Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment

Drug repurposing for diabetes mellitus: In silico and in vitro investigation of DrugBank database for α-glucosidase inhibitors

The process of developing novel compounds/drugs is arduous, time-intensive, and financially burdensome, characterized by a notably low success rate and relatively high attrition rates. To alleviate these challenges, compound/drug repositioning strategies are employed to predict potential therapeutic effects for DrugBank-approved compounds across various diseases. In this study, we devised a computational and enzyme inhibitory mechanistic approach to identify promising compounds from the pool of DrugBank-approved substances targeting Diabetes Mellitus (DM). Molecular docking analyses were employed to validate the binding interaction patterns and conformations of the screened compounds within the active site of α-glucosidase. Notably, Asp352 and Glu277 participated in interactions within the α-glucosidase-ligand complexes, mediated by conventional hydrogen bonding and van der Waals forces, respectively. The stability of the docked complexes (α-glucosidase-compounds) was scrutinized through Molecular Dynamics (MD) simulations. Subsequent in vitro analyses assessed the therapeutic potential of the repositioned compounds against α-glucosidase. Kinetic studies revealed that "Forodesine" exhibited a lower IC50 (0.24 ± 0.04 mM) compared to the control, and its inhibitory pattern corresponds to that of competitive inhibitors. In-depth in silico secondary structure content analysis detailed the interactions between Forodesine and α-glucosidase, unveiling significant alterations in enzyme conformation upon binding, impacting its catalytic activity. Overall, our findings underscore the potential of Forodesine as a promising candidate for DM treatment through α-glucosidase inhibition. Further validation through in vitro and in vivo studies is imperative to confirm the therapeutic benefits of Forodesine in conformational diseases such as DM.

Exploring cutting-edge strategies for drug repurposing in female cancers - An insight into the tools of the trade

Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.

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.

Current trends and future prospects of drug repositioning in gastrointestinal oncology

Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.

Enhancing Immunotherapy in Ovarian Cancer: The Emerging Role of Metformin and Statins

Ovarian cancer metastization is accompanied by the development of malignant ascites, which are associated with poor prognosis. The acellular fraction of this ascitic fluid contains tumor-promoting soluble factors, bioactive lipids, cytokines, and extracellular vesicles, all of which communicate with the tumor cells within this peritoneal fluid. Metabolomic profiling of ovarian cancer ascites has revealed significant differences in the pathways of fatty acids, cholesterol, glucose, and insulin. The proteins involved in these pathways promote tumor growth, resistance to chemotherapy, and immune evasion. Unveiling the key role of this liquid tumor microenvironment is crucial for discovering more efficient treatment options. This review focuses on the cholesterol and insulin pathways in ovarian cancer, identifying statins and metformin as viable treatment options when combined with standard chemotherapy. These findings are supported by clinical trials showing improved overall survival with these combinations. Additionally, statins and metformin are associated with the reversal of T-cell exhaustion, positioning these drugs as potential combinatory strategies to improve immunotherapy outcomes in ovarian cancer patients.

Repurposing approved non-oncology drugs for cancer therapy: a comprehensive review of mechanisms, efficacy, and clinical prospects

Cancer poses a significant global health challenge, with predictions of increasing prevalence in the coming years due to limited prevention, late diagnosis, and inadequate success with current therapies. In addition, the high cost of new anti-cancer drugs creates barriers in meeting the medical needs of cancer patients, especially in developing countries. The lengthy and costly process of developing novel drugs further hinders drug discovery and clinical implementation. Therefore, there has been a growing interest in repurposing approved drugs for other diseases to address the urgent need for effective cancer treatments. The aim of this comprehensive review is to provide an overview of the potential of approved non-oncology drugs as therapeutic options for cancer treatment. These drugs come from various chemotherapeutic classes, including antimalarials, antibiotics, antivirals, anti-inflammatory drugs, and antifungals, and have demonstrated significant antiproliferative, pro-apoptotic, immunomodulatory, and antimetastatic properties. A systematic review of the literature was conducted to identify relevant studies on the repurposing of approved non-oncology drugs for cancer therapy. Various electronic databases, such as PubMed, Scopus, and Google Scholar, were searched using appropriate keywords. Studies focusing on the therapeutic potential, mechanisms of action, efficacy, and clinical prospects of repurposed drugs in cancer treatment were included in the analysis. The review highlights the promising outcomes of repurposing approved non-oncology drugs for cancer therapy. Drugs belonging to different therapeutic classes have demonstrated notable antitumor effects, including inhibiting cell proliferation, promoting apoptosis, modulating the immune response, and suppressing metastasis. These findings suggest the potential of these repurposed drugs as effective therapeutic approaches in cancer treatment. Repurposing approved non-oncology drugs provides a promising strategy for addressing the urgent need for effective and accessible cancer treatments. The diverse classes of repurposed drugs, with their demonstrated antiproliferative, pro-apoptotic, immunomodulatory, and antimetastatic properties, offer new avenues for cancer therapy. Further research and clinical trials are warranted to explore the full potential of these repurposed drugs and optimize their use in treating various cancer types. Repurposing approved drugs can significantly expedite the process of identifying effective treatments and improve patient outcomes in a cost-effective manner.

