disorder

Substance-use disorder represents a frequently hidden non-communicable chronic disease. Patients with intravenous drug addiction are at high risk of direct exposure to a variety of viral infections and are considered to be the largest subpopulation infected with the hepatitis C virus. Ribavirin is a synthetic nucleoside analog that has been used as an integral component of hepatitis C therapy. However, ribavirin medication is quite often associated with pronounced psychiatric adverse effects. It is not well understood to what extent ribavirin per se contributes to changes in drug-related neurobehavioral disturbances, especially in the case of psychostimulant drugs, such as amphetamine. It is now well-known that repeated amphetamine usage produces psychosis in humans and behavioral sensitization in animals. On the other hand, ribavirin has an affinity for adenosine A1 receptors that antagonistically modulate the activity of dopamine D1 receptors, which play a critical role in the development of behavioral sensitization. This review will focus on the current knowledge of neurochemical/ neurobiological changes that exist in the psychostimulant drug-addicted brain itself and the antipsychotic-like efficiency of adenosine agonists. Particular attention will be paid to the potential side effects of ribavirin therapy, and the opportunities and challenges related to its application in already existing psychostimulant-use disorder.

A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases

Millions of Americans are affected by rare diseases, many of which have poor survival rates. However, the small market size of individual rare diseases, combined with the time and capital requirements of pharmaceutical R&D, have hindered the development of new drugs for these cases. A promising alternative is drug repurposing, whereby existing FDA-approved drugs might be used to treat diseases different from their original indications. In order to generate drug repurposing hypotheses in a systematic and comprehensive fashion, it is essential to integrate information from across the literature of pharmacology, genetics, and pathology. To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). GNBR is a large, heterogeneous knowledge graph comprising drug, disease, and gene (or protein) entities linked by a small set of semantic themes derived from the abstracts of biomedical literature. We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. This approach achieves high performance on a gold-standard test set of known drug indications (AUROC = 0.89) and is capable of generating novel repurposing hypotheses, which we independently validate using external literature sources and protein interaction networks. Finally, we demonstrate the ability of our model to produce explanations of its predictions.

Machine learning applications in drug development

Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.

The Role of Drug Repurposing in the Development of Novel Antimicrobial Drugs: Non-Antibiotic Pharmacological Agents as Quorum Sensing-Inhibitors

Background: The emergence of multidrug-resistant organisms (MDROs) is a global public health issue, severely hindering clinicians in administering appropriate antimicrobial therapy. Drug repurposing is a drug development strategy, during which new pharmacological applications are identified for already approved drugs. From the viewpoint of the development of virulence inhibitors, inhibition of quorum sensing (QS) is a promising route because various important features in bacterial physiology and virulence are mediated by QS-dependent gene expression. Methods: Forty-five pharmacological agents, encompassing a wide variety of different chemical structures and mechanisms of action, were tested during our experiments. The antibacterial activity of the compounds was tested using the broth microdilution method. Screening and semi-quantitative assessment of QS-inhibition by the compounds was performed using QS-signal molecule-producing and indicator strains. Results: Fourteen pharmaceutical agents showed antibacterial activity in the tested concentration range, while eight drugs (namely 5-fluorouracil, metamizole-sodium, cisplatin, methotrexate, bleomycin, promethazine, chlorpromazine, and thioridazine) showed dose-dependent QS-inhibitory activity in the in vitro model systems applied during the experiments. Conclusions: Virulence inhibitors represent an attractive alternative strategy to combat bacterial pathogens more efficiently. Some of the tested compounds could be considered potential QS-inhibitory agents, warranting further experiments involving additional model systems to establish the extent of their efficacy.

Repurposing of Iloperidone: Antihypertensive and ocular hypotensive activity in animals

PURPOSE

Iloperidone, second generation antipsychotic drug, reported in clinical trial to produce orthostatic hypotension as side effect. It was claimed to be antagonistic at alpha adrenergic receptor in central nervous system. We evaluated effect of Iloperidone on peripheral alpha 1 adrenoreceptor by in silico and in vitro methods while in vivo hypotensive, antihypertensive and ocular hypotensive activity was evaluated in animals.

