Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Huang L

PROVIDER: S-EPMC5244366 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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

Huang Lu L Jiang Yuyang Y Chen Yuzong Y

Scientific reports 20170119

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 agains ...[more]

PMID: 28102344

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Project description:Compared with traditional oral and injection administration, the transdermal administration of traditional Chinese medicine has distinctive characteristics and advantages, which can avoid the "first pass effect" of the liver and the destruction of the gastrointestinal tract, maintain a stable blood concentration, and prolong drug action time. However, the basic theory and technology research in transdermal drug delivery are relatively limited at present, especially regarding research on new carriers of transdermal drug delivery and pharmacokinetic studies of the skin, which has become a bottleneck of transdermal drug delivery development. Triptolide is one of the main active components of Tripterygium wilfordii, which displays activities against mouse models of polycystic kidney disease and pancreatic cancer but its physical properties and severe toxicity limit its therapeutic potential. Due to the previously mentioned advantages of transdermal administration, in this study, we performed a detail analysis of the pharmacokinetics of a new transdermal triptolide delivery system. Triptolide nanoemulsion gels were prepared and served as new delivery systems, and the ex vivo characteristics were described. The metabolic characteristics of the different triptolide transdermal drug delivery formulations were investigated via skin-blood synchronous microdialysis combined with LC/MS. A multiscale modeling framework, molecular dynamics and finite element modeling were adopted to simulate the transport process of triptolide in the skin and to explore the pharmacokinetics and mathematical patterns. This study shows that the three-layer model can be used for transdermal drug delivery system drug diffusion research. Therefore, it is profitable for transdermal drug delivery system design and the optimization of the dosage form. Based on the drug concentration of the in vivo microdialysis measurement technology, the diffusion coefficient of drugs in the skin can be more accurately measured, and the numerical results can be verified. Therefore, the microdialysis technique combined with mathematical modeling provides a very good platform for the further study of transdermal delivery systems. This research will provide a new technology and method for the study of the pharmacokinetics of traditional Chinese medicine transdermal drug delivery. It has important theoretical and practical significance in clarifying the metabolic transformation of percutaneous drug absorption and screening for appropriate drugs and dosage forms of transdermal drug delivery.

Project description:BackgroundExperimental studies have demonstrated that both the extracellular vasculature or microenvironment and intracellular molecular network (e.g., epidermal growth factor receptor (EGFR) signaling pathway) are important for brain tumor growth. Additionally, some drugs have been developed to inhibit EGFR signaling pathways. However, how angiogenesis affects the response of tumor cells to drug treatment has rarely been mechanistically studied. Therefore, a multiscale model is required to investigate such complex biological systems that contain interactions and feedback among multiple levels.ResultsIn this study, we developed a single cell-based multiscale spatiotemporal model to simulate vascular tumor growth and the drug response based on the vascular endothelial growth factor receptor (VEGFR) signaling pathway, the EGFR signaling pathway and the cell cycle as well as several microenvironmental factors that determine cell fate switches in a temporal and spatial context. By incorporating the EGFRI treatment effect, the model showed an interesting phenomenon in which the survival rate of tumor cells decreased in the early stage but rebounded in a later stage, revealing the emergence of drug resistance. Moreover, we revealed the critical role of angiogenesis in acquired drug resistance, since inhibiting blood vessel growth using a VEGFR inhibitor prevented the recovery of the survival rate of tumor cells in the later stage. We further investigated the optimal timing of combining VEGFR inhibition with EGFR inhibition and predicted that the drug combination targeting both the EGFR pathway and VEGFR pathway has a synergistic effect. The experimental data validated the prediction of drug synergy, confirming the effectiveness of our model. In addition, the combination of EGFR and VEGFR genes showed clinical relevance in glioma patients.ConclusionsThe developed multiscale model revealed angiogenesis-induced drug resistance mechanisms of brain tumors to EGFRI treatment and predicted a synergistic drug combination targeting both EGFR and VEGFR pathways with optimal combination timing. This study explored the mechanistic and functional mechanisms of the angiogenesis underlying tumor growth and drug resistance, which advances our understanding of novel mechanisms of drug resistance and provides implications for designing more effective cancer therapies.

Dataset Information

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

Publications

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

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