Drug Repositioning for Effective Prostate Cancer Treatment

Prostate cancer and the cell cycle: Focusing on the role of microRNAs

Prostate cancer is the most frequent solid tumor in terms of incidence and ranks second only to lung cancer in terms of cancer mortality among men. It has a considerably high mortality rate; around 375,000 deaths occurred worldwide in 2020. In 2024, the American Cancer Society estimated that the number of new prostate cancer cases will be around 299,010 cases, and the estimated deaths will be around 32,250 deaths only in the USA. Cell cycle dysregulation is inevitable in cancer etiology and is targeted by various therapies in cancer treatment. MicroRNAs (miRNAs) are small, endogenous, non-coding regulatory molecules involved in both normal and abnormal cellular events. One of the cellular processes regulated by miRNAs is the cell cycle. Although there are some exceptions, tumor suppressor miRNAs could potentially arrest the cell cycle by downregulating several molecular machineries involved in catalyzing the cell cycle progression. In contrast, oncogenic miRNAs (oncomirs) help the cell cycle to progress by targeting various regulatory proteins such as retinoblastoma (Rb) or cell cycle inhibitors such as p21 or p27, and hence may contribute to prostate cancer progression; however, this is not always the case. In this review, we emphasize how a dysregulated miRNA expression profile is linked to an abnormal cell cycle progression in prostate cancer, which subsequently paves the way to a new therapeutic option for prostate cancer.

DiPPI: A Curated Data Set for Drug-like Molecules in Protein-Protein Interfaces

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.

In silico exploration of anti-prostate cancer compounds from differential expressed genes

Prostate cancer (PCa) is a complex and biologically diverse disease with no curative treatment options at present. This study aims to utilize computational methods to explore potential anti-PCa compounds based on differentially expressed genes (DEGs), with the goal of identifying novel therapeutic indications or repurposing existing drugs. The methods employed in this study include DEGs-to-drug prediction, pharmacokinetics prediction, target prediction, network analysis, and molecular docking. The findings revealed a total of 79 upregulated DEGs and 110 downregulated DEGs in PCa, which were used to identify drug compounds capable of reversing the dysregulated conditions (dexverapamil, emetine, parthenolide, dobutamine, terfenadine, pimozide, mefloquine, ellipticine, and trifluoperazine) at a threshold probability of 20% on several molecular targets, such as serotonin receptors 2a/2b/2c, HERG protein, adrenergic receptors alpha-1a/2a, dopamine D3 receptor, inducible nitric oxide synthase (iNOS), epidermal growth factor receptor erbB1 (EGFR), tyrosine-protein kinases, and C-C chemokine receptor type 5 (CCR5). Molecular docking analysis revealed that terfenadine binding to inducible nitric oxide synthase (-7.833 kcal.mol-1) and pimozide binding to HERG (-7.636 kcal.mol-1). Overall, binding energy ΔGbind (Total) at 0 ns was lower than that of 100 ns for both the Terfenadine-iNOS complex (-101.707 to -103.302 kcal.mol-1) and Ellipticine-TOPIIα complex (-42.229 to -58.780 kcal.mol-1). In conclusion, this study provides insight on molecular targets that could possibly contribute to the molecular mechanisms underlying PCa. Further preclinical and clinical studies are required to validate the therapeutic effectiveness of these identified drugs in PCa disease.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

System biology approaches for drug repurposing

Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.

Liposomes for the treatment of prostate cancer therapy: A review

One of the cancers that affect men, prostate cancer considerably raises mortality rates for males around the world. Patients with prostate cancer can have a localized or advanced form of the illness. Digital rectal examinations, prostate-specific antigen analyses, and prostate biopsies are all used to identify prostate cancer. The onset, development, and spread of cancer are all correlated with mutations in specific genes. Radical prostatectomy, ablative radiation, and active surveillance are all forms of treatment for localized prostate cancer. Androgen deprivation therapy (ADT), radiation, and chemotherapy are given to men who have metastatic prostate cancer or have experienced a relapse. When compared to traditional cancer chemotherapeutic methods, the liposome-based drug delivery technology offers less toxic, biodegradable, and biocompatible nanomedicine. Liposomes offer great advantages for use in nanomedicines by improving the sensitivity, specificity, and persistence of these anti-malignant cell agents in the body. Liposomal formulations are undergoing clinical trials of variety of cancers including prostate cancer. The present narrative review describes the composition and types of liposomes, its advantages, disadvantages, and the methods of preparation, research studies, clinical applications, drug repurposing and administration.

Comprehensive Review on Recent Strategies for Management of Prostate Cancer: Therapeutic Targets and SAR

Prostate cancer is a disease that is affecting a large population worldwide. Androgen deprivation therapy (ADT) has become a foundation for the treatment of advanced prostate cancer, as used in most clinical settings from neo-adjuvant to metastatic stage. In spite of the success of ADT in managing the disease in the majority of men, hormonal manipulation fails eventually. New molecules are developed for patients with various hormone-refractory diseases. Advancements in molecular oncology have increased understanding of numerous cellular mechanisms which control cell death in the prostate and these insights can lead to the development of more efficacious and tolerable therapies for carcinoma of the prostate. This review is focused on numerous therapies that might be a boon for prostate therapy like signaling inhibitors, vaccines, and inhibitors of androgen receptors. Along with these, various bioactive molecules and their derivatives are highlighted, which act as potential antiprostate cancer agents. This article also emphasized the recent advances in the field of medicinal chemistry of prostate cancer agents.

