A formal model for analyzing drug combination effects and its application in TNF-alpha-induced NFkappaB pathway

Synergistic Potential of Argentatins A and B to Improve 5-Fluorouracil Cytotoxicity in Colorectal Cancer Cell Models

Colorectal cancer is the third most commonly diagnosed cancer worldwide and the second most common cause of cancer-related death in both men and women. Although a number of treatments are available to combat this malignancy, the antimetabolite 5-fluorouracil has been the cornerstone of therapy since its synthesis in the 1950s. Unfortunately, the prolonged use of 5-fluorouracil can lead to chemoresistance, which has prompted research into combination regimens to improve efficacy and quality of life and reduce resistance. Here, we evaluated the synergistic potential of two compounds isolated from guayule, and argentatins A and B, alone and in combination with 5-fluorouracil in a panel of colorectal cancer cell lines. Cell viability assays showed that the combination treatment (argentatin A with 5 fluorouracil) significantly enhanced cytotoxicity, especially in RKO, where the analysis using the Bliss independence model indicated a remarkable synergistic effect with the lowest doses of both compounds. In contrast to the combination with argentatin B, in which the additive effect was only found in the HCT-116 cell line. Finally, immunocytometric analysis revealed that combination treatments induced higher rates of apoptosis than single-agent treatments. Collectively, our findings indicate that argentatins A and B may enhance the anti-tumour effects of 5-fluorouracil and may represent a promising strategy to improve the efficacy of anticancer therapies based on this antimetabolite.

Investigation of the effects of catharanthine and Q10 on Nrf2 and its association with MMP-9, MRP1, and Bcl-2 and apoptosis in a model of hepatocellular carcinoma

Since the role of Nrf2 in cancer cell survival has been highlighted, the pharmacological modulation of the Nrf2-Keap1 pathway may provide new opportunities for cancer treatment. This study purposed to use ubiquinone (Q10) as an antioxidant and catharanthine alkaloid as a cAMP inducer suppressing HepG2 cells by reducing Nrf2 level. The effects of Q10 and catharanthine on HepG2 cells in terms of viability were analyzed by MTT test. MTT results were used to determine the effective concentration of both drugs for the subsequent treatment and analysis. Subsequently, the effects of Q10 and catharanthine in a single and combined manner on oxidant/antioxidant status, apoptosis, metastasis, and drug resistance of HepG2 cells were investigated by related methods. Both Q10 and catharanthine decreased the level of oxidative stress products and increased antioxidant capacity in HepG2 cells. Nrf2 gene expression decreased by Q10, but catharanthine unexpectedly increased it. Following Nrf2 alterations, the expression levels of MMP-9 and MRP1 involved in metastasis and drug resistance were significantly and dose-dependently decreased by Q10, while catharanthine slightly increased both. However, both drugs increased caspase 3/7 activity and apoptosis rate, and the effect of Q10 on apoptosis was stronger than that of catharanthine. Most of the effects of the combination treatments were similar to those of the Q10 single treatment and indicated the dominant effect over the catharanthine component. Despite the antioxidant and apoptotic properties of both agents, Q10 was better than catharanthine in inducing apoptosis, counteracting drug resistance, and metastasis in HepG2 cells.

Functionally-instructed modifiers of response to ATR inhibition in experimental glioma

BACKGROUND

The DNA damage response (DDR) is a physiological network preventing malignant transformation, e.g. by halting cell cycle progression upon DNA damage detection and promoting DNA repair. Glioblastoma are incurable primary tumors of the nervous system and DDR dysregulation contributes to acquired treatment resistance. Therefore, DDR targeting is a promising therapeutic anti-glioma strategy. Here, we investigated Ataxia telangiectasia and Rad3 related (ATR) inhibition (ATRi) and functionally-instructed combination therapies involving ATRi in experimental glioma.

METHODS

We used acute cytotoxicity to identify treatment efficacy as well as RNAseq and DigiWest protein profiling to characterize ATRi-induced modulations within the molecular network in glioma cells. Genome-wide CRISPR/Cas9 functional genomic screens and subsequent validation with functionally-instructed compounds and selected shRNA-based silencing were employed to discover and investigate molecular targets modifying response to ATRi in glioma cell lines in vitro, in primary cultures ex vivo and in zebrafish and murine models in vivo.

RESULTS

ATRi monotherapy displays anti-glioma efficacy in vitro and ex vivo and modulates the molecular network. We discovered molecular targets by genome-wide CRISPR/Cas9 loss-of-function and activation screens that enhance therapeutic ATRi effects. We validated selected druggable targets by a customized drug library and functional assays in vitro, ex vivo and in vivo.

CONCLUSION

In conclusion, our study leads to the identification of novel combination therapies involving ATRi that could inform future preclinical studies and early phase clinical trials.

Differential mRNA expression in the induction of DNA damage, G2/M arrest, and cell death by zerumbone in HepG2/C3A cells

Zerumbone (ZER) is a phytochemical with antioxidant and antiproliferative properties. This study evaluated the cytoxicity of ZER combined with chemotherapeutic agents and the expression of mRNA genes related to cell cycle, cell death, xenobiotic metabolism, DNA damage, and endoplasmic reticulum (ER) stress in HepG2/C3A cells. ZER was cytotoxic (IC₅₀, 44.31 μM). ZER-induced apoptosis was related to BBC3 and ERN1 upregulation (ER stress), and its antiproliferative effects were attributable to MYC, IGF1, and NF-kB mRNA inhibition. ZER-induced G₂/M arrest and DNA damage was associated with mRNA expression of cell cycle (CDKN1A) and DNA damage (GADD45A) genes. Increased CYP1A2 and CYP2C19 mRNA expression suggested ZER metabolization, and reduced CYP1A1 and CYP2D6 expression indicated a longer time of action of ZER in the cell, enhancing its pharmacological effect. ZER downregulated TP53, PARP1, BIRC5 (apoptosis), and MAP1LC3A (autophagy). In apoptosis assay, the data of the association treatments with ZER suggested antagonism. In cytotoxicity assay, the data of the association treatments with ZER suggested synergism action to cisplatin and antagonism action to doxorubicin and 5-fluorouracil. Thus, ZER has potential for application in chemotherapy as it modulates mRNA targets; however, it may not have the desired efficiency when combined with other chemotherapeutic agents.

