The structure and function of drug pumps

Resistance to drugs has emerged in biological systems as diverse as cancer cells undergoing chemotherapy and microbial pathogens undergoing treatment with antimicrobials. This medical problem is escalating and there is an urgent need for the development of new classes of drugs. In the case of pathogenic bacteria, we are rapidly approaching a scenario where there will be no effective antibiotics in the armoury of drugs available for treating the infectious diseases that these bacteria cause, returning us to the pre-antibiotic era when infectious diseases were rife because they were untreatable. One of the most frequently employed resistance strategies in both prokaryotes and eukaryotes is the transmembrane-protein-catalysed extrusion of drugs from the cell, with these proteins acting like bilge pumps, reducing the intracellular drug concentration to subtoxic levels. There is currently much scientific interest in understanding how these pumps operate, so that we might design transport inhibitors that would block them, allowing a renaissance for drugs that are no longer effective owing to their efflux.     

Targeting mitochondria: Strategies,innovations and challenges: The future of medicine will come through mitochondria

Mitochondrial dysfunction has been associated with the aging process and a large variety of human disorders, such as cardiovascular and neurodegenerative diseases, cancer, migraine, infertility, kidney and liver diseases, toxicity of drugs and many more. It is well recognized that the physiological role of mitochondria widely exceeds that of solely being the biochemical power plant of our cells. Over the recent years, mitochondria have become an interesting target for drug therapy, and the research field aimed at “targeting mitochondria” is active and expanding as witnessed by this already third edition of the world congress on targeting mitochondria. It is becoming a necessity and an urge to know why and how to target mitochondria with bioactive molecules and drugs in order to treat and prevent mitochondria-based pathologies and chronic diseases. This special issue covers a variety of new strategies and innovations as well as clinical applications in mitochondrial medicine.     

Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state “signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.     

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．     

HDAC inhibitors in parasitic diseases

Parasitic diseases cause significant global morbidity and mortality, particularly in underdeveloped regions of the world. Malaria alone causes ~800000 deaths each year, with children and pregnant women being at highest risk. There is no licensed vaccine available for any human parasitic disease and drug resistance is compromising the efficacy of many available anti-parasitic drugs. This is driving drug discovery research on new agents with novel modes of action. Histone deacetylase (HDAC) inhibitors are being investigated as drugs for a range of diseases, including cancers and infectious diseases such as HIV/AIDS, and several parasitic diseases. This review focuses on the current state of knowledge of HDAC inhibitors targeted to the major human parasitic diseases malaria, schistosomiasis, trypanosomiasis, toxoplasmosis and leishmaniasis. Insights are provided into the unique challenges that will need to be considered if HDAC inhibitors are to be progressed towards clinical development as potential new anti-parasitic drugs.     

Dendritic cells: emerging pharmacological targets of immunosuppressive drugs

Immunosuppressive drugs have revolutionized organ transplantation and improved the therapeutic management of autoimmune diseases. The development of immunosuppressive drugs and understanding of their action traditionally has been focused on lymphocytes, but recent evidence indicates that these agents interfere with immune responses at the earliest stage, targeting key functions of dendritic cells (DCs). Here, we review our present understanding of how classical and new immunosuppressive agents interfere with DC development and function. This knowledge might provide a rational basis for the selection of immunosuppressive drugs in different clinical settings and for the generation of tolerogenic DCs in the laboratory.     

A Network-Based Classification Model for Deriving Novel Drug-Disease Associations and Assessing Their Molecular Actions

The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug) was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer’s disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer’s disease.     

Genomics, systems biology and drug development for infectious diseases

Although a variety of drugs are available for many infectious diseases that predominantly affect the developing world reasons remain for continuing to search for new chemotherapeutics. First, the development of microbial resistance has made some of the most effective and inexpensive drug regimes unreliable and dangerous to use on severely ill patients. Second, many existing antimicrobial drugs show toxicity or are too expensive for countries where the per capita income is in the order of hundreds of dollars per year. In recognition of this, new publicly and privately financed drug discovery efforts have been established to identify and develop new therapies for diseases such as tuberculosis, malaria and AIDS. This in turn, has intensified the need for tools to facilitate drug identification for those microbes whose molecular biology is poorly understood, or which are difficult to grow in the laboratory. While much has been written about how functional genomics can be used to find novel protein targets for chemotherapeutics this review will concentrate on how genome-wide, systems biology approaches may be used following whole organism, cell-based screening to understand the mechanism of drug action or to identify biological targets of small molecules. Here we focus on protozoan parasites, however, many of the approaches can be applied to pathogenic bacteria or parasitic helminths, insects or disease-causing fungi.     

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/.     

