Network pharmacology and drug discovery

The pharmaceutical industry has historically relied on particular families of 'druggable' proteins against which to develop compounds with desired actions. But proteins rarely function in isolation in and outside the cell; rather, proteins operate as part of highly interconnected cellular networks. Network pharmacology is an emerging area of pharmacology which utilizes network analysis of drug action. By considering drug actions in the context of the cellular networks, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. Network pharmacology can provide new approaches for drug discovery for complex diseases. This review introduced the recent progress of network pharmacology and its importance to understand the mechanism of drug actions and drug discovery.     

Effects of cellular pharmacology on drug distribution in tissues.

The efficacy of targeted therapeutics such as immunotoxins is directly related to both the extent of distribution achievable and the degree of drug internalization by individual cells in the tissue of interest. The factors that influence the tissue distribution of such drugs include drug transport; receptor/drug binding; and cellular pharmacology, the processing and routing of the drug within cells. To examine the importance of cellular pharmacology, previously treated only superficially, we have developed a mathematical model for drug transport in tissues that includes drug and receptor internalization, recycling, and degradation, as well as drug diffusion in the extracellular space and binding to cell surface receptors. We have applied this "cellular pharmacology model" to a model drug/cell system, specifically, transferrin and the well-defined transferrin cycle in CHO cells. We compare simulation results to models with extracellular diffusion only or diffusion with binding to cell surface receptors and present a parameter sensitivity analysis. The comparison of models illustrates that inclusion of intracellular trafficking significantly increases the total transferrin concentration throughout much of the tissue while decreasing the penetration depth. Increasing receptor affinity or tissue receptor density reduces permeation of extracellular drug while increasing the peak value of the intracellular drug concentration, resulting in "internal trapping" of transferrin near the source; this could account for heterogeneity of drug distributions observed in experimental systems. Other results indicate that the degree of drug internalization is not predicted by the total drug profile. Hence, when intracellular drug is required for a therapeutic effect, the optimal treatment may not result from conditions that produce the maximal total drug distribution. Examination of models that include cellular pharmacology may help guide rational drug design and provide useful information for whole body pharmacokinetic studies.     

Differences in the subcellular localization of alpha1-adrenoceptor subtypes can affect the subtype selectivity of drugs in a study with the fluorescent ligand BODIPY FL-prazosin

G protein-coupled receptor (GPCR) subtypes are differentially distributed in the cell; however, it remains unclear how this affects the subtype selectivity of particular drugs. In the present study, we used flow cytometry analysis with the fluorescent ligand, BODIPY FL-prazosin, to study the relationship between the subcellular distribution of subtype receptors and the subtype-selective character of ligands using alpha1a and alpha1b-adrenoceptors (ARs). Alpha1a-ARs predominantly localize inside the cell, while alpha1b-ARs on the cell surface. Flow cytometry analysis and confocal laser-scanning micrographs of living cells showed that BODIPY FL-prazosin can label not only alpha1-ARs on the cell surface, but also those localized inside the cell. Furthermore, flow cytometry analysis of alpha1A-AR-selective drug, KMD-3213, and alpha1B-AR-selective drug, CEC, revealed that the major determinant of the subtype selectivity of each drug is different. The alpha1A-AR selectivity of KMD-3213 can be explained by its much higher affinity for alpha1a-AR than alpha1b-AR (affinity-dependent selectivity), while the alpha1B-AR selectivity of the hydrophilic alkylating agent CEC is due to preferential inactivation of alpha1-ARs on the cell surface (receptor localization-dependent selectivity). This study illustrates that factors in addition to the affinity of the drug for the receptor, such as subcellular localization of the receptor, should be taken into account in assessing the subtype selectivity of a drug.     

Modulating effects of drugs on cell cycle kinetics

The effects of individual drugs on cell cycle progression can often be determined by analyzing the DNA distribution of cultured cells at appropriate times after drug administration. In addition, to cell counts, RNA and/or protein content, the alterations in cell cycle distribution of drug-treated cells can yield information on the potential cell cycle phase specificity of the drug. However single parameter DNA analysis, as currently used, can give inaccurate information when drug effects are associated with cytokinesis perturbations, in spite of various statistical and mathematical analyses. Scanning flow cytometry could be an interesting alternative for the detection of binucleate cells.     

