Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease

For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.     

Old drugs,new tricks: Emerging role of drug repurposing in the management of atopic dermatitis

Atopic dermatitis is a chronic recurring pruritic inflammatory skin disease manifested by increased pro-inflammatory mediators which lead to dry, thickened, cracked, scaly skin. The current treatment options for atopic dermatitis management comprise drawbacks and leave unmet effective clinical needs. So, the approach for repurposing existing drugs for atopic dermatitis management may potentially overcome these unmet needs. Diseases that share the common pathophysiological pathways with atopic dermatitis can serve as a foundation for the repurposing of drugs. Drugs used in the management of cancer, rheumatoid arthritis, and other immune-mediated diseases such as psoriasis are under investigation to know the potential in atopic dermatitis management by utilizing repurposing strategies for a novel therapeutic indication. This review mainly envisages the probable repurposing of drugs for the management of atopic dermatitis disease; the barriers and regulatory aspects involved in the repurposing of existing drugs.     

Recent advances in drug repurposing using machine learning

Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase inhibitors, Alzheimer’s disease as well as COVID-19.     

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.     

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．     

Old Weapon for New Enemy: Drug Repurposing for Treatment of Newly Emerging Viral Diseases

正Emerging and re-emerging viral diseases are a public health concern for the whole world and pose a major threat to human health and life. In last decades, numerous major outbreaks of emerging and re-emerging viral diseases with gross public concern were recorded in different regions,including Ebola in western Africa, Zika in South America,     

Pharmacologically active metabolites,combination screening and target identification-driven drug repositioning in antituberculosis drug discovery

There has been renewed interest in alternative strategies to address bottlenecks in antibiotic development. These include the repurposing of approved drugs for use as novel anti-infective agents, or their exploitation as leads in drug repositioning. Such approaches are especially attractive for tuberculosis (TB), a disease which remains a leading cause of morbidity and mortality globally and, increasingly, is associated with the emergence of drug-resistance. In this review article, we introduce a refinement of traditional drug repositioning and repurposing strategies involving the development of drugs that are based on the active metabolite(s) of parental compounds with demonstrated efficacy. In addition, we describe an approach to repositioning the natural product antibiotic, fusidic acid, for use against Mycobacterium tuberculosis. Finally, we consider the potential to exploit the chemical matter arising from these activities in combination screens and permeation assays which are designed to confirm mechanism of action (MoA), elucidate potential synergies in polypharmacy, and to develop rules for drug permeability in an organism that poses a special challenge to new drug development.     

Drug databases and their contributions to drug repurposing

Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.     

Database identifies FDA-approved drugs with potential to be repurposed for treatment of orphan diseases

Facing substantial obstacles to developing new therapies for rare diseases, some sponsors are looking to 'repurpose' drugs already approved for other conditions and use those therapies to treat rare diseases. In an effort to facilitate such repurposing and speed the delivery of new therapies to people who need them, we have established a new resource, the Rare Disease Repurposing Database (RDRD). The advantages of repurposed compounds include their demonstrated efficacy (in some clinical contexts), their observed toxicity profiles and their clearly described manufacturing controls. To create the RDRD, we matched the US Food and Drug Administration (FDA) orphan designation database to FDA drug and biological product approval lists. The RDRD lists 236 products that have received orphan status designation--that is, were found to be 'promising' for the treatment of a rare disease--and though not yet approved for marketing for that rare disease, they are already approved for marketing to treat some other disease or condition. The RDRD contains three tables: Orphan-designated products with at least one marketing approval for a common disease indication (N = 109); orphan-designated products with at least one marketing approval for a rare disease indication (N = 76); and orphan-designated products with marketing approvals for both common and rare disease indications (N = 51). While the data included in the database is a re-configuration/cross-indexing of information already released by the FDA, it offers sponsors a new tool for finding special opportunities to develop niche therapies for rare disease patients.     

Recent Advances in Drug Repositioning for the Discovery of New Anticancer Drugs

Drug repositioning (also referred to as drug repurposing), the process of finding new uses of existing drugs, has been gaining popularity in recent years. The availability of several established clinical drug libraries and rapid advances in disease biology, genomics and bioinformatics has accelerated the pace of both activity-based and in silico drug repositioning. Drug repositioning has attracted particular attention from the communities engaged in anticancer drug discovery due to the combination of great demand for new anticancer drugs and the availability of a wide variety of cell- and target-based screening assays. With the successful clinical introduction of a number of non-cancer drugs for cancer treatment, drug repositioning now became a powerful alternative strategy to discover and develop novel anticancer drug candidates from the existing drug space. In this review, recent successful examples of drug repositioning for anticancer drug discovery from non-cancer drugs will be discussed.     

