Current Pharmaceutical Design - Volume 20, Issue 1, 2014

A complex disease like cancer is hardly caused by one gene or one protein singly. It is usually caused by the perturbation of the network formed by several genes or proteins. In the last decade several research teams have attempted to construct interaction maps of genes and proteins either experimentally or reverse engineer interaction maps using computational techniques. These networks were usually created under a certain condition such as an environmental condition, a particular disease, or a specific tissue type. Lately, however, there has been greater emphasis on finding the differential structure of the existing network topology under a novel condition or disease status to elucidate the perturbation in a biological system. In this review/tutorial article we briefly mention some of the research done in this area; we mainly illustrate the computational/statistical methods developed by our team in recent years for differential network analysis using publicly available gene expression data collected from a well known cancer study. This data includes a group of patients with acute lymphoblastic leukemia and a group with acute myeloid leukemia. In particular, we describe the statistical tests to detect the change in the network topology based on connectivity scores which measure the association or interaction between pairs of genes. The tests under various scores are applied to this data set to perform a differential network analysis on gene expression for human leukemia. We believe that, in the future, differential network analysis will be a standard way to view the changes in gene expression and protein expression data globally and these types of tests could be useful in analyzing the complex differential signatures.

Reductionist approaches and linear experimentation have expanded our knowledge in biology over the past century and represent till today the basis for the prevention, diagnosis and treatment of all diseases in clinical medicine. However, major diseases still remain incurable. All currently available drugs target a single gene or protein ignoring dynamics of highly complex biomolecular networks driving collectively gene expression and cell’s function. No surprise that most of these agents don’t cure common multifactorial disorders while available diagnostics and biomarkers are unable to predict tissue-specific cellular reactions to genetic and epigenetic alterations as well as drug effects in individual patients and populations. In this review we discuss latest advances in genome localization of genomewide association studies variants, whole genome/whole exome data analysis, protein-protein interactions networks databases, and more recent Encyclopedia of DNA Elements (ENCODE) data on regulatory networks including transcription factors-binding sites and genegene interactions. In addition challenges for a comprehensive analysis of intracellular signaling pathways network is described. Such analysis, despite genome-scale scarce data and lack of sophisticated methods to predict dynamics of a global hierarchy or ‘cloud”of biological networks, appears essential for the discovery of new therapeutic network targets, which could dramatically increase treatment efficacy, while minimizing at the same time major adverse effects. In this review we describe potential and challenges of modern approaches for applying next-generation sequencing and patient’s personal whole genome analysis for personalized treatment using available drugs. Additionally, we report why the discovery of next-generation drugs should be shifted from our linear world to motifsand network-associated disease integrating genome science and dynamics of network biology advances.

Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.

Understanding and improving the effects of combined drug treatments in metastatic colorectal Cancer (mCRC) is a multidisciplinary and multiscale problem, that can benefit from a systems biology approach. Although a quite limited number of active drugs have been approved for clinical applications, a variety of combined delivery regimen options are actually used in the clinic, so that choosing between them, or designing new ones, is not an obvious task, which calls for some rationalization based on physiological principles. We propose some physiologically based molecular pharmacokinetics-pharmacodynamics models for the main cytotoxic drugs used in the clinic and call for others describing more recently used agents, such as associated with monoclonal antibodies. We also advocate simultaneously designing models of the proliferating cell populations under therapeutic control, as cancer is primarily a disruption of physiological control on tissue proliferation. These two types of models are based on differential equations to continuously describe both the fate of drugs in the organism, from infusion until pharmacological effects, and their impact on the proliferation of cell populations, healthy and tumor. The multiscale nature of colorectal cancer, from the disruption of intracellular pathways to tumor growth observed at the macroscopic level, together with its frequent multilocal extension by simultaneous metastases in various healthy tissues of the organism at the time of diagnosis, and later, call for multiscale mathematical models. We thus propose a multi-level vision of cytotoxic drug use in the clinic, in which the weapon in the hands of clinicians, a drug combination regimen, the targets -wanted and unwanted –on which it exerts its effects, molecular pathways in proliferating cell populations, and the environment of the latter in a whole organism, are all considered in order to design a rationale for appropriate shooting, i.e., treatment optimization under patient-tailored constraints.