Drug Repurposing to Circumvent Immune Checkpoint Inhibitor Resistance in Cancer Immunotherapy

Immune checkpoint inhibitors (ICI) have achieved unprecedented clinical success in cancer treatment. However, drug resistance to ICI therapy is a major hurdle that prevents cancer patients from responding to the treatment or having durable disease control. Drug repurposing refers to the application of clinically approved drugs, with characterized pharmacological properties and known adverse effect profiles, to new indications. It has also emerged as a promising strategy to overcome drug resistance. In this review, we summarized the latest research about drug repurposing to overcome ICI resistance. Repurposed drugs work by either exerting immunostimulatory activities or abolishing the immunosuppressive tumor microenvironment (TME). Compared to the de novo drug design strategy, they provide novel and affordable treatment options to enhance cancer immunotherapy that can be readily evaluated in the clinic. Biomarkers are exploited to identify the right patient population to benefit from the repurposed drugs and drug combinations. Phenotypic screening of chemical libraries has been conducted to search for T-cell-modifying drugs. Genomics and integrated bioinformatics analysis, artificial intelligence, machine and deep learning approaches are employed to identify novel modulators of the immunosuppressive TME.

Unlocking therapeutic potential: integration of drug repurposing and immunotherapy for various disease targeting

Drug repurposing, also known as drug repositioning, entails the application of pre-approved or formerly assessed drugs having potentially functional therapeutic amalgams for curing various disorders or disease conditions distinctive from their original remedial indication. It has surfaced as a substitute for the development of drugs for treating cancer, cardiovascular diseases, neurodegenerative disorders, and various infectious diseases like Covid-19. Although the earlier lines of findings in this area were serendipitous, recent advancements are based on patient centered approaches following systematic, translational, drug targeting practices that explore pathophysiological ailment mechanisms. The presence of definite information and numerous records with respect to beneficial properties, harmfulness, and pharmacologic characteristics of repurposed drugs increase the chances of approval in the clinical trial stages. The last few years have showcased the successful emergence of repurposed drug immunotherapy in treating various diseases. In this light, the present review emphasises on incorporation of drug repositioning with Immunotherapy targeted for several disorders.

In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer

BACKGROUND

Epigenomic dysregulation has been linked to solid tumour malignancies, including ovarian cancers. Profiling of re-programmed enhancer locations associated with disease has the potential to improve stratification and thus therapeutic choices. Ovarian cancers are subdivided into histological subtypes that have significant molecular and clinical differences, with high-grade serous carcinoma representing the most common and aggressive subtype.

METHODS

We interrogated the enhancer landscape(s) of normal ovary and subtype-specific ovarian cancer states using publicly available data. With an initial focus on H3K27ac histone mark, we developed a computational pipeline to predict drug compound activity based on epigenomic stratification. Lastly, we substantiated our predictions in vitro using patient-derived clinical samples and cell lines.

RESULTS

Using our in silico approach, we highlighted recurrent and privative enhancer landscapes and identified the differential enrichment of a total of 164 transcription factors involved in 201 protein complexes across the subtypes. We pinpointed SNS-032 and EHMT2 inhibitors BIX-01294 and UNC0646 as therapeutic candidates in high-grade serous carcinoma, as well as probed the efficacy of specific inhibitors in vitro.

CONCLUSION

Here, we report the first attempt to exploit ovarian cancer epigenomic landscapes for drug discovery. This computational pipeline holds enormous potential for translating epigenomic profiling into therapeutic leads.

Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the one of the most common subtypes in lung cancer. Although various targeted therapies have been used in the clinical practice, the 5-year overall survival rate of patients is still low. Thus, it is urgent to identify new therapeutic targets and develop new drugs for the treatment of the LUAD patients.

METHODS

Survival analysis was used to identify the prognostic genes. Gene co-expression network analysis was used to identify the hub genes driving the tumor development. A profile-based drug repositioning approach was used to repurpose the potentially useful drugs for targeting the hub genes. MTT and LDH assay were used to measure the cell viability and drug cytotoxicity, respectively. Western blot was used to detect the expression of the proteins.

FINDINGS

We identified 341 consistent prognostic genes from two independent LUAD cohorts, whose high expression was associated with poor survival outcomes of patients. Among them, eight genes were identified as hub genes due to their high centrality in the key functional modules in the gene-co-expression network analysis and these genes were associated with the various hallmarks of cancer (e.g., DNA replication and cell cycle). We performed drug repositioning analysis for three of the eight genes (CDCA8, MCM6, and TTK) based on our drug repositioning approach. Finally, we repurposed five drugs for inhibiting the protein expression level of each target gene and validated the drug efficacy by performing in vitro experiments.

INTERPRETATION

We found the consensus targetable genes for the treatment of LUAD patients with different races and geographic characteristics. We also proved the feasibility of our drug repositioning approach for the development of new drugs for disease treatment.

Relationship between drug targets and drug-signature networks: a network-based genome-wide landscape

Drugs produce pharmaceutical and adverse effects that arise from the complex relationship between drug targets and signatures; by considering such relationships, we can begin to understand the cellular mechanisms of drugs. In this study, we selected 463 genes from the DSigDB database corresponding to targets and signatures for 382 FDA-approved drugs with both protein binding information for a drug-target score (KDTN, i.e., the degree to which the protein encoded by the gene binds to a number of drugs) and microarray signature information for a drug-sensitive score (KDSN, i.e., the degree to which gene expression is stimulated by the drug). Accordingly, we constructed two drug-gene bipartite network models, a drug-target network and drug-signature network, which were merged into a multidimensional model. Analysis revealed that the KDTN and KDSN were in mutually exclusive and reciprocal relationships in terms of their biological network structure and gene function. A symmetric balance between the KDTN and KDSN of genes facilitates the possibility of therapeutic drug effects in whole genome. These results provide new insights into the relationship between drugs and genes, specifically drug targets and drug signatures.