METHODS

Pharmacological activity prediction of Iloperidone was done using PASSOnline and SwissTargetPrediction softwares and molecular docking with Alpha 1A adrenoreceptor using AutoDock Vina. Hypotensive activity in normotensive and antihypertensive activity against DOCA-salt induced hypertension in rats were evaluated at doses 0.03 mg/Kg and 0.1 mg/Kg, i.p of Iloperidone. Blood pressure was measured by invasive blood pressure measurement technique using PowerLab 4/30 and intraocular pressure was measured using digital tonometer.

RESULTS

Iloperidone (0.1 mg/Kg) showed significant decrease in blood pressure (38.96 ± 1.1%) in normotensive rats, while in DOCA salt induced hypertensive rats, systolic blood pressure was found to be decreased by 29.04 ± 1.45% and 31.43 ± 1.21% in 0.03 mg/Kg and 0.1 mg/Kg treated rats respectively. Iloperidone prevented rise in systolic BP with adrenaline. Intraocular pressure was found to be decreased by 36.66 ± 3.15% in rabbits after 1 h of instillation of 0.1% Iloperidone.

CONCLUSION

Iloperidone exerted hypotensive and/or anti-hypertensive activity in rats and ocular hypotensive activity in rabbits.

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.

The assessment of efficient representation of drug features using deep learning for drug repositioning

BACKGROUND

De novo drug discovery is a time-consuming and expensive process. Nowadays, drug repositioning is utilized as a common strategy to discover a new drug indication for existing drugs. This strategy is mostly used in cases with a limited number of candidate pairs of drugs and diseases. In other words, they are not scalable to a large number of drugs and diseases. Most of the in-silico methods mainly focus on linear approaches while non-linear models are still scarce for new indication predictions. Therefore, applying non-linear computational approaches can offer an opportunity to predict possible drug repositioning candidates.

RESULTS

In this study, we present a non-linear method for drug repositioning. We extract four drug features and two disease features to find the semantic relations between drugs and diseases. We utilize deep learning to extract an efficient representation for each feature. These representations reduce the dimension and heterogeneity of biological data. Then, we assess the performance of different combinations of drug features to introduce a pipeline for drug repositioning. In the available database, there are different numbers of known drug-disease associations corresponding to each combination of drug features. Our assessment shows that as the numbers of drug features increase, the numbers of available drugs decrease. Thus, the proposed method with large numbers of drug features is as accurate as small numbers.

CONCLUSION

Our pipeline predicts new indications for existing drugs systematically, in a more cost-effective way and shorter timeline. We assess the pipeline to discover the potential drug-disease associations based on cross-validation experiments and some clinical trial studies.

Drug repurposing in neurological diseases: an integrated approach to reduce trial and error

Identifying effective disease-modifying therapies for neurological diseases remains an important challenge in drug discovery and development. Drug repurposing attempts to determine new indications for pre-existing compounds and represents a major opportunity to address this clinically unmet need. It is potentially more cost-effective and time-efficient than de novo drug development and has yielded notable successes in neurological disorders. However, across all medical disciplines, only 30% of repurposed drugs, and 10% of novel candidate molecules, gain market approval. One potentially significant contributor towards this limited success rate is an incomplete knowledge of the exposure-response relationships for the compounds of interest, and how these relate to the new indication, prior to commencing a new trial. We provide an overview of the current approach to early-stage drug repurposing and consider the issues contributing to inconclusive, or possibly falsely negative, Phase II and III trial outcomes in neurological diseases by highlighting examples that illustrate the limitations of empirical evidence generation without a strong scientific basis for the dose rationale. We conclude with a framework suggesting a translational, iterative approach, that integrates pharmacological, pharmaceutical and clinical expertise, towards preclinical and early clinical drug development. This ensures appropriate dosing regimen, route of administration and/or formulation are selected for the new indication before their evaluation in prospective clinical trials.

