Publications by authors named "Aleksander S Popel"

200 Publications

A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization.

iScience 2021 Feb 29;24(2):102112. Epub 2021 Jan 29.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 720 Rutland Avenue, 613 Traylor Bldg, Baltimore, MD 21205, USA.

Macrophages are highly plastic immune cells that dynamically integrate microenvironmental signals to shape their own functional phenotypes, a process known as polarization. Here we develop a large-scale mechanistic computational model that for the first time enables a systems-level characterization, from quantitative, temporal, dose-dependent, and single-cell perspectives, of macrophage polarization driven by a complex multi-pathway signaling network. The model was extensively calibrated and validated against literature and focused on in-house experimental data. Using the model, we generated dynamic phenotype maps in response to numerous combinations of polarizing signals; we also probed into an population of model-based macrophages to examine the impact of polarization continuum at the single-cell level. Additionally, we analyzed the model under an condition of peripheral arterial disease to evaluate strategies that can potentially induce therapeutic macrophage repolarization. Our model is a key step toward the future development of a network-centric, comprehensive "virtual macrophage" simulation platform.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.isci.2021.102112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895754PMC
February 2021

Quantitative Systems Pharmacology Modeling of PBMC-Humanized Mouse to Facilitate Preclinical Immuno-oncology Drug Development.

ACS Pharmacol Transl Sci 2021 Feb 3;4(1):213-225. Epub 2020 Dec 3.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States.

Progress in immunotherapy has resulted in explosively increased new therapeutic interventions and they have shown promising results in the treatment of cancer. Animal testing is performed to provide preliminary efficacy and safety data for drugs under development prior to clinical trials. However, translational challenges remain for preclinical studies such as study design and the relevance of animal models to humans. Hence, only a small fraction of cancer patients showed response. The explosion of drug candidates and therapies makes preclinical assessment of every plausible option impossible, but it can be easily tested using Quantitative System Pharmacology (QSP) models. Here, we developed a QSP model for humanized mice. Tumor growth dynamics, T cell dynamics, cytokine release, immune checkpoint expression, and drug administration were modeled and calibrated using experimental data. Tumor growth inhibition data were used for model validation. Pharmacokinetics of T cell engager (TCE), tumor growth profile, T cell expansion in the blood and infiltration into tumor, T cell dissemination from primary tumor, cytokine release profile, and expression of additional PD-L1 induced by IFN-γ were modeled and calibrated using a variety of experimental data and showed good consistency. Mouse-specific response to T cell engager monotherapy also showed the key features of efficacy of TCE. This novel QSP model, designed for human peripheral blood mononuclear cells (PBMC) engrafted xenograft mice, incorporating the most critical components of the mouse model with key cancer and immune cells, can become an integral part of preclinical drug development.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acsptsci.0c00178DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887858PMC
February 2021

Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer.

J Immunother Cancer 2021 Feb;9(2)

Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.

Background: Immune checkpoint blockade therapy has clearly shown clinical activity in patients with triple-negative breast cancer, but less than half of the patients benefit from the treatments. While a number of ongoing clinical trials are investigating different combinations of checkpoint inhibitors and chemotherapeutic agents, predictive biomarkers that identify patients most likely to benefit remains one of the major challenges. Here we present a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that incorporates detailed mechanisms of immune-cancer cell interactions to make efficacy predictions and identify predictive biomarkers for treatments using atezolizumab and nab-paclitaxel.

Methods: A QSP model was developed based on published data of triple-negative breast cancer. With the model, we generated a virtual patient cohort to conduct in silico virtual clinical trials and make retrospective analyses of the pivotal IMpassion130 trial that led to the accelerated approval of atezolizumab and nab-paclitaxel for patients with programmed death-ligand 1 (PD-L1) positive triple-negative breast cancer. Available data from clinical trials were used for model calibration and validation.

Results: With the calibrated virtual patient cohort based on clinical data from the placebo comparator arm of the IMpassion130 trial, we made efficacy predictions and identified potential predictive biomarkers for the experimental arm of the trial using the proposed QSP model. The model predictions are consistent with clinically reported efficacy endpoints and correlated immune biomarkers. We further performed a series of virtual clinical trials to compare different doses and schedules of the two drugs for simulated therapeutic optimization.