Drug Repurposing at the Interface of Melanoma Immunotherapy and Autoimmune Disease

Cancer cells have a remarkable ability to evade recognition and destruction by the immune system. At the same time, cancer has been associated with chronic inflammation, while certain autoimmune diseases predispose to the development of neoplasia. Although cancer immunotherapy has revolutionized antitumor treatment, immune-related toxicities and adverse events detract from the clinical utility of even the most advanced drugs, especially in patients with both, metastatic cancer and pre-existing autoimmune diseases. Here, the combination of multi-omics, data-driven computational approaches with the application of network concepts enables in-depth analyses of the dynamic links between cancer, autoimmune diseases, and drugs. In this review, we focus on molecular and epigenetic metastasis-related processes within cancer cells and the immune microenvironment. With melanoma as a model, we uncover vulnerabilities for drug development to control cancer progression and immune responses. Thereby, drug repurposing allows taking advantage of existing safety profiles and established pharmacokinetic properties of approved agents. These procedures promise faster access and optimal management for cancer treatment. Together, these approaches provide new disease-based and data-driven opportunities for the prediction and application of targeted and clinically used drugs at the interface of immune-mediated diseases and cancer towards next-generation immunotherapies.

The Antineoplastic Effect of Carboplatin Is Potentiated by Combination with Pitavastatin or Metformin in a Chemoresistant High-Grade Serous Carcinoma Cell Line

The combination of Carboplatin with Paclitaxel is the mainstay treatment for high-grade serous carcinoma; however, many patients with advanced disease undergo relapse due to chemoresistance. Drug repurposing coupled with a combination of two or more compounds with independent mechanisms of action has the potential to increase the success rate of the antineoplastic treatment. The purpose of this study was to explore whether the combination of Carboplatin with repurposed drugs led to a therapeutic benefit. Hence, we assessed the cytotoxic effects of Carboplatin alone and in combination with several repurposed drugs (Pitavastatin, Metformin, Ivermectin, Itraconazole and Alendronate) in two tumoral models, i.e., Carboplatin (OVCAR8) and Carboplatin-Paclitaxel (OVCAR8 PTX R P) chemoresistant cell lines and in a non-tumoral (HOSE6.3) cell line. Cellular viability was measured using the Presto Blue assay, and the synergistic interactions were evaluated using the Chou-Talalay, Bliss Independence and Highest Single Agent reference models. Combining Carboplatin with Pitavastatin or Metformin displayed the highest cytotoxic effect and the strongest synergism among all combinations for OVCAR8 PTX R P cells, resulting in a chemotherapeutic effect superior to Carboplatin as a single agent. Concerning HOSE6.3 cells, combining Carboplatin with almost all the repurposed drugs demonstrated a safe pharmacological profile. Overall, we propose that Pitavastatin or Metformin could act synergistically in combination with Carboplatin for the management of high-grade serous carcinoma patients with a Carboplatin plus Paclitaxel resistance profile.

Quantification of the growth suppression of HER2+ breast cancer colonies under the effect of trastuzumab and PD-1/PD-L1 inhibitor

Introduction

Immune checkpoint blockade (ICB)-based therapy is revolutionizing cancer treatment by fostering successful immune surveillance and effector cell responses against various types of cancers. However, patients with HER2+ cancers are yet to benefit from this therapeutic strategy. Precisely, several questions regarding the right combination of drugs, drug modality, and effective dose recommendations pertaining to the use of ICB-based therapy for HER2+ patients remain unanswered.

Methods

In this study, we use a mathematical modeling-based approach to quantify the growth inhibition of HER2+ breast cancer (BC) cell colonies (ZR75) when treated with anti-HER2; trastuzumab (TZ) and anti-PD-1/PD-L1 (BMS-202) agents.

Results and discussion

Our data show that a combination therapy of TZ and BMS-202 can significantly reduce the viability of ZR75 cells and trigger several morphological changes. The combination decreased the cell's invasiveness along with altering several key pathways, such as Akt/mTor and ErbB2 compared to monotherapy. In addition, BMS-202 causes dose-dependent growth inhibition of HER2+ BC cell colonies alone, while this effect is significantly improved when used in combination with TZ. Based on the in-vitro monoculture experiments conducted, we argue that BMS-202 can cause tumor growth suppression not only by mediating immune response but also by interfering with the growth signaling pathways of HER2+BC. Nevertheless, further studies are imperative to substantiate this argument and to uncover the potential crosstalk between PD-1/PD-L1 inhibitors and HER2 growth signaling pathways in breast cancer.

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.

