Harvesting Candidate Genes Responsible for Serious Adverse Drug Reactions from a Chemical-Protein Interactome

Identifying genetic factors responsible for serious adverse drug reaction (SADR) is of critical importance to personalized medicine. However, genome-wide association studies are hampered due to the lack of case-control samples, and the selection of candidate genes is limited by the lack of understanding of the underlying mechanisms of SADRs. We hypothesize that drugs causing the same type of SADR might share a common mechanism by targeting unexpectedly the same SADR-mediating protein. Hence we propose an approach of identifying the common SADR-targets through constructing and mining an in silico chemical-protein interactome (CPI), a matrix of binding strengths among 162 drug molecules known to cause at least one type of SADR and 845 proteins. Drugs sharing the same SADR outcome were also found to possess similarities in their CPI profiles towards this 845 protein set. This methodology identified the candidate gene of sulfonamide-induced toxic epidermal necrolysis (TEN): all nine sulfonamides that cause TEN were found to bind strongly to MHC I (Cw*4), whereas none of the 17 control drugs that do not cause TEN were found to bind to it. Through an insight into the CPI, we found the Y116S substitution of MHC I (B*5703) enhances the unexpected binding of abacavir to its antigen presentation groove, which explains why B*5701, not B*5703, is the risk allele of abacavir-induced hypersensitivity. In conclusion, SADR targets and the patient-specific off-targets could be identified through a systematic investigation of the CPI, generating important hypotheses for prospective experimental validation of the candidate genes.     

Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.     

Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods

Elucidation of chemical-protein interactions (CPI) is the basis of target identification and drug discovery. It is time-consuming and costly to determine CPI experimentally, and computational methods will facilitate the determination of CPI. In this study, two methods, multitarget quantitative structure-activity relationship (mt-QSAR) and computational chemogenomics, were developed for CPI prediction. Two comprehensive data sets were collected from the ChEMBL database for method assessment. One data set consisted of 81?689 CPI pairs among 50?924 compounds and 136 G-protein coupled receptors (GPCRs), while the other one contained 43?965 CPI pairs among 23?376 compounds and 176 kinases. The range of the area under the receiver operating characteristic curve (AUC) for the test sets was 0.95 to 1.0 and 0.82 to 1.0 for 100 GPCR mt-QSAR models and 100 kinase mt-QSAR models, respectively. The AUC of 5-fold cross validation were about 0.92 for both 176 kinases and 136 GPCRs using the chemogenomic method. However, the performance of the chemogenomic method was worse than that of mt-QSAR for the external validation set. Further analysis revealed that there was a high false positive rate for the external validation set when using the chemogenomic method. In addition, we developed a web server named CPI-Predictor, , which is available for free. The methods and tool have potential applications in network pharmacology and drug repositioning.     

Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network

Abstract

Nipah Virus (NiV) is a newly emergent paramyxovirus that has caused various outbreaks in Asian countries. Despite its acute pathogenicity and lack of approved therapeutics for human use, there is an urgent need to determine inhibitors against NiV. Hence, this work includes prospection of potential entry inhibitors by implementing an integrative structure- and network-based drug discovery approach. FDA-approved drugs were screened against attachment glycoprotein (NiV-G, PDB: 2VSM), one of the prime targets to inhibit viral entry, using a molecular docking approach that was benchmarked both on CCDC/ASTEX and known NIV-G inhibitor set. The predicted small molecules were prioritized on the basis of topological analysis of the chemical-protein interaction network, which was inferred by integrating the drug-target network, NiV-human interaction network, and human protein-protein interaction network. A total of 17 drugs were predicted to be NiV-G inhibitors using molecular docking studies that were further prioritized to 3 novel leads???Nilotinib, Deslanoside and Acetyldigitoxin???on the basis of topological analysis of inferred chemical-protein interaction network. While Deslanoside and Acetyldigitoxin belong to an already known class of anti-NiV inhibitors, Nilotinib belongs to Benzenoids chemical class that has not been reported hitherto for developing anti-NiV inhibitors. These identified drugs are expected to be successful in further experimental evaluation and therefore could be used for anti-Nipah drug discovery. Apart, we also obtained various insights into the underlying chemical-protein interaction network, based on which several important network nodes were predicted. The applicability of our proposed approach was also demonstrated by prospecting for anti-NiV phytochemicals on an independent dataset.