Lamellipodin-RICTOR Signaling Mediates Glioblastoma Cell Invasion and Radiosensitivity Downstream of EGFR

Glioblastoma is a tumor type of unmet need despite the development of multimodal treatment strategies. The main factors contributing to the poor prognosis of glioblastoma patients are diverse genetic and epigenetic changes driving glioblastoma persistence and recurrence. Complemented are these factors by extracellular cues mediated through cell surface receptors, which further aid in fostering pro-invasion and pro-survival signaling contributing to glioblastoma therapy resistance. The underlying mechanisms conferring this therapy resistance are poorly understood. Here, we show that the cytoskeleton regulator Lamellipodin (Lpd) mediates invasiveness, proliferation and radiosensitivity of glioblastoma cells. Phosphoproteome analysis identified the epidermal growth factor receptor (EGFR) signaling axis commonly hyperactive in glioblastoma to depend on Lpd. Mechanistically, EGFR signaling together with an interaction between Lpd and the Rapamycin-insensitive companion of mammalian target of rapamycin (RICTOR) jointly regulate glioblastoma radiosensitivity. Collectively, our findings demonstrate an essential function of Lpd in the radiation response and invasiveness of glioblastoma cells. Thus, we uncover a novel Lpd-driven resistance mechanism, which adds an additional critical facet to the complex glioblastoma resistance network.

Antileukemic efficacy of a potent artemisinin combined with sorafenib and venetoclax

Artemisinins are active against human leukemia cell lines and have low clinical toxicity in worldwide use as antimalarials. Because multiagent combination regimens are necessary to cure fully evolved leukemias, we sought to leverage our previous finding that artemisinin analogs synergize with kinase inhibitors, including sorafenib (SOR), by identifying additional synergistic antileukemic drugs with low toxicity. Screening of a targeted antineoplastic drug library revealed that B-cell lymphoma 2 (BCL2) inhibitors synergize with artemisinins, and validation assays confirmed that the selective BCL2 inhibitor, venetoclax (VEN), synergized with artemisinin analogs to inhibit growth and induce apoptotic cell death of multiple acute leukemia cell lines in vitro. An oral 3-drug "SAV" regimen (SOR plus the potent artemisinin-derived trioxane diphenylphosphate 838 dimeric analog [ART838] plus VEN) killed leukemia cell lines and primary cells in vitro. Leukemia cells cultured in ART838 had decreased induced myeloid leukemia cell differentiation protein (MCL1) levels and increased levels of DNA damage-inducible transcript 3 (DDIT3; GADD153) messenger RNA and its encoded CCATT/enhancer-binding protein homologous protein (CHOP), a key component of the integrated stress response. Thus, synergy of the SAV combination may involve combined targeting of MCL1 and BCL2 via discrete, tolerable mechanisms, and cellular levels of MCL1 and DDIT3/CHOP may serve as biomarkers for action of artemisinins and SAV. Finally, SAV treatment was tolerable and resulted in deep responses with extended survival in 2 acute myeloid leukemia (AML) cell line xenograft models, both harboring a mixed lineage leukemia gene rearrangement and an FMS-like receptor tyrosine kinase-3 internal tandem duplication, and inhibited growth in 2 AML primagraft models.

Synergy of Anaerobic Soil Disinfestation and Trichoderma spp. in Rhizoctonia Root Rot Suppression

Potential synergy between anaerobic soil disinfestation (ASD) and Trichoderma spp. in suppression of Rhizoctonia root rot in radish was evaluated. A split-plot design with three replications was used; main plots were Trichoderma harzianum T22, Trichoderma asperellum NT25 and a non-Trichoderma control. Subplots were ASD carbon sources wheat bran, molasses, chicken manure, and mustard greens and two non-amended controls: anaerobic (covered and flooded) and aerobic (not covered or flooded). Carbon sources and Rhizoctonia solani inoculant were mixed with soil, placed in pots, and flooded, followed by drenching Trichoderma spore suspensions and sealing the pots in zip-lock bags. After 3 weeks, bags were removed, soil was aired for 1 week and radish “SSR-RR-27” was seeded. Rhizoctonia root rot severity and incidence were lowest in radish plants grown in ASD-treated soil amended with wheat bran, molasses, or mustard greens across all Trichoderma treatments. Disease severity was lower in radish plants treated with NT25 than with T22 or the non-Trichoderma control across all ASD treatments, and in radish grown in ASD-treated soil amended with wheat bran plus NT25 compared to ASD-wheat bran or NT25 alone. Rhizoctonia solani populations were significantly reduced by ASD treatment regardless of carbon source, while Trichoderma populations were not affected by ASD treatment with the exception of ASD-mustard greens. The interactions of either Trichoderma isolate and ASD with most carbon sources were additive, while T22 with ASD-molasses and NT25 with ASD–wheat bran interactions were synergistic in reducing disease severity. One interaction, T22 with ASD-chicken manure was antagonistic. Enhancement of ASD efficacy in suppressing soilborne diseases such as Rhizoctonia root rot by additional soil amendment with Trichoderma spp. during the process appears to be dependent on both Trichoderma isolate and ASD carbon source.

Crepis vesicaria L. subsp. taraxacifolia Leaves: Nutritional Profile, Phenolic Composition and Biological Properties

Crepis vesicaria subsp. taraxacifolia (Cv) of Asteraceae family is used as food and in traditional medicine. However there are no studies on its nutritional value, phenolic composition and biological activities. In the present work, a nutritional analysis of Cv leaves was performed and its phenolic content and biological properties evaluated. The nutritional profile was achieved by gas chromatography (GC). A 70% ethanolic extract was prepared and characterized by HLPC-PDA-ESI/MSn. The quantification of chicoric acid was determined by HPLC-PDA. Subsequently, it was evaluated its antioxidant activity by DPPH, ABTS and FRAP methods. The anti-inflammatory activity and cellular viability was assessed in Raw 264.7 macrophages. On wet weight basis, carbohydrates were the most abundant macronutrients (9.99%), followed by minerals (2.74%) (mainly K, Ca and Na), protein (1.04%) and lipids (0.69%), with a low energetic contribution (175.19 KJ/100 g). The Cv extract is constituted essentially by phenolic acids as caffeic, ferulic and quinic acid derivatives being the major phenolic constituent chicoric acid (130.5 mg/g extract). The extract exhibited antioxidant activity in DPPH, ABTS and FRAP assays and inhibited the nitric oxide (NO) production induced by LPS (IC50 = 0.428 ± 0.007 mg/mL) without cytotoxicity at all concentrations tested. Conclusions: Given the nutritional and phenolic profile and antioxidant and anti-inflammatory properties, Cv could be a promising useful source of functional food ingredients.