Entecavir: A Review and Considerations for Its Application in Oncology

Entecavir (ETV) is a drug used as a first-line treatment for chronic hepatitis B (CHB) virus infection because it is a guanosine nucleoside analogue with activity against the hepatitis B virus polymerase. The ETV dosage can range from 0.5 mg to 1 mg once a day and the most common side effects include headache, insomnia, fatigue, dizziness, somnolence, vomiting, diarrhea, nausea, dyspepsia, and increased liver enzyme levels. In addition to its conventional use, ETV acts as an inhibitor of lysine-specific demethylase 5B (KDM5B), an enzyme that is overexpressed in breast, lung, skin, liver, and prostate tumors and is involved in the hormonal response, stem cell regeneration, genomic stability, cell proliferation, and differentiation. The KDM5B enzyme acts as a transcriptional repressor in tumor suppressor genes, silencing them, and its overexpression leads to drug resistance in certain tumor types. Furthermore, the literature suggests that KDM5B activates the PI3K/AKT signaling pathway, while reducing KDM5B expression decreases AKT signaling, resulting in decreased tumor cell proliferation. In silico studies have demonstrated that ETV can inhibit tumor cell proliferation and induce apoptosis by reducing KDM5B expression. ETV also appears to inhibit PARP-1, has a high genetic barrier, reducing the chance of resistance development, and can also prevent the reactivation of the hepatitis B virus in cancer patients, which have proven to be significant advantages regarding its use as a repurposed drug in oncology. Therefore, ETV holds promise beyond its original therapeutic indication.

Prostate Cancer: Genetics, Epigenetics and the Need for Immunological Biomarkers

Epidemiological data highlight prostate cancer as a significant global health issue, with high incidence and substantial impact on patients' quality of life. The prevalence of this disease is associated with various factors, including age, heredity, and race. Recent research in prostate cancer genetics has identified several genetic variants that may be associated with an increased risk of developing the disease. However, despite the significance of these findings, genetic markers for prostate cancer are not currently utilized in clinical practice as reliable indicators of the disease. In addition to genetics, epigenetic alterations also play a crucial role in prostate cancer development. Aberrant DNA methylation, changes in chromatin structure, and microRNA (miRNA) expression are major epigenetic events that influence oncogenesis. Existing markers for prostate cancer, such as prostate-specific antigen (PSA), have limitations in terms of sensitivity and specificity. The cost of testing, follow-up procedures, and treatment for false-positive results and overdiagnosis contributes to the overall healthcare expenditure. Improving the effectiveness of prostate cancer diagnosis and prognosis requires either narrowing the risk group by identifying new genetic factors or enhancing the sensitivity and specificity of existing markers. Immunological biomarkers (both circulating and intra-tumoral), including markers of immune response and immune dysfunction, represent a potentially useful area of research for enhancing the diagnosis and prognosis of prostate cancer. Our review emphasizes the need for developing novel immunological biomarkers to improve the diagnosis, prognosis, and management of prostate cancer. We highlight the most recent achievements in the identification of biomarkers provided by circulating monocytes and tumor-associated macrophages (TAMs). We highlight that monocyte-derived and TAM-derived biomarkers can enable to establish the missing links between genetic predisposition, hormonal metabolism and immune responses in prostate cancer.

Repurposing Drugs in Controlling Recurrent Platinum-Resistant Clear-Cell Ovarian Cancer

Background

Recurrent platinum-resistant clear-cell ovarian cancer has a low overall survival duration of 7-8 months, making it a fatal disease. Currently, chemotherapy is the major kind of treatment, but it offers little advantage. Repurposed conventional drugs have recently been found to offer the ability to control cancer with few side effects and at a reasonable cost to healthcare organizations. Case Presentation. In this case report, we present the case of a 41-year-old Thai female patient diagnosed with recurrent platinum-resistant clear-cell ovarian cancer (PRCCC) in the year 2020. After undergoing chemotherapy for two courses and failing to respond to treatment, she began alternative medicine with repurposing drugs in November 2020. Simvastatin, metformin, niclosamide, mebendazole, itraconazole, loratadine, and chloroquine were also administered. Two months after therapy, a computerized tomography (CT) scan revealed a conflict between a decline in tumor marker levels (CA 125, CA 19-9) and an increase in the number of lymph nodes. However, after continuing all medications for 4 months, the CA 125 level decreased from 303.6 to 54 U/ml, and the CA 19-9 level decreased from 1210.3 to 386.10 U/ml. The patient's EQ-5D-5L score increased from 0.631 to 0.829 (abdominal pain and depression), indicating improved quality of life. Overall survival was 8.5 months, and progression-free survival was 2 months.

Conclusion

The response to drug repurposing is demonstrated by a four-month-long improvement in symptoms. This work introduces a novel strategy for the management of recurrent platinum-resistant clear-cell ovarian cancer that needs further evaluation in large-scale studies.

Pharmacological Efficacy of Repurposing Drugs in the Treatment of Prostate Cancer

Worldwide, prostate cancer (PC) is the second most frequent cancer among men and the fifth leading cause of death; moreover, standard treatments for PC have several issues, such as side effects and mechanisms of resistance. Thus, it is urgent to find drugs that can fill these gaps, and instead of developing new molecules requiring high financial and time investments, it would be useful to select non-cancer approved drugs that have mechanisms of action that could help in PC treatment, a process known as repurposing drugs. In this review article, drugs that have potential pharmacological efficacy are compiled to be repurposed for PC treatment. Thus, these drugs will be presented in the form of pharmacotherapeutic groups, such as antidyslipidemic drugs, antidiabetic drugs, antiparasitic drugs, antiarrhythmic drugs, anti-inflammatory drugs, antibacterial drugs, antiviral drugs, antidepressant drugs, antihypertensive drugs, antifungal drugs, immunosuppressant drugs, antipsychotic drugs, antiepileptic and anticonvulsant drugs, bisphosphonates and drugs for alcoholism, among others, and we will discuss their mechanisms of action in PC treatment.

Drug Repurposing: A New Hope in Drug Discovery for Prostate Cancer

Prostate cancer (PCA), the most common cancer in men, accounted for 1.3 million new incidences in 2018. An increase in incidences is an issue of concern that should be addressed. Of all the reported prostate cancers, 85% were detected in stages III and IV, making them difficult to treat. Conventional drugs gradually lose their efficacy due to the developed resistance against them, thus requiring newer therapeutic agents to be used as monotherapy or combination. Recent research regarding treatment options has attained remarkable speed and development. Therefore, in this context, drug repurposing comes into the picture, which is defined as the "investigation of the off-patent, approved and marketed drugs for a novel therapeutic indication" which saves at least 30% of the time and cost, reducing the cost of treatment for patients, which usually runs high in cancer patients. The anticancer property of cardiac glycosides in cancers was tested in the early 1980s. The trend then shifts toward treating prostate cancer by repurposing other cardiovascular drugs. The current review mainly emphasizes the advantageous antiprostate cancer profile of conventional CVS drugs like cardiac glycosides, RAAS inhibitors, statins, heparin, and beta-blockers with underlying mechanisms.

