DRUGSURV: a resource for repositioning of approved and experimental drugs in oncology based on patient survival information

The use of existing drugs for new therapeutic applications, commonly referred to as drug repositioning, is a way for fast and cost-efficient drug discovery. Drug repositioning in oncology is commonly initiated by in vitro experimental evidence that a drug exhibits anticancer cytotoxicity. Any independent verification that the observed effects in vitro may be valid in a clinical setting, and that the drug could potentially affect patient survival in vivo is of paramount importance. Despite considerable recent efforts in computational drug repositioning, none of the studies have considered patient survival information in modelling the potential of existing/new drugs in the management of cancer. Therefore, we have developed DRUGSURV; this is the first computational tool to estimate the potential effects of a drug using patient survival information derived from clinical cancer expression data sets. DRUGSURV provides statistical evidence that a drug can affect survival outcome in particular clinical conditions to justify further investigation of the drug anticancer potential and to guide clinical trial design. DRUGSURV covers both approved drugs (∼1700) as well as experimental drugs (∼5000) and is freely available at http://www.bioprofiling.de/drugsurv.     

药物重定位的计算分析方法

全新结构药物的研发存在周期长、耗资大、风险高的问题.通过各种技术预测已有药物的新适应症,即药物重定位,可以缩短药物研发时间、降低研发成本和风险.由于疾病种类和已知药物的数量繁多,完全通过实验筛选已知药物的新用途仍然具有很高的成本.随着组学和药物信息学数据的积累,药物重定位进入到了理性设计和实验筛选相结合的阶段,药物重定位的计算预测已经成为计算生物学和系统生物学的重要研究方向.本文将目前药物重定位计算分析的策略归纳为药物-靶标关系分析、药物-药物关系分析和药物-疾病关系分析,对已报道的技术方法及其成功应用实例进行了综述.     

Repositioning drugs for inflammatory disease – fishing for new anti-inflammatory agents

Inflammation is an important and appropriate host response to infection or injury. However, dysregulation of this response, with resulting persistent or inappropriate inflammation, underlies a broad range of pathological processes, from inflammatory dermatoses to type 2 diabetes and cancer. As such, identifying new drugs to suppress inflammation is an area of intense interest. Despite notable successes, there still exists an unmet need for new effective therapeutic approaches to treat inflammation. Traditional drug discovery, including structure-based drug design, have largely fallen short of satisfying this unmet need. With faster development times and reduced safety and pharmacokinetic uncertainty, drug repositioning – the process of finding new uses for existing drugs – is emerging as an alternative strategy to traditional drug design that promises an improved risk-reward trade-off. Using a zebrafish in vivo neutrophil migration assay, we undertook a drug repositioning screen to identify unknown anti-inflammatory activities for known drugs. By interrogating a library of 1280 approved drugs for their ability to suppress the recruitment of neutrophils to tail fin injury, we identified a number of drugs with significant anti-inflammatory activity that have not previously been characterized as general anti-inflammatories. Importantly, we reveal that the ten most potent repositioned drugs from our zebrafish screen displayed conserved anti-inflammatory activity in a mouse model of skin inflammation (atopic dermatitis). This study provides compelling evidence that exploiting the zebrafish as an in vivo drug repositioning platform holds promise as a strategy to reveal new anti-inflammatory activities for existing drugs.KEY WORDS: Drug repositioning, Zebrafish, Inflammation, Neutrophil, Atopic dermatitis, Immunity     

Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.     

eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs

About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/.     