Preclinical Activity of PI3K Inhibitor Copanlisib in Gastrointestinal Stromal Tumor

KIT or PDGFRA gain-of-function mutations are the primary drivers of gastrointestinal stromal tumor (GIST) growth and progression throughout the disease course. The PI3K/mTOR pathway is critically involved in the transduction of KIT/PDGFRA oncogenic signaling regardless of the type of primary and secondary mutations, and therefore emerges as a relevant targetable node in GIST biology. We evaluated in GIST preclinical models the antitumor activity of copanlisib, a novel pan-class-I PI3K inhibitor with predominant activity against p110α and p110δ isoforms, as single-agent and in combination with first-line KIT inhibitor imatinib. In vitro studies undertaken in one imatinib-sensitive (GIST-T1) and two imatinib-resistant (GIST-T1/670 and GIST430/654) GIST cell models showed that single-agent copanlisib effectively suppressed PI3K pathway activation leading to decreased cell viability and proliferation in both imatinib-sensitive and -resistant cells irrespective of the type of primary or secondary KIT mutations. Simultaneous PI3K and KIT inhibition with copanlisib and imatinib resulted in enhanced impairment of cell viability in both imatinib-sensitive and -resistant GIST cell models, although apoptosis was mostly triggered in GIST-T1. Single-agent copanlisib inhibited GIST growth in vivo, and conjoined inhibition of PI3K and KIT was the most active therapeutic intervention in imatinib-sensitive GIST-T1 xenografts. IHC stain for cleaved-caspase 3 and phospho-S6 support a predominant antiproliferative effect of copanlisib in GIST. In conclusion, copanlisib has single-agent antitumor activity in GIST regardless KIT mutational status or sensitivity to imatinib. Effective KIT inhibition is necessary to achieve synergistic or additive effects with the combination of imatinib and any given PI3K/mTOR pathway inhibition.

Advances in computational approaches in identifying synergistic drug combinations

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.

Controlling Nuclear NF-κB Dynamics by β-TrCP-Insights from a Computational Model

The canonical nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway regulates central processes in mammalian cells and plays a fundamental role in the regulation of inflammation and immunity. Aberrant regulation of the activation of the transcription factor NF-κB is associated with severe diseases such as inflammatory bowel disease and arthritis. In the canonical pathway, the inhibitor IκB suppresses NF-κB’s transcriptional activity. NF-κB becomes active upon the degradation of IκB, a process that is, in turn, regulated by the β-transducin repeat-containing protein (β-TrCP). β-TrCP has therefore been proposed as a promising pharmacological target in the development of novel therapeutic approaches to control NF-κB’s activity in diseases. This study explores the extent to which β-TrCP affects the dynamics of nuclear NF-κB using a computational model of canonical NF-κB signaling. The analysis predicts that β-TrCP influences the steady-state concentration of nuclear NF-κB, as well as changes characteristic dynamic properties of nuclear NF-κB, such as fold-change and the duration of its response to pathway stimulation. The results suggest that the modulation of β-TrCP has a high potential to regulate the transcriptional activity of NF-κB.

Drosophila Cancer Models Identify Functional Differences between Ret Fusions

We generated and compared Drosophila models of RET fusions CCDC6-RET and NCOA4-RET. Both RET fusions directed cells to migrate, delaminate, and undergo EMT, and both resulted in lethality when broadly expressed. In all phenotypes examined, NCOA4-RET was more severe than CCDC6-RET, mirroring their effects on patients. A functional screen against the Drosophila kinome and a library of cancer drugs found that CCDC6-RET and NCOA4-RET acted through different signaling networks and displayed distinct drug sensitivities. Combining data from the kinome and drug screens identified the WEE1 inhibitor AZD1775 plus the multi-kinase inhibitor sorafenib as a synergistic drug combination that is specific for NCOA4-RET. Our work emphasizes the importance of identifying and tailoring a patient's treatment to their specific RET fusion isoform and identifies a multi-targeted therapy that may prove effective against tumors containing the NCOA4-RET fusion.

Antitumor Synergism and Enhanced Survival with a Tumor Vasculature-Targeted Enzyme Prodrug System, Rapamycin, and Cyclophosphamide

Mutant cystathionine gamma-lyase was targeted to phosphatidylserine exposed on tumor vasculature through fusion with Annexin A1 or Annexin A5. Cystathionine gamma-lyase E58N, R118L, and E338N mutations impart nonnative methionine gamma-lyase activity, resulting in tumor-localized generation of highly toxic methylselenol upon systemic administration of nontoxic selenomethionine. The described therapeutic system circumvents systemic toxicity issues using a novel drug delivery/generation approach and avoids the administration of nonnative proteins and/or DNA required with other enzyme prodrug systems. The enzyme fusion exhibits strong and stable in vitro binding with dissociation constants in the nanomolar range for both human and mouse breast cancer cells and in a cell model of tumor vascular endothelium. Daily administration of the therapy suppressed growth of highly aggressive triple-negative murine 4T1 mammary tumors in immunocompetent BALB/cJ mice and MDA-MB-231 tumors in SCID mice. Treatment did not result in the occurrence of negative side effects or the elicitation of neutralizing antibodies. On the basis of the vasculature-targeted nature of the therapy, combinations with rapamycin and cyclophosphamide were evaluated. Rapamycin, an mTOR inhibitor, reduces the prosurvival signaling of cells in a hypoxic environment potentially exacerbated by a vasculature-targeted therapy. IHC revealed, unsurprisingly, a significant hypoxic response (increase in hypoxia-inducible factor 1 α subunit, HIF1A) in the enzyme prodrug-treated tumors and a dramatic reduction of HIF1A upon rapamycin treatment. Cyclophosphamide, an immunomodulator at low doses, was combined with the enzyme prodrug therapy and rapamycin; this combination synergistically reduced tumor volumes, inhibited metastatic progression, and enhanced survival. Mol Cancer Ther; 16(9); 1855-65. ©2017 AACR.

Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

Advances in Synergistic Combinations of Chinese Herbal Medicine for the Treatment of Cancer

The complex pathology of cancer development requires correspondingly complex treatments. The traditional application of individual single-target drugs fails to sufficiently treat cancer with durable therapeutic effects and tolerable adverse events. Therefore, synergistic combinations of drugs represent a promising way to enhance efficacy, overcome toxicity and optimize safety. Chinese Herbal Medicines (CHMs) have long been used as such synergistic combinations. Therefore, we summarized the synergistic combinations of CHMs used in the treatment of cancer and their roles in chemotherapy in terms of enhancing efficacy, reducing side effects, immune modulation, as well as abrogating drug resistance. Our conclusions support the development of further science-based holistic modalities for cancer care.