A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures

Delivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.

Integrative Analysis of Motor Neuron and Microglial Transcriptomes from SOD1G93A Mice Models Uncover Potential Drug Treatments for ALS

Amyotrophic lateral sclerosis (ALS) is a fatal disease of motor neurons that mainly affects the motor cortex, brainstem, and spinal cord. Under disease conditions, microglia could possess two distinct profiles, M1 (toxic) and M2 (protective), with the M2 profile observed at disease onset. SOD1 (superoxide dismutase 1) gene mutations account for up to 20% of familial ALS cases. Comparative gene expression differences in M2-protective (early) stage SOD1^G93A microglia and age-matched SOD1^G93A motor neurons are poorly understood. We evaluated the differential gene expression profiles in SOD1^G93A microglia and SOD1^G93A motor neurons utilizing publicly available transcriptomics data and bioinformatics analyses, constructed biomolecular networks around them, and identified gene clusters as potential drug targets. Following a drug repositioning strategy, 5 small compounds (belinostat, auranofin, BRD-K78930611, AZD-8055, and COT-10b) were repositioned as potential ALS therapeutic candidates that mimic the protective state of microglia and reverse the toxic state of motor neurons. We anticipate that this study will provide new insights into the ALS pathophysiology linking the M2 state of microglia and drug repositioning.

Drug repurposing-an emerging strategy in cancer therapeutics

Cancer is a complex disease affecting millions of people around the world. Despite advances in surgical and radiation therapy, chemotherapy continues to be an important therapeutic option for the treatment of cancer. The current treatment is expensive and has several side effects. Also, over time, cancer cells develop resistance to chemotherapy, due to which there is a demand for new drugs. Drug repurposing is a novel approach that focuses on finding new applications for the old clinically approved drugs. Current advances in the high-dimensional multiomics landscape, especially proteomics, genomics, and computational omics-data analysis, have facilitated drug repurposing. The drug repurposing approach provides cheaper, effective, and safe drugs with fewer side effects and fastens the process of drug development. The review further delineates each repurposed drug's original indication and mechanism of action in cancer. Along with this, the article also provides insight upon artificial intelligence and its application in drug repurposing. Clinical trials are vital for determining medication safety and effectiveness, and hence the clinical studies for each repurposed medicine in cancer, including their stages, status, and National Clinical Trial (NCT) identification, are reported in this review article. Various emerging evidences imply that repurposing drugs is critical for the faster and more affordable discovery of anti-cancerous drugs, and the advent of artificial intelligence-based computational tools can accelerate the translational cancer-targeting pipeline.

Prostate Cancer Review: Genetics, Diagnosis, Treatment Options, and Alternative Approaches

Simple Summary

Prostate cancer affects men of all racial and ethnic groups and leads to higher rates of mortality in those belonging to a lower socioeconomic status due to late detection of the disease. There is growing evidence that suggests the contribution of an individual’s genetic profile to prostate cancer. Currently used prostate cancer treatments have serious adverse effects; therefore, new research is focusing on alternative treatment options such as the use of genetic biomarkers for targeted gene therapy, nanotechnology for controlled targeted treatment, and further exploring medicinal plants for new anticancer agents. In this review, we describe the recent advances in prostate cancer research.

Abstract

Prostate cancer is one of the malignancies that affects men and significantly contributes to increased mortality rates in men globally. Patients affected with prostate cancer present with either a localized or advanced disease. In this review, we aim to provide a holistic overview of prostate cancer, including the diagnosis of the disease, mutations leading to the onset and progression of the disease, and treatment options. Prostate cancer diagnoses include a digital rectal examination, prostate-specific antigen analysis, and prostate biopsies. Mutations in certain genes are linked to the onset, progression, and metastasis of the cancer. Treatment for localized prostate cancer encompasses active surveillance, ablative radiotherapy, and radical prostatectomy. Men who relapse or present metastatic prostate cancer receive androgen deprivation therapy (ADT), salvage radiotherapy, and chemotherapy. Currently, available treatment options are more effective when used as combination therapy; however, despite available treatment options, prostate cancer remains to be incurable. There has been ongoing research on finding and identifying other treatment approaches such as the use of traditional medicine, the application of nanotechnologies, and gene therapy to combat prostate cancer, drug resistance, as well as to reduce the adverse effects that come with current treatment options. In this article, we summarize the genes involved in prostate cancer, available treatment options, and current research on alternative treatment options.

Unboxing the molecular modalities of mutagens in cancer

The etiology of the majority of human cancers is associated with a myriad of environmental causes, including physical, chemical, and biological factors. DNA damage induced by such mutagens is the initial step in the process of carcinogenesis resulting in the accumulation of mutations. Mutational events are considered the major triggers for introducing genetic and epigenetic insults such as DNA crosslinks, single- and double-strand DNA breaks, formation of DNA adducts, mismatched bases, modification in histones, DNA methylation, and microRNA alterations. However, DNA repair mechanisms are devoted to protect the DNA to ensure genetic stability, any aberrations in these calibrated mechanisms provoke cancer occurrence. Comprehensive knowledge of the type of mutagens and carcinogens and the influence of these agents in DNA damage and cancer induction is crucial to develop rational anticancer strategies. This review delineated the molecular mechanism of DNA damage and the repair pathways to provide a deep understanding of the molecular basis of mutagenicity and carcinogenicity. A relationship between DNA adduct formation and cancer incidence has also been summarized. The mechanistic basis of inflammatory response and oxidative damage triggered by mutagens in tumorigenesis has also been highlighted. We elucidated the interesting interplay between DNA damage response and immune system mechanisms. We addressed the current understanding of DNA repair targeted therapies and DNA damaging chemotherapeutic agents for cancer treatment and discussed how antiviral agents, anti-inflammatory drugs, and immunotherapeutic agents combined with traditional approaches lay the foundations for future cancer therapies.