Bioactive Microbial Metabolites in Cancer Therapeutics: Mining, Repurposing, and Their Molecular Targets

The persistence and resurgence of cancer, characterized by abnormal cell growth and differentiation, continues to be a serious public health concern critically affecting public health, social life, and the global economy. Hundreds of putative drug molecules of synthetic and natural origin were approved for anticancer therapy in the last few decades. Although conventional anticancer treatment strategies have promising aspects, several factors such as their limitations, drug resistance, and side effects associated with them demand more effort in repositioning or developing novel therapeutic regimens. The rich heritage of microbial bioactive components remains instrumental in providing novel avenues for cancer therapeutics. Actinobacteria, Firmicutes, and fungi have a plethora of bioactive compounds, which received attention for their efficacy in cancer treatment targeting different pathways responsible for abnormal cell growth and differentiation. Yet the full potential remains underexplored to date, and novel compounds from such microbes are reported regularly. In addition, the advent of computational tools has further augmented the mining of microbial secondary metabolites and identifying their molecular targets in cancer cells. Furthermore, the drug-repurposing strategy has facilitated the use of approved drugs of microbial origin in regulating cancer cell growth and progression. The wide diversity of microbial compounds, different mining approaches, and multiple modes of action warrant further investigations on the current status of microbial metabolites in cancer therapeutics. Hence, in this review, we have critically discussed the untapped potential of microbial products in mitigating cancer progression. The review also summarizes the impact of drug repurposing in cancer therapy and discusses the novel avenues for future therapeutic drug development against cancer.

Target-Based Small Molecule Drug Discovery for Colorectal Cancer: A Review of Molecular Pathways and In Silico Studies

Colorectal cancer is one of the most prevalent cancer types. Although there have been breakthroughs in its treatments, a better understanding of the molecular mechanisms and genetic involvement in colorectal cancer will have a substantial role in producing novel and targeted treatments with better safety profiles. In this review, the main molecular pathways and driver genes that are responsible for initiating and propagating the cascade of signaling molecules reaching carcinoma and the aggressive metastatic stages of colorectal cancer were presented. Protein kinases involved in colorectal cancer, as much as other cancers, have seen much focus and committed efforts due to their crucial role in subsidizing, inhibiting, or changing the disease course. Moreover, notable improvements in colorectal cancer treatments with in silico studies and the enhanced selectivity on specific macromolecular targets were discussed. Besides, the selective multi-target agents have been made easier by employing in silico methods in molecular de novo synthesis or target identification and drug repurposing.

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

BACKGROUND

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

METHODS

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

FINDINGS

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

INTERPRETATION

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

FUNDING

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

A Gene Co-Expression Network-Based Drug Repositioning Approach Identifies Candidates for Treatment of Hepatocellular Carcinoma

Simple Summary

Hepatocellular carcinoma (HCC) is the most common malignancy of liver cancer. However, treatment of HCC is still severely limited due to limitation of drug therapy. We aimed to screen more possible target genes and candidate drugs for HCC, exploring the possibility of drug treatments from systems biological perspective. We identified ten candidate target genes, which are hub genes in HCC co-expression networks, which also possess significant prognostic value in two independent HCC cohorts. The rationality of these target genes was well demonstrated through variety analyses of patient expression profiles. We then screened candidate drugs for target genes and finally identified withaferin-a and mitoxantrone as the candidate drug for HCC treatment. The drug effectiveness was validated in in vitro model and computational analysis, providing more evidence for our drug repositioning method and results.

Abstract

Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.

Drug repurposing for cancer therapy in the era of precision medicine

Drug repurposing refers to the identification of clinically approved drugs, with the known safety profiles and defined pharmacokinetic properties, to new indications. Despite the advances in oncology research, cancers are still associated with the most unmet medical needs. Drug repurposing has emerged as a useful approach for the search for effective and durable cancer treatment. It may also represent a promising strategy to facilitate precision cancer treatment and to overcome drug resistance. The repurposing of non-cancer drugs for precision oncology effectively extends the inventory of actionable molecular targets and thus increases the number of patients who may benefit from precision cancer treatment. In cancer types where genetic heterogeneity is so high that it is not feasible to identify strong repurposed drug candidates for standard treatment, the precision oncology approach offers individual patients access to novel treatment options. For repurposed candidates with low potency, a combination of multiple repurposed drugs may produce a synergistic therapeutic effect. Precautions should be taken when combining repurposed drugs with anticancer agents to avoid detrimental drug-drug interactions and unwanted side effects. New multifactorial data analysis and artificial intelligence methods are needed to untangle the complex association of molecular signatures influencing specific cancer subtypes to facilitate drug repurposing in precision oncology.

DDREL: From drug-drug relationships to drug repurposing

Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.

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.

Levofloxacin exerts broad-spectrum anticancer activity via regulation of THBS1, LAPTM5, SRD5A3, MFAP5 and P4HA1

One cost-effective way for identifying novel cancer therapeutics is in the repositioning of available drugs for which current therapies are inadequate. Levofloxacin prevents DNA duplication in bacteria by inhibiting the activity of DNA helicase. As eukaryotic cells have similar intracellular biologic characteristics as prokaryotic cells, we speculate that antibiotics inhibiting DNA duplication in bacteria may also affect the survival of cancer cells. Here we report that levofloxacin significantly inhibited the proliferation and clone formation of cancer cells and xenograft tumor growth through cell cycle arrest at G2/M and by enhancing apoptosis. Levofloxacin significantly altered gene expression in a direction favoring anticancer activity. THBS1 and LAPTM5 were dose-dependently upregulated whereas SRD5A3, MFAP5 and P4HA1 were downregulated. Pathway analysis revealed that levofloxacin significantly regulated canonical oncogenic pathways. Specific network enrichment included a MAPK/apoptosis/cytokine-cytokine receptor interaction pathway network that associates with cell growth, differentiation, cell death, angiogenesis and development and repair processes and a bladder cancer/P53 signaling pathway network mediating the inhibition of angiogenesis and metastasis. THBS1 overlapped in 16 of the 22 enriched apoptotic pathways and the 2 pathways in the bladder cancer/P53 signaling pathway network. P4HA1 enriched in 7 of the top 10 molecular functions regulated by differential downregulated genes. Our results indicate that levofloxacin has broad-spectrum anticancer activity with the potential to benefit cancer patients already treated or requiring prophylaxis for an infectious syndrome. The efficacy we find with levofloxacin may provide insight into the discovery and the design of novel less toxic anticancer drugs.