Targeting of β1 integrins impairs DNA repair for radiosensitization of head and neck cancer cells

β1 Integrin-mediated cell-extracellular matrix interactions allow cancer cell survival and confer therapy resistance. It was shown that inhibition of β1 integrins sensitizes cells to radiotherapy. Here, we examined the impact of β1 integrin targeting on the repair of radiation-induced DNA double-strand breaks (DSBs). β1 Integrin inhibition was accomplished using the monoclonal antibody AIIB2 and experiments were performed in three-dimensional cell cultures and tumor xenografts of human head and neck squamous cell carcinoma (HNSCC) cell lines. AIIB2, X-ray irradiation, small interfering RNA-mediated knockdown and Olaparib treatment were performed and residual DSB number, protein and gene expression, non-homologous end joining (NHEJ) activity as well as clonogenic survival were determined. β1 Integrin targeting impaired repair of radiogenic DSB (γH2AX/53BP1, pDNA-PKcs T2609 foci) in vitro and in vivo and reduced the protein expression of Ku70, Rad50 and Nbs1. Further, we identified Ku70, Ku80 and DNA-PKcs but not poly(ADP-ribose) polymerase (PARP)-1 to reside in the β1 integrin pathway. Intriguingly, combined inhibition of β1 integrin and PARP using Olaparib was significantly more effective than either treatment alone in non-irradiated and irradiated HNSCC cells. Here, we support β1 integrins as potential cancer targets and highlight a regulatory role for β1 integrins in the repair of radiogenic DNA damage via classical NHEJ. Further, the data suggest combined targeting of β1 integrin and PARP as promising approach for radiosensitization of HNSCC.

Regulation of signal transduction by spatial parameters: a case in NF-κB oscillation

NF-κB is a transcription factor regulating expression of more than 500 genes, and its dysfunction leads to the autoimmune and inflammatory diseases. In malignant cancer cells, NF-κB is constitutively activated. Thus the elucidation of mechanisms for NF-κB regulation is important for the establishment of therapeutic treatment caused by incorrect NF-κB responses. Cytoplasmic NF-κB translocates to the nucleus by the application of extracellular stimuli such as cytokines. Nuclear NF-κB is known to oscillate with the cycle of 1.5-4.5 h, and it is thought that the oscillation pattern regulates the expression profiles of genes. In this review, first we briefly describe regulation mechanisms of NF-κB. Next, published computational simulations on the oscillation of NF-κB are summarised. There are at least 60 reports on the computational simulation and analysis of NF-κB oscillation. Third, the importance of a 'space' for the regulation of oscillation pattern of NF-κB is discussed, showing altered oscillation pattern by the change in spatial parameters such as diffusion coefficient, nuclear to cytoplasmic volume ratio (N/C ratio), and transport through nuclear membrane. Finally, simulations in a true intracellular space (TiCS), which is an intracellular 3D space reconstructed in a computer with organelles such as nucleus and mitochondria are discussed.

Understanding the Genetic Mechanisms of Cancer Drug Resistance Using Genomic Approaches

A major obstacle in precision cancer medicine is the inevitable resistance to targeted therapies. Tremendous effort and progress has been made over the past few years to understand the biochemical and genetic mechanisms underlying drug resistance, with the goal to eventually overcome such daunting challenges. Diverse mechanisms, such as secondary mutations, oncogene bypass, and epigenetic alterations, can all lead to drug resistance, and the number of known involved genes is growing rapidly, thus providing many possibilities to overcome resistance. The finding of these mechanisms and genes invariably requires the application of genomic and functional genomic approaches to tumors or cancer models. In this review, we briefly highlight the major drug-resistance mechanisms known today, and then focus primarily on the technological approaches leading to the advancement of this field.

Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

In-vivo evaluation of human recombinant Co-arginase against A375 melanoma xenografts

Metastatic melanoma is a deadly form of cancer with few therapeutic options and the cause of more than 9480 deaths annually in the USA alone. Novel treatment options for this disease are urgently needed. Here we test the efficacy of a novel melanoma drug, the human recombinant Co-arginase (CoArgIPEG), against an aggressive A375 melanoma mouse model. CoArgIPEG is a modification of the naturally occurring human enzyme with improved stability, catalytic activity, and potentially lower immunogenicity compared with current amino acid-depleting drugs. Marked tumor growth reductions (mean P=0.0057) with apoptosis induction and proliferation inhibition are noted with CoArgIPEG treatment, both in the presence and in the absence of supplemental citrulline. Further, improved therapeutic efficacy has been noted against A375 xenografts relative to the naturally occurring human recombinant arginase enzyme at lower doses of CoArgIPEG. Unfortunately, after 1 month, half of the relapsing tumors showed argininosuccinate synthase induction, which correlated with Ser62-phosphorylated cMyc. Although argininosuccinate synthase induction could not be induced in vitro, a drug targeting pathway previously demonstrated to be associated with Ser62 cMyc phosphorylation - U0126 - in combination with CoArgIPEG demonstrated an in-vitro synergistic response (combination indices 0.13±0.10 and 0.14±0.10 with or without citrulline, respectively). Overall, favorable efficacy and potential synergy with other antimelanoma drugs support CoArgIPEG as a potent, novel cancer therapeutic.

Phenotype-dependent alteration of pathways and networks reveals a pure synergistic mechanism for compounds treating mouse cerebral ischemia

AIM

Our previous studies have showed that ursodeoxycholic acid (UA) and jasminoidin (JA) effectively reduce cerebral infarct volume in mice. In this study we explored the pure synergistic mechanism of these compounds in treatment of mouse cerebral ischemia, which was defined as synergistic actions specific for phenotype variations after excluding interference from ineffective compounds.

METHODS

Mice with focal cerebral ischemia were treated with UA, JA or a combination JA and UA (JU). Concha margaritifera (CM) was taken as ineffective compound. Cerebral infarct volume of the mice was determined, and the hippocampi were taken for microarray analysis. Particular signaling pathways and biological functions were enriched based on differentially expressed genes, and corresponding networks were constructed through Ingenuity Pathway Analysis.

RESULTS

In phenotype analysis, UA, JA, and JU significantly reduced the ischemic infarct volume with JU being superior to UA or JA alone, while CM was ineffective. As a result, 4 pathways enriched in CM were excluded. Core pathways in the phenotype-positive groups (UA or JA) were involved in neuronal homeostasis and neuropathology. JU-contributing pathways included all UA-contributing and the majority (71.7%) of JA-contributing pathways, and 10 new core pathways whose effects included inflammatory immunity, apoptosis and nervous system development. The functions of JU group included all functions of JA group, the majority (93.1%) of UA-contributing functions, and 3 new core functions, which focused on physiological system development and function.