Repurposing approach identifies new treatment options for invasive fungal disease

Drug repositioning is the process of discovery, validation and marketing of previously approved drugs for new indications. Our aim was drug repositioning, using ligand-based and structure-based computational methods, of compounds that are similar to two hit compounds previously selected by our group that show promising antifungal activity. Through the ligand-based method, 100 compounds from each of three databases (MDDR, DrugBank and TargetMol) were selected by the Tanimoto coefficient, as similar to LMM5 or LMM11. These compounds were analyzed by the scaffold trees, and up to 10 compounds from each database were selected. The structure-based method (molecular docking) using thioredoxin reductase as the target drug was performed as a complementary approach, resulting in six compounds that were tested in an in vitro assay. All compounds, particularly raltegravir, showed antifungal activity against the genus Paracoccidioides. Raltegravir, an antiviral drug, showed promising antifungal activity against the experimental murine paracoccidioidomycosis, with significant reduction of the fungal burden and decreased alterations in the lung structure of mice treated with 1 mg/kg of raltegravir. In conclusion, the combination of two in silico methods for drug repositioning was able to select an antiviral drug with promising antifungal activity for treatment of paracoccidioidomycosis.     

Drug Repositioning by Kernel-Based Integration of Molecular Structure,Molecular Activity,and Phenotype Data

Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems.     

A systematic analysis of FDA-approved anticancer drugs

Background

The discovery of novel anticancer drugs is critical for the pharmaceutical research and development, and patient treatment. Repurposing existing drugs that may have unanticipated effects as potential candidates is one way to meet this important goal. Systematic investigation of efficient anticancer drugs could provide valuable insights into trends in the discovery of anticancer drugs, which may contribute to the systematic discovery of new anticancer drugs.

Results

In this study, we collected and analyzed 150 anticancer drugs approved by the US Food and Drug Administration (FDA). Based on drug mechanism of action, these agents are divided into two groups: 61 cytotoxic-based drugs and 89 target-based drugs. We found that in the recent years, the proportion of targeted agents tended to be increasing, and the targeted drugs tended to be delivered as signal drugs. For 89 target-based drugs, we collected 102 effect-mediating drug targets in the human genome and found that most targets located on the plasma membrane and most of them belonged to the enzyme, especially tyrosine kinase. From above 150 drugs, we built a drug-cancer network, which contained 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations. The network indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. From 89 targeted drugs, we built a cancer-drug-target network, which contained 214 nodes (23 cancer types, 89 drugs, and 102 targets) and 313 edges (118 drug-cancer associations and 195 drug-target associations). Starting from the network, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types by applying the common target-based approach. Most novel drug-cancer associations (116, 87%) are supported by at least one clinical trial study.

Conclusions

In this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to identify novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

     

现代生物学对药物发现的影响

随着后基因组时代的到来,药物发现研究领域不断涌现出一系列新思路、新技术、新方法,从而迅速推进药物发现的多元化发展。一方面,基因组学、蛋白质组学、转录组学、代谢组学、生物信息学、系统生物学等新兴学科的崛起与发展,为药物发现提供更为广泛而深刻的理论基础;另一方面,计算机辅助药物设计、高通量筛选、高内涵筛选、生物芯片、转基因和RNA干扰等高新技术的发展和完善,为药物发现提供了新的技术手段和有力工具,极大地拓宽了药物发现的途径。本文结合近年来现代生物学的研究进展,综述现代生物学对药物发现过程的影响。     

生物信息技术加速开发旧药新用途

传统的技术路线研发新药,不仅周期很长而且耗资巨大,开发已获批准药物新的治疗用途,又称为药物重定位,比传统的新药研发具有明显的优势．基于芯片的基因表达谱分析,已常规地广泛用于各种人类疾病的临床研究,提供了在全基因组水平描述疾病状态的特征信号．同时,基因芯片也广泛地用于对比药物处理前后细胞基因表达模式的变化,这也提供了反映药物效应的高质量信号．最近出版的Science Translational Medicine杂志同时发表了一个研究组的两篇论文,为我们展示了如何利用生物信息学手段重新解析和比较全基因组基因表达谱数据,以高效地预测药物的新用途．这两篇论文使用了公共数据库中的100种疾病基因表达谱数据,以及164种药物处理前后细胞基因表达谱数据,通过比较和配对疾病与药物基因表达谱,得到了一些可以逆转疾病异常表达基因的药物,其中证实了一些已知的药物-疾病组合,也预测了一些新的药物-疾病组合．最后通过实验验证了抗溃疡药可用于治疗肺癌,而抗癫痫药可治疗炎症性肠道疾病,进一步证实了他们所采用研究策略的正确性．于是,肺癌和炎性肠道疾病这两种临床上难治的疾病有了新的候选治疗药物,我们也有了一种挖掘已有数据快速发现药物新用途的思路和方法．     