Conclusions: This study provides a QSP platform, which can be used to generate virtual patient cohorts and conduct virtual clinical trials. Our findings demonstrate its potential for making efficacy predictions for immunotherapies and chemotherapies, identifying predictive biomarkers, and guiding future clinical trial designs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/jitc-2020-002100DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7883871PMC
February 2021

Peptides that immunoactivate the tumor microenvironment.

Biochim Biophys Acta Rev Cancer 2021 01 1;1875(1):188486. Epub 2020 Dec 1.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA.

Cancer immunotherapy has achieved positive clinical outcomes and is revolutionizing cancer treatment. However, cancer immunotherapy has thus far failed to improve outcomes for most "cold tumors", which are characterized by low infiltration of immune cells and immunosuppressive tumor microenvironment. Enhancing the responsiveness of cold tumors to cancer immunotherapy by stimulating the components of the tumor microenvironment is a strategy pursued in the last decade. Currently, most of the agents used to modify the tumor microenvironment are small molecules or antibodies. Small molecules exhibit low affinity and specificity towards the target and antibodies have shortcomings such as poor tissue penetration and high production cost. Peptides may overcome these drawbacks and therefore are promising materials for immunomodulating agents. Here we systematically summarize the currently developed immunoactivating peptides and discuss the potential of peptide therapeutics in cancer immunology.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.bbcan.2020.188486DOI Listing
January 2021

Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer.

Front Physiol 2020 19;11:583333. Epub 2020 Oct 19.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Overwhelming evidence has shown the significant role of the tumor microenvironment (TME) in governing the triple-negative breast cancer (TNBC) progression. Digital pathology can provide key information about the spatial heterogeneity within the TME using image analysis and spatial statistics. These analyses have been applied to CD8+ T cells, but quantitative analyses of other important markers and their correlations are limited. In this study, a digital pathology computational workflow is formulated for characterizing the spatial distributions of five immune markers (CD3, CD4, CD8, CD20, and FoxP3) and then the functionality is tested on whole slide images from patients with TNBC. The workflow is initiated by digital image processing to extract and colocalize immune marker-labeled cells and then convert this information to point patterns. Afterward invasive front (IF), central tumor (CT), and normal tissue (N) are characterized. For each region, we examine the intra-tumoral heterogeneity. The workflow is then repeated for all specimens to capture inter-tumoral heterogeneity. In this study, both intra- and inter-tumoral heterogeneities are observed for all five markers across all specimens. Among all regions, IF tends to have higher densities of immune cells and overall larger variations in spatial model fitting parameters and higher density in cell clusters and hotspots compared to CT and N. Results suggest a distinct role of IF in the tumor immuno-architecture. Though the sample size is limited in the study, the computational workflow could be readily reproduced and scaled due to its automatic nature. Importantly, the value of the workflow also lies in its potential to be linked to treatment outcomes and identification of predictive biomarkers for responders/non-responders, and its application to parameterization and validation of computational immuno-oncology models.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fphys.2020.583333DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604437PMC
October 2020

Regulation of the tumor immune microenvironment and vascular normalization in TNBC murine models by a novel peptide.

Oncoimmunology 2020 05 14;9(1):1760685. Epub 2020 May 14.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Triple-negative breast cancer (TNBC) is a highly metastatic and aggressive disease with limited treatment options. Recently, the combination of the immune checkpoint inhibitor (ICI) atezolizumab (anti-PD-L1) with nab-paclitaxel was approved following a clinical trial that showed response rates in at least 43% of patients. While this approval marks a major advance in the treatment of TNBC it may be possible to improve the efficacy of ICI therapies through further modulation of the suppressive tumor immune microenvironment (TIME). Several factors may limit immune response in TNBC including aberrant growth factor signaling, such as VEGFR2 and cMet signaling, inefficient vascularization, poor delivery of drugs and immune cells, and the skewing of immune cell populations toward immunosuppressive phenotypes. Here we investigate the immune-modulating properties of AXT201, a novel 20 amino-acid integrin-binding peptide in two syngeneic mouse TNBC models: 4T1-BALB/c and NT4-FVB. AXT201 treatment improved survival in the NT4 model by 20% and inhibited the growth of 4T1 tumors by 47% over 22 days post-inoculation. Subsequent immunohistochemical analyses of 4T1 tumors also showed a 53% reduction in vascular density and a 184% increase in pericyte coverage following peptide treatment. Flow cytometry analyses demonstrated evidence of a more favorable anti-tumor immune microenvironment following treatment with AXT201, including significant decreases in the populations of T regulatory cells, monocytic myeloid-derived suppressor cells, and PD-L1 expressing cells and increased expression of T cell functional markers. Together, these findings demonstrate immune-activating properties of AXT201 that could be developed in combination with other immunomodulatory agents in the treatment of TNBC.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/2162402X.2020.1760685DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7458646PMC
May 2020

Combination therapy with T cell engager and PD-L1 blockade enhances the antitumor potency of T cells as predicted by a QSP model.