Repositioning of Anti-Inflammatory Drugs for the Treatment of Cervical Cancer Sub-Types

Cervical cancer is the fourth most commonly diagnosed cancer worldwide and, in almost all cases is caused by infection with highly oncogenic Human Papillomaviruses (HPVs). On the other hand, inflammation is one of the hallmarks of cancer research. Here, we focused on inflammatory proteins that classify cervical cancer patients by considering individual differences between cancer patients in contrast to conventional treatments. We repurposed anti-inflammatory drugs for therapy of HPV-16 and HPV-18 infected groups, separately. In this study, we employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes. We performed a meta-analysis of cervical cancer-associated transcriptomic datasets considering subtype differences of samples and identified the differentially expressed genes (DEGs). Using gene signature reversal on HPV-16 and HPV-18, we performed both signature- and network-based drug reversal to identify anti-inflammatory drug candidates against inflammation-associated nodes. The anti-inflammatory drug candidates were evaluated using molecular docking to determine the potential of physical interactions between the anti-inflammatory drug and inflammation-associated nodes as drug targets. We proposed 4 novels anti-inflammatory drugs (AS-601245, betamethasone, narciclasin, and methylprednisolone) for the treatment of HPV-16, 3 novel drugs for the treatment of HPV-18 (daphnetin, phenylbutazone, and tiaprofenoic acid), and 5 novel drugs (aldosterone, BMS-345541, etodolac, hydrocortisone, and prednisolone) for the treatment of both subtypes. We proposed anti-inflammatory drug candidates that have the potential to be therapeutic agents for the prevention and/or treatment of cervical cancer.

Itraconazole Reverts ABCB1-Mediated Docetaxel Resistance in Prostate Cancer

Docetaxel (DTX) was the first chemotherapeutic agent to demonstrate significant efficacy in the treatment of men with metastatic castration-resistant prostate cancer. However, response to DTX is generally short-lived, and relapse eventually occurs due to emergence of drug-resistance. We previously established two DTX-resistant prostate cancer cell lines, LNCaP^R and C4-2B^R, derived from the androgen‐dependent LNCaP cell line, and from the LNCaP lineage-derived androgen-independent C4-2B sub-line, respectively. Using an unbiased drug screen, we identify itraconazole (ITZ), an oral antifungal drug, as a compound that can efficiently re-sensitize drug-resistant LNCaP^R and C4-2B^R prostate cancer cells to DTX treatment. ITZ can re-sensitize multiple DTX-resistant cell models, not only in prostate cancer derived cells, such as PC-3 and DU145, but also in docetaxel-resistant breast cancer cells. This effect is dependent on expression of ATP-binding cassette (ABC) transporter protein ABCB1, also known as P-glycoprotein (P-gp). Molecular modeling of ITZ bound to ABCB1, indicates that ITZ binds tightly to the inward-facing form of ABCB1 thereby inhibiting the transport of DTX. Our results suggest that ITZ may provide a feasible approach to re-sensitization of DTX resistant cells, which would add to the life-prolonging effects of DTX in men with metastatic castration-resistant prostate cancer.

A New Approach to Drug Repurposing with Two-Stage Prediction, Machine Learning, and Unsupervised Clustering of Gene Expression

Drug repurposing has broad importance in planetary health for therapeutics innovation in infectious diseases as well as common or rare chronic human diseases. Drug repurposing has also proved important to develop interventions against the COVID-19 pandemic. We propose a new approach for drug repurposing involving two-stage prediction and machine learning. First, diseases are clustered by gene expression on the premise that similar patterns of altered gene expression imply critical pathways shared in different disease conditions. Next, drug efficacy is assessed by the reversibility of abnormal gene expression, and results are clustered to identify repurposing targets. To cluster similar diseases, gene expression data from 262 cases of 31 diseases and 268 controls were analyzed by Uniform Manifold Approximation and Projection for Dimension Reduction followed by k-means to optimize the number of clusters. For evaluation, we examined disease-specific gene expression data for inclusion, body myositis, polymyositis, and dermatomyositis (DM), and used LINCS L1000 characteristic direction signatures search engine (L1000CDS2) to obtain lists of small-molecule compounds that reversed the expression patterns of these specifically altered genes as candidates for drug repurposing. Finally, the functions of affected genes were analyzed by Gene Set Enrichment Analysis to examine consistency with expected drug efficacy. Consequently, we found disease-specific gene expression, and importantly, identified 20 drugs such as BMS-387032, phorbol-12-myristate-13-acetate, mitoxantrone, alvocidib, and vorinostat as candidates for repurposing. These were previously noted to be effective against two of the three diseases, and have a high probability of being effective against the other. That is, inclusion body myositis and DM. The two-stage prediction approach to drug repurposing presented here offers innovation to inform future drug discovery and clinical trials in a variety of human diseases.

Triclosan: A Small Molecule with Controversial Roles

Triclosan (TCS), a broad-spectrum antimicrobial agent, has been widely used in personal care products, medical products, plastic cutting boards, and food storage containers. Colgate Total^® toothpaste, containing 10 mM TCS, is effective in controlling biofilm formation and maintaining gingival health. Given its broad usage, TCS is present ubiquitously in the environment. Given its strong lipophilicity and accumulation ability in organisms, it is potentially harmful to biohealth. Several reports suggest the toxicity of this compound, which is inserted in the class of endocrine disrupting chemicals (EDCs). In September 2016, TCS was banned by the U.S. Food and Drug Administration (FDA) and the European Union in soap products. Despite these problems, its application in personal care products within certain limits is still allowed. Today, it is still unclear whether TCS is truly toxic to mammals and the adverse effects of continuous, long-term, and low concentration exposure remain unknown. Indeed, some recent reports suggest the use of TCS as a repositioned drug for cancer treatment and cutaneous leishmaniasis. In this scenario it is necessary to investigate the advantages and disadvantages of TCS, to understand whether its use is advisable or not. This review intends to highlight the pros and cons that are associated with the use of TCS in humans.