Protein microarray technology is one of the most powerful tools presently available for proteomic studies. Numerous types of protein microarrays have been widely and successfully applied for both basic biological studies and clinical researches, including those designed to characterize protein-protein, protein-nucleic acid, protein-drug/small molecule and antibody-antigen interactions. In the past decade, a variety of protein microarrays have been developed, including those spotted with whole proteomes, smaller peptides, antibodies, and lectins. Featured as high-throughput, miniaturized, and capable of parallel analysis, the power of protein microarrays has already been demonstrated many times in both basic research and clinical applications. In this review, we have summarized the latest developments in the production and application of protein microarrays. We discuss several of the most important applications of protein microarray, ranging from proteome microarrays for large scale identification of protein-protein interactions to lectin microarrays for live cell surface glycan profiling, with special emphasis on their use in studies of drug mechanisms and biomarker discovery. Already with tremendous success, we envision protein microarrays will become an indispensible tool for any systems-wide studies, fostering the integration of basic research observations to clinically useful applications.

The nuclear transport protein Exportin 1 (XPO1), also called chromosome region maintenance 1 (CRM1), is over-expressed 2- 4 fold in cancer. XPO1 is one of seven nuclear exporter proteins, and is solely responsible for the transport of the major tumor suppressor proteins (TSPs) from the nucleus to the cytoplasm. XPO1 exports any protein that carries a leucine-rich, hydrophobic nuclear export sequence (NES). A number of inhibitors have been discovered that block XPO1 function and thereby restore TSPs to the nucleus of both malignant and normal cells. However, natural product, irreversible XPO1 antagonists such as leptomycin B (LMB) have proven toxic in both preclinical models and in the clinic. Recently, orally bioavailable, drug-like small molecule, potent and selective inhibitors of XPO1 mediated nuclear export (“SINE”) have been designed and are undergoing clinical evaluations in both humans and canines with cancer. The breadth of clinical applicability and long-term viability of an XPO1 inhibition strategy requires a deeper evaluation of the consequence of global re-organization of proteins in cancer and normal cells. Unfortunately, most of the studies on XPO1 inhibitors have focused on evaluating a limited number of TSPs or other proteins. Because XPO1 carries ~220 mammalian proteins out of the nucleus, such reductionism has not permitted a global understanding of cellular behavior upon drug-induced disruption of XPO1 function. The consequence of XPO1 inhibition requires holistic investigations that consider the entire set of XPO1 targets and their respective pathways modulated without losing key details. Systems biology is one such holistic approach that can be applied to understand XPO1 regulated proteins along with the downstream players involved. This review provides comprehensive evaluations of the different computational tools that can be utilized in the better understanding of XPO1 and its target. We anticipate that such holistic approaches can allow for the development of a clinically successful XPO1 targeted therapeutic strategy against cancer.

A growing body of evidence suggests that glioma stem cells (GSCs) account for tumor initiation, therapy resistance, and the subsequent regrowth of gliomas. Thus, continuous efforts have been undertaken to further characterize this subpopulation of less differentiated tumor cells. Although we are able to enrich GSCs, we still lack a comprehensive understanding of GSC phenotypes and behavior. The advent of high-throughput technologies raised hope that incorporation of these newly developed platforms would help to tackle such questions. Since then a couple of comparative genome-, transcriptome- and proteome-wide studies on GSCs have been conducted giving new insights in GSC biology. However, lessons had to be learned in designing high-throughput experiments and some of the resulting conclusions fell short of expectations because they were performed on only a few GSC lines or at one molecular level instead of an integrative poly-omics approach. Despite these shortcomings, our knowledge of GSC biology has markedly expanded due to a number of survival-associated biomarkers as well as glioma-relevant signaling pathways and therapeutic targets being identified. In this article we review recent findings obtained by comparative high-throughput analyses of GSCs. We further summarize fundamental concepts of systems biology as well as its applications for glioma stem cell research.