Repurposing Drugs by In Silico Methods to Target BCR Kinase Domain in Chronic Myeloid Leukemia

BACKGROUND

Targeted therapy in the form of highly selective tyrosine kinase inhibitors (TKIs) has transformed the treatment of chronic myeloid leukemia (CML). However, mutations in the kinase domain contribute to drug resistance against TKIs which compromises the treatment response. Our aim is to explore regions outside the BCR-ABL oncoprotein to identify potential therapeutic targets to curb drug resistance by targeting growth factor receptor-bound protein-2 (Grb-2) which binds to BCR-ABL at the phosphorylated tyrosine (Y177) thereby activating the Ras and PI3K/AKT signaling pathway.

METHODS

We have used in silico methods to repurpose drugs for identifying their potential to inhibit the binding of Grb-2 with Y177 by occupying the active binding site of the BCR domain.

RESULTS

Differentially expressed genes from GEO dataset were found to be associated with hematopoietic cell lineage, NK cell-mediated cytotoxicity, NF-κB and chemokine signaling, cytokine-cytokine receptor interaction, histidine metabolism and transcriptional misregulation in cancer. The fold recognition method of SPARKS-X tool was used to model the BCR domain (Z-score = 8.21). Connectivity Map generated a drug list based on the gene expression profile, which were docked with BCR. Schrodinger XP glide docking identified Diphosphopyridine nucleotide, Hesperidin, Butirosin, Ovoflavin, and Nor-dihydroguaiaretic acid to show strong interaction in close proximity to the active binding pocket containing Y177 of the target protein and was further validated using iGEMDOCK and Parallelized Open Babel and AutoDock suite Pipeline (POAP).

CONCLUSION

Our study not only extends our current knowledge about repurposing drugs for newer indications but also provides a route towards combinatorial therapy with standard drugs used for CML treatment. However, the efficacy of these repurposed drugs needs to be further investigated using in vitro and in vivo studies.<br />.

Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics

Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as game-changing technologies, AI and machine learning algorithms have slowly but surely begun to revolutionize pharmaceutical industry and drug development over the past 5 years. In this expert review, we describe the most frequently used machine learning algorithms in drug development pipelines and the -omics databases well poised to support machine learning and drug discovery. Subsequently, we analyze the emerging new computational approaches to drug discovery and the in silico pipelines for drug repositioning and the synergies among -omics system sciences, AI and machine learning. As with system sciences, AI and machine learning embody a system scale and Big Data driven vision for drug discovery and development. We conclude with a future outlook on the ways in which machine learning approaches can be implemented to buttress and expedite drug discovery and precision medicine. As AI and machine learning are rapidly entering pharmaceutical industry and the art and science of drug development, we need to critically examine the attendant prospects and challenges to benefit patients and public health.

Novel Antiviral Activities of Obatoclax, Emetine, Niclosamide, Brequinar, and Homoharringtonine

Viruses are the major causes of acute and chronic infectious diseases in the world. According to the World Health Organization, there is an urgent need for better control of viral diseases. Repurposing existing antiviral agents from one viral disease to another could play a pivotal role in this process. Here, we identified novel activities of obatoclax and emetine against herpes simplex virus type 2 (HSV-2), echovirus 1 (EV1), human metapneumovirus (HMPV) and Rift Valley fever virus (RVFV) in cell cultures. Moreover, we demonstrated novel activities of emetine against influenza A virus (FLUAV), niclosamide against HSV-2, brequinar against human immunodeficiency virus 1 (HIV-1), and homoharringtonine against EV1. Our findings may expand the spectrum of indications of these safe-in-man agents and reinforce the arsenal of available antiviral therapeutics pending the results of further in vitro and in vivo tests.

An Integrated System Biology Approach Yields Drug Repositioning Candidates for the Treatment of Heart Failure