Past, Present, and Future of Therapies for Pituitary Neuroendocrine Tumors: Need for Omics and Drug Repositioning Guidance

Innovation roadmaps are important, because they encourage the actors in an innovation ecosystem to creatively imagine multiple possible science future(s), while anticipating the prospects and challenges on the innovation trajectory. In this overarching context, this expert review highlights the present unmet need for therapeutic innovations for pituitary neuroendocrine tumors (PitNETs), also known as pituitary adenomas. Although there are many drugs used in practice to treat PitNETs, many of these drugs can have negative side effects and show highly variable outcomes in terms of overall recovery. Building innovation roadmaps for PitNETs' treatments can allow incorporation of systems biology approaches to bring about insights at multiple levels of cell biology, from genes to proteins to metabolites. Using the systems biology techniques, it will then be possible to offer potential therapeutic strategies for the convergence of preventive approaches and patient-centered disease treatment. Here, we first provide a comprehensive overview of the molecular subtypes of PitNETs and therapeutics for these tumors from the past to the present. We then discuss examples of clinical trials and drug repositioning studies and how multi-omics studies can help in discovery and rational development of new therapeutics for PitNETs. Finally, this expert review offers new public health and personalized medicine approaches on cases that are refractory to conventional treatment or recur despite currently used surgical and/or drug therapy.

Identification of Druggable Genes for Asthma by Integrated Genomic Network Analysis

Asthma is a common and heterogeneous disease characterized by chronic airway inflammation. Currently, the two main types of asthma medicines are inhaled corticosteroids and long-acting β2-adrenoceptor agonists (LABAs). In addition, biological drugs provide another therapeutic option, especially for patients with severe asthma. However, these drugs were less effective in preventing severe asthma exacerbation, and other drug options are still limited. Herein, we extracted asthma-associated single nucleotide polymorphisms (SNPs) from the genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) catalog and prioritized candidate genes through five functional annotations. Genes enriched in more than two categories were defined as “biological asthma risk genes.” Then, DrugBank was used to match target genes with FDA-approved medications and identify candidate drugs for asthma. We discovered 139 biological asthma risk genes and identified 64 drugs targeting 22 of these genes. Seven of them were approved for asthma, including reslizumab, mepolizumab, theophylline, dyphylline, aminophylline, oxtriphylline, and enprofylline. We also found 17 drugs with clinical or preclinical evidence in treating asthma. In addition, eleven of the 40 candidate drugs were further identified as promising asthma therapy. Noteworthy, IL6R is considered a target for asthma drug repurposing based on its high target scores. Through in silico drug repurposing approach, we identified sarilumab and satralizumab as the most promising drug for asthma treatment.

System and network biology-based computational approaches for drug repositioning

Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.

MicroRNA-Mediated Drug Repurposing Unveiled Potential Candidate Drugs for Prolactinoma Treatment

INTRODUCTION

Prolactinomas, also called lactotroph adenomas, are the most encountered type of hormone-secreting pituitary neuroendocrine tumors in the clinic. The preferred first-line therapy is a medical treatment with dopamine agonists (DAs), mainly cabergoline, to reduce serum prolactin levels, tumor volume, and mass effect. However, in some cases, patients have displayed DA resistance with aggressive tumor behavior or are faced with recurrence after drug withdrawal. Also, currently used therapeutics have notorious side effects and impair the life quality of the patients.

METHODS

Since the amalgamation of clinical and laboratory data besides tumor histopathogenesis and transcriptional regulatory features of the tumor emerges to exhibit essential roles in the behavior and progression of prolactinomas; in this work, we integrated mRNA- and microRNA (miRNA)-level transcriptome data that exploit disease-specific signatures in addition to biological and pharmacological data to elucidate a rational prioritization of pathways and drugs in prolactinoma.

RESULTS

We identified 8 drug candidates through drug repurposing based on mRNA-miRNA-level data integration and evaluated their potential through in vitro assays in the MMQ cell line. Seven repurposed drugs including 5-fluorocytosine, nortriptyline, neratinib, puromycin, taxifolin, vorinostat, and zileuton were proposed as potential drug candidates for the treatment of prolactinoma. We further hypothesized possible mechanisms of drug action on MMQ cell viability through analyzing the PI3K/Akt signaling pathway and cell cycle arrest via flow cytometry and Western blotting.

DISCUSSION

We presented the transcriptomic landscape of prolactinoma through miRNA and mRNA-level data integration and proposed repurposed drug candidates based on this integration. We validated our findings through testing cell viability, cell cycle phases, and PI3K/Akt protein expressions. Effects of the drugs on cell cycle phases and inhibition of the PI3K/Akt pathway by all drugs gave us promising output for further studies using these drugs in the treatment of prolactinoma. This is the first study that reports miRNA-mediated repurposed drugs for prolactinoma treatment via in vitro experiments.

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

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

A data-driven methodology towards evaluating the potential of drug repurposing hypotheses

Drug repurposing has become a widely used strategy to accelerate the process of finding treatments. While classical de novo drug development involves high costs, risks, and time-consuming paths, drug repurposing allows to reuse already-existing and approved drugs for new indications. Numerous research has been carried out in this field, both in vitro and in silico. Computational drug repurposing methods make use of modern heterogeneous biomedical data to identify and prioritize new indications for old drugs. In the current paper, we present a new complete methodology to evaluate new potentially repurposable drugs based on disease-gene and disease-phenotype associations, identifying significant differences between repurposing and non-repurposing data. We have collected a set of known successful drug repurposing case studies from the literature and we have analysed their dissimilarities with other biomedical data not necessarily participating in repurposing processes. The information used has been obtained from the DISNET platform. We have performed three analyses (at the genetical, phenotypical, and categorization levels), to conclude that there is a statistically significant difference between actual repurposing-related information and non-repurposing data. The insights obtained could be relevant when suggesting new potential drug repurposing hypotheses.