Drugs repurposed: An advanced step towards the treatment of breast cancer and associated challenges

Breast cancer (BC) is mostly observed in women and is responsible for huge mortality in women subjects globally. Due to the continued development of drug resistance and other contributing factors, the scientific community needs to look for new alternatives, and drug repurposing is one of the best opportunities. Here we light upon the drug repurposing with a major focus on breast cancer. BC is a division of cancer known as the leading cause of death of 2.3 million women globally, with 685,000 fatalities. This number is steadily rising, necessitating the development of a treatment that can extend survival time. All available treatments for BC are very costly as well as show side effects. This unfulfilled requirement of the anti-cancer drugs ignited an enthusiasm for drug repositioning, which means finding out the anti-cancer use of already marketed drugs for other complications. With the advancement in proteomics, genomics, and computational approaches, the drug repurposing process hastens. So many drugs are repurposed for the BC, including alkylating agents, antimetabolite, anthracyclines, an aromatase inhibitor, mTOR, and many more. The drug resistance in breast cancer is rising, so reviewing how the challenges in breast cancer can be combated with drug repurposing. This paper provides the updated information on all the repurposed drugs candidates for breast cancer with the molecular mechanism responsible for their anti-tumor activity. Additionally, all the challenges that occur during the repurposing of the drugs are discussed.

Gut-microbiota-microglia-brain interactions in Alzheimer's disease: knowledge-based, multi-dimensional characterization

BACKGROUND

Interactions between the gut microbiota, microglia, and aging may modulate Alzheimer's disease (AD) pathogenesis but the precise nature of such interactions is not known.

METHODS

We developed an integrated multi-dimensional, knowledge-driven, systems approach to identify interactions among microbial metabolites, microglia, and AD. Publicly available datasets were repurposed to create a multi-dimensional knowledge-driven pipeline consisting of an integrated network of microbial metabolite-gene-pathway-phenotype (MGPPN) consisting of 34,509 nodes (216 microbial metabolites, 22,982 genes, 1329 pathways, 9982 mouse phenotypes) and 1,032,942 edges.

RESULTS

We evaluated the network-based ranking algorithm by showing that abnormal microglia function and physiology are significantly associated with AD pathology at both genetic and phenotypic levels: AD risk genes were ranked at the top 6.4% among 22,982 genes, P < 0.001. AD phenotypes were ranked at the top 11.5% among 9982 phenotypes, P < 0.001. A total of 8094 microglia-microbial metabolite-gene-pathway-phenotype-AD interactions were identified for top-ranked AD-associated microbial metabolites. Short-chain fatty acids (SCFAs) were ranked at the top among prioritized AD-associated microbial metabolites. Through data-driven analyses, we provided evidence that SCFAs are involved in microglia-mediated gut-microbiota-brain interactions in AD at both genetic, functional, and phenotypic levels.

CONCLUSION

Our analysis produces a novel framework to offer insights into the mechanistic links between gut microbial metabolites, microglia, and AD, with the overall goal to facilitate disease mechanism understanding, therapeutic target identification, and designing confirmatory experimental studies.

Drug repurposing for opioid use disorders: integration of computational prediction, clinical corroboration, and mechanism of action analyses

Morbidity and mortality from opioid use disorders (OUD) and other substance use disorders (SUD) is a major public health crisis, yet there are few medications to treat them. There is an urgency to accelerate SUD medication development. We present an integrated drug repurposing strategy that combines computational prediction, clinical corroboration using electronic health records (EHRs) of over 72.9 million patients and mechanisms of action analysis. Among top-ranked repurposed candidate drugs, tramadol, olanzapine, mirtazapine, bupropion, and atomoxetine were associated with increased odds of OUD remission (adjusted odds ratio: 1.51 [1.38-1.66], 1.90 [1.66-2.18], 1.38 [1.31-1.46], 1.37 [1.29-1.46], 1.48 [1.25-1.76], p value < 0.001, respectively). Genetic and functional analyses showed these five candidate drugs directly target multiple OUD-associated genes including BDNF, CYP2D6, OPRD1, OPRK1, OPRM1, HTR1B, POMC, SLC6A4 and OUD-associated pathways, including opioid signaling, G-protein activation, serotonin receptors, and GPCR signaling. In summary, we developed an integrated drug repurposing approach and identified five repurposed candidate drugs that might be of value for treating OUD patients, including those suffering from comorbid conditions.

In silico drug repositioning using deep learning and comprehensive similarity measures

BACKGROUND

Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized.

METHODS

In this work, we develop a deep gated recurrent units model to predict potential drug-disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug-disease interactions.

RESULTS

The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out.

CONCLUSION

The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.