Identifying effective pharmacological treatments for heart failure (HF) patients remains critically important. Given that the development of drugs de novo is expensive and time consuming, drug repositioning has become an increasingly important branch. In the present study, we propose a two-step drug repositioning pipeline and investigate the novel therapeutic effects of existing drugs approved by the US Food and Drug Administration to discover potential therapeutic drugs for HF. In the first step, we compared the gene expression pattern of HF patients with drug-induced gene expression profiles to obtain preliminary candidates. In the second step, we performed a systems biology approach based on the known protein–protein interaction information and targets of drugs to narrow down preliminary candidates to obtain final candidates. Drug set enrichment analysis and literature search were applied to assess the performance of our repositioning approach. We also constructed a mode of action network for each candidate and performed pathway analysis for each candidate using genes contained in their mode of action network to uncover pathways that potentially reflect the mechanisms of candidates’ therapeutic efficacy to HF. We discovered numerous preliminary candidates, some of which are used in clinical practice and supported by the literature. The final candidates contained nearly all of the preliminary candidates supported by previous studies. Drug set enrichment analysis and literature search support the validity of our repositioning approach. The mode of action network for each candidate not only displayed the underlying mechanisms of drug efficacy but also uncovered potential biomarkers and therapeutic targets for HF. Our two-step drug repositioning approach is efficient to find candidates with potential therapeutic efficiency to HF supported by the literature and might be of particular use in the discovery of novel effective pharmacological therapies for HF.

Spironolactone, a Classic Potassium-Sparing Diuretic, Reduces Survivin Expression and Chemosensitizes Cancer Cells to Non-DNA-Damaging Anticancer Drugs

Spironolactone, a classical diuretic drug, is used to treat tumor-associated complications in cancer patients. Spironolactone was recently reported to exert anti-cancer effects by suppressing DNA damage repair. However, it currently remains unclear whether spironolactone exerts combinational effects with non-DNA-damaging anti-cancer drugs, such as gemcitabine and epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Using the cancer cells of lung cancer, pancreatic cancer, and glioblastoma, the combinational effects of spironolactone with gemcitabine and osimertinib, a third-generation EGFR-TKI, were examined in vitro with cell viability assays. To elucidate the underlying mechanisms, we investigated alterations induced in survivin, an anti-apoptotic protein, by spironolactone as well as the chemosensitization effects of the suppression of survivin by YM155, an inhibitor of survivin, and siRNA. We also examined the combinational effects in a mouse xenograft model. The results obtained revealed that spironolactone augmented cell death and the suppression of cell growth by gemcitabine and osimertinib. Spironolactone also reduced the expression of survivin in these cells, and the pharmacological and genetic suppression of survivin sensitized cells to gemcitabine and osimertinib. This combination also significantly suppressed tumor growth without apparent adverse effects in vivo. In conclusion, spironolactone is a safe candidate drug that exerts anti-cancer effects in combination with non-DNA-damaging drugs, such as gemcitabine and osimertinib, most likely through the suppression of survivin.

Activation of pCREB/Nrf-2 signaling mediates re-positioning of liraglutide as hepato-protective for methotrexate -induced liver injury (MILI)

Methotrexate (MTX) is commonly used to treat several types of cancer and autoimmune diseases. However, there is increasing concern over its organs toxicities particularly liver toxicity. Liraglutide, a glucagon like peptide-1 agonist, possesses antioxidant and anti-inflammatory features. This study aimed to explore the potential protective effect of liraglutide pre-treatment in ameliorating MTX-induced hepatotoxicity and to further investigate the underlying mechanisms. Rats received 1.2 mg/kg liraglutide intraperitoneal twice daily for 7 days before MTX. Results revealed that liraglutide significantly decreased activities of liver enzymes and oxidative stress in hepatocytes. Furthermore, NF-kB expression and related inflammatory markers (TNF-α, COX-2 and IL-6) were reduced in the pre-treatment group of liraglutide. These data validate the advantageous effects of liraglutide in MTX hepatotoxic animals. In addition, liraglutide increased the expression of the antioxidant transcription factor nuclear factor-erythroid 2-related factor 2 (Nrf-2), along with the transcription of downstream phosphorylated cAMP response element-binding protein (pCREB) which increases the activity of Nrf-2. Additionally, caspase-3 expression/activity and BAX/Bcl-2 ratio were decreased following liraglutide pre-treatment. In conclusion, it was confirmed that liraglutide enhanced the antioxidant activity of liver cells by activating the Nrf-2 and pCREB signaling, thereby, reducing liver cell inflammation and apoptosis induced by MTX.

Systems biology based drug repositioning for development of cancer therapy

Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.