Pancreatic cancer [PC] is a complex disease harboring multiple genetic alterations. It is now well known that deregulation in the expression and function of oncogenes and tumor suppressor genes contributes to the development and progression of PC. The last 40 years have not seen any major improvements in the dismal overall cure rate for PC where drug resistance is an emerging and recurring obstacle for successful treatment of PC. Additionally, the lack of molecular biomarkers for patient selection limits drug availabilities for tailored therapy for patients diagnosed with PC. The very high failure rate of new drugs in Phase III clinical trials in PC calls for a more robust pre-clinical and clinical testing of new compounds. In order to rationally choose combinations of targeted agents that may improve therapeutic outcome by overcoming drug resistance, one needs to apply newer research tools such as systems and network biology. These newer tools are expected to assist in the design of effective drug combinations for the treatment of PC and are expected to become an important part in any future clinical trials. In this review we will provide background information on the current state of PC research, the reasons for drug failure and how to overcome these issues using systems sciences. We conclude this review with an example on how systems and network methodologies can help in the design efficacious drug combinations for this deadly and by far incurable disease.

Background: Hepatocellular carcinoma (HCC) has high mortality partly due to acquiring drug resistance during chemotherapy treatment. Therefore, it is necessary to explore the underlying mechanism of drug resistance. Methods: We used 2-DE and MALDI-TOF-MS analysis to explore the possible molecular insight into 5-FU resistance in HCC. The differentially expressed proteins were validated by Western blot analysis. Results: We identified 102 unique proteins including p16, maspin, PRDX6, PSMB7, MYL6, PHB, and HSP27 with alteration in SMMC- 7721/5-FU. Furthermore, down-regulation of PRDX6 and PSMB7 enhanced SMMC-7721/5-FU cells to 5-FU sensitivity. Conclusions: Our study suggests that targeting drug resistant genes such as PRDX6 and PSMB7 could be a novel approach to overcome 5-FU resistance in HCC cells.

The mTOR signaling plays an integral role in cellular homeostasis controlling the transition between the catabolic and anabolic states. Originally approved as immunosuppressive agents preventing allograft rejection, inhibitors of mTOR signaling have recently entered the arena of cancer therapy. Using rapamycin derivative (RAD001) as a prototype inhibitor, we aimed to systematically analyze the molecular mechanisms underlying the pleiotropic effects of mTOR signaling. Using proliferation- and clonogenic survival assays, a preferential sensitivity of microvascular endothelial cells (HDMVEC) followed by fibroblasts and U87 gliblastoma to RAD001 treatment was found. In contrast, lung- and prostate tumor cells demonstrated relative resistance against RAD001 treatment. In co-culture with fibroblasts, RAD001 exerted potent antiangiogenic effects by inhibiting endothelial cell tube formation. Further, RAD001 treatment efficiently prevented tumor growth in U87 tumor xenografts. Integrative transcriptome analysis was performed to decipher the molecular mechanism underlying RAD001 -induced anti-tumor and antiangiogenic effects. The predominant expression pattern was downregulation of genes after RAD001 treatment in all three sensitive cell types. Among the RAD001 downregulated genes, a transcriptional network was discovered enriched for genes related to angiogenesis processes and extracellular matrix remodeling, e.g., VEGF, HIF1A, CXCLs, IL6, FN, PAI-1 or NRP1. Of note, key components of PI3K upstream (PDK1) as well as mTORC2 downstream signaling (SGK1, NDRG) were downregulated by RAD001. Decreased expression of IMPDH and 139 common gene targets between mycophenolic acid and RAD001 suggested in part shared mechanisms underlying their antiangiogenic and immunosuppressive effects. In summary, key genetic participants governing anti-tumor and anti-angiogenic effects of mTOR inhibition were identified.

The heat stress cognate 70 is one of the major cytoplasmic chaperones to supply a multitude of the housekeeping chaperoning functions. In addition to its high constitutive expression, recent studies have demonstrated that it is also inducible. Another exciting discovery is that the regulation of heat stress cognate 70 plays important roles in the aging process and aging-related diseases. Besides the chaperone functions, heat stress cognate 70 is involved in the inflammatory signal pathways via extracellular interaction with TLR2/TLR4. Furthermore, studies have validated the ability of extracellular heat stress cognate 70 to regulate cancer cell proliferation and sperm storage. The discovery of heat stress cognate 70 secretion, in normal and cancer cells undergoing stress, presents novel therapeutic strategies.

