Effect of Combination Treatment of Rapamycin and Isoflavones on mTOR Pathway in Human Glioblastoma (U87) Cells

Puli, Shilpa; Jain, Aditi; Lai, James C. K.; Bhushan, Alok

doi:10.1007/s11064-010-0142-2

Cited by 30 publications

(28 citation statements)

References 56 publications

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“…The simulation results show good agreement with experimental data. The effects of the rapamycin are qualitatively consistent with the experimental observations in the literature [44,45,47,49]. As the concentration of rapamycin increases, a decline is observed in the overall growth rate of the tumor.…”

Section: Resultssupporting

confidence: 89%

A fully coupled space–time multiscale modeling framework for predicting tumor growth

Rahman

Feng

Yankeelov

et al. 2017

Computer Methods in Applied Mechanics and Engineering

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Most biological systems encountered in living organisms involve highly complex heterogeneous multi-component structures that exhibit different physical, chemical, and biological behavior at different spatial and temporal scales. The development of predictive mathematical and computational models of multiscale events in such systems is a major challenge in contemporary computational biomechanics, particularly the development of models of growing tumors in humans. The aim of this study is to develop a general framework for tumor growth prediction by considering major biological events at tissue, cellular, and subcellular scales. The key to developing such multiscale models is how to bridge spatial and temporal scales that range from 10−3 to 103 mm in space and from 10−6 to 107 s in time. In this paper, a fully coupled space–time multiscale framework for modeling tumor growth is developed. The framework consists of a tissue scale model, a model of cellular activities, and a subcellular transduction signaling pathway model. The tissue, cellular, and subcellular models in this framework are solved using partial differential equations for tissue growth, agent-based model for cellular events, and ordinary differential equations for signaling transduction pathway as a network at subcellular scale. The model is calibrated using experimental observations. Moreover, this model is biologically-driven from a signaling pathway, volumetrically-consistent between cellular and tissue scale in terms of tumor volume evolution in time, and a biophysically-sound tissue model that satisfies all conservation laws. The results show that the model is capable of predicting major characteristics of tumor growth such as the morphological instability, growth patterns of different cell phenotypes, compact regions of the higher cell density at the tumor region, and the reduction of growth rate due to drug delivery. The predicted treatment outcomes show a reduction in proliferation at different rates in response to different drug dosages. Moreover, the results of several 3D applications to tumor growth and the evolution of cellular and subcellular events are presented.

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Section: Resultssupporting

confidence: 89%

A fully coupled space–time multiscale modeling framework for predicting tumor growth

Rahman

Feng

Yankeelov

et al. 2017

Computer Methods in Applied Mechanics and Engineering

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“…As shown in Table 1, the synergy scores of both 5-fluorouracil (5-FU) combined with Vinblastine [33] and 5-FU combined with Rapamycin [34] entered the top three. Three other synergistic pairs, Vinblastine and Camptothecin [35], Genistein and Camptothecin [36], and Genistein and Rapamycin [37], also earned high marks and ranked in the top layer. We then used, respectively, three global background networks including the global protein-protein interaction (PPI) network and two kinds of global pathway networks (Keep Node Content and Merge Node Content, KNC and MNC) ( See Methods ) to calculate the synergy score.…”

Section: Resultsmentioning

confidence: 99%

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

2011

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BackgroundMulticomponent 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.ResultsIn 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.ConclusionsNIMS 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.

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“…Previous work has used the mTORC1 inhibitor rapamycin to inhibit the mTOR/PI3K pathway in an attempt to inhibit the growth of GBM [30-32]. It was therefore a logical progression to investigate specific mTOR signalling inhibition in GBM cells and their possible use in conjunction with TMZ.…”

Section: Discussionmentioning

confidence: 99%

mTOR inhibition and levels of the DNA repair protein MGMT in T98G glioblastoma cells

2014

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BackgroundGlioblastoma multiforme (GBM), the most common and most aggressive type of primary adult brain tumour, responds poorly to conventional treatment. Temozolomide (TMZ) chemotherapy remains the most commonly used treatment, despite a large proportion of tumours displaying TMZ resistance. 60% of GBM tumours have unmethylated MGMT promoter regions, resulting in an overexpression of the DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT), which is responsible for tumour resistance to TMZ chemotherapy. Tumours also often exhibit hyperactive PI3-kinase/mTOR signalling, which enables them to resynthesise proteins quickly. Since MGMT is a suicide protein that is degraded upon binding to and repairing TMZ-induced O6-methylguanine adducts, it has been hypothesized that inhibition of translation via the mTOR signalling pathway could generate a tumour-specific reduction in MGMT protein and increase TMZ sensitivity.MethodsMGMT was monitored at the post-transcriptional, translational and protein levels, to determine what effect mTOR inhibition was having on MGMT protein expression in vitro.ResultsWe show that inhibiting mTOR signalling is indeed associated with acute inhibition of protein synthesis. Western blots show that despite this, relative to loading control proteins, steady state levels of MGMT protein increased and MGMT mRNA was retained in heavy polysomes. Whilst TMZ treatment resulted in maintained MGMT protein levels, concomitant treatment of T98G cells with TMZ and KU0063794 resulted in increased MGMT protein levels without changes in total mRNA levels.ConclusionsThese in vitro data suggest that, counterintuitively, mTOR inhibition may not be a useful adjunct to TMZ therapy and that more investigation is needed before applying mTOR inhibitors in a clinical setting.

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Effect of Combination Treatment of Rapamycin and Isoflavones on mTOR Pathway in Human Glioblastoma (U87) Cells

Cited by 30 publications

References 56 publications

A fully coupled space–time multiscale modeling framework for predicting tumor growth

A fully coupled space–time multiscale modeling framework for predicting tumor growth

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

mTOR inhibition and levels of the DNA repair protein MGMT in T98G glioblastoma cells

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