Computational Drug Repurposing Algorithm Targeting TRPA1 Calcium Channel as a Potential Therapeutic Solution for Multiple Sclerosis

Multiple sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system (CNS) through neurodegeneration and demyelination, leading to physical/cognitive disability and neurological defects. A viable target for treating MS appears to be the Transient Receptor Potential Ankyrin 1 (TRPA1) calcium channel, whose inhibition has been shown to have beneficial effects on neuroglial cells and protect against demyelination. Using computational drug discovery and data mining methods, we performed an in silico screening study combining chemical graph mining, quantitative structure-activity relationship (QSAR) modeling, and molecular docking techniques in a global prediction model in order to identify repurposable drugs as potent TRPA1 antagonists that may serve as potential treatments for MS patients. After screening the DrugBank database with the combined generated algorithm, 903 repurposable structures were selected, with 97 displaying satisfactory inhibition probabilities and pharmacokinetics. Among the top 10 most probable inhibitors of TRPA1 with good blood brain barrier (BBB) permeability, desvenlafaxine, paliperidone, and febuxostat emerged as the most promising repurposable agents for treating MS. Molecular docking studies indicated that desvenlafaxine, paliperidone, and febuxostat are likely to induce allosteric TRPA1 channel inhibition. Future in vitro and in vivo studies are needed to confirm the biological activity of the selected hit molecules.

A paradigm shift in medicine: A comprehensive review of network-based approaches

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Insights into Computational Drug Repurposing for Neurodegenerative Disease

Computational drug repurposing has the ability to remarkably reduce drug development time and cost in an era where these factors are prohibitively high. Several examples of successful repurposed drugs exist in fields such as oncology, diabetes, leprosy, inflammatory bowel disease, among others, however computational drug repurposing in neurodegenerative disease has presented several unique challenges stemming from the lack of validation methods and difficulty in studying heterogenous diseases of aging. Here, we examine existing approaches to computational drug repurposing, including molecular, clinical, and biophysical methods, and propose data sources and methods to advance computational drug repurposing in neurodegenerative disease using Alzheimer's disease as an example.

Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer

Purpose: Most prostate cancers (PCs) initially respond to androgen deprivation therapy (ADT), but eventually many PC patients develop castration resistant PC (CRPC). Currently, available drugs that have been approved for the treatment of CRPC patients are limited. Computational drug repositioning methods using public databases represent a promising and efficient tool for discovering new uses for existing drugs. The purpose of the present study is to predict drug candidates that can treat CRPC using a computational method that integrates publicly available gene expression data of tumors from CRPC patients, drug-induced gene expression data and drug response activity data. Methods: Gene expression data from tumoral and normal or benign prostate tissue samples in CRPC patients were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in CRPC were determined with a meta-signature analysis by a metaDE R package. Additionally, drug activity data were downloaded from the ChEMBL database. Furthermore, the drug-induced gene expression data were downloaded from the LINCS database. The reversal relationship between the CRPC and drug gene expression signatures as the Reverse Gene Expression Scores (RGES) were computed. Drug candidates to treat CRPC were predicted using summarized scores (sRGES). Additionally, synergic effects of drug combinations were predicted with a Target Inhibition interaction using the Minimization and Maximization Averaging (TIMMA) algorithm. Results: The drug candidates of sorafenib, olaparib, elesclomol, tanespimycin, and ponatinib were predicted to be active for the treatment of CRPC. Meanwhile, CRPC-related genes, in this case MYL9, E2F2, APOE, and ZFP36, were identified as having gene expression data that can be reversed by these drugs. Additionally, lenalidomide in combination with pazopanib was predicted to be most potent for CRPC. Conclusion: These findings support the use of a computational reversal gene expression approach to identify new drug and drug combination candidates that can be used to treat CRPC.

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods

Recent data indicate that up-to 30–40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have been identified through their molecular biological network commonality with clinically approved anti-cancer therapies. A machine-learning algorithm of random walks on graphs (operating within the supercomputing DreamLab platform) was used to simulate drug actions on human interactome networks to obtain genome-wide activity profiles of 1962 approved drugs (199 of which were classified as “anti-cancer” with their primary indications). A supervised approach was employed to predict cancer-beating molecules using these ‘learned’ interactome activity profiles. The validated model performance predicted anti-cancer therapeutics with classification accuracy of 84–90%. A comprehensive database of 7962 bioactive molecules within foods was fed into the model, which predicted 110 cancer-beating molecules (defined by anti-cancer drug likeness threshold of >70%) with expected capacity comparable to clinically approved anti-cancer drugs from a variety of chemical classes including flavonoids, terpenoids, and polyphenols. This in turn was used to construct a ‘food map’ with anti-cancer potential of each ingredient defined by the number of cancer-beating molecules found therein. Our analysis underpins the design of next-generation cancer preventative and therapeutic nutrition strategies.