Communicated by Ramaswamy H. Sarma     

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.     

A Multidimensional Strategy to Detect Polypharmacological Targets in the Absence of Structural and Sequence Homology

Conventional drug design embraces the “one gene, one drug, one disease” philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology.     

A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.     

Regulators of G protein signaling & drugs of abuse

Drugs of abuse such as opioids and stimulants share a common dopaminergic reward pathway; however, in response to continual intermittent exposure to such drugs, there are neuronal alterations leading to changes in behavior. Regulators of G protein signaling (RGS) are proteins that negatively regulate G protein signaling and are expressed in brain areas important for the pharmacology of abused drugs. Moreover, the level of expression of several of these proteins is regulated by abused drugs. In this article, we discuss RGS proteins, their regulation by morphine and stimulants, and how altered levels of these proteins affect cell signaling to contribute to the pharmacology and behavioral consequence of abused drugs. Finally, we consider if RGS proteins represent viable targets for drug abuse medications.     

蛋白质组学技术在网络药理学中的应用

蛋白质组学发展至今已日趋成熟,在生物医药相关领域研究中的应用显著增加,与之相关的样品制备技术、蛋白定量方法及先进的质谱仪器也得到了快速发展。网络药理学是近年来提出的新药发现新策略,是药理学的新兴分支学科,它从整体的角度探索药物与疾病的关联性,发现药物靶标,指导新药研发。将蛋白质组学技术应用于网络药理学研究,能使研究人员系统地预测和解释药物的作用,加速药物靶点的确认,从而设计多靶点药物或药物组合。综述了蛋白质组学技术的新近研究进展,并简单概述了其在网络药理学中的应用。     

Network Topology Reveals Key Cardiovascular Disease Genes

The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and “driver genes.” We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies “key” genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs.     

Network-Based Relating Pharmacological and Genomic Spaces for Drug Target Identification

Background

Identifying drug targets is a critical step in pharmacology. Drug phenotypic and chemical indexes are two important indicators in this field. However, in previous studies, the indexes were always isolated and the candidate proteins were often limited to a small subset of the human genome.

Methodology/Principal Findings

Based on the correlations observed in pharmacological and genomic spaces, we develop a computational framework, drugCIPHER, to infer drug-target interactions in a genome-wide scale. Three linear regression models are proposed, which respectively relate drug therapeutic similarity, chemical similarity and their combination to the relevance of the targets on the basis of a protein-protein interaction network. Typically, the model integrating both drug therapeutic similarity and chemical similarity, drugCIPHER-MS, achieved an area under the Receiver Operating Characteristic (ROC) curve of 0.988 in the training set and 0.935 in the test set. Based on drugCIPHER-MS, a genome-wide map of drug biological fingerprints for 726 drugs is constructed, within which unexpected drug-drug relations emerged in 501 cases, implying possible novel applications or side effects.

Conclusions/Significance

Our findings demonstrate that the integration of phenotypic and chemical indexes in pharmacological space and protein-protein interactions in genomic space can not only speed the genome-wide identification of drug targets but also find new applications for the existing drugs.     

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.     

Determining the Degree of Promiscuity of Extensively Assayed Compounds

In the context of polypharmacology, an emerging concept in drug discovery, promiscuity is rationalized as the ability of compounds to specifically interact with multiple targets. Promiscuity of drugs and bioactive compounds has thus far been analyzed computationally on the basis of activity annotations, without taking assay frequencies or inactivity records into account. Most recent estimates have indicated that bioactive compounds interact on average with only one to two targets, whereas drugs interact with six or more. In this study, we have further extended promiscuity analysis by identifying the most extensively assayed public domain compounds and systematically determining their promiscuity. These compounds were tested in hundreds of assays against hundreds of targets. In our analysis, assay promiscuity was distinguished from target promiscuity and separately analyzed for primary and confirmatory assays. Differences between the degree of assay and target promiscuity were surprisingly small and average and median degrees of target promiscuity of 2.6 to 3.4 and 2.0 were determined, respectively. Thus, target promiscuity remained at a low level even for most extensively tested active compounds. These findings provide further evidence that bioactive compounds are less promiscuous than drugs and have implications for pharmaceutical research. In addition to a possible explanation that drugs are more extensively tested for additional targets, the results would also support a “promiscuity enrichment model” according to which promiscuous compounds might be preferentially selected for therapeutic efficacy during clinical evaluation to ultimately become drugs.     

Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.     

基于网络药理学的大黄治疗阿尔茨海默病作用机制研究

基于网络药理学探讨大黄治疗阿尔茨海默病(AD)的作用机制.借助TCMSP数据库及Uniprot数据库筛选出大黄有效成分及靶点基因.通过Drugbank、Dis Ge NET和TTD数据库筛选出阿尔茨海默病的靶点基因;成分靶点与疾病靶点映射后使用Cytoscape 3.7.1软件构建药物有效成分-靶点蛋白相互作用网络,使...     

An expanding role for cell biologists in drug discovery and pharmacology

The profound challenges facing clinicians, who must prescribe drugs in the face of dramatic variability in response, and the pharmaceutical industry, which must develop new drugs despite ever-rising costs, represent opportunities for cell biologists interested in rethinking the conceptual basis of pharmacology and drug discovery. Much better understanding is required of the quantitative behaviors of networks targeted by drugs in cells, tissues, and organisms. Cell biologists interested in these topics should learn more about the basic structure of drug development campaigns and hone their quantitative and programming skills. A world of conceptual challenges and engaging industry–academic collaborations awaits, all with the promise of delivering real benefit to patients and strained healthcare systems.Four decades of molecular and cellular biology has fundamentally improved our understanding of human disease, but this undeniable revolution has had less impact than hoped on human health, particularly in the area of discovery and use of therapeutic drugs. The missing link between basic science and useful therapeutics is the quantitative, multifactorial understanding of networks that operate within and between cells and of the changes that drugs induce in these networks (Berger and Iyengar, 2009 ). Contributing to this understanding of drugs and network dynamics represents a significant opportunity for cell biologists interested in careers in industry and for academic scientists seeking industrial collaborations. Success in such “translational” research is not simply a matter of applying known concepts to practical problems; interesting new ideas and science are required (Loscalzo and Barabasi, 2011 ). Fifty years ago, pharmacology and pathophysiology provided cell biologists with many fundamental research problems, and there is every reason to believe this will also be true in the future.Insufficient understanding of pathological and therapeutic mechanisms at a cellular level has contributed to the growing difficulty of bringing new drugs to market. Even when drugs win approval, it is rare that we can predict which patients will benefit from them. As a result, patients have too few treatment options, many serious illnesses remain difficult to treat, and the cost of new medicines is too high (often at the limit of what healthcare systems can support). High-throughput “-omic” approaches have been hailed as a means to understand disease and develop new drugs, but an outstanding opportunity exists for fundamental contributions from cell biologists. A central feature of cell biology is its emphasis on applying diverse conceptual and analytical approaches to biological processes that are inherently multifactorial. This is in contrast to “-omic” approaches, in which the focus is usually on one type of data collected in volume (gene sequences being one example).The role of cell biology in unraveling disease mechanisms is well established, but the value of cell biology in drug development is less well appreciated. Cell and molecular biologists currently play a role during the earliest preclinical stages of drug development in the identification and evaluation of potential drug targets (Figure 1). However, it is increasingly apparent that existing procedures for qualifying targets are inadequate, and this manifests itself as frequent and expensive late-stage failures of efficacy (typically during phase II and III clinical studies (Paul et al., 2010 ). To overcome this problem, we require a much better understanding of the functions of target proteins within the context of cellular networks in normal and diseased cells, both in culture and in the organism (“network biology”). Opportunities exist for cell biologists to help define optimal therapeutic strategies (e.g., aiding in the choice between using a recombinant antibody or small molecule) and to ascertain exposure/response relationships in tissues. Cell biologists also have an important role to play in understanding acquired resistance. A lack of durable responses is the bane of many recently approved targeted drugs. Finally, in diseases such as cancer, we have many plausible targets (the Akt kinase, for example), but it is not clear how to inhibit the target without causing excessive toxicity. It is also unclear why only a subset of patients responds to even the most potent and selective inhibitors. In our opinion, many drugs fail because cell biology is ignored during the later stages of drug development, when selecting indications and drug combinations and determining dosing schedules are the key tasks.Open in a separate windowFIGURE 1:Traditional and emerging roles for cell biologists in drug development and pharmacology. Traditionally, cell biologists have worked on the earliest phases of drug discovery, during the identification and validation of targets. However, by expanding their horizons and adding new skills, cell biologists can become well-suited to other roles later in development, roles in which the stakes are higher and sophisticated understanding of the underlying biology less common. Some of these fields are traditional (e.g., pharmacokinetics and