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/.     

微流控芯片在乳腺癌骨转移研究中的应用

乳腺癌骨转移患者死亡率高达70%～80%,目前缺乏有效的治疗药物.微流控芯片技术能够有效模拟骨组织的生化和生物物理微环境,便捷地实现模拟骨微环境中乳腺癌骨转移的研究,这将为探索乳腺癌骨转移的细胞和分子机制、进而进行抗乳腺癌骨转移药物高通量筛选提供有价值的技术方法和平台.本综述简要介绍了乳腺癌骨转移的分子机制和治疗药物研究现状,详细阐述了乳腺癌骨转移的微流控芯片模型,分析了基于微流控芯片技术进行抗乳腺癌骨转移药物高通量筛选的优势和挑战,旨在为乳腺癌骨转移机制研究和药物筛选提供参考.     

Graph Theory Enables Drug Repurposing – How a Mathematical Model Can Drive the Discovery of Hidden Mechanisms of Action

We introduce a methodology to efficiently exploit natural-language expressed biomedical knowledge for repurposing existing drugs towards diseases for which they were not initially intended. Leveraging on developments in Computational Linguistics and Graph Theory, a methodology is defined to build a graph representation of knowledge, which is automatically analysed to discover hidden relations between any drug and any disease: these relations are specific paths among the biomedical entities of the graph, representing possible Modes of Action for any given pharmacological compound. We propose a measure for the likeliness of these paths based on a stochastic process on the graph. This measure depends on the abundance of indirect paths between a peptide and a disease, rather than solely on the strength of the shortest path connecting them. We provide real-world examples, showing how the method successfully retrieves known pathophysiological Mode of Action and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. Applications of this methodology are presented, and prove the efficacy of the method for selecting drugs as treatment options for rare diseases.     

Gene expression‐based drug repurposing to target aging

Aging is the largest risk factor for a variety of noncommunicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both lifespan and healthspan. Aging is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target‐centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat aging in human brain. Using multiple gene expression data sets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterize aging. Then, we compared these changes in gene expression with drug‐perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as antiaging drugs by reversing the detrimental changes that occur during aging, others by mimicking the cellular defence mechanisms. The drugs that we identified included significant number of already identified prolongevity drugs, indicating that the method can discover de novo drugs that meliorate aging. The approach has the advantages that using data from human brain aging data, it focuses on processes relevant in human aging and that it is unbiased, making it possible to discover new targets for aging studies.     