Comparison of synchronized Chinese hamster ovary cells obtained by mitotic shake-off, hydroxyurea, aphidicolin, or methotrexate

Synchronized cell populations are necessary to study many aspects of cell biology. We have developed a method to obtain highly synchronized Chinese hamster ovary cell populations in S phase or G2 phase by utilizing mitotic selection followed by incubation with either hydroxyurea, aphidicolin, or methotrexate for 12 h. Flow cytometry analysis shows that the coefficient of variation in the spread of the cell population in S phase is as low as 6%. Drug toxicity studies compare the effects of the various drugs on G1 and S phase cells. The use of aphidicolin or hydroxyurea results in the most highly synchronized cell populations, but methotrexate yields inadequate synchronization. These results demonstrate that both aphidicolin and hydroxyurea are useful drugs for obtaining highly synchronized cell populations after an initial synchrony in mitosis. Aphidicolin is perhaps the best choice because of less toxicity to S phase cells when used in low concentrations.     

Analysis of virus-infected cells by flow cytometry

Flow cytometry has been used to study virus-cell interactions for many years. This article critically reviews a number of reports on the use of flow cytometry for the detection of virus-infected cells directly in clinical samples and in virus-infected cultured cells. Examples are presented of the use of flow cytometry to screen antiviral drugs against human immunodeficiency virus (HIV), human cytomegalovirus, and herpes simplex viruses (HSV) and to perform drug susceptibility testing for these viruses. The use of reporter genes such as green fluorescent protein incorporated into HIV or HSV or into cells for the detection of the presence of virus, for drug susceptibility assay, and for viral pathogenesis is also covered. Finally, studies on the use of flow cytometry for studying the effect of virus infection on apoptosis and the cell cycle are summarized. It is hoped that this article will give the reader some understanding of the great potential of this technology for studying virus cell interactions.     

流式细胞仪的原理、应用及最新进展

流式细胞术是一种采用激光束激发单行流动的细胞,对它的散射光和携带的荧光进行探测,从而完成细胞分析和分选的技术。以流式细胞术为核心技术,流式细胞仪集光学、电子学、生物学、免疫学等多门学科和技术于一体,能够高效分析微小颗粒（如细胞,细菌）的先进科技设备。它对社会产生了深远的影响,成为了科学研究的必要工具。最近几年,流式细胞仪取得了长足进步。为了深入的了解它,本文从流式细胞仪的工作原理和技术指标,在临床医学、生物学、生殖学和制药学中的应用,以及它的世界格局、仪器功能的最新进展三方面,进行了简明、扼要的论述。展望未来：功能专业化、自动化,体积小型化,多色多参数分析能力提高和分析分选速度更快成为流式细胞仪发展的趋势。     

Effects of oxygen-free radicals on proliferation kinetics of cultured rabbit articular chondrocytes

It can be postulated that among the factors implicated in cartilaginous lesions, oxygen-derived free radicals seem to have a prominent part. To investigate this hypothesis, rabbit articular chondrocyte cultures have been exposed to oxygen-derived reactive species generated by the hypoxanthine-xanthine oxydase system. We observed a dose-dependent decrease of cellular growth. In order to explain this result, cell cycle progression and binucleate cell fractions have been studied. A greater number of binucleate cells and an increase in cell volume were observed. Flow cytometry analysis revealed a perturbation in cell cycle progression leading to a significant increase in the proportion of cells in G2 phase and an important augmentation in cell protein content confirmed by biochemical assays. This model shows which type of alteration can be induced by oxygen-derived free radicals in vitro. In addition, we deem this model to be useful for studying degenerative processes and for screening drugs that can scavenge oxygen-free radicals.     