J Immunother Cancer 2020 08;8(2)

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Background: T cells have been recognized as core effectors for cancer immunotherapy. How to restore the anti-tumor ability of suppressed T cells or improve the lethality of cytotoxic T cells has become the main focus in immunotherapy. Bispecific antibodies, especially bispecific T cell engagers (TCEs), have shown their unique ability to enhance the patient's immune response to tumors by stimulating T cell activation and cytokine production in an MHC-independent manner. Antibodies targeting the checkpoint inhibitory molecules such as programmed cell death protein 1 (PD-1), PD-ligand 1 (PD-L1) and cytotoxic lymphocyte activated antigen 4 are able to restore the cytotoxic effect of immune suppressed T cells and have also shown durable responses in patients with malignancies. However, both types have their own limitations in treating certain cancers. Preclinical and clinical results have emphasized the potential of combining these two antibodies to improve tumor response and patients' survival. However, the selection and evaluation of combination partners clinically is a costly endeavor. In addition, despite advances made in immunotherapy, there are subsets of patients who are non-responders, and reliable biomarkers for different immunotherapies are urgently needed to improve the ability to prospectively predict patients' response and improve clinical study design. Therefore, mathematical and computational models are essential to optimize patient benefit, and guide combination approaches with lower cost and in a faster manner.

Method: In this study, we continued to extend the quantitative systems pharmacology (QSP) model we developed for a bispecific TCE to explore efficacy of combination therapy with an anti-PD-L1 monoclonal antibody in patients with colorectal cancer.

Results: Patient-specific response to TCE monotherapy, anti-PD-L1 monotherapy and the combination therapy were predicted using this model according to each patient's individual characteristics.

Conclusions: Individual biomarkers for TCE monotherapy, anti-PD-L1 monotherapy and their combination have been determined based on the QSP model. Best treatment options for specific patients could be suggested based on their own characteristics to improve clinical trial efficiency. The model can be further used to assess plausible combination strategies for different TCEs and immune checkpoint inhibitors in different types of cancer.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/jitc-2020-001141DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454244PMC
August 2020

Suppression of Ocular Vascular Inflammation through Peptide-Mediated Activation of Angiopoietin-Tie2 Signaling.

Int J Mol Sci 2020 Jul 21;21(14). Epub 2020 Jul 21.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Persistent inflammation is a complication associated with many ocular diseases. Changes in ocular vessels can amplify disease responses and contribute to vision loss by influencing the delivery of leukocytes to the eye, vascular leakage, and perfusion. Here, we report the anti-inflammatory activity for AXT107, a non-RGD, 20-mer αvβ3 and α5β1 integrin-binding peptide that blocks vascular endothelial growth factor (VEGF)-signaling and activates tyrosine kinase with immunoglobulin and EGF-like domains 2 (Tie2) using the normally inhibitory ligand angiopoietin 2 (Ang2). Tumor necrosis factor α (TNFα), a central inflammation mediator, induces Ang2 release from endothelial cells to enhance its stimulation of inflammation and vascular leakage. AXT107 resolves TNFα-induced vascular inflammation in endothelial cells by converting the endogenously released Ang2 into an agonist of Tie2 signaling, thereby disrupting both the synergism between TNFα and Ang2 while also preventing inhibitor of nuclear factor-κB α (IκBα) degradation directly through Tie2 signaling. This recovery of IκBα prevents nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) nuclear localization, thereby blocking NF-κB-induced inflammatory responses, including the production of VCAM-1 and ICAM-1, leukostasis, and vascular leakage in cell and mouse models. AXT107 also decreased the levels of pro-inflammatory TNF receptor 1 (TNFR1) without affecting levels of the more protective TNFR2. These data suggest that AXT107 may provide multiple benefits in the treatment of retinal/choroidal and other vascular diseases by suppressing inflammation and promoting vascular stabilization.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ijms21145142DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7404316PMC
July 2020

QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications.