CONCLUSION

The pure synergism between UA and JA underlies 10 new core pathways and 3 new core functions, which are involved in inflammation, immune responses, apoptosis and nervous system development.

Development of potent autophagy inhibitors that sensitize oncogenic BRAF V600E mutant melanoma tumor cells to vemurafenib

Autophagy is a dynamic cell survival mechanism by which a double-membrane vesicle, or autophagosome, sequesters portions of the cytosol for delivery to the lysosome for recycling. This process can be inhibited using the antimalarial agent chloroquine (CQ), which impairs lysosomal function and prevents autophagosome turnover. Despite its activity, CQ is a relatively inadequate inhibitor that requires high concentrations to disrupt autophagy, highlighting the need for improved small molecules. To address this, we screened a panel of antimalarial agents for autophagy inhibition and chemically synthesized a novel series of acridine and tetrahydroacridine derivatives. Structure-activity relationship studies of the acridine ring led to the discovery of VATG-027 as a potent autophagy inhibitor with a high cytotoxicity profile. In contrast, the tetrahydroacridine VATG-032 showed remarkably little cytotoxicity while still maintaining autophagy inhibition activity, suggesting that both compounds act as autophagy inhibitors with differential effects on cell viability. Further, knockdown of autophagy-related genes showed no effect on cell viability, demonstrating that the ability to inhibit autophagy is separate from the compound cytotoxicity profiles. Next, we determined that both inhibitors function through lysosomal deacidification mechanisms and ultimately disrupt autophagosome turnover. To evaluate the genetic context in which these lysosomotropic inhibitors may be effective, they were tested in patient-derived melanoma cell lines driven by oncogenic BRAF (v-raf murine sarcoma viral oncogene homolog B). We discovered that both inhibitors sensitized melanoma cells to the BRAF V600E inhibitor vemurafenib. Overall, these autophagy inhibitors provide a means to effectively block autophagy and have the potential to sensitize mutant BRAF melanomas to first-line therapies.

Synergy of silver nanoparticles and aztreonam against Pseudomonas aeruginosa PAO1 biofilms

Pathogenic bacterial biofilms, such as those found in the lungs of patients with cystic fibrosis (CF), exhibit increased antimicrobial resistance, due in part to the inherent architecture of the biofilm community. The protection provided by the biofilm limits antimicrobial dispersion and penetration and reduces the efficacy of antibiotics that normally inhibit planktonic cell growth. Thus, alternative antimicrobial strategies are required to combat persistent infections. The antimicrobial properties of silver have been known for decades, but silver and silver-containing compounds have recently seen renewed interest as antimicrobial agents for treating bacterial infections. The goal of this study was to assess the efficacy of citrate-capped silver nanoparticles (AgNPs) of various sizes, alone and in combination with the monobactam antibiotic aztreonam, to inhibit Pseudomonas aeruginosa PAO1 biofilms. Among the different sizes of AgNPs examined, 10-nm nanoparticles were most effective in inhibiting the recovery of P. aeruginosa biofilm cultures and showed synergy of inhibition when combined with sub-MIC levels of aztreonam. Visualization of biofilms treated with combinations of 10-nm AgNPs and aztreonam indicated that the synergistic bactericidal effects are likely caused by better penetration of the small AgNPs into the biofilm matrix, which enhances the deleterious effects of aztreonam against the cell envelope of P. aeruginosa within the biofilms. These data suggest that small AgNPs synergistically enhance the antimicrobial effects of aztreonam against P. aeruginosa in vitro, and they reveal a potential role for combinations of small AgNPs and antibiotics in treating patients with chronic infections.

Synergistic and antagonistic drug combinations depend on network topology

Drug combinations may exhibit synergistic or antagonistic effects. Rational design of synergistic drug combinations remains a challenge despite active experimental and computational efforts. Because drugs manifest their action via their targets, the effects of drug combinations should depend on the interaction of their targets in a network manner. We therefore modeled the effects of drug combinations along with their targets interacting in a network, trying to elucidate the relationships between the network topology involving drug targets and drug combination effects. We used three-node enzymatic networks with various topologies and parameters to study two-drug combinations. These networks can be simplifications of more complex networks involving drug targets, or closely connected target networks themselves. We found that the effects of most of the combinations were not sensitive to parameter variation, indicating that drug combinational effects largely depend on network topology. We then identified and analyzed consistent synergistic or antagonistic drug combination motifs. Synergistic motifs encompass a diverse range of patterns, including both serial and parallel combinations, while antagonistic combinations are relatively less common and homogenous, mostly composed of a positive feedback loop and a downstream link. Overall our study indicated that designing novel synergistic drug combinations based on network topology could be promising, and the motifs we identified could be a useful catalog for rational drug combination design in enzymatic systems.

Computational analyses of synergism in small molecular network motifs

Cellular functions and responses to stimuli are controlled by complex regulatory networks that comprise a large diversity of molecular components and their interactions. However, achieving an intuitive understanding of the dynamical properties and responses to stimuli of these networks is hampered by their large scale and complexity. To address this issue, analyses of regulatory networks often focus on reduced models that depict distinct, reoccurring connectivity patterns referred to as motifs. Previous modeling studies have begun to characterize the dynamics of small motifs, and to describe ways in which variations in parameters affect their responses to stimuli. The present study investigates how variations in pairs of parameters affect responses in a series of ten common network motifs, identifying concurrent variations that act synergistically (or antagonistically) to alter the responses of the motifs to stimuli. Synergism (or antagonism) was quantified using degrees of nonlinear blending and additive synergism. Simulations identified concurrent variations that maximized synergism, and examined the ways in which it was affected by stimulus protocols and the architecture of a motif. Only a subset of architectures exhibited synergism following paired changes in parameters. The approach was then applied to a model describing interlocked feedback loops governing the synthesis of the CREB1 and CREB2 transcription factors. The effects of motifs on synergism for this biologically realistic model were consistent with those for the abstract models of single motifs. These results have implications for the rational design of combination drug therapies with the potential for synergistic interactions.