Flow cytometry for high-throughput, high-content screening

Flow cytometry is a mature platform for quantitative multi-parameter measurement of cell fluorescence. Recent innovations allow up to 30-fold faster serial processing of bulk cell samples. Homogeneous discrimination of free and cell-bound fluorescent probe eliminates wash steps to streamline sample processing. Compound screening throughput may be further enhanced by multiplexing of assays on color-coded bead or cell suspension arrays and by integrating computational techniques to create smaller, focused compound libraries. Novel bead-based assay systems allow studies of real-time interactions between solubilized receptors, ligands and molecular signaling components that recapitulate and extend measurements in intact cells. These new developments, and its broad usage, position flow cytometry as an attractive analysis platform for high-throughput, high-content biological testing and drug discovery.     

Putative molecular determinants mediating sensitivity or resistance towards carnosic acid tumor cell responses

BackgroundCarnosic acid (CA) is one of the main constituents in rosemary extract. It possesses valuable pharmacological properties, including anti-oxidant, anti-inflammatory, anti-microbial and anti-cancer activities. Numerous in vitro and in vivo studies investigated the anticancer profile of CA and emphasized its potentiality for cancer treatment. Nevertheless, the role of multidrug-resistance (MDR) related mechanisms for CA's anticancer effect is not yet known.PurposeWe investigated the cytotoxicity of CA against known mechanisms of anticancer drug resistance (P-gp, ABCB5, BCRP, EGFR and p53) and determined novel putative molecular factors associated with cellular response towards CA.Study designCytotoxicity assays, bioinformatic analysis, flow cytometry and western blotting were performed to identify the mode of action of CA towards cancer cells.MethodsThe cytotoxicity to CA was assessed using the resazurin assays in cell lines expressing the mentioned resistance mechanisms. A pharmacogenomic characterization of the NCI 60 cell line panel was applied via COMPARE, hierarchical cluster and network analyses. Flow cytometry was used to detect cellular mode of death and ROS generation. Changes in proteins-related to apoptosis were determined by Western blotting.ResultsCell lines expressing ABC transporters (P-gp, BCRP or ABCB5), mutant EGFR or p53 were not cross-resistant to CA compared to their parental counterparts. By pharmacogenomic approaches, we identified genes that belong to different functional groups (e.g. signal transduction, regulation of cytoskeleton and developmental regulatory system). These genes were predicted as molecular determinants that mediate CA tumor cellular responses. The top affected biofunctions included cellular development, cellular proliferation and cellular death and survival. The effect of CA-mediated apoptosis in leukemia cells, which were recognized as the most sensitive tumor type, was confirmed via flow cytometry and western blot analysis.ConclusionCA may provide a novel treatment option to target refractory tumors and to effectively cooperate with established chemotherapy. Using pharmacogenomic approaches and network pharmacology, the relationship between cancer complexity and multi-target potentials of CA was analyzed and many putative molecular determinants were identified. They could serve as novel targets for CA and further studies are needed to translate the possible implications to clinical cancer treatment.     

Update on the concept of the cell cycle: the contribution of flow cytometry

Flow cytometry has been extensively used to provide accurate estimates of the relative amounts of various cellular constituents (DNA, RNA, proteins) for cell kinetic studies. Multiparametric analysis also supports the recent concept that cell growth and the DNA division cycle may be under distinct regulatory mechanisms. Moreover, metabolic subcompartments of the cell cycle, distinguished by flow cytometry, have offered a highly sensitive cell classification in comparison with the conventional distinction of the four main phases of the cell cycle. Finally, a new sensitive and powerful technology, BrdU/DNA analysis, represents a remarkable maturing of a very useful alternative for the study of DNA synthesis and cell cycle traverse.     