Cascade Deep Forest With Heterogeneous Similarity Measures for Drug-Target Interaction Prediction

Drug repositioning is a method of systematically identifying potential molecular targets that known drugs may act on. Compared with traditional methods, drug repositioning has been extensively studied due to the development of multi-omics technology and system biology methods. Because of its biological network properties, it is possible to apply machine learning related algorithms for prediction. Based on various heterogeneous network model, this paper proposes a method named THNCDF for predicting drug-target interactions. Various heterogeneous networks are integrated to build a tripartite network, and similarity calculation methods are used to obtain similarity matrix. Then, the cascade deep forest method is used to make prediction. Results indicate that THNCDF outperforms the previously reported methods based on the 10-fold cross-validation on the benchmark data sets proposed by Y. Yamanishi. The area under Precision Recall curve (AUPR) value on the Enzyme, GPCR, Ion Channel, and Nuclear Receptor data sets is 0.988, 0.980, 0.938, and 0.906 separately. The experimental results well illustrate the feasibility of this method.

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

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

Repurposing approved drugs for cancer therapy

BACKGROUND

Many drugs approved for other indications can control the growth of tumor cells and limit adverse events (AE).

DATA SOURCES

Literature searches with keywords 'repurposing and cancer' books, websites: https://clinicaltrials.gov/, for drug structures: https://pubchem.ncbi.nlm.nih.gov/.

AREAS OF AGREEMENT

Introducing approved drugs, such as those developed to treat diabetes (Metformin) or inflammation (Thalidomide), identified to have cytostatic activity, can enhance chemotherapy or even replace more cytotoxic drugs. Also, anti-inflammatory compounds, cytokines and inhibitors of proteolysis can be used to control the side effects of chemo- and immuno-therapies or as second-line treatments for tumors resistant to kinase inhibitors (KI). Drugs specifically developed for cancer therapy, such as interferons (IFN), the tyrosine KI abivertinib TKI (tyrosine kinase inhibitor) and interleukin-6 (IL-6) receptor inhibitors, may help control symptoms of Covid-19.

AREAS OF CONTROVERSY

Better knowledge of mechanisms of drug activities is essential for repurposing. Chemotherapies induce ER stress and enhance mutation rates and chromosome alterations, leading to resistance that cannot always be related to mutations in the target gene. Metformin, thalidomide and cytokines (IFN, tumor necrosis factor (TNF), interleukin-2 (IL-2) and others) have pleiomorphic activities, some of which can enhance tumorigenesis. The small and fragile patient pools available for clinical trials can cloud the data on the usefulness of cotreatments.

GROWING POINTS

Better understanding of drug metabolism and mechanisms should aid in repurposing drugs for primary, adjuvant and adjunct treatments.

AREAS TIMELY FOR DEVELOPING RESEARCH

Optimizing drug combinations, reducing cytotoxicity of chemotherapeutics and controlling associated inflammation.

Repurposing of the Antiepileptic Drug Levetiracetam to Restrain Neuroendocrine Prostate Cancer and Inhibit Mast Cell Support to Adenocarcinoma

A relevant fraction of castration-resistant prostate cancers (CRPC) evolve into fatal neuroendocrine (NEPC) tumors in resistance to androgen deprivation and/or inhibitors of androgen receptor pathway. Therefore, effective drugs against both CRPC and NEPC are needed. We have previously described a dual role of mast cells (MCs) in prostate cancer, being capable to promote adenocarcinoma but also to restrain NEPC. This finding suggests that a molecule targeting both MCs and NEPC cells could be effective against prostate cancer. Using an in silico drug repurposing approach, here we identify the antiepileptic drug levetiracetam as a potential candidate for this purpose. We found that the protein target of levetiracetam, SV2A, is highly expressed by both NEPC cells and MCs infiltrating prostate adenocarcinoma, while it is low or negligible in adenocarcinoma cells. In vitro, levetiracetam inhibited the proliferation of NEPC cells and the degranulation of MCs. In mice bearing subcutaneous tumors levetiracetam was partially active on both NEPC and adenocarcinoma, the latter effect due to the inhibition of MMP9 release by MCs. Notably, in TRansgenic Adenocarcinoma of the Mouse Prostate (TRAMP) mice subjected to surgical castration to mimic androgen deprivation therapy, levetiracetam reduced onset and frequency of both high grade prostatic intraepithelial neoplasia, adenocarcinoma and NEPC, thus increasing the number of cured mice showing only signs of tumor regression. Our results demonstrate that levetiracetam can directly restrain NEPC development after androgen deprivation, and that it can also block adenocarcinoma progression through the inhibition of some MCs functions. These findings open the possibility of further testing levetiracetam for the therapy of prostate cancer or of MC-mediated diseases.

A numerical study on tumor-on-chip performance and its optimization for nanodrug-based combination therapy

Microfluidic devices, such as the tumor-on-a-chip (ToC), allow for the delivery of multiple drugs as desired for various therapies such as cancer treatment. Due to the complexity involved, visualizing, and gaining knowledge of the performance of such devices through experimentation alone is difficult if not impossible. In this paper, we performed a numerical simulation study on ToC performance, which focuses on the ability to combine multiple nanodrugs and optimized ToC performance. The numerical simulations of the chip performance were performed based on the typical chip design and operating parameters, as well as the established governing equations, boundary conditions, and fluid-structure interaction. The effect of cell injection time and position, inlet flow rate, number of inlets, medium viscosity, and cell concentration on the chip performance in terms of shear stress and cell distribution were examined. The results illustrate the profound effect of operation parameters, thus allowing for rigorously determining operational parameters to prevent spheroids ejection from microwells and to restrict the shear stresses within a physiological range. Also, the results show that triple-inlets can increase the uniformity of cell distribution in comparison with single or double inlets. Based on the simulation results, the architecture of the primary ToC was further optimized, resulting in a novel design that enables applying multiple, yet simultaneous, nanodrugs with optimal drug combination as desired for an individual patient. Furthermore, our simulations on the optimized chip showed a uniform cell distribution required for uniform-sized tumor spheroids generation, and complete medium exchange. Taken together, this study not only illustrates that numerical simulations are effective to visualize the ToCs performance, but also develops a novel ToC design optimized for nanodrug-based combination therapy.