CPT Pharmacometrics Syst Pharmacol 2020 09 7;9(9):484-497. Epub 2020 Sep 7.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune-related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole-body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno-oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/psp4.12546DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7499194PMC
September 2020

A Quantitative Systems Pharmacology Model of T Cell Engager Applied to Solid Tumor.

AAPS J 2020 06 12;22(4):85. Epub 2020 Jun 12.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Cancer immunotherapy has recently drawn remarkable attention as promising results in the clinic have shown its ability to improve the overall survival, and T cells are considered to be one of the primary effectors for cancer immunotherapy. Enhanced and restored T cell tumoricidal activity has shown great potential for killing cancer cells. Bispecific T cell engagers (TCEs) are a growing class of molecules that are designed to bind two different antigens on the surface of T cells and cancer cells to bring them in close proximity and selectively activate effector T cells to kill target cancer cells. New T cell engagers are being investigated for the treatment of solid tumors. The activity of newly developed T cell engagers showed a strong correlation with tumor target antigen expression. However, the correlation between tumor-associated antigen expression and overall response of cancer patients is poorly understood. In this study, we used a well-calibrated quantitative systems pharmacology (QSP) model extended to bispecific T cell engagers to explore their efficacy and identify potential biomarkers. In principle, patient-specific response can be predicted through this model according to each patient's individual characteristics. This extended QSP model has been calibrated with available experimental data and provides predictions of patients' response to TCE treatment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1208/s12248-020-00450-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293198PMC
June 2020

Cytokines secreted by stromal cells in TNBC microenvironment as potential targets for cancer therapy.

Cancer Biol Ther 2020 06 26;21(6):560-569. Epub 2020 Mar 26.

Department of Pharmaceutical Science, Albany College of Pharmacy and Health Science, Albany, NY, USA.

In triple-negative breast cancer (TNBC), the lack of therapeutic markers and effective targeted therapies result in an incurable metastatic disease associated with a poor prognosis. Crosstalks within the tumor microenvironment (TME), including those between cancer and stromal cells, affect the tumor heterogeneity, growth, and metastasis. Previously, we have demonstrated that IL-6, IL-8, and CCL5 play a significant role in TNBC growth and metastasis. In this study, we performed a systematic analysis of cytokine factors secreted from four stromal components (fibroblasts, macrophages, lymphatic endothelial cells, and blood microvascular endothelial cells) induced by four TNBC cell types. Through bioinformatic analysis, we selected putative candidates of secreted factors from stromal cells, which are involved in EMT activity, cell proliferation, metabolism, and matrisome pathways. Among the candidates, LCN2, GM-CSF, CST3, IL-6, IL-8, and CHI3L1 are ranked highly. Significantly, Lipocalin-2 (LCN2) is upregulated in the crosstalk of stromal cells and four different TNBC cells. We validated the increase of LCN2 secreted from four stromal cells induced by TNBC cells. Using a specific LCN2 antibody, we observed the inhibition of TNBC cell growth and migration. Taken together, these results propose secreted factors as molecular targets to treat TNBC progression via crosstalk with stromal components.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/15384047.2020.1739484DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515526PMC
June 2020

Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model With an Epigenetic Modulator and Immune Checkpoint Inhibitors.

Front Bioeng Biotechnol 2020 25;8:141. Epub 2020 Feb 25.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

The survival rate of patients with breast cancer has been improved by immune checkpoint blockade therapies, and the efficacy of their combinations with epigenetic modulators has shown promising results in preclinical studies. In this prospective study, we propose an ordinary differential equation (ODE)-based quantitative systems pharmacology (QSP) model to conduct an virtual clinical trial and analyze potential predictive biomarkers to improve the anti-tumor response in HER2-negative breast cancer. The model is comprised of four compartments: central, peripheral, tumor, and tumor-draining lymph node, and describes immune activation, suppression, T cell trafficking, and pharmacokinetics and pharmacodynamics (PK/PD) of the therapeutic agents. We implement theoretical mechanisms of action for checkpoint inhibitors and the epigenetic modulator based on preclinical studies to investigate their effects on anti-tumor response. According to model-based simulations, we confirm the synergistic effect of the epigenetic modulator and that pre-treatment tumor mutational burden, tumor-infiltrating effector T cell (Teff) density, and Teff to regulatory T cell (Treg) ratio are significantly higher in responders, which can be potential biomarkers to be considered in clinical trials. Overall, we present a readily reproducible modular model to conduct virtual clinical trials on patient cohorts of interest, which is a step toward personalized medicine in cancer immunotherapy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fbioe.2020.00141DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7051945PMC
February 2020

Immunoactivating the tumor microenvironment enhances immunotherapy as predicted by integrative computational model.