pharmacodynamics [PK/PD]; black) and others are newly emerging (e.g., systems pharmacology; red).Cell biology also has an important role to play in discerning the precise mechanisms of action of existing drugs; it is a remarkable fact that we understand very few drug responses in mechanistic detail. This is as true of the latest generations of targeted therapeutics (many of which aim for selective inhibition of disease-specific mutants) as for older drugs that constitute the mainstay of standard-of-care therapy. The challenge lies less in the interaction between a drug and its intended target than in the consequences of target inhibition for cellular phenotype. This is particularly true when we consider genetic variation from one patient to the next and from one cell to the next within a single patient (particularly with diseases such as cancer). Cellular responses to the microtubule inhibitor and anticancer drug Taxol are an excellent example. Despite being an “old-fashioned” cytotoxic drug, Taxol and its various derivatives are a mainstay of contemporary cancer care, and more patients have probably benefited from taxanes than from all the targeted anticancer drugs combined (Ni Chonghaile et al., 2011 ). Understanding responses to taxanes at a cellular level has also been central to understanding the biology of the spindle assembly checkpoint and mitosis in general. Over the past two decades, checkpoint pathways have been identified and studied in many organisms, and we now understand in detail how processes such as mitotic catastrophe cause cell death (Mitchison, 2012 ). Remarkably, however, the factors that determine whether a cell lives or dies when exposed to Taxol differ dramatically between cultured cells and xenografted tumors (never mind real human tumors); progress through mitosis is always required in culture, but apparently not in the mouse (Orth et al., 2011 ). Understanding this difference represents a fascinating problem in cell biology likely to reveal how cell-autonomous processes, such as mitosis, interact with factors from the local environment in controlling cell fate. Such understanding could also have a real and immediate impact on cancer care.Over the past decade, the success of classical antimitotic chemotherapeutics, such as Taxol, has given rise to efforts to develop other antimitotic agents. For example, drugs that target spindle motors promised to combine the therapeutic antimitotic effects of Taxol, while minimizing neuropathy (motors such as Eg5 are not expressed in neurons [ Huszar et al., 2009 ]). Despite a massive effort by multiple companies, these drugs have proven disappointing in the clinic, as have many drugs that target mitotic kinases. It is now clear that inhibiting mitosis in cancer cells simply does not have the effects we have assumed for the past 50 years, and those antimitotic drugs that do work must do something fundamentally more. Working this out is likely to advance our understanding of the complexities of cell division in humans and animals. However, given the time pressures in industry, there is little opportunity to pursue “failed” drugs, and academic cell biologists have largely ignored problems such as the mechanisms of cell killing by antimitotic agents in real tumors. We must adopt a more holistic and physiological perspective in which we admit that detailed mechanistic understanding is required not only in model organisms and HeLa cells, but also in myriad normal and diseased tissues that have low mitotic index, unusual forms of endo-replication, and complex interactions with neighboring cells. New programs sponsored by the National Center for Advancing Translational Sciences promise to provide some support for this type of research (Allison, 2012 ).More generally, while we all recognize that the “one gene–one disease” paradigm is insufficient for understanding human disease and for selecting patients who will respond to therapy, an effective alternative remains to be developed. Even when the multiplicity of factors involved in a particular disease can be discerned, this understanding does not necessarily reveal how to develop a treatment or cure. For therapy, we must elucidate not only the nature of the initial insult (e.g., a cancer-causing mutation) but also the operation of biological networks that attempt to compensate for the insult (to reestablish homeostasis) and variation in network properties from one individual to the next. It is also important that we identify and understand factors that determine the concentrations and biodistribution of drugs in patients with diverse genotypes. This, in turn, requires a multiscale, network-based approach involving systemic and quantitative study of biological processes at the cellular, tissue, and organismal levels and of the effects of drugs on these processes—precisely the areas in which cell biology has much to contribute.Despite these opportunities, several factors stand in the way of a greater role for cell biologists in drug discovery and development. The first is an unfamiliar vocabulary. We are repeatedly amazed by postdocs who have decided they want to pursue a career in biotechnology or the pharmaceutical industry but who have not spent the time to learn the basics of the drug discovery process from preclinical development to phased clinical trials. Anyone interested in an industrial career should stay abreast of the lively and interesting debates about the best ways to structure and evaluate trials (Kelloff and Sigman, 