Pathway Interactions Based on Drug-Induced Datasets

In this study, we identified enrichment pathway connections from MCF7 breast cancer epithelial cells that were treated with 87 drugs. We extracted drug-treated samples, where the sample size was greater than or equal to 5. The drugs included 17-allylamino-geldanamycin, LY294002, trichostatin A, valproic acid, sirolimus, and wortmannin, which had sample sizes of 11, 8, 7, 7, 7, and 5, respectively. We found meaningful pathways using gene set enrichment analysis and identified intradrug and interdrug pathway interactions, which implied the influence of drug combination. Among the top 20 enrichment pathways that were wortmannin induced, there were a total of 37 intradrug pathway interactions via common genes. Thirty-seven pathway interactions were induced by valproic acid, 11 induced by trichostatin A, 20 induced by LY294002, and 59 induced by sirolimus, all via common genes. The number of interdrug-induced pathway interactions ranged from one pair of pathways to 23. The pair of ERBB_SIGNALING and INSULIN_SIGNALING pathways showed the highest score from a pair of 2 individual drugs. The highest number of pathway interactions was observed between the drugs 17-allylamino-geldanamycin and LY294002.

Use of big data in drug development for precision medicine: an update

INTRODUCTION

Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios.

AREAS COVERED

Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery.

EXPERT OPINION

In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.

Controlled and tuneable drug release from electrospun fibers and a non-invasive approach for cytotoxicity testing

Electrospinning is an attractive method to generate drug releasing systems. In this work, we encapsulated the cell death-inducing drug Diclofenac (DCF) in an electrospun poly-L-lactide (PLA) scaffold. The scaffold offers a system for a sustained and controlled delivery of the cytotoxic DCF over time making it clinically favourable by achieving a prolonged therapeutic effect. We exposed human dermal fibroblasts (HDFs) to the drug-eluting scaffold and employed multiphoton microscopy and fluorescence lifetime imaging microscopy. These methods were suitable for non-invasive and marker-independent assessment of the cytotoxic effects. Released DCF induced changes in cell morphology and glycolytic activity. Furthermore, we showed that drug release can be influenced by adding dimethyl sulfoxide as a co-solvent for electrospinning. Interestingly, without affecting the drug diffusion mechanism, the resulting PLA scaffolds showed altered fibre morphology and enhanced initial DCF burst release. The here described model could represent an interesting way to control the diffusion of encapsulated bio-active molecules and test them using a marker-independent, non-invasive approach.

Re-positioning of known drugs for Pim-1 kinase target using molecular docking analysis

The Concept of reusing existing drugs for new targets is gaining momentum in recent years because of cost-effectiveness as safety and toxicology data are already available. Therefore, it is of interest to re-profile known drugs against the Pim-1 kinase target using molecular docking analysis. Results show that known drugs such as nilotinib, vemurafenib, Idelalisib, and other small kinases inhibitors have high binding ability with Pim-1 kinase for consideration as potential inhibitors.

Identification of repurposed protein kinase B binders from FDA-approved drug library: a hybrid-structure activity relationship and systems modeling based approach

Food and Drug Administration (FDA)-approved drugs may be repurposed against those diseases, for which their therapeutic action has not been described. The present study deals with repurposing FDA-approved drugs for selective targeting of protein kinase B (PKB/Akt) for anti-cancer activity, through a two-tier (Cell and Target) model hybridization protocol implemented with support vector machine-based learning method. The hybridization was done as per rules of reaction kinetics. The hybridization process was facilitated as a standalone application for free access at https://github.com/undwivedi/Akt-Selective.git. The selectivity of the ligands for PKB/Akt binding was also evaluated on the basis of mitophagy system model for anti-apoptotic activity. Screening of the FDA-approved drug library, using the developed H- SAR model, led to identification of four compounds (Cas nos. 94749-08-3, 57808-66-9, 62-13-5, 76-43-7), bearing the selectivity for PKB/Akt. Since, the identified compounds have already crossed the barriers of absorption, distribution, metabolism, excretion, toxicity in clinical trials, therefore are safe to be considered for repurposing individually or in combination with other drugs.Communicated by Ramaswamy H. Sarma.