Repurposing of antipsychotic trifluoperazine for treating brain metastasis, lung metastasis and bone metastasis of melanoma by disrupting autophagy flux

Targeted therapies and immunotherapy have brought substantial benefits to patients with melanoma. However, brain metastases remain the biggest threat to the survival and quality of life of melanoma patients. One of the major challenges to an effective therapy is the inability of drugs to penetrate the blood-brain barrier (BBB). Anti-schizophrenic drugs can cross the BBB, and many of them have demonstrated anti-cancer effects. Repurposing existing drugs for new clinical indications is an alluring strategy for anticancer drug discovery. Herein, we applied this strategy and screened a small collection of existing anti-schizophrenic drugs to use as anti-melanoma agents. Among them, trifluoperazine dihydrochloride (TFP) exhibited promising potencies for suppressing the growth and metastasis of melanoma, both in vitro and in vivo. TFP obviously suppressed the viability of melanoma cells within the micromolar range and inhibited the growth of melanoma in the subcutaneous mice models. Notably, intraperitoneal (i.p.) administration of TFP (40 mg/kg/day) obviously inhibited the growth of intra-carotid-injection established melanoma brain metastasis and extended the survival of brain metastasis-bearing mice. Moreover, TFP significantly suppressed lung metastasis and bone metastasis of melanoma in preclinical metastasis models. Mechanistically, TFP caused G0/G1 cell cycle arrest and mitochondrial-dependent intrinsic apoptosis of melanoma cells. In addition, TFP treatment increased the expression of microtubule associated protein 1 light chain 3 beta-II (LC3B-II) and p62 in vitro, suggesting an inhibition of autophagic flux. TFP decreased LysoTracker Red uptake after treatment, indicating impaired acidification of lysosomes. Moreover, the colocalization of LC3 with lysosomal-associated membrane protein 1 (LAMP1), a lysosome marker, was also suppressed after TFP treatment, suggesting that TFP might block the fusion of autophagosomes with lysosomes, which led to autophagosome accumulation. Taken together, our data highlight the potential of repurposing TFP as a new adjuvant drug for treating melanoma patients with brain, lung, and bone metastases.

Drug Repositioning of the α1-Adrenergic Receptor Antagonist Naftopidil: A Potential New Anti-Cancer Drug?

Failure of conventional treatments is often observed in cancer management and this requires the development of alternative therapeutic strategies. However, new drug development is known to be a high-failure process because of the possibility of a lower efficacy than expected for the drug or appearance of non-manageable side effects. Another way to find alternative therapeutic drugs consists in identifying new applications for drugs already approved for a particular disease: a concept named "drug repurposing". In this context, several studies demonstrated the potential anti-tumour activity exerted by α1-adrenergic receptor antagonists and notably renewed interest for naftopidil as an anti-cancer drug. Naftopidil is used for benign prostatic hyperplasia management in Japan and a retrospective study brought out a reduced incidence of prostate cancer in patients that had been prescribed this drug. Further studies showed that naftopidil exerted anti-proliferative and cytotoxic effects on prostate cancer as well as several other cancer types in vitro, as well as ex vivo and in vivo. Moreover, naftopidil was demonstrated to modulate the expression of Bcl-2 family pro-apoptotic members which could be used to sensitise cancer cells to targeting therapies and to overcome resistance of cancer cells to apoptosis. For most of these anti-cancer effects, the molecular pathway is either not fully deciphered or shown to involve α1-adrenergic receptor-independent pathway, suggesting off target transduction signals. In order to improve its efficacy, naftopidil analogues were designed and shown to be effective in several studies. Thereby, naftopidil appears to display anti-cancer properties on different cancer types and could be considered as a candidate for drug repurposing although its anti-cancerous activities need to be studied more deeply in prospective randomized clinical trials.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

Drug repositioning in cancer: The current situation in Japan

Cancer is a leading cause of death worldwide, and the incidence continues to increase. Despite major research aimed at discovering and developing novel and effective anticancer drugs, oncology drug development is a lengthy and costly process, with high attrition rates. Drug repositioning (DR, also referred to as drug repurposing), the process of finding new uses for approved noncancer drugs, has been gaining popularity in the past decade. DR has become a powerful alternative strategy for discovering and developing novel anticancer drug candidates from the existing approved drug space. Indeed, the availability of several large established libraries of clinical drugs and rapid advances in disease biology, genomics/transcriptomics/proteomics and bioinformatics has accelerated the pace of activity‐based, literature‐based and in silico DR, thereby improving safety and reducing costs. However, DR still faces financial obstacles in clinical trials, which could limit its practical use in the clinic. Here, we provide a brief review of DR in cancer and discuss difficulties in the development of DR for clinical use. Furthermore, we introduce some promising DR candidates for anticancer therapy in Japan.

Biomedical data and computational models for drug repositioning: a comprehensive review

Drug repositioning can drastically decrease the cost and duration taken by traditional drug research and development while avoiding the occurrence of unforeseen adverse events. With the rapid advancement of high-throughput technologies and the explosion of various biological data and medical data, computational drug repositioning methods have been appealing and powerful techniques to systematically identify potential drug-target interactions and drug-disease interactions. In this review, we first summarize the available biomedical data and public databases related to drugs, diseases and targets. Then, we discuss existing drug repositioning approaches and group them based on their underlying computational models consisting of classical machine learning, network propagation, matrix factorization and completion, and deep learning based models. We also comprehensively analyze common standard data sets and evaluation metrics used in drug repositioning, and give a brief comparison of various prediction methods on the gold standard data sets. Finally, we conclude our review with a brief discussion on challenges in computational drug repositioning, which includes the problem of reducing the noise and incompleteness of biomedical data, the ensemble of various computation drug repositioning methods, the importance of designing reliable negative samples selection methods, new techniques dealing with the data sparseness problem, the construction of large-scale and comprehensive benchmark data sets and the analysis and explanation of the underlying mechanisms of predicted interactions.