Proc Natl Acad Sci U S A 2020 03 26;117(9):4447-4449. Epub 2020 Feb 26.

Systems Biology Laboratory, School of Medicine, Johns Hopkins University, Baltimore, MD 21205;

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1073/pnas.2001050117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060734PMC
March 2020

A mechanistic integrative computational model of macrophage polarization: Implications in human pathophysiology.

PLoS Comput Biol 2019 11 18;15(11):e1007468. Epub 2019 Nov 18.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.

Macrophages respond to signals in the microenvironment by changing their functional phenotypes, a process known as polarization. Depending on the context, they acquire different patterns of transcriptional activation, cytokine expression and cellular metabolism which collectively constitute a continuous spectrum of phenotypes, of which the two extremes are denoted as classical (M1) and alternative (M2) activation. To quantitatively decode the underlying principles governing macrophage phenotypic polarization and thereby harness its therapeutic potential in human diseases, a systems-level approach is needed given the multitude of signaling pathways and intracellular regulation involved. Here we develop the first mechanism-based, multi-pathway computational model that describes the integrated signal transduction and macrophage programming under M1 (IFN-γ), M2 (IL-4) and cell stress (hypoxia) stimulation. Our model was calibrated extensively against experimental data, and we mechanistically elucidated several signature feedbacks behind the M1-M2 antagonism and investigated the dynamical shaping of macrophage phenotypes within the M1-M2 spectrum. Model sensitivity analysis also revealed key molecular nodes and interactions as targets with potential therapeutic values for the pathophysiology of peripheral arterial disease and cancer. Through simulations that dynamically capture the signal integration and phenotypic marker expression in the differential macrophage polarization responses, our model provides an important computational basis toward a more quantitative and network-centric understanding of the complex physiology and versatile functions of macrophages in human diseases.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1007468DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6860420PMC
November 2019

Angiopoietin-Tie Signaling Pathway in Endothelial Cells: A Computational Model.

iScience 2019 Oct 3;20:497-511. Epub 2019 Oct 3.

Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.

The angiopoietin-Tie signaling pathway is an important vascular signaling pathway involved in angiogenesis, vascular stability, and quiescence. Dysregulation in the pathway is linked to the impairments in vascular function associated with many diseases, including cancer, ocular diseases, systemic inflammation, and cardiovascular diseases. The present study uses a computational signaling pathway model validated against experimental data to quantitatively study various mechanistic aspects of the angiopoietin-Tie signaling pathway, including receptor activation, trafficking, turnover, and molecular mechanisms of its regulation. The model provides mechanistic insights into the controversial role of Ang2 and its regulators vascular endothelial protein tyrosine phosphatase (VE-PTP) and Tie1 and predicts synergistic effects of inhibition of VE-PTP, Tie1, and Tie2 cleavage on enhancing the vascular protective actions of Tie2.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.isci.2019.10.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806670PMC
October 2019

Mechanistically detailed systems biology modeling of the HGF/Met pathway in hepatocellular carcinoma.

NPJ Syst Biol Appl 2019 16;5:29. Epub 2019 Aug 16.

1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA.

Hepatocyte growth factor (HGF) signaling through its receptor Met has been implicated in hepatocellular carcinoma tumorigenesis and progression. Met interaction with integrins is shown to modulate the downstream signaling to Akt and ERK (extracellular-regulated kinase). In this study, we developed a mechanistically detailed systems biology model of HGF/Met signaling pathway that incorporated specific interactions with integrins to investigate the efficacy of integrin-binding peptide, AXT050, as monotherapy and in combination with other therapeutics targeting this pathway. Here we report that the modeled dynamics of the response to AXT050 revealed that receptor trafficking is sufficient to explain the effect of Met-integrin interactions on HGF signaling. Furthermore, the model predicted patient-specific synergy and antagonism of efficacy and potency for combination of AXT050 with sorafenib, cabozantinib, and rilotumumab. Overall, the model provides a valuable framework for studying the efficacy of drugs targeting receptor tyrosine kinase interaction with integrins, and identification of synergistic drug combinations for the patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41540-019-0107-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697704PMC
April 2020

A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade.