Multiple independent analyses reveal only transcription factors as an enriched functional class associated with microRNAs

Background

The process of drug discovery and development is time-consuming and costly, and the probability of success is low. Therefore, there is rising interest in repositioning existing drugs for new medical indications. When successful, this process reduces the risk of failure and costs associated with de novo drug development. However, in many cases, new indications of existing drugs have been found serendipitously. Thus there is a clear need for establishment of rational methods for drug repositioning.

Results

In this study, we have established a database we call “PharmDB” which integrates data associated with disease indications, drug development, and associated proteins, and known interactions extracted from various established databases. To explore linkages of known drugs to diseases of interest from within PharmDB, we designed the Shared Neighborhood Scoring (SNS) algorithm. And to facilitate exploration of tripartite (Drug-Protein-Disease) network, we developed a graphical data visualization software program called phExplorer, which allows us to browse PharmDB data in an interactive and dynamic manner. We validated this knowledge-based tool kit, by identifying a potential application of a hypertension drug, benzthiazide (TBZT), to induce lung cancer cell death.

Conclusions

By combining PharmDB, an integrated tripartite database, with Shared Neighborhood Scoring (SNS) algorithm, we developed a knowledge platform to rationally identify new indications for known FDA approved drugs, which can be customized to specific projects using manual curation. The data in PharmDB is open access and can be easily explored with phExplorer and accessed via BioMart web service (http://www.i-pharm.org/, http://biomart.i-pharm.org/).     

From G Protein-coupled Receptor Structure Resolution to Rational Drug Design

A number of recent technical solutions have led to significant advances in G protein-coupled receptor (GPCR) structural biology. Apart from a detailed mechanistic view of receptor activation, the new structures have revealed novel ligand binding sites. Together, these insights provide avenues for rational drug design to modulate the activities of these important drug targets. The application of structural data to GPCR drug discovery ushers in an exciting era with the potential to improve existing drugs and discover new ones. In this review, we focus on technical solutions that have accelerated GPCR crystallography as well as some of the salient findings from structures that are relevant to drug discovery. Finally, we outline some of the approaches used in GPCR structure based drug design.     

Mining small-molecule screens to repurpose drugs

Repurposing and repositioning drugs--discovering new uses for existing and experimental medicines-is an attractive strategy for rescuing stalled pharmaceutical projects, finding treatments for neglected diseases, and reducing the time, cost and risk of drug development. As this strategy emerged, academic researchers began performing high-throughput screens (HTS) of small molecules--the type of experiments once exclusively conducted in industry--and making the data from these screens available to all. Several methods can mine this data to inform repurposing and repositioning efforts. Despite these methods' limitations, it is hopeful that they will accelerate the discovery of new uses for known drugs, but this hope has not yet been realized.     

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large‐scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross‐validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue‐specific expression information on the drug targets. We further show that disease‐specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease‐specific signatures.     

SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug–disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity (i.e., SARS), comorbidity (e.g., cardiovascular diseases), or for their association to drugs tentatively repurposed to treat COVID-19 (e.g., malaria, HIV, rheumatoid arthritis). Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments (e.g., chloroquine, hydroxychloroquine, tocilizumab, heparin), as well as a new combination therapy of 5 drugs (hydroxychloroquine, chloroquine, lopinavir, ritonavir, remdesivir), actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies (e.g., anti-IFNγ, anti-TNFα, anti-IL12, anti-IL1β, anti-IL6), and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.     