Identification of the targeted therapeutic potential of doxycycline for a subset of gastric cancer patients

The identification of new drugs for the targeted therapy of gastric cancer remains an important need. The RAS/RAF/MEK/ERK/ELK1 signaling cascade is activated in many cancers, including gastric cancer. To identify the targetable inhibitors of the ERK/MAPK pathway, we performed a repurposing screening of a panel of antimicrobial agents in gastric cancer cells using an ERK/MAPK-driven firefly luciferase reporter assay. Multiple antibiotics were identified to inhibit ERK-mediated transcriptional activity. Among them, doxycycline showed high inhibition of ERK/MAPK-regulated transcriptional activity and the levels of ERK proteins. Doxycycline was further identified to inhibit the proliferation and the colony- and spheroid-forming potential of gastric cancer cells. By in vitro signaling pathway and genome-wide expression profiling analyses, doxycycline was identified to inhibit signaling pathways and transcriptional activities regulated by ER, Myc, E2F1, Wnt, SMAD2/3/4, Notch, and OCT4. Doxycycline was also found to activate p53-, ATF6-, NRF1/2-, and MTF1-mediated transcription and inhibit the transcription of histones, proteasomal genes, fibroblast growth factor, and other oncogenic factors. These observations show the multitargeting and targeted therapeutic features of doxycycline for a subset of gastric tumors.

Repurposing Dihydropyridines for Treatment of Helicobacter pylori Infection

Antibiotic resistance is a major cause of the increasing failures in the current eradication therapies against Helicobacter pylori. In this scenario, repurposing drugs could be a valuable strategy to fast-track novel antimicrobial agents. In the present study, we analyzed the inhibitory capability of 1,4-dihydropyridine (DHP) antihypertensive drugs on the essential function of the H. pylori response regulator HsrA and investigated both the in vitro antimicrobial activities and the in vivo efficacy of DHP treatments against H. pylori. Six different commercially available and highly prescribed DHP drugs-namely, Nifedipine, Nicardipine, Nisoldipine, Nimodipine, Nitrendipine, and Lercanidipine-noticeably inhibited the DNA binding activity of HsrA and exhibited potent bactericidal activities against both metronidazole- and clarithromycin-resistant strains of H. pylori, with minimal inhibitory concentration (MIC) values in the range of 4 to 32 mg/L. The dynamics of the decline in the bacterial counts at 2 × MIC appeared to be correlated with the lipophilicity of the drugs, suggesting different translocation efficiencies of DHPs across the bacterial membrane. Oral treatments with 100 mg/kg/day of marketed formulations of Nimodipine or Nitrendipine in combination with omeprazole significantly reduced the H. pylori gastric colonization in mice. The results presented here support a novel therapeutic solution for treatment of antibiotic-resistant H. pylori infections.

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.

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.

Antipsychotic Drug Trifluoperazine Suppresses Colorectal Cancer by Inducing G0/G1 Arrest and Apoptosis

Repurposing existing drugs for cancer treatment is an effective strategy. An approved antipsychotic drug, trifluoperazine (TFP), has been reported to have potential anticancer effects against several cancer types. Here, we investigated the effect and molecular mechanism of TFP in colorectal cancer (CRC). In vitro studies showed that TFP induced G0/G1 cell cycle arrest to dramatically inhibit CRC cell proliferation through downregulating cyclin-dependent kinase (CDK) 2, CDK4, cyclin D1, and cyclin E and upregulating p27. TFP also induced apoptosis, decreased mitochondrial membrane potential, and increased reactive oxygen species levels in CRC cells, indicating that TFP induced mitochondria-mediated intrinsic apoptosis. Importantly, TFP significantly suppressed tumor growth in two CRC subcutaneous tumor models without side effects. Interestingly, TFP treatment increased the expression levels of programmed death-1 ligand 1 (PD-L1) in CRC cells and programmed death-1 (PD-1) in tumor-infiltrating CD4+ and CD8+ T cells, implying that the combination of TFP with an immune checkpoint inhibitor, such as an anti-PD-L1 or anti-PD-1 antibody, might have synergistic anticancer effects. Taken together, our study signifies that TFP is a novel treatment strategy for CRC and indicates the potential for using the combination treatment of TFP and immune checkpoint blockade to increase antitumor efficiency.

Enabling Web-scale data integration in biomedicine through Linked Open Data

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.

Co-carcinogenic effects of vitamin E in prostate

A large number of basic researches and observational studies suggested the cancer preventive activity of vitamin E, but large-scale human intervention trials have yielded disappointing results and actually showed a higher incidence of prostate cancer although the mechanisms underlying the increased risk remain largely unknown. Here we show through in vitro and in vivo studies that vitamin E produces a marked inductive effect on carcinogen-bioactivating enzymes and a pro-oxidant status promoting both DNA damage and cell transformation frequency. First, we found that vitamin E in the human prostate epithelial RWPE-1 cell line has the remarkable ability to upregulate the expression of various phase-I activating cytochrome P450 (CYP) enzymes, including activators of polycyclic aromatic hydrocarbons (PAHs), giving rise to supraphysiological levels of reactive oxygen species. Furthermore, our rat model confirmed that vitamin E in the prostate has a powerful booster effect on CYP enzymes associated with the generation of oxidative stress, thereby favoring lipid-derived electrophile spread that covalently modifies proteins. We show that vitamin E not only causes DNA damage but also promotes cell transformation frequency induced by the PAH-prototype benzo[a]pyrene. Our findings might explain why dietary supplementation with vitamin E increases the prostate cancer risk among healthy men.

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.

Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning

BACKGROUND

Genome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment.

METHODS

In this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions.

FINDINGS

We identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line.

INTERPRETATION

Our approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.