Sci Rep 2019 08 2;9(1):11286. Epub 2019 Aug 2.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Over the past decade, several immunotherapies have been approved for the treatment of melanoma. The most prominent of these are the immune checkpoint inhibitors, which are antibodies that block the inhibitory effects on the immune system by checkpoint receptors, such as CTLA-4, PD-1 and PD-L1. Preclinically, blocking these receptors has led to increased activation and proliferation of effector cells following stimulation and antigen recognition, and subsequently, more effective elimination of cancer cells. Translation from preclinical to clinical outcomes in solid tumors has shown the existence of a wide diversity of individual patient responses, linked to several patient-specific parameters. We developed a quantitative systems pharmacology (QSP) model that looks at the mentioned checkpoint blockade therapies administered as mono-, combo- and sequential therapies, to show how different combinations of specific patient parameters defined within physiological ranges distinguish different types of virtual patient responders to these therapies for melanoma. Further validation by fitting and subsequent simulations of virtual clinical trials mimicking actual patient trials demonstrated that the model can capture a wide variety of tumor dynamics that are observed in the clinic and can predict median clinical responses. Our aim here is to present a QSP model for combination immunotherapy specific to melanoma.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-019-47802-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677731PMC
August 2019

Anisotropic poly(lactic-co-glycolic acid) microparticles enable sustained release of a peptide for long-term inhibition of ocular neovascularization.

Acta Biomater 2019 10 30;97:451-460. Epub 2019 Jul 30.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD 21205, USA; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Department of Materials Science & Engineering, Johns Hopkins University, Baltimore, MD 21205, USA. Electronic address:

Leading causes of vision loss include neovascular age-related macular degeneration (NVAMD) and macular edema (ME), which both require frequent intravitreal injections for treatment. A safe, poly(lactic-co-glycolic acid) (PLGA)-based biodegradable polymeric microparticle (MP) delivery system was developed that encapsulates and protects a biomimetic peptide from degradation, allows sustained intraocular release through polymer hydrolysis, and demonstrates a prolonged anti-angiogenic effect in vivo in three different NVAMD animal models (a laser-induced choroidal neovascularization mouse model, a rhoVEGF transgenic mouse model, and a Tet/opsin/VEGF transgenic mouse model) following intravitreal administration. The role of copolymer composition and microparticle shape was explored and 85:15 lactide-to-glycolide PLGA formed into ellipsoidal microparticles was found to be effective at inhibiting neovascularization for at least 16 weeks in vivo. Treatments were found to not only inhibit the growth of neovascularization, but also to cause regression of the neovasculature, reduce vascular leakage, and prevent exudative retinal detachment. These particulate devices are promising for the sustained release of biologics in the eye and may be useful for treating retinal diseases. STATEMENT OF SIGNIFICANCE: Devastating retinal diseases cause blindness in millions of people around the world. Current protein-based treatments have insufficient efficacy for many patients and also necessitate frequent intravitreal injections. Here, we demonstrate a new treatment consisting of a peptide encapsulated in biodegradable microparticles. We explore the effects of copolymer composition and physical shape of polymeric microparticles and find that both modulate peptide release. Efficacy of the treatment was validated in three different mouse models and the lead formulation was determined to be effective long-term, for at least 16 weeks in vivo, following a single injection. Treatments inhibited and regressed neovascularization as well as reduced vascular leakage. Anisotropic polymeric microparticles are promising for the sustained release of biologics in the eye.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.actbio.2019.07.054DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6939309PMC
October 2019

A Computational Model of Neoadjuvant PD-1 Inhibition in Non-Small Cell Lung Cancer.