Drug discovery targeting cell division proteins,microtubules and FtsZ

Eukaryotic cell division or cytokinesis has been a major target for anticancer drug discovery. After the huge success of paclitaxel and docetaxel, microtubule-stabilizing agents (MSAs) appear to have gained a premier status in the discovery of next-generation anticancer agents. However, the drug resistance caused by MDR, point mutations, and overexpression of tubulin subtypes, etc., is a serious issue associated with these agents. Accordingly, the discovery and development of new-generation MSAs that can obviate various drug resistances has a significant meaning. In sharp contrast, prokaryotic cell division has been largely unexploited for the discovery and development of antibacterial drugs. However, recent studies on the mechanism of bacterial cytokinesis revealed that the most abundant and highly conserved cell division protein, FtsZ, would be an excellent new target for the drug discovery of next-generation antibacterial agents that can circumvent drug-resistances to the commonly used drugs for tuberculosis, MRSA and other infections. This review describes an account of our research on these two fronts in drug discovery, targeting eukaryotic as well as prokaryotic cell division.     

Drug discovery from medicinal plants

Current research in drug discovery from medicinal plants involves a multifaceted approach combining botanical, phytochemical, biological, and molecular techniques. Medicinal plant drug discovery continues to provide new and important leads against various pharmacological targets including cancer, HIV/AIDS, Alzheimer's, malaria, and pain. Several natural product drugs of plant origin have either recently been introduced to the United States market, including arteether, galantamine, nitisinone, and tiotropium, or are currently involved in late-phase clinical trials. As part of our National Cooperative Drug Discovery Group (NCDDG) research project, numerous compounds from tropical rainforest plant species with potential anticancer activity have been identified. Our group has also isolated several compounds, mainly from edible plant species or plants used as dietary supplements, that may act as chemopreventive agents. Although drug discovery from medicinal plants continues to provide an important source of new drug leads, numerous challenges are encountered including the procurement of plant materials, the selection and implementation of appropriate high-throughput screening bioassays, and the scale-up of active compounds.     

The continuing search for antitumor agents from higher plants

Plant secondary metabolites and their semi-synthetic derivatives continue to play an important role in anticancer drug therapy. In this short review, selected single chemical entity antineoplastic agents from higher plants that are currently in clinical trials as cancer chemotherapy drug candidates are described. These compounds are representative of a wide structural diversity. In addition, the approaches taken toward the discovery of anticancer agents from tropical plants in the laboratory of the authors are summarized. The successful clinical utilization of cancer chemotherapeutic agents from higher plants has been evident for about half a century, and, when considered with the promising pipeline of new plant-derived compounds now in clinical trials, this augurs well for the continuation of drug discovery research efforts to elucidate additional candidate substances of this type.     

Drug Repositioning for Diabetes Based on 'Omics' Data Mining

Drug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical ‘omics’ data. We analyzed data from genome wide association studies (GWAS), proteomics and metabolomics studies and revealed a total of 992 proteins as potential anti-diabetic targets in human. Information on the drugs that target these 992 proteins was retrieved from the Therapeutic Target Database (TTD) and 108 of these proteins are drug targets with drug projects information. Research and preclinical drug targets were excluded and 35 of the 108 proteins were selected as druggable proteins. Among them, five proteins were known targets for treating diabetes. Based on the pathogenesis knowledge gathered from the OMIM and PubMed databases, 12 protein targets of 58 drugs were found to have a new indication for treating diabetes. CMap (connectivity map) was used to compare the gene expression patterns of cells treated by these 58 drugs and that of cells treated by known anti-diabetic drugs or diabetes risk causing compounds. As a result, 9 drugs were found to have the potential to treat diabetes. Among the 9 drugs, 4 drugs (diflunisal, nabumetone, niflumic acid and valdecoxib) targeting COX2 (prostaglandin G/H synthase 2) were repurposed for treating type 1 diabetes, and 2 drugs (phenoxybenzamine and idazoxan) targeting ADRA2A (Alpha-2A adrenergic receptor) had a new indication for treating type 2 diabetes. These findings indicated that ‘omics’ data mining based drug repositioning is a potentially powerful tool to discover novel anti-diabetic indications from marketed drugs and clinical candidates. Furthermore, the results of our study could be related to other disorders, such as Alzheimer’s disease.     

Systematic drug repositioning based on clinical side-effects

Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naïve Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this “clinical phenotypic assay”.