2012 ). An industrial career usually requires writing more but shorter reports than an academic career, and familiarity with the language of drug discovery makes report writing much easier. A career in industry also benefits from knowledge of the diverse scientific, medical, and business factors that determine success in a drug development campaign. At the same time, it is important to note that some key drug discovery concepts, such as “target identification” or “target qualification,” are widely used but elusive. They imply that the key task is identifying (or cloning) a specific target protein and then screening for agonists and antagonists. As mentioned above, the current challenge increasingly involves understanding targets in the context of biological networks, homeostatic processes, and pathophysiological mechanisms (Wang et al., 2012 ). This implies a more nuanced and holistic approach to understanding the ways the targets and drugs interact (Chene, 2012 ).Many cell biologists in industry find themselves involved in the development or evaluation of assays, particularly for high-throughput screening. Evaluating such screens requires basic understanding of statistics and the trade-offs between false-positive and false-negative results (Atkinson and Lalonde, 2007 ). If high-content screening by imaging is involved, then it is necessary to develop and apply machine vision approaches. Unfortunately, many cell biologists are insufficiently trained in basic statistics, and they have poor programming skills. In our experience, this can be a significant impediment to employment in industry that can be overcome by taking courses in probability and statistics and by gaining practical experience with MatLab or languages such as Python and R. Particularly in biotech, learning the rudiments of intellectual property law can also be a real asset, since it makes it easier to spot patentable inventions.Even the largest drug companies have come to doubt their ability to pursue development projects all the way from target identification to drug approval. It is widely believed that more frequent and effective collaborations between industry and academe are part of the solution (Rubin and Gilliland, 2012 ). This obviously represents a significant opportunity for academic cell biologists. However, the days in which companies were willing to shower academic institutions with generous and unrestricted financial support are long gone. It is now necessary to develop research programs that revolve around concrete goals and deliverables. In our experience, this can be an exciting process for academics accustomed to the conservatism of federal grants, since industry is often willing to pursue ideas that are risky and innovative. Moreover, we have rarely found the perceived difference between applied and basic research to be a significant issue. However, very different expectations over the duration of projects are a major challenge. Industry typically works on 12- to 18-month time lines and academe on a schedule that is at least twice as long. In our experience, even the most effective industry–academic projects tend to underdeliver over the first 18 months, and then only prove their worth in subsequent years. Industry must be more sensitive to the fact that starting a new project in an academic setting means recruiting a new student or postdoc and that there is no way for such an individual to be trained and to succeed with only 18 months of support. However, academics must learn to accommodate the real need for industrial partners to reevaluate projects after approximately 18 months. In our opinion, academics could speed up the initial stages of a project and industry should slow down. We have personally witnessed many industrial projects that were discontinued without reaching a firm conclusion, only to result in an exciting opportunity being missed or to leave open questions that impede progress many years later. A frank discussion of these issues is essential at the outset of any collaborative project.Despite obvious challenges, we envision an expanding role for cell biologists in drug discovery that extends beyond their traditional involvement in early-stage target identification. Significant opportunities exist in better qualifying potential targets and in identifying the role of target proteins in cellular function and pathophysiology. Better understanding of targets in the context of cellular and tissue networks should make it possible to design better therapeutics based on optimizing selectivity, affinity, and type of molecule. Cell biologists can also become more involved in clinical development of new and standard-of-care drugs, particularly with respect to identifying indications, developing diagnostics, and stratifying populations. In this case, learning more about the clinical phases of drug development is valuable. In our personal experience, the most effective approaches are those that involve quantitative analysis and combine experimentation and modeling. This often goes under the name “systems biology” but can easily be viewed as a natural evolution of cell biology in the face of ever-larger data sets and more complex cellular mechanisms. Thus, if we had a single piece of advice for cell biologists interested in pharmacology or drug discovery, it is to acquire or hone skills in statistics, bioinformatics, programming, and applied mathematics in general.Open in a separate windowP. K. SorgerOpen in a separate windowP. K. Sorger     