Harnessing Drug Repurposing for Exploration of New Diseases: An Insight to Strategies and Case Studies

BACKGROUND

Traditional drug discovery is time consuming, costly, and risky process. Owing to the large investment, excessive attrition, and declined output, drug repurposing has become a blooming approach for the identification and development of new therapeutics. The method has gained momentum in the past few years and has resulted in many excellent discoveries. Industries are resurrecting the failed and shelved drugs to save time and cost. The process accounts for approximately 30% of the new US Food and Drug Administration approved drugs and vaccines in recent years.

METHODS

A systematic literature search using appropriate keywords were made to identify articles discussing the different strategies being adopted for repurposing and various drugs that have been/are being repurposed.

RESULTS

This review aims to describe the comprehensive data about the various strategies (Blinded search, computational approaches, and experimental approaches) used for the repurposing along with success case studies (treatment for orphan diseases, neglected tropical disease, neurodegenerative diseases, and drugs for pediatric population). It also inculcates an elaborated list of more than 100 drugs that have been repositioned, approaches adopted, and their present clinical status. We have also attempted to incorporate the different databases used for computational repurposing.

CONCLUSION

The data presented is proof that drug repurposing is a prolific approach circumventing the issues poised by conventional drug discovery approaches. It is a highly promising approach and when combined with sophisticated computational tools, it also carries high precision. The review would help researches in prioritizing the drugrepositioning method much needed to flourish the drug discovery research.

Cephalosporin antibiotics specifically and selectively target nasopharyngeal carcinoma through HMOX1-induced ferroptosis

AIMS

Many antibiotics derived from mold metabolites have been found to possess anticarcinogenic properties. We aimed to investigate whether they may elicit anticancer activity, especially against nasopharyngeal carcinoma.

MAIN METHODS

The response of nasopharyngeal and other carcinoma cell lines to cephalosporin antibiotics was evaluated in vitro and in vivo. MTT and clonogenic colony formation assays assessed the viability and proliferation of cultured cells. Flow cytometry was used to assess cell cycle parameters and apoptotic markers. Tumor growth was determined using a xenograft model in vivo. Microarray and RT-qPCR expression analyses investigate differential gene expression. Mechanistic assessment of HMOX1 in cefotaxime-mediated ferroptosis was tested with Protoporphyrin IX zinc.

KEY FINDINGS

Cephalosporin antibiotics showed highly specific and selective anticancer activity on nasopharyngeal carcinoma CNE2 cells both in vitro and vivo with minimal toxicity. Cefotaxime sodium significantly regulated 11 anticancer relevant genes in CNE2 cells in a concentration-dependent manner. Pathway analyses indicate apoptotic and the ErbB-MAPK-p53 signaling pathways are significantly enriched. HMOX1 represents the top one ranked upregulated gene by COS and overlaps with 16 of 42 enriched apoptotic signaling pathways. Inhibition of HMOX1 significantly reduced the anticancer efficacy of cefotaxime in CNE2 cells.

SIGNIFICANCE

Our discovery is the first to highlight the off-label potential of cephalosporin antibiotics as a specific and selective anticancer drug for nasopharyngeal carcinoma. We mechanistically show that induction of ferroptosis through HMOX1 induction mediates cefotaxime anticancer activity. Our findings provide an alternative treatment for nasopharyngeal carcinoma by showing that existing cephalosporin antibiotics are specific and selective anticancer drugs.

Web-based Tools for Drug Repurposing: Successful Examples of Collaborative Research

Computational approaches have been proven to be complementary tools of interest in identifying potential candidates for drug repurposing. However, although the methods developed so far offer interesting opportunities and could contribute to solving issues faced by the pharmaceutical sector, they also come with their constraints. Indeed, specific challenges ranging from data access, standardization and integration to the implementation of reliable and coherent validation methods must be addressed to allow systematic use at a larger scale. In this mini-review, we cover computational tools recently developed for addressing some of these challenges. This includes specific databases providing accessibility to a large set of curated data with standardized annotations, web-based tools integrating flexible user interfaces to perform fast computational repurposing experiments and standardized datasets specifically annotated and balanced for validating new computational drug repurposing methods. Interestingly, these new databases combined with the increasing number of information about the outcomes of drug repurposing studies can be used to perform a meta-analysis to identify key properties associated with successful drug repurposing cases. This information could further be used to design estimation methods to compute a priori assessment of the repurposing possibilities.

Integrative Genomic-Epigenomic Analysis of Clozapine-Treated Patients with Refractory Psychosis

Clozapine (CLZ) is the only antipsychotic drug that has been proven to be effective in patients with refractory psychosis, but it has also been proposed as an effective mood stabilizer; however, the complex mechanisms of action of CLZ are not yet fully known. To find predictors of CLZ-associated phenotypes (i.e., the metabolic ratio, dosage, and response), we explore the genomic and epigenomic characteristics of 44 patients with refractory psychosis who receive CLZ treatment based on the integration of polygenic risk score (PRS) analyses in simultaneous methylome profiles. Surprisingly, the PRS for bipolar disorder (BD-PRS) was associated with the CLZ metabolic ratio (pseudo-R2 = 0.2080, adjusted p-value = 0.0189). To better explain our findings in a biological context, we assess the protein-protein interactions between gene products with high impact variants in the top enriched pathways and those exhibiting differentially methylated sites. The GABAergic synapse pathway was found to be enriched in BD-PRS and was associated with the CLZ metabolic ratio. Such interplay supports the use of CLZ as a mood stabilizer and not just as an antipsychotic. Future studies with larger sample sizes should be pursued to confirm the findings of this study.