Aneuploidy in stem cells

Stem cells hold enormous promise for regenerative medicine as well as for engineering of model systems to study diseases and develop new drugs. The discovery of protocols that allow for generating induced pluripotent stem cells(IPSCs) from somatic cells has brought this promise steps closer to reality. However,as somatic cells might have accumulated various chromosomal abnormalities,including aneuploidies throughout their lives,the resulting IPSCs might no longer carry the perfect blueprint for the tissue to be generated,or worse,become at risk of adopting a malignant fate. In this review,we discuss the contribution of aneuploidy to healthy tissues and how aneuploidy can lead to disease. Furthermore,we review the differences between how somatic cells and stem cells respond to aneuploidy.     

IN DEFENCE OF PRIORITY REVIEW VOUCHERS

Infectious and parasitic diseases cause enormous health problems in the developing world whereas they leave the developed one relatively unscathed. Research and development (R&D) of drugs for diseases that mainly affect people in developing countries is limited. The problem that relatively few drugs are available for diseases that cause an enormous burden of disease in the developing world is called the 'availability problem'. In recent years, the availability problem has received quite a bit of attention. A number of proposals have been fielded as to how this problem might be minimized. Wild-card patent extensions, advance market commitments, cash prizes and the Health Impact Fund are prominent examples of such proposals. These proposals can be thought of as pull-mechanisms for R&D of drugs for neglected diseases. What has been coined a 'priority review voucher' is another pull-mechanism. This paper is a critical discussion of this pull-mechanism. First, the original priority review voucher scheme, as proposed by Ridley et al. (2006), is described. A number of objections to this scheme are thereafter presented. A few amendments to the original scheme are then suggested, and it is argued that with these amendments in place, the priority review voucher scheme constitutes an attractive way of stimulating R&D of drugs for neglected diseases.     

Modularity in protein complex and drug interactions reveals new polypharmacological properties

Recent studies have highlighted the importance of interconnectivity in a large range of molecular and human disease-related systems. Network medicine has emerged as a new paradigm to deal with complex diseases. Connections between protein complexes and key diseases have been suggested for decades. However, it was not until recently that protein complexes were identified and classified in sufficient amounts to carry out a large-scale analysis of the human protein complex system. We here present the first systematic and comprehensive set of relationships between protein complexes and associated drugs and analyzed their topological features. The network structure is characterized by a high modularity, both in the bipartite graph and in its projections, indicating that its topology is highly distinct from a random network and that it contains a rich and heterogeneous internal modular structure. To unravel the relationships between modules of protein complexes, drugs and diseases, we investigated in depth the origins of this modular structure in examples of particular diseases. This analysis unveils new associations between diseases and protein complexes and highlights the potential role of polypharmacological drugs, which target multiple cellular functions to combat complex diseases driven by gain-of-function mutations.     

On the microstructurally driven heterogeneous response of brain white matter to drug infusion pressure

Delivering therapeutic agents into the brain via convection-enhanced delivery (CED), a mechanically controlled infusion method, provides an efficient approach to bypass the blood–brain barrier and deliver drugs directly to the targeted focus in the brain. Mathematical methods based on Darcy’s law have been widely adopted to predict drug distribution in the brain to improve the accuracy and reduce the side effects of this technique. However, most of the current studies assume that the hydraulic permeability and porosity of brain tissue are homogeneous and constant during the infusion process, which is less accurate due to the deformability of the axonal structures and the extracellular matrix in brain white matter. To solve this problem, a multiscale model was established in this study, which takes into account the pressure-driven deformation of brain microstructure to quantify the change of local permeability and porosity. The simulation results were corroborated using experiments measuring hydraulic permeability in ovine brain samples. Results show that both hydraulic pressure and drug concentration in the brain would be significantly underestimated by classical Darcy’s law, thus highlighting the great importance of the present multiscale model in providing a better understanding of how drugs transport inside the brain and how brain tissue responds to the infusion pressure. This new method can assist the development of both new drugs for brain diseases and preoperative evaluation techniques for CED surgery, thus helping to improve the efficiency and precision of treatments for brain diseases.

     

Strategies for developing protein tyrosine phosphatase inhibitors

Protein tyrosine phosphatases (PTPs) play vital roles in numerous cellular processes and are implicated in a growing number of human diseases, ranging from cancer to cardiovascular, immunological, infectious, neurological, and metabolic diseases. Here we present methods for developing small molecule inhibitors for these enzymes, starting with how to set up a high throughput chemical library screening for PTP inhibitors, how to confirm and prioritize hits, and how to circumnavigate possible pitfalls. Next, we present the relatively new hit generating method of in silico or virtual screening. We give an overview of existing software tools, describe how to choose and generate protein target structures and illustrate the procedure with examples. We then discuss how three-dimensional PTP structures can be analyzed in terms of their potential to bind small molecule inhibitors selectively over homologous proteins and how computer tools can be applied for lead optimization efforts. We finish with a perspective of how well these PTP inhibitors might perform as future drugs to treat human disease.     