Yeast "N"-hybrid systems for protein-protein and drug-protein interaction discovery

The majority of small molecule drugs act on protein targets to exert a therapeutic function. It has become apparent in recent years that many small molecule drugs act on more than one particular target and consequently, approaches which profile drugs to uncover their target binding spectrum have become increasingly important. Classical yeast two-hybrid systems have mainly been used to discover and characterize protein-protein interactions, but recent modifications and improvements have opened up new routes towards screening for small molecule-protein interactions. Such yeast "n"-hybrid systems hold great promise for the development of drugs which interfere with protein-protein interactions and for the discovery of drug-target interactions. In this review, we discuss several yeast two-hybrid based approaches with applications in drug discovery and describe a protocol for yeast three-hybrid screening of small molecules to identify their direct targets.     

Characterizing the Network of Drugs and Their Affected Metabolic Subpathways

A fundamental issue in biology and medicine is illustration of the overall drug impact which is always the consequence of changes in local regions of metabolic pathways (subpathways). To gain insights into the global relationship between drugs and their affected metabolic subpathways, we constructed a drug–metabolic subpathway network (DRSN). This network included 3925 significant drug–metabolic subpathway associations representing drug dual effects. Through analyses based on network biology, we found that if drugs were linked to the same subpathways in the DRSN, they tended to share the same indications and side effects. Furthermore, if drugs shared more subpathways, they tended to share more side effects. We then calculated the association score by integrating drug-affected subpathways and disease-related subpathways to quantify the extent of the associations between each drug class and disease class. The results showed some close drug–disease associations such as sex hormone drugs and cancer suggesting drug dual effects. Surprisingly, most drugs displayed close associations with their side effects rather than their indications. To further investigate the mechanism of drug dual effects, we classified all the subpathways in the DRSN into therapeutic and non-therapeutic subpathways representing drug therapeutic effects and side effects. Compared to drug side effects, the therapeutic effects tended to work through tissue-specific genes and these genes tend to be expressed in the adrenal gland, liver and kidney; while drug side effects always occurred in the liver, bone marrow and trachea. Taken together, the DRSN could provide great insights into understanding the global relationship between drugs and metabolic subpathways.     

Mapping the genome of Plasmodium falciparum on the drug-like chemical space reveals novel anti-malarial targets and potential drug leads

The parasite Plasmodium falciparum is the main agent responsible for malaria. In this study, we exploited a recently published chemical library from GlaxoSmithKline (GSK) that had previously been confirmed to inhibit parasite growth of the wild type (3D7) and the multi-drug resistance (D2d) strains, in order to uncover the weak links in the proteome of the parasite. We predicted 293 proteins of P. falciparum, including the six out of the seven verified targets for P. falciparum malaria treatment, as targets of 4645 GSK active compounds. Furthermore, we prioritized druggable targets, based on a number of factors, such as essentiality for growth, lack of homology with human proteins, and availability of experimental data on ligand activity with a non-human homologue of a parasite protein. We have additionally prioritized predicted ligands based on their polypharmacology profile, with focus on validated essential proteins and the effect of their perturbations on the metabolic network of P. falciparum, as well as indication of drug resistance emergence. Finally, we predict potential off-target effects on the human host with associations to cancer, neurological and dermatological disorders, based on integration of available chemical-protein and protein-protein interaction data. Our work suggests that a large number of the P. falciparum proteome is potentially druggable and could therefore serve as novel drug targets in the fight against malaria. At the same time, prioritized compounds from the GSK library could serve as lead compounds to medicinal chemists for further optimization.     

抗糖尿病药物治疗阿尔茨海默病的研究进展

阿尔茨海默病（Alzheimers disease, AD）是以认知缺陷为主要特征的慢性疾病,目前尚无有效根治药物。由于患者数量显著增长,探究治疗AD的药物成为国内外的研究热点。近年流行病学研究表明,2型糖尿病是AD的危险因素,两者具有共同的病理生理机制,如胰岛素抵抗、淀粉样蛋白沉积、Tau蛋白过度磷酸化、炎症反应和氧化应激等。因此,从现有的抗糖尿病药物中筛选AD的治疗药物是目前研究的一种趋势。越来越多的研究已证实降糖药物（如胰岛素、二甲双胍等）具有改善AD病变的有益作用。从AD与2型糖尿病的相关性、抗糖尿病药物治疗AD两个方面综述了抗糖尿病药物治疗AD的研究进展,以期为AD的治疗拓宽思路。