Cellular responses to stimuli are controlled by complex regulatory networks that comprise many molecular components. Understanding such networks is critical for understanding normal cellular functions and pathological conditions. Because the complexity of these networks often precludes intuitive insights, a useful approach is to study mathematical models of small network motifs having reduced complexity yet consisting of key regulatory components of the more complex networks. Computational studies have analyzed the behavior of small motifs, and have begun to describe the ways in which variations in parameters affect their functional properties. Here, we investigated how variations in pairs of parameters act synergistically (or antagonistically) to alter responses of ten common network motifs. Simulations identified parameter variations that maximized synergism, and examined the ways in which synergism was affected by stimulus protocols and motif architecture. The results have implications for the rational design of combination drug therapies where a goal is to identify drugs that when administered together have a greater effect than would be predicted by simple addition of single-drug effects (i.e., super-additive effects), thereby allowing for lower drug doses, minimizing undesirable effects.

In silico identification of potential therapeutic targets in the TGF-β signal transduction pathway

The transforming growth factor-β (TGF-β) superfamily of cytokines controls fundamental cellular processes, such as proliferation, motility, differentiation, and apoptosis. This fundamental role is emphasized by the widespread presence of mutations of the core components of the TGF-β signal transduction pathway in a number of human diseases. Therefore, there is an increasing interest in the development of therapies to specifically target this pathway. Here we develop a computational approach to identify potential intervention points that are capable of restoring the normal signaling dynamics to the mutated system while maintaining the behavior of normal cells substantially unperturbed. We apply this approach explicitly to the TGF-β pathway to study the signaling dynamics of mutated and normal cells treated with inhibitory drugs and identify the processes in the pathway that are most susceptible to therapeutic intervention.

Traditional Chinese medicine network pharmacology: theory, methodology and application

Traditional Chinese medicine (TCM) has a long history of viewing an individual or patient as a system with different statuses, and has accumulated numerous herbal formulae. The holistic philosophy of TCM shares much with the key ideas of emerging network pharmacology and network biology, and meets the requirements of overcoming complex diseases, such as cancer, in a systematic manner. To discover TCM from a systems perspective and at the molecular level, a novel TCM network pharmacology approach was established by updating the research paradigm from the current "one target, one drug" mode to a new "network target, multi-components" mode. Subsequently, a set of TCM network pharmacology methods were created to prioritize disease-associated genes, to predict the target profiles and pharmacological actions of herbal compounds, to reveal drug-gene-disease co-module associations, to screen synergistic multi-compounds from herbal formulae in a high-throughput manner, and to interpret the combinatorial rules and network regulation effects of herbal formulae. The effectiveness of the network-based methods was demonstrated for the discovery of bioactive compounds and for the elucidation of the mechanisms of action of herbal formulae, such as Qing-Luo-Yin and the Liu-Wei-Di-Huang pill. The studies suggest that the TCM network pharmacology approach provides a new research paradigm for translating TCM from an experience-based medicine to an evidence-based medicine system, which will accelerate TCM drug discovery, and also improve current drug discovery strategies.

Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles

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.

A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus

The use of plants as natural medicines in the treatment of type II diabetes mellitus (T2DM) has long been of special interest. In this work, we developed a docking score-weighted prediction model based on drug-target network to evaluate the efficacy of medicinal plants for T2DM. High throughput virtual screening from chemical library of natural products was adopted to calculate the binding affinity between natural products contained in medicinal plants and 33 T2DM-related proteins. The drug-target network was constructed according to the strength of the binding affinity if the molecular docking score satisfied the threshold. By linking the medicinal plant with T2DM through drug-target network, the model can predict the efficacy of natural products and medicinal plant for T2DM. Eighteen thousand nine hundred ninety-nine natural products and 1669 medicinal plants were predicted to be potentially bioactive.

The effect of network biology on drug toxicology

INTRODUCTION

The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity.

AREAS COVERED

This review describes the strengths and limitations of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity.

EXPERT OPINION

There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network biology has the opportunity to contribute to a better understanding of a drug's safety profile. The authors believe that considering a drug action and protein's function in a global physiological environment may benefit our understanding of the impact some chemicals have on human health and toxicity. The next step for network biology will be to better integrate differential and quantitative data.

Targeted cytolysins synergistically potentiate cytoplasmic delivery of gelonin immunotoxin

Targeted endocytic uptake is a first step toward tissue-specific cytoplasmic macromolecular delivery; however, inefficient escape from the endolysosomal compartment makes this generally impractical at present. We report here a targeted cytolysin approach that dramatically potentiates endosomal release of an independently targeted potent gelonin immunotoxin. Fibronectin domains engineered for affinity to EGF receptor or carcinoembryonic antigen were fused to the plant toxin gelonin or bacterial pore-forming cytolysins. These fusion proteins display synergistic activity in both antigen-specific cytotoxicity in vitro, enhancing potency by several orders of magnitude, and in tumor growth inhibition in vivo. In addition, the number of internalized gelonin molecules required to induce apoptosis is reduced from approximately 5 × 10(6) to less than 10(3). Targeted potentiation shows promise for enhancing cytoplasmic delivery of other macromolecular payloads such as DNA, siRNA, and miRNA.

Optimizing combinations of flavonoids deriving from astragali radix in activating the regulatory element of erythropoietin by a feedback system control scheme

Identifying potent drug combination from a herbal mixture is usually quite challenging, due to a large number of possible trials. Using an engineering approach of the feedback system control (FSC) scheme, we identified the potential best combinations of four flavonoids, including formononetin, ononin, calycosin, and calycosin-7-O-β-D-glucoside deriving from Astragali Radix (AR; Huangqi), which provided the best biological action at minimal doses. Out of more than one thousand possible combinations, only tens of trials were required to optimize the flavonoid combinations that stimulated a maximal transcriptional activity of hypoxia response element (HRE), a critical regulator for erythropoietin (EPO) transcription, in cultured human embryonic kidney fibroblast (HEK293T). By using FSC scheme, 90% of the work and time can be saved, and the optimized flavonoid combinations increased the HRE mediated transcriptional activity by ~3-fold as compared with individual flavonoid, while the amount of flavonoids was reduced by ~10-fold. Our study suggests that the optimized combination of flavonoids may have strong effect in activating the regulatory element of erythropoietin at very low dosage, which may be used as new source of natural hematopoietic agent. The present work also indicates that the FSC scheme is able to serve as an efficient and model-free approach to optimize the drug combination of different ingredients within a herbal decoction.