Complex diseases require complex therapies

Understanding disease causes and drug action at the molecular and systems levels could help to identify combinations of drugs that are more effective than individual drugs alone.Since the rise of modern pharmaceutical research and industry in the 1950s, drugs have been used to treat an increasingly wide range of diseases. From antibiotics for treating infections, to antivirals to treat HIV/AIDS, to drugs for hypertension and cancer, drug-based therapies have had enormous effects in curing or converting often fatal diseases into manageable conditions. Even pathophysiologies, such as peptic ulcers, that once required surgery are now routinely treated by drugs.Along with the many successes, several limitations have also become evident. Many diseases, especially those that progress in severity, remain difficult to treat with drugs. The list of such disorders is long and includes aneurysms, congestive heart failure, diabetes, kidney disease and many types of cancer. Even drugs that are efficacious do not work for everybody. Effective drugs cause serious adverse events in a subset of users. As we often cannot predict who might suffer from these side effects, the drug is typically taken off the market.These problems have generated a sense that our current approaches might have reached their limits and that we need new thinking to drive both drug discovery and usage. The extensive advances in our understanding of the basic molecular and cell biology of humans, other mammalian organisms and model organisms indicate that there are probably many more cellular components that could be targeted by drugs to fight disease. Another general insight is that cellular components interact with one another to form extensive networks. These networks have the capability to regulate and coordinate a range of subcellular functions, which gives rise to cellular phenotypes [1,2]. These cellular phenotypes underlie the tissue and organ functions that are characteristics of both health and disease.Malfunctions at the molecular level, when propagated to a higher level of organization, give rise to diseaseGenomics, molecular and cell biology and biochemistry are steadily becoming the basic elements for systems biology. As we continue to identify and characterize parts of cells and tissues, the next step in biology is to understand how these parts come together to form functional systems. The focus is not only to understand the characteristics and functions of individual entities, such as genes, proteins, lipids, sugars and so on, but also to understand how these entities interact with one another and what functions emerge from these interactions [3]. In this line of reasoning, almost all tissue and organ functions as well as organismal behaviour arise from molecular interactions. This has been explicitly demonstrated for coupled biochemical components that form positive feedback loops, which function as bistable switches. Such switches underlie, for instance, long-term depression of synaptic responses in the hippocampus [4] or hunger in mice [5].The systems-biology view that complex networks underlie many diseases is being increasingly demonstrated for many diseases, including heart disease, kidney disease, diabetes, metabolic diseases and cancers. To cast systems of interacting entities as networks is useful because it allows the use of graph theory, a branch of mathematics that analyses how complex systems are organized and how such organization enables system-level functions. When one thinks of complex regulatory networks, we often tend to think of molecular networks, but it is important to remember that networks exist at the level of tissues and organs and between organs at the level of organisms. Tissue-level networks are best recognized in the brain, where the activity of circuits—that is, networks of neurons—can be correlated with the behaviour of animals.The combining of drugs that act on different targets within a network could be more efficacious than treating disease with one drugAt the organismal level, current therapies for hypertension, which include multiple drugs acting at various tissues and organs—β-blockers on the heart, angiotensin-converting-enzyme inhibitors on blood vessels and diuretics on the kidney—provide compelling evidence of how blood pressure is a function of interactions between multiple tissues and organs in the body. Overall, it is reasonable to conclude that there are networks at different levels of organization: molecular networks within and between cells, cellular networks within tissues and organs, and networks of organs that functionally give rise to organismal physiology. Between each of these networks there are multiple connections, which are essential for a healthy organism (Fig 1). Malfunctions at the molecular level, when propagated to a higher level of organization, give rise to disease. Sometimes these malfunctions differ from person to person owing to variations and changes in the person''s genome. These variations indicate that different malfunctions can give rise to the same disease and knowing the molecular malfunctions is essential for developing personalized therapy. The various streams of data show overall that there is reasonable evidence to support a systems-biology approach that uses a network perspective of disease genes and mechanisms [6].Open in a separate windowFigure 1A schematic representation of the layers of networks that underlie organismal function, such as control of blood pressure (hypertension) or glucose levels in the blood (type 2 diabetes). Organismal functions arise from functional interactions between multiple organs. Organ and tissue functions arise from the functions of the multiple cell types of which they are comprised. Molecular networks exist within and between cell types that give rise to cellular functions. Drugs typically change the activity of the molecular components, and this change in activity percolates up to eventually affect organismal functions or malfunctions in disease states.Drugs, by and large, work at the molecular level, just as diseases originate from molecular malfunctions. From penicillin, which inhibits enzymes that make the bacterial cell wall, to β-blockers, such as propranolol, that inhibit β-adrenergic receptors to regulate heart function, to cancer drugs, such as imatinib, that block tyrosine kinases to inhibit the proliferation of cells, the effects of drugs start with molecular interactions. These effects are propagated across scales of organization to alter tissue or organ function to cure or relieve disease. The transmission of the drug effect is not linear. Rather, it occurs through the networks at each level of organization. This type of percolation at various scales of organization can sometimes have harmful consequences in addition to the intended good effect of treating the disease. These are called side effects, where effective treatment of one disease or its symptoms is associated with occurrence of a different type of disease in some individuals taking the drug.Such systems-biology-based approaches are likely to be of increasing value in the treatment of cancer because most cancers undergo multiple molecular changes as they progressWell-known examples of side effects are the occurrence of heart attacks and strokes associated with rofecoxib, which is used to treat osteoarthritis, and rosiglitazone, which is used to treat type 2 diabetes mellitus. In each case the drug is efficacious in treating the disease it is intended to treat but the risk of a serious side effect is too great and these drugs have been largely withdrawn from the market. In both cases, it appears that the side effects were a result of the networks in which the intended drug targets participate in different cell types and tissues.Sometimes, drugs bind to unintended targets and such interactions can lead to serious side effects. Many classes of drug, for reasons that are not always clear, cause arrhythmias by binding to the HERG channel in the heart. As one of its preclinical safety checks, the US Food and Drug Administration (FDA) therefore recommends that the developers of new drugs demonstrate that their drug does not interact with the HERG channel protein. Unintended targets of drugs are also part of cellular networks and, therefore, effects