AAPS J 2019 06 24;21(5):79. Epub 2019 Jun 24.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Immunotherapy and immune checkpoint blocking antibodies such as anti-PD-1 are approved and significantly improve the survival of advanced non-small cell lung cancer (NSCLC) patients, but there has been little success in identifying biomarkers capable of separating the responders from non-responders before the onset of the therapy. In this study, we developed a quantitative system pharmacology (QSP) model to represent the anti-tumor immune response in human NSCLC that integrated our knowledge of tumor growth, antigen processing and presentation, T cell activation and distribution, antibody pharmacokinetics, and immune checkpoint dynamics. The model was calibrated with the available data and was used to identify potential biomarkers as well as patient-specific response based on the patient parameters. The model predicted that in addition to tumor mutational burden (TMB), a known biomarker for anti-PD-1 therapy in NSCLC, the number of effector T cells and regulatory T cells in the tumor and blood is a predictor of the responders. Furthermore, the model simulated a set of 12 patients with known TMB and MHC/antigen-binding affinity from a recent clinical trial ( ClinicalTrials.gov number, NCT02259621) on neoadjuvant nivolumab therapy in resectable lung cancer and predicted an augmented durable response in patients with adjuvant nivolumab treatment in addition to the clinical trial protocol of neoadjuvant nivolumab treatment followed by resection. Overall, the model provides a valuable framework to model tumor immunity and response to immune checkpoint blockers to enhance biomarker discovery and performing virtual clinical trials to aid in design and interpretation of the current trials with fewer patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1208/s12248-019-0350-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6591205PMC
June 2019

simulation of a clinical trial with anti-CTLA-4 and anti-PD-L1 immunotherapies in metastatic breast cancer using a systems pharmacology model.

R Soc Open Sci 2019 May 22;6(5):190366. Epub 2019 May 22.

Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.

The low response rate of immune checkpoint blockade in breast cancer has highlighted the need for predictive biomarkers to identify responders. While a number of clinical trials are ongoing, testing all possible combinations is not feasible. In this study, a quantitative systems pharmacology model is built to integrate immune-cancer cell interactions in patients with breast cancer, including central, peripheral, tumour-draining lymph node (TDLN) and tumour compartments. The model can describe the immune suppression and evasion in both TDLN and the tumour microenvironment due to checkpoint expression, and mimic the tumour response to checkpoint blockade therapy. We investigate the relationship between the tumour response to checkpoint blockade therapy and composite tumour burden, PD-L1 expression and antigen intensity, including their individual and combined effects on the immune system, using model-based simulations. The proposed model demonstrates the potential to make predictions of tumour response of individual patients given sufficient clinical measurements, and provides a platform that can be further adapted to other types of immunotherapy and their combination with molecular-targeted therapies. The patient predictions demonstrate how this systems pharmacology model can be used to individualize immunotherapy treatments. When appropriately validated, these approaches may contribute to optimization of breast cancer treatment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1098/rsos.190366DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6549962PMC
May 2019

Tumor Ensemble-Based Modeling and Visualization of Emergent Angiogenic Heterogeneity in Breast Cancer.

Sci Rep 2019 03 27;9(1):5276. Epub 2019 Mar 27.

Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Maryland, USA.

There is a critical need for new tools to investigate the spatio-temporal heterogeneity and phenotypic alterations that arise in the tumor microenvironment. However, computational investigations of emergent inter- and intra-tumor angiogenic heterogeneity necessitate 3D microvascular data from 'whole-tumors' as well as "ensembles" of tumors. Until recently, technical limitations such as 3D imaging capabilities, computational power and cost precluded the incorporation of whole-tumor microvascular data in computational models. Here, we describe a novel computational approach based on multimodality, 3D whole-tumor imaging data acquired from eight orthotopic breast tumor xenografts (i.e. a tumor 'ensemble'). We assessed the heterogeneous angiogenic landscape from the microvascular to tumor ensemble scale in terms of vascular morphology, emergent hemodynamics and intravascular oxygenation. We demonstrate how the abnormal organization and hemodynamics of the tumor microvasculature give rise to unique microvascular niches within the tumor and contribute to inter- and intra-tumor heterogeneity. These tumor ensemble-based simulations together with unique data visualization approaches establish the foundation of a novel 'cancer atlas' for investigators to develop their own in silico systems biology applications. We expect this hybrid image-based modeling framework to be adaptable for the study of other tissues (e.g. brain, heart) and other vasculature-dependent diseases (e.g. stroke, myocardial infarction).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-019-40888-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437174PMC
March 2019

Dynamic Changes in Microvascular Flow Conductivity and Perfusion After Myocardial Infarction Shown by Image-Based Modeling.