Drugs Repurposing in High-Grade Serous Ovarian Cancer

BACKGROUND

Ovary Carcinoma (OC) is the most lethal gynecological neoplasm due to the late diagnoses and to the common development of resistance to platinum-based chemotherapy. Thus, novel therapeutic approaches are urgently required. In this regard, the strategy of drug repurposing is becoming attractive. By this approach, the effectiveness of a drug originally developed for another indication is tested in a different pathology. The advantage is that data about pharmacokinetic properties and toxicity are already available. Thus, in principle, it is possible to reduce research costs and to speed up drug usage/marketing.

RESULTS

Here, some noticeable examples of repurposed drugs for OC, such as amiodarone, ruxolitinib, statins, disulfiram, ormeloxifenem, and Quinacrine, are reported. Amiodarone, an antiarrhythmic agent, has shown promising anti-OC activity, although the systemic toxicity should not be neglected. The JAK inhibitor, Ruxolitinib, may be employed particularly in coadministration with standard OC therapy as it synergistically interacts with platinum-based drugs. Particularly interesting is the use of statin which represent one of the most commonly administered drugs in aged population to treat hypercholesterolemia. Disulfiram, employed in the treatment of chronic alcoholism, has shown anti-OC properties. Ormeloxifene, commonly used for contraception, seems to be promising, especially due to the negligible side effects. Finally, Quinacrine used as an antimicrobial and anti-inflammatory drug, is able to downregulate OC cell growth and promote cell death.

CONCLUSION

Whereas further testing in patients are necessary to better clarify the therapeutic potential of repurposed drugs for OC, it is believed that their use, better if combined with OC targeted delivery systems, can significantly contribute to the development of novel and effective anti-OC treatments.

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors-let us call that the silver bullet approach to cancer therapeutics-to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns-we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases "shrapnel gunshots" may become more effective than "silver bullets". Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.

Drug Repurposing Approach for Developing Novel Therapy Against Mupirocin-Resistant Staphylococcus aureus

Multidrug resistance (MDR) is a major health issue for the treatment of infectious diseases throughout the world. Staphylococcus aureus (S. aureus) is a Gram-positive bacteria, responsible for various local and systemic infections in humans. The continuous and abrupt use of antibiotics against bacteria such as S. aureus results in the development of resistant strains. Presently, mupirocin (MUP) is the drug of choice against S. aureus and MDR (methicillin-resistant). However, S. aureus has acquired resistance against MUP as well due to isoleucyl-tRNA synthetase (IleS) mutation at sites 588 and 631. Thus, the aim of the present study was to discover novel bioactives against MUP-resistant S. aureus using in silico drug repurposing approaches. In silico drug repurposing techniques were used to obtain suitable bioactive lead molecules such as buclizine, tasosartan, emetine, medrysone, and so on. These lead molecules might be able to resolve this issue. These leads were obtained through molecular docking simulation based virtual screening, which could be promising for the treatment of MUP-resistant S. aureus. The findings of the present work need to be validated further through in vitro and in vivo studies for their clinical application.

Overcoming cancer therapeutic bottleneck by drug repurposing

Ever present hurdles for the discovery of new drugs for cancer therapy have necessitated the development of the alternative strategy of drug repurposing, the development of old drugs for new therapeutic purposes. This strategy with a cost-effective way offers a rare opportunity for the treatment of human neoplastic disease, facilitating rapid clinical translation. With an increased understanding of the hallmarks of cancer and the development of various data-driven approaches, drug repurposing further promotes the holistic productivity of drug discovery and reasonably focuses on target-defined antineoplastic compounds. The "treasure trove" of non-oncology drugs should not be ignored since they could target not only known but also hitherto unknown vulnerabilities of cancer. Indeed, different from targeted drugs, these old generic drugs, usually used in a multi-target strategy may bring benefit to patients. In this review, aiming to demonstrate the full potential of drug repurposing, we present various promising repurposed non-oncology drugs for clinical cancer management and classify these candidates into their proposed administration for either mono- or drug combination therapy. We also summarize approaches used for drug repurposing and discuss the main barriers to its uptake.

Eupatilin Promotes Cell Death by Calcium Influx through ER-Mitochondria Axis with SERPINB11 Inhibition in Epithelial Ovarian Cancer

Ovarian cancer is the leading cause of gynecological cancer-related mortality. The anticancer effect of eupatilin, a family of flavonoids, is known in many cancer types, but it is unclear what mechanism it plays in ovarian cancer. In this study, eupatilin promoted cell death of ovarian cancer cells by activating caspases, cell cycle arrest, reactive oxygen species (ROS) generation, calcium influx, disruption of the endoplasmic reticulum (ER)–mitochondria axis with SERPINB11 inhibition, and downregulation of phosphoinositide 3-kinase (PI3K) and mitogen activated protein kinase (MAPK) pathways. Additionally, eupatilin-reduced SERPINB11 expression enhanced the effect of conventional chemotherapeutic agents against ovarian cancer cell progression. Cotreatment with siSERPINB11 and eupatilin increased calcium-ion-dependent apoptotic activity in ovarian cancer cells. Although there were no significant toxic effects of eupatilin on embryos, eupatilin completely inhibited tumorigenesis in a zebrafish xenograft model. In addition, eupatilin suppressed angiogenesis in zebrafish transgenic models. Collectively, downregulating SERPINB11 with eupatilin against cancer progression may improve therapeutic activity.