on these targets can be propagated through networks.Drug combinations can also cause unanticipated side effects. Analysis of the FDA Adverse Event Reporting System database (FAERS) by Altman and colleagues [7] showed that paroxetine, an antidepressant, and pravastatin, a cholesterol-lowering drug, raised blood glucose levels when administered in combination, whereas each drug on its own did not. Such an increase in blood glucose is an important consideration for patients with diabetes. This study showed the potential usefulness of analysing large databases, such as FAERS, to identify unanticipated biological effects associated with drug combinations and provided support for the idea that systems biology underlies combination drug therapy.Systems pharmacology is the name that is increasingly being used for the new systems-based approach that is being used to understand drug actions and for drug discovery. Systems pharmacology will take into account genomic variations and molecular complexity in defining physiological and pathophysiological responses at the tissue, organ and organism levels. My colleagues and I have used it to understand drug actions by studying how drug targets function within cellular networks. One hypothesis we have pursued is that, in addition to networks enabling drugs to do bad things, they can also enable good effects.Combining drugs that act on different targets within a network could be more efficacious than treating disease with one drug. Sometimes, complex diseases cannot be treated effectively by modulating a single target. Asthma is a good example: long-acting stimulators of the β-adrenergic receptors and corticosteroids together are effective and are widely used in combination. The combined effects are through drug action at varying timescales in cellular and tissue networks: the long-acting β2-adrenergic activator acutely relaxes the airways while the corticosteroids suppress inflammation with a slower time course.The combination of long-acting β2-adrenergic activators with muscarinic-receptor blockers is going through the approval process for treatment of chronic obstructive pulmonary disease [8]. These drug combinations are based on knowledge of how the targets of these drugs work in the context of cellular regulatory networks, and represent good examples of how systems-level thinking can lead to useful therapies. Such systems-biology-based approaches are likely to be of increasing value in the treatment of cancer because most cancers undergo multiple molecular changes as they progress. The combination of drugs that block the effects of multiple activators and inhibitors of cell growth are likely to become efficacious targeted therapy as we start to obtain detailed knowledge of the molecular networks underlying many cancers.Not all drug combinations are based on network logic. The commonly used antibacterial, Augmentin, combines the antibiotic amoxicillin with clavulanic acid, an inhibitor of the β-lactamase that breaks down the antibiotic. Here, the second drug extends the life of the first drug thus making it more efficacious.A novel systems approach in cancer has been described for treatment of some types of leukaemia and involves the use of genetically engineered T cells, which produce a cytokine storm that can kill off cancerous cells [9]. However, there are serious life-threatening side effects. An article in the New York Times describes how physicians have combined the genetically engineered T cells with antibodies against interleukin-6 by using tocilizumab to keep the effects of the T cells within a therapeutic range [10]. Although the news report suggests that this combination was developed empirically for a medical emergency, post hoc it is clear that the physicians have used an implicit systems approach to select a second drug to manage the risk–benefit ratio of the first drug by considering the source and target cells as part of a multicellular response network.A study from my laboratory [11] also shows that combination therapy can substantially reduce the serious adverse effects associated with a useful drug. We analysed FAERS and found many cases in which a drug B was given for a different reason and reduced a serious adverse event associated with drug A. We studied the combination of rosiglitazone and exenatide in some depth. Patients who were prescribed rosiglitazone and exenatide had a greatly reduced risk of heart attack than did patients prescribed rosiglitazone in combination with other drugs. This finding suggests that exenatide selectively reduces the risk of heart attacks and stroke associated with rosiglitazone. We were able to build molecular networks to show how signals from the targets of these two drugs might intersect and found that the blood protein PAI1 might be involved. PAI1 regulates the protease that breaks down blood clots. Increases in PAI1 levels lead to an increased risk of clots. We validated the network-based molecular mechanisms underlying the drug combination effects in a mouse model of diabetes.…the studies described here and many others are starting to show that systems-level analysis can be a powerful driver for understanding drug actionThis case is not unique. We identified nearly 19,000 other drug combinations in FAERS in which a second drug mitigated a serious adverse event associated with a first drug. Some of these combinations and effects are surprising. H2 antagonists, typically given for acid reflux diseases, were associated with a decreased number of suicides associated with selective serotonin reuptake inhibitors, and the blood-pressure medication lisinopril reduces statin-associated muscle wasting. We have been able to build plausible molecular networks for several of these drug combinations, suggesting that current molecular and cell biological knowledge could be used to develop a network-based understanding of the beneficial effects of drug combinations. Of note, the second drug is often given to treat an entirely different disease and the decreased side effects are unanticipated benefits of drug combination.At a general level, the studies described here and many others show that systems-level analysis can be a powerful driver for understanding drug action. One can envisage three kinds of new knowledge coming from such analyses (Fig 2). First is the identification of unanticipated adverse events that each drug might not produce on its own. Identification and prediction of such adverse effects could prove useful to guide physicians regarding which medicines can be co-prescribed. The second kind of knowledge is the opposite of the first: identification of unanticipated beneficial effects by drug combinations, such as mitigation of side effects. This type of knowledge might lead to repurposing of approved drugs if their efficacy in suppressing adverse events could be established in rigorous clinical trials. The third kind of knowledge, which is the most forward-looking, is that network biology can be used for the discovery of new drugs. Network analysis can provide a rational basis for identifying targets, which, when modulated together by drug combinations, might be distinctively efficacious in treating complex diseases.Open in a separate windowFigure 2A flow chart of how systems biology can affect various facets of pharmacology and therapeutics.Combination therapy based on network biology could become efficacious for the treatment of progressive diseases, such as type 2 diabetes, kidney disease, congestive heart failure and, of course, many cancers. While the necessary knowledge is not yet available, the path forward can be readily seen. Large databases, such as FAERs, can provide empirical knowledge of good and bad outcomes associated with combination therapies in humans. As large amounts of genomic and molecular data are integrated with clinical data when electronic medical records become more widely used and molecular characterization of patients becomes more standardized, it will probably generate a wealth of systems-level information to analyse and generate hypotheses. These hypotheses might help with the design of studies to better understand the progression of diseases, and design new drugs or repurpose existing drugs that, in combination, are more effective for treating complex diseases.? Open in a separate windowRavi Iyengar     