Atherosclerosis is a progressive disease starting with accumulation of lipids, lipoproteins, and immune cells in the arterial wall. Inflammation and the innate immune response are involved in the formation of early atherosclerotic lesion. A protein complex known as the inflammasome is stimulated to activate interleukin-1β (IL-1β) and IL-18, which are responsible for activation of inflammatory processes. Inflammasome-mediated processes are important in the process of atherosclerosis. The front of structure domains as well as IL-1, and IL-18 stands at the threshold of the adaptive immune response that accelerates full-blown atherosclerotic disease progression. This review is intended to provide new insights into the pathogenesis of atherosclerosis and indicate new potential molecular targets for therapy of this disease.

The mammalian target of rapamycin (mTOR) is a PI3K-related serine/threonine kinase and plays a critical role in modulating proliferation, growth, survival, invasion and chemoresistance of multiple myeloma, a malignancy of plasma cells. Since it was identified as the therapeutic target of rapamycin, mTOR has been applied for anti-cancer drug discovery. More and more mTOR inhibitors have been developed and demonstrated with great clinical potentials for multiple myeloma and other cancers. In this review, we highlighted advances in drug discovery targeting the mTOR signaling pathway for the treatment of multiple myeloma with an input from our recent studies.

The OB-receptor or leptin receptor (LR) is crucial for energy homeostasis and regulation of food uptake. Leptin is a 16 kDa hormone that is mainly secreted by fat cells into the bloodstream. Under normal circumstances, circulating leptin levels are proportionate to the fat body mass. Sensing of elevated leptin levels by the hypothalamic neuro-circuitry activates a negative feedback loop resulting in reduced food intake and increased energy expenditure. Decreased leptin concentrations lead to opposite effects. Therefore, rational design of leptin agonists/antagonists could be an appealing challenge in the battle against obesity. The Leptin/LR interactions have been studied in several works by means of different molecular modelling approaches, spreading from homology modelling to manual docking. No small molecules have ever been proposed as agonists of the Ob receptor but researchers’ efforts focused only on leptin-related synthetic peptides as receptor antagonists and on peptidomimetics. In this review we try to track a timeline of obtained in silico information to clarify the mechanism of interaction between leptin and its receptor, together to summarize the more recent efforts to propose new drugs usable in anti-obesity therapy. Final considerations could be useful starting points for the rational drug design of new lead compounds.

Leflunomide is a disease-modifying antirheumatic drug (DMARD) for the treatment of rheumatoid arthritis (RA). Structurally, it is a derivative of 5-methylisoxazole-4-carboxamide. Upon metabolism, the N-O bond in the isoxazole ring is cleaved to form the active metabolite, teriflunomide, which was recently approved by the FDA for the treatment of multiple sclerosis. Both leflunomide and teriflunomide inhibit dihydroorotate dehydrogenase (DHODH) thereby inhibiingt the synthesis of pyrimidine. For both drugs, the two major concerns are potential liver toxicity and teratogenicity. It was suspected that these undesirable effects might be related to the cleavage of the N-O bond. We herein summarize the metabolites-toxicity issues related to leflunomide/teriflunomide and discuss two related molecular platforms, UTL-4 and UTL-5. UTL-4 compounds are based on the same scaffold of leflunomide; their toxicological and pharmacological effects are not significantly different from those of leflunomide/teriflunomide. In UTL-5 series, the leflunomide scaffold is changed into 5-methylisoxazole-3-carboxamide. Unlike leflunomide, the N-O bond of a UTL-5 compound, UTL-5b, is not cleaved upon metabolism; instead, the peptide bond is cleaved to form its major metabolites. UTL-5b and its metabolites do not inhibit DHODH in vitro. In addition, UTL-5b and all other UTL-5 compounds have lower acute toxicity than leflunomide/teriflunomide. Furthermore, from leflunomide to UTL-5b/UTL-5g, the potential liver toxicity becomes liver protective effect. With the reduced toxicity, UTL-5 compounds still maintain significant pharmacological effects including anti-inflammatory and antiarthritic effects. In summary, our observations provide a valuable direction in drug optimization based on the modification of the leflunomide scaffold.