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     

Analysis of free intracellular calcium by flow cytometry: multiparameter and pharmacologic applications

Flow cytometry offers numerous advantages over traditional techniques for measuring intracellular Ca(2+) in lymphoid and nonlymphoid cells. In particular, the heterogeneity of cell responses can be defined by flow cytometry, and multiparameter analyses permit the determination of intracellular Ca(2+) in surface-marker-defined target cells as well as correlation of changes in Ca(2+) with other biochemical markers, including ligand binding. This article presents several established methods for measuring intracellular Ca(2+) by flow cytometry in lymphoid and nonlymphoid cells. Examples are provided for determination of Ca(2+) in human peripheral blood leukocytes and two human epithelial cell lines grown in monolayer. In addition, applications are reviewed or presented for correlating changes in intracellular Ca(2+) with other cell parameters, including cell cycle analysis, changes in cell membrane integrity, and the induction of apoptosis markers. Finally, a number of novel sample handling capabilities useful for performing kinetic analyses of Ca(2+) changes by flow cytometry are now available and one application is presented which is finding utility in pharmacologic studies.     

The apoptotic effect of sarsasapogenin from Anemarrhena asphodeloides on HepG2 human hepatoma cells

Sarsasapogenin, a kind of mainly effective components of Anemarrhena asphodeloides Bunge (Liliaceae) has the effects of being anti-diabetes and improving memory. However, there are few reports focusing on its anti-tumor effects. In this study, the sarsasapogenin was extracted from rhizomes of A. asphodeloides Bunge and applied to inhibit HepG2 human hepatoma cells. MTT assay showed that sarsasapogenin induced a distinct dose- and time-dependent diminution of cell viability with IC(50) of 42.4+/-1.0microg/ml for 48h. Furthermore, sarsasapogenin-induced apoptosis of HepG2 cells was verified by Hoechst 33258 staining, electron microscopy, DNA fragmentation and PI staining. Flow cytometry analysis showed that sarsasapogenin-induced cell apoptosis was through arrest of cell cycle in G(2)/M phase. Hence we proposed that sarsasapogenin could be used as an anti-liver cancer drug for future studies.     

网络药理学与药物发现研究进展

将生物学网络与药物作用网络整合,分析药物在网络中与节点或网络模块的关系,由寻找单一靶点转向综合网络分析,就形成了网络药理学.通过系统生物学的研究方法进行网络药理学分析,能够在分子水平上更好的理解细胞以及器官的行为,加速药物靶点的确认以及发现新的生物标志物.这使得我们有可能系统地预测和解释药物的作用,优化药物设计,发现影响药物作用有效性和安全性的因素,从而设计多靶点药物或药物组合.本文综述了网络药理学的新近研究进展,介绍在生物学网络的各个层面上网络药理学的研究和应用,展望网络药理未来的发展方向,对药物发现具有重要意义.     

Assessment of electroporation by flow cytometry

BACKGROUND: Electroporation accomplishes transient permeabilization of cells and thus aids in the uptake of drugs. The method has been employed clinically in the treatment of dermatological tumors with bleomycin. The conditions of electroporation are still largely empirical and information is lacking as to the interrelationships among voltage pulse height, pulse number and toxicity, cell permeation, drug uptake, and effects on drug toxicity. We used propidium iodide (PI) and flow cytometry to define cell permeation into cytoplasmic and nuclear compartments to determine the improvements of drug toxicity that can be accomplished by electroporation. METHODS: Human squamous carcinoma cells of defined TP53 status and normal human epithelial cells were subjected to electroporation using a square wave pulse generator in the range of 0-5,000 V/cm. Flow cytometry served to establish entry of the drug reporter, PI, into the cytoplasm and nucleus. A dye staining method served to establish cell survival and to determine the toxicity of bleomycin alone, electroporation alone, and electroporation with bleomycin. RESULTS: The electric field intensity (EFI) required to produce 50% permeabilization (EP(50)) is cell type dependent. The EP(50) varied from 1,465 to 2,027 V/cm. An EFI below 900 V/cm is growth stimulatory whereas an EFI in excess of 1,000 V/cm is growth inhibitory. An EFI of 1,000 V/cm is sufficient to increase bleomycin toxicity by a factor of 2-3. A differential electroporation efficiency is observed between normal and tumor cells. CONCLUSIONS: Tumor cells can be targeted preferentially at electroporation voltages where normal cells are less permeable.