Advances in systems biology are enhancing our understanding of disease and moving us closer to novel disease treatments

With tens of billions of dollars spent each year on the development of drugs to treat human diseases, and with fewer and fewer applications for investigational new drugs filed each year despite this massive spending, questions now abound on what changes to the drug discovery paradigm can be made to achieve greater success. The high rate of failure of drug candidates in clinical development, where the great majority of these drugs fail due to lack of efficacy, speak directly to the need for more innovative approaches to study the mechanisms of disease and drug discovery. Here we review systems biology approaches that have been devised over the last several years to understand the biology of disease at a more holistic level. By integrating a diversity of data like DNA variation, gene expression, protein–protein interaction, DNA–protein binding, and other types of molecular phenotype data, more comprehensive networks of genes both within and between tissues can be constructed to paint a more complete picture of the molecular processes underlying physiological states associated with disease. These more integrative, systems-level methods lead to networks that are demonstrably predictive, which in turn provides a deeper context within which single genes operate such as those identified from genome-wide association studies or those targeted for therapeutic intervention. The more comprehensive views of disease that result from these methods have the potential to dramatically enhance the way in which novel drug targets are identified and developed, ultimately increasing the probability of success for taking new drugs through clinical development. We highlight a number of the integrative approaches via examples that have resulted not only in the identification of novel genes for diabetes and cardiovascular disease, but in more comprehensive networks as well that describe the context in which the disease genes operate.     

SWEETLEAD: an In Silico Database of Approved Drugs,Regulated Chemicals,and Herbal Isolates for Computer-Aided Drug Discovery

In the face of drastically rising drug discovery costs, strategies promising to reduce development timelines and expenditures are being pursued. Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success. Herein, we report the creation of a highly-curated in silico database of chemical structures representing approved drugs, chemical isolates from traditional medicinal herbs, and regulated chemicals, termed the SWEETLEAD database. The motivation for SWEETLEAD stems from the observance of conflicting information in publicly available chemical databases and the lack of a highly curated database of chemical structures for the globally approved drugs. A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical. Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database. The publically available release of SWEETLEAD (https://simtk.org/home/sweetlead) provides an important tool to enable the successful completion of computer-aided repurposing and drug discovery campaigns.