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

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

Non-Negative Matrix Factorization for Drug Repositioning: Experiments with the repoDB Dataset

Computational methods for drug repositioning are gaining mainstream attention with the availability of experimental gene expression datasets and manually curated relational information in knowledge bases. When building repurpos-ing tools, a fundamental limitation is the lack of gold standard datasets that contain realistic true negative examples of drug-disease pairs that were shown to be non-indications. To address this gap, the repoDB dataset was created in 2017 as a first of its kind realistic resource to benchmark drug repositioning methods - its positive examples are drawn from FDA approved indications and negatives examples are derivedfrom failed clinical trials. In this paper, we present the first effort for repositioning that directly tests against repoDB instances. By using hand-curated drug-disease indications from the UMLS Metathesaurus and automatically extracted relations from the SemMedDB database, we employ non-negative matrix factorization (NMF) methods to recover repoDB positive indications. Among recoverable approved indications, our NMF methods achieve 96% recall with 80% precision providing further evidence that hand-curated knowledge and matrix completion methods can be exploited for hypothesis generation.

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.

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

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

Phyotochemical candidates repurposing for cancer therapy and their molecular mechanisms

Though limited success through chemotherapy, radiotherapy and surgery has been obtained for efficient cancer therapy for modern decades, cancers are still considered high burden to human health worldwide to date. Recently repurposing drugs are attractive with lower cost and shorter time compared to classical drug discovery, just as Metformin from Galega officinalis, originally approved for treating Type 2 diabetes by FDA, is globally valued at millions of US dollars for cancer therapy. As most previous reviews focused on FDA approved drugs and synthetic agents, current review discussed the anticancer potential of phytochemicals originally approved for treatment of cardiovascular diseases, diabetes, infectious diarrhea, depression and malaria with their molecular mechanisms and efficacies and suggested future research perspectives.

Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning

BACKGROUND

Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc. RESULTS: We propose a novel computational method for drug repositioning. We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds of entities. The proposed method outperforms several baseline methods in recovering missing associations. Most of the top predictions are validated by literature search and computational docking. Latent factors are used to cluster the drugs, targets and diseases into functional groups. Topological Data Analysis (TDA) is applied to investigate the properties of the clusters. We find that the latent factors are able to capture the functional patterns and underlying molecular mechanisms of drugs, targets and diseases. In addition, we focus on repurposing drugs for cancer and discover not only new therapeutic use but also adverse effects of the drugs. In the in-depth study of associations among the clusters of drugs, targets and cancer subtypes, we find there exist strong associations between particular clusters.

CONCLUSIONS

The proposed method is able to recover missing associations, discover new predictions and uncover functional clusters of drugs, targets and diseases. The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.

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.

Computational Drug Repurposing: Current Trends

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Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre-existing drugs has been accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or incorporating molecular structure information of drugs and protein targets with other kinds of data derived from systems biology provide great potential to accelerate drug discovery and improve the success of drug repurposing attempts. In this review, we highlight commonly used computational drug repurposing strategies, including bioinformatics and cheminformatics tools, to integrate large-scale data emerging from the systems biology, and consider both the challenges and opportunities of using this approach. Moreover, we provide successful examples and case studies that combined various in silico drug-repurposing strategies to predict potential novel uses for known therapeutics.

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.

EK-DRD: A Comprehensive Database for Drug Repositioning Inspired by Experimental Knowledge

Drug repositioning, or the identification of new indications for approved therapeutic drugs, has gained substantial traction with both academics and pharmaceutical companies because it reduces the cost and duration of the drug development pipeline and the likelihood of unforeseen adverse events. To date there has not been a systematic effort to identify such opportunities, in part because of the lack of a comprehensive resource for an enormous amount of unsystematic drug repositioning information to support scientists who could benefit from this endeavor. To address this challenge, we developed a new database, the Experimental Knowledge-Based Drug Repositioning Database (EK-DRD), by using text and data mining as well as manual curation. EK-DRD contains experimentally validated drug repositioning annotation for 1861 FDA-approved and 102 withdrawn small-molecule drugs. Annotation was done at four levels using 30 944 target assay records, 3999 cell assay records, 585 organism assay records, and 8889 clinical trial records. Additionally, approximately 1799 repositioning protein or target sequences coupled with 856 related diseases and 1332 pathways are linked to the drug entries. Our web-based software displays a network of integrative relationships between drugs, their repositioning targets, and related diseases. The database is fully searchable and supports extensive text, sequence, chemical structure, and relational query searches. EK-DRD is freely accessible at http://www.idruglab.com/drd/index.php .

Identification of novel analgesics through a drug repurposing strategy

The identification of new indications for approved or failed drugs is a process called drug repositioning or drug repurposing. The motivation includes overcoming the productivity gap that exists in drug development, which is a high-cost–high-risk process. Repositioning also includes rescuing drugs that have safely entered the market but have failed to demonstrate sufficient efficiency for the initial clinical indication. Considering the high prevalence of chronic pain, the lack of sufficient efficacy and the safety issues of current analgesics, repositioning seems to be an attractive approach. This review presents example of drugs that already have been repositioned and highlights new technologies that are available for the identification of additional compounds to stimulate the curiosity of readers for further exploration.

Predicting drug–disease associations by network embedding and biomedical data integration

Purpose

The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue.

Design/methodology/approach

Here, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation.

Findings

This model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs.

Originality/value

This study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.