Multicomponent phytotherapeutic approach gaining momentum: Is the “one drug to fit all” model breaking down?

Natural product based drugs constitute a substantial proportion of the pharmaceutical market particularly in the therapeutic areas of infectious diseases and oncology. The primary focus of any drug development program so far has been to design selective ligands (drugs) that act on single selective disease targets to obtain highly efficacious and safe drugs with minimal side effects. Although this approach has been successful for many diseases, yet there is a significant decline in the number of new drug candidates being introduced into clinical practice over the past few decades. This serious innovation deficit that the pharmaceutical industries are facing is due primarily to the post-marketing failures of blockbuster drugs. Many analysts believe that the current capital-intensive model-“the one drug to fit all” approach will be unsustainable in future and that a new “less investment, more drugs” model is necessary for further scientific growth. It is now well established that many diseases are multi-factorial in nature and that cellular pathways operate more like webs than highways. There are often multiple ways or alternate routes that may be switched on in response to the inhibition of a specific target. This gives rise to the resistant cells or resistant organisms under the specific pressure of a targeted agent, resulting in drug resistance and clinical failure of the drug. Drugs designed to act against individual molecular targets cannot usually combat multifactorial diseases like cancer, or diseases that affect multiple tissues or cell types such as diabetes and immunoinflammatory diseases. Combination drugs that affect multiple targets simultaneously are better at controlling complex disease systems and are less prone to drug resistance. This multicomponent therapy forms the basis of phytotherapy or phytomedicine where the holistic therapeutic effect arises as a result of complex positive (synergistic) or negative (antagonistic) interactions between different components of a cocktail. In this approach, multicomponent therapy is considered to be advantageous for multifactorial diseases, instead of a “magic bullet” the metaphor of a “herbal shotgun” might better explain the state of affairs. The different interactions between various components might involve the protection of an active substance from decomposition by enzymes, modification of transport across membranes of cells or organelles, evasion of multidrug resistance mechanisms among others.     

Drug discovery and development focusing on existing medicines: drug re-profiling strategy

As a new strategy for drug discovery and development, I focus on drug re-profiling as a way to identify new treatments for diseases. In this strategy, the actions of existing medicines, whose safety and pharmacokinetic effects in humans have already been confirmed clinically and approved for use, are examined comprehensively at the molecular level and the results used for the development of new medicines. This strategy is based on the fact that we still do not understand the underlying mechanisms of action of many existing medicines, and as such the cellular responses that give rise to their main effects and side effects are yet to be elucidated. To this extent, identification of the mechanisms underlying the side effects of medicines offers a means for us to develop safer drugs. The results can also be used for developing existing drugs for use as medicines for the treatment of other diseases. Promoting this research strategy could provide breakthroughs in drug discovery and development.     

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.     

中枢神经系统疾病治疗性抗体药物应用进展

单克隆抗体药物是一种新兴的治疗药物,具有高选择性,被用于多种疾病的治疗,如肿瘤、免疫疾病等,也可以用于中枢神经系统疾病,如阿尔茨海默病、帕金森病、中风和脑肿瘤等。然而,因为血脑屏障低通透性,限制了抗体药物在中枢神经系统疾病治疗中的应用,在很多神经系统疾病临床试验中,抗体药物并没有取得预期效果。如今,人们利用血脑屏障上内源性转运蛋白介导,设计了可以通过血脑屏障的抗体药物。对通过血脑屏障治疗性抗体药物研发进展及其应用前景进行了综述。     

Literature Mining for the Discovery of Hidden Connections between Drugs,Genes and Diseases

The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs.     

QSAR analyses on ginkgolides and their analogues using CoMFA, CoMSIA, and HQSAR

Ginkgolides, isolated from ginkgo balba leaves, were found to be powerful as natural antagonists of human platelet activating factor (PAF) in treatment of some diseases such as acute inflammation, tissue rejection, asthma, and ischemic injury. Ginkgolides have a cage skeleton consisting of six five-membered rings, therefore, are very tough to be synthesized. For finding new powerful substitutes of the natural ginkgolides for treating those diseases, three methods, viz. CoMFA, CoMSIA, and HQSAR, were used to investigate the relationship between 117 ginkgolide analogues with great structural diversity and their bioactivities against PAF receptor. The high q2 released from the different QSAR methods, ranging from 0.583 to 0.684, suggests that three rational and predictive QSAR models were successfully built. These models also show clearly how steric, electrostatic, hydrophobicity, and individual atom affect molecular bioactivity as antagonists of PAF. These results could also be used to account for the unusually higher bioactivity of ginkgolide B than other ginkgolides. The possible binding mechanism between ginkgolides and human PAF receptor was also deduced based on the QSAR models. Therefore, this study should be very helpful in discovering new drugs as PAF antagonists in fighting against various diseases related to PAF and PAF receptor.