An Integrative Platform of TCM Network Pharmacology and Its Application on a Herbal Formula, Qing-Luo-Yin

The scientific understanding of traditional Chinese medicine (TCM) has been hindered by the lack of methods that can explore the complex nature and combinatorial rules of herbal formulae. On the assumption that herbal ingredients mainly target a molecular network to adjust the imbalance of human body, here we present a-self-developed TCM network pharmacology platform for discovering herbal formulae in a systematic manner. This platform integrates a set of network-based methods that we established previously to catch the network regulation mechanism and to identify active ingredients as well as synergistic combinations for a given herbal formula. We then provided a case study on an antirheumatoid arthritis (RA) formula, Qing-Luo-Yin (QLY), to demonstrate the usability of the platform. We revealed the target network of QLY against RA-related key processes including angiogenesis, inflammatory response, and immune response, based on which we not only predicted active and synergistic ingredients from QLY but also interpreted the combinatorial rule of this formula. These findings are either verified by the literature evidence or have the potential to guide further experiments. Therefore, such a network pharmacology strategy and platform is expected to make the systematical study of herbal formulae achievable and to make the TCM drug discovery predictable.

Understanding synergy

Analysis of the interactive effects of combinations of hormones or other manipulations with qualitatively similar individual effects is an important topic in basic and clinical endocrinology as well as other branches of basic and clinical research related to integrative physiology. Functional, as opposed to mechanistic, analyses of interactions rely on the concept of synergy, which can be defined qualitatively as a cooperative action or quantitatively as a supra-additive effect according to some metric for the addition of different dose-effect curves. Unfortunately, dose-effect curve addition is far from straightforward; rather, it requires the development of an axiomatic mathematical theory. I review the mathematical soundness, face validity, and utility of the most frequently used approaches to supra-additive synergy. These criteria highlight serious problems in the two most common synergy approaches, response additivity and Loewe additivity, which is the basis of the isobole and related response surface approaches. I conclude that there is no adequate, generally applicable, supra-additive synergy metric appropriate for endocrinology or any other field of basic and clinical integrative physiology. I recommend that these metrics be abandoned in favor of the simpler definition of synergy as a cooperative, i.e., nonantagonistic, effect. This simple definition avoids mathematical difficulties, is easily applicable, meets regulatory requirements for combination therapy development, and suffices to advance phenomenological basic research to mechanistic studies of interactions and clinical combination therapy research.

Neighbor communities in drug combination networks characterize synergistic effect

Combination therapies are urgently needed for optimal clinical benefit, but an efficient strategy for rational discovery of drug combinations, especially combinations of experimental drugs, is still lacking. Consequently, we proposed here a network-based computational method to identify novel synergistic drug combinations. A large-scale drug combination network (DCN), which provides an alternative way to study the underlying mechanisms of drug combinations, was constructed by integrating 345 drug combination relationships, 1293 drug-target interactions and 15134 target-protein interactions. It was illustrated that synergistic drugs seldom have identical or directly connected targets, while most targets in DCN can be reached from every other by 2 to 4 edges (interactions). Accordingly, the concept 'neighbor community' was introduced to characterize the relationships between synergistic drugs by specifying the interactions between drug targets and their neighbor proteins in the context of DCN. A subsequent study revealed that the integrated topological and functional properties of neighbor communities can be employed to successfully predict drug combinations. It was shown that this method can achieve 88% prediction accuracy and 0.95 AUC (Area Under ROC Curve), demonstrating its good performance in specificity and sensitivity. Moreover, ten predicted synergistic drug combinations unknown to the method were confirmed by recent literature, and three predicted new combinations of experimental drug BI-2536 were validated by in vitro assays. The results suggested that this method provides a means to explore promising drug combinations at an earlier stage of the drug development process.

EGFR/JIP-4/JNK2 signaling attenuates cetuximab-mediated radiosensitization of squamous cell carcinoma cells

EGF receptor (EGFR) promotes tumor growth as well as radio- and chemoresistance in various human malignancies including squamous cell carcinomas (SCC). In addition to deactivation of prosurvival signaling, cetuximab-mediated EGFR targeting might concomitantly induce self-attenuating signaling bypasses. Identification of such bypass mechanisms is key to improve the efficacy of targeted approaches. Here, we show great similarity of EGFR signaling and radiation survival in cetuximab-treated SCC cells grown in a more physiologic three-dimensional extracellular matrix and as tumor xenografts in contrast to conventional monolayer cell cultures. Using phosphoproteome arrays, we observed strong induction of JNK2 phosphorylation potentially resulting from cetuximab-inhibited EGFR through c-jun-NH(2)-kinase (JNK)-interacting protein-4 (JIP-4), which was identified using an immunoprecipitation-mass spectrometric approach. Inhibition of this signaling bypass by JIP-4 or JNK2 knockdown or pharmacologic JNK2 inhibition enhanced cetuximab efficacy and tumor cell radiosensitivity. Our findings add new facets to EGFR signaling and indicate signaling bypass possibilities of cancer cells to improve their survival on cetuximab treatment. By deactivation of cetuximab-self-attenuating JNK2-dependent signaling, the cytotoxicity, and radiosensitizing potential of cetuximab can be augmented.

Network-based drug discovery by integrating systems biology and computational technologies

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple '-omics' databases. The newly developed algorithm- or network-based computational models can tightly integrate '-omics' databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various '-omics' platforms and computational tools would accelerate development of network-based drug discovery and network medicine.

Combinatorial drug therapy for cancer in the post-genomic era

Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy.

Time-course network analysis reveals TNF-α can promote G1/S transition of cell cycle in vascular endothelial cells

MOTIVATION

Tumor necrosis factor-alpha (TNF-α), a major inflammatory cytokine, is closely related to several cardiovascular pathological processes. However, its effects on the cell cycle of vascular endothelial cells (VECs) have been the subject of some controversy. To investigate the molecular mechanism underlying this process, we constructed time-course protein-protein interaction (PPI) networks of TNF-α induced regulation of cell cycle in VECs using microarray datasets and genome-wide PPI datasets. Then, we analyzed the topological properties of the responsive PPI networks and calculated the node degree and node betweenness centralization of each gene in the networks. We found that p21, p27 and cyclinD1, key genes of the G1/S checkpoint, are in the center of responsive PPI networks and their roles in PPI networks are significantly altered with induction of TNF-α. According to the following biological experiments, we proved that TNF-α can promote G(1)/S transition of cell cycle in VECs and facilitate the cell cycle activation induced by vascular endothelial growth factor.