Computational Drug Repositioning for Gastric Cancer using Reversal Gene Expression Profiles

Treatment of gastric cancer (GC) often produces poor outcomes. Moreover, predicting which GC treatments will be effective remains challenging. Computational drug repositioning using public databases is a promising and efficient tool for discovering new uses for existing drugs. Here we used a computational reversal of gene expression approach based on effects on gene expression signatures by GC disease and drugs to explore new GC drug candidates. Gene expression profiles for individual GC tumoral and normal gastric tissue samples were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in GC were determined with a meta-signature analysis. Profiles drug activity and drug-induced gene expression were downloaded from the ChEMBL and the LINCS databases, respectively. Candidate drugs to treat GC were predicted using reversal gene expression score (RGES). Drug candidates including sorafenib, olaparib, elesclomol, tanespimycin, selumetinib, and ponatinib were predicted to be active for treatment of GC. Meanwhile, GC-related genes such as PLOD3, COL4A1, UBE2C, MIF, and PRPF5 were identified as having gene expression profiles that can be reversed by drugs. These findings support the use of a computational reversal gene expression approach to identify new drug candidates that can be used to treat GC.

Drug repositioning in epilepsy reveals novel antiseizure candidates

Objective

Epilepsy treatment falls short in ~30% of cases. A better understanding of epilepsy pathophysiology can guide rational drug development in this difficult to treat condition. We tested a low-cost, drug-repositioning strategy to identify candidate epilepsy drugs that are already FDA-approved and might be immediately tested in epilepsy patients who require new therapies.

Methods

Biopsies of spiking and nonspiking hippocampal brain tissue from six patients with unilateral mesial temporal lobe epilepsy were analyzed by RNA-Seq. These profiles were correlated with transcriptomes from cell lines treated with FDA-approved drugs, identifying compounds which were tested for therapeutic efficacy in a zebrafish seizure assay.

Results

In spiking versus nonspiking biopsies, RNA-Seq identified 689 differentially expressed genes, 148 of which were previously cited in articles mentioning seizures or epilepsy. Differentially expressed genes were highly enriched for protein-protein interactions and formed three clusters with associated GO-terms including myelination, protein ubiquitination, and neuronal migration. Among the 184 compounds, a zebrafish seizure model tested the therapeutic efficacy of doxycycline, metformin, nifedipine, and pyrantel tartrate, with metformin, nifedipine, and pyrantel tartrate all showing efficacy.

Interpretation

This proof-of-principle analysis suggests our powerful, rapid, cost-effective approach can likely be applied to other hard-to-treat diseases.

Nifuroxazide induces apoptosis, inhibits cell migration and invasion in osteosarcoma

Osteosarcoma is the most common primary malignancy of bone and characterized by an appendicular primary tumor with a high rate of metastasis to the lungs. Unfortunately, there is no effective strategy to treat osteosarcoma in current clinical practice. In this study, the anticancer effects and potential mechanisms of nifuroxazide, an oral nitrofuran antibiotic, on two osteosarcoma cell lines were investigated. The results of the antiproliferative activity in vitro showed that nifuroxazide inhibited cell proliferation of UMR106 and MG63 cells in a dose- and time-dependent manner. Interestingly, nifuroxazide showed low toxicity to non-tumor cells (HEK 293 T). In addition, ROS-mitochondrial mediated apoptosis was observed after treatment of nifuroxazide. Moreover, nifuroxazide could significantly inhibit osteosarcoma cells migration and invasion via p-Stat3, MMP-2 and MMP-9 mediated signaling pathway. Taken together, our results suggested that nifuroxazide could be a promising agent for osteosarcoma treatment by inhibiting cell proliferation, inducing cell apoptosis and impairing cell migration and invasion.