CONTACT

shaoli@mail.tsinghua.edu.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Time-resolved in silico modeling of fine-tuned cAMP signaling in platelets: feedback loops, titrated phosphorylations and pharmacological modulation

Background

Hemostasis is a critical and active function of the blood mediated by platelets. Therefore, the prevention of pathological platelet aggregation is of great importance as well as of pharmaceutical and medical interest. Endogenous platelet inhibition is predominantly based on cyclic nucleotides (cAMP, cGMP) elevation and subsequent cyclic nucleotide-dependent protein kinase (PKA, PKG) activation. In turn, platelet phosphodiesterases (PDEs) and protein phosphatases counterbalance their activity. This main inhibitory pathway in human platelets is crucial for countervailing unwanted platelet activation. Consequently, the regulators of cyclic nucleotide signaling are of particular interest to pharmacology and therapeutics of atherothrombosis. Modeling of pharmacodynamics allows understanding this intricate signaling and supports the precise description of these pivotal targets for pharmacological modulation.

Results

We modeled dynamically concentration-dependent responses of pathway effectors (inhibitors, activators, drug combinations) to cyclic nucleotide signaling as well as to downstream signaling events and verified resulting model predictions by experimental data. Experiments with various cAMP affecting compounds including anti-platelet drugs and their combinations revealed a high fidelity, fine-tuned cAMP signaling in platelets without cross-talk to the cGMP pathway. The model and the data provide evidence for two independent feedback loops: PKA, which is activated by elevated cAMP levels in the platelet, subsequently inhibits adenylyl cyclase (AC) but as well activates PDE3. By multi-experiment fitting, we established a comprehensive dynamic model with one predictive, optimized and validated set of parameters. Different pharmacological conditions (inhibition, activation, drug combinations, permanent and transient perturbations) are successfully tested and simulated, including statistical validation and sensitivity analysis. Downstream cyclic nucleotide signaling events target different phosphorylation sites for cAMP- and cGMP-dependent protein kinases (PKA, PKG) in the vasodilator-stimulated phosphoprotein (VASP). VASP phosphorylation as well as cAMP levels resulting from different drug strengths and combined stimulants were quantitatively modeled. These predictions were again experimentally validated. High sensitivity of the signaling pathway at low concentrations is involved in a fine-tuned balance as well as stable activation of this inhibitory cyclic nucleotide pathway.

Conclusions

On the basis of experimental data, literature mining and database screening we established a dynamic in silico model of cyclic nucleotide signaling and probed its signaling sensitivity. Thoroughly validated, it successfully predicts drug combination effects on platelet function, including synergism, antagonism and regulatory loops.

Combined use of biocontrol agents to manage plant diseases in theory and practice

Effective use of biological control agents (BCAs) is a potentially important component of sustainable agriculture. Recently, there has been an increasing interest among researchers in using combinations of BCAs to exploit potential synergistic effects among them. The methodology for investigating such synergistic effects was reviewed first and published results were then assessed for available evidence for synergy. Correct formulation of hypotheses based on the theoretical definition of independence (Bliss independence or Loewe additivity) and the subsequent and statistical testing for the independence-synergistic-antagonistic interactions have rarely been carried out thus far in studies on biocontrol of plant diseases. Thus, caution must be taken when interpreting reported "synergistic" effects without assessing the original publications. Recent theoretical modeling work suggested that disease suppression from combined use of two BCAs was, in general, very similar to that achieved by the more efficacious one, indicating no synergistic but more likely antagonistic interactions. Only in 2% of the total 465 published treatments was there evidence for synergistic effects among BCAs. In the majority of the cases, antagonistic interactions among BCAs were indicated. Thus, both theoretical and experimental studies suggest that, in combined use of BCAs, antagonistic interactions among BCAs are more likely to occur than synergistic interactions. Several research strategies, including formulation of synergy hypotheses in relation to biocontrol mechanisms, are outlined to exploit microbial mixtures for uses in biocontrol of plant diseases.

Network target for screening synergistic drug combinations with application to traditional Chinese medicine

BACKGROUND

Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge.

RESULTS

In this work, we proposed for the first time a "network target"-based paradigm instead of the traditional "single target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological network as a therapeutic target and assumes that the relationship among agents can be transferred to network interactions among the molecular level entities (targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a network target perspective.

CONCLUSIONS

NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The network target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.

Modeling the TNFα-induced apoptosis pathway in hepatocytes

The proinflammatory cytokine TNFα fails to provoke cell death in isolated hepatocytes but has been implicated in hepatocyte apoptosis during liver diseases associated with chronic inflammation. Recently, we showed that TNFα is able to sensitize primary murine hepatocytes cultured on collagen to Fas ligand-induced apoptosis and presented a mathematical model of the sensitizing effect. Here, we analyze how TNFα induces apoptosis in combination with the transcriptional inhibitor actinomycin D (ActD). Accumulation of reactive oxygen species (ROS) in response to TNFR activation turns out to be critical for sustained activation of JNK which then triggers mitochondrial pathway-dependent apoptosis. In addition, the amount of JNK is strongly upregulated in a ROS-dependent way. In contrast to TNFα plus cycloheximide no cFLIP degradation is observed suggesting a different apoptosis pathway in which the Itch-mediated cFLIP degradation and predominantly caspase-8 activation is not involved. Time-resolved data of the respective pro- and antiapoptotic factors are obtained and subjected to mathematical modeling. On the basis of these data we developed a mathematical model which reproduces the complex interplay regulating the phosphorylation status of JNK and generation of ROS. This model was fully integrated with our model of TNFα/Fas ligand sensitizing as well as with a published NF-κB-model. The resulting comprehensive model delivers insight in the dynamical interplay between the TNFα and FasL pathways, NF-κB and ROS and gives an example for successful model integration.

Gambogic acid inhibits Hsp90 and deregulates TNF-α/NF-κB in HeLa cells

Gambogic acid (GB) is an important anti-cancer drug candidate, but the target protein by which it exerts its anti-cancer effects has not been identified. This study is the first to show that GB inhibits heat shock protein 90 (Hsp90) and down-regulates TNF-α/NF-κB in HeLa cells. The effects of GB on Hsp90 were studied by characterizing its physical interactions with Hsp90 upon binding, the noncompetitive inhibition of Hsp90 ATPase activity, and the degradation of Hsp90 client proteins (i.e., Akt, IKK) in HeLa cells. GB seems to bind to the N-terminal ATP-binding domain of Hsp90. Additionally, GB suppresses the activation of TNF-α/NF-κB and decreases XIAP expression levels and the ratio of Bcl-2/Bax, which in turn induces HeLa cell apoptosis. Thus, GB represents a promising therapeutic agent for cancer; it may also be useful as a probe to increase understanding of the biological functions of Hsp90.