J Am Heart Assoc 2019 04;8(7):e011058

1 Centro Nacional de Investigaciones Cardiovasculares (CNIC) Madrid Spain.

Background Microcirculation is a decisive factor in tissue reperfusion inadequacy following myocardial infarction ( MI ). Nonetheless, experimental assessment of blood flow in microcirculation remains a bottleneck. We sought to model blood flow properties in coronary microcirculation at different time points after MI and to compare them with healthy conditions to obtain insights into alterations in cardiac tissue perfusion. Methods and Results We developed an image-based modeling framework that permitted feeding a continuum flow model with anatomical data previously obtained from the pig coronary microvasculature to calculate physiologically meaningful permeability tensors. The tensors encompassed the microvascular conductivity and were also used to estimate the arteriole-venule drop in pressure and myocardial blood flow. Our results indicate that the tensors increased in a bimodal pattern at infarcted areas on days 1 and 7 after MI while a nonphysiological decrease in arteriole-venule drop in pressure was observed; contrary, the tensors and the arteriole-venule drop in pressure on day 3 after MI , and in remote areas, were closer to values for healthy tissue. Myocardial blood flow calculated using the condition-dependent arteriole-venule drop in pressure decreased in infarcted areas. Last, we simulated specific modes of vascular remodeling, such as vasodilation, vasoconstriction, or pruning, and quantified their distinct impact on microvascular conductivity. Conclusions Our study unravels time- and region-dependent alterations of tissue perfusion related to the structural changes occurring in the coronary microvasculature due to MI . It also paves the way for conducting simulations in new therapeutic interventions in MI and for image-based microvascular modeling by applying continuum flow models in other biomedical scenarios.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/JAHA.118.011058DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509718PMC
April 2019

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment.

Processes (Basel) 2019 Jan 13;7(1). Epub 2019 Jan 13.

Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.

Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/pr7010037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6349239PMC
January 2019

Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases.

Int J Mol Sci 2019 Jan 19;20(2). Epub 2019 Jan 19.

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

MicroRNAs (miRs) are endogenous non-coding RNA molecules that play important roles in human health and disease by regulating gene expression and cellular processes. In recent years, with the increasing scientific knowledge and new discovery of miRs and their gene targets, as well as the plentiful experimental evidence that shows dysregulation of miRs in a wide variety of human diseases, the computational modeling approach has emerged as an effective tool to help researchers identify novel functional associations between differential miR expression and diseases, dissect the phenotypic expression patterns of miRs in gene regulatory networks, and elucidate the critical roles of miRs in the modulation of disease pathways from mechanistic and quantitative perspectives. Here we will review the recent systems biology studies that employed different kinetic modeling techniques to provide mechanistic insights relating to the regulatory function and therapeutic potential of miRs in human diseases. Some of the key computational aspects to be discussed in detail in this review include (i) models of miR-mediated network motifs in the regulation of gene expression, (ii) models of miR biogenesis and miR⁻target interactions, and (iii) the incorporation of such models into complex disease pathways in order to generate mechanistic, molecular- and systems-level understanding of pathophysiology. Other related bioinformatics tools such as computational platforms that predict miR-disease associations will also be discussed, and we will provide perspectives on the challenges and opportunities in the future development and translational application of data-driven systems biology models that involve miRs and their regulatory pathways in human diseases.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ijms20020421DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358731PMC
January 2019

A collagen IV-derived peptide disrupts α5β1 integrin and potentiates Ang2/Tie2 signaling.

JCI Insight 2019 02 21;4(4). Epub 2019 Feb 21.

Department of Biomedical Engineering and.

The angiopoietin (Ang)/Tie2 signaling pathway is essential for maintaining vascular homeostasis, and its dysregulation is associated with several diseases. Interactions between Tie2 and α5β1 integrin have emerged as part of this control; however, the mechanism is incompletely understood. AXT107, a collagen IV-derived peptide, has strong antipermeability activity and has enabled the elucidation of this previously undetermined mechanism. Previously, AXT107 was shown to inhibit VEGFR2 and other growth factor signaling via receptor tyrosine kinase association with specific integrins. AXT107 disrupts α5β1 and stimulates the relocation of Tie2 and α5 to cell junctions. In the presence of Ang2 and AXT107, junctional Tie2 is activated, downstream survival signals are upregulated, F-actin is rearranged to strengthen junctions, and, as a result, endothelial junctional permeability is reduced. These data suggest that α5β1 sequesters Tie2 in nonjunctional locations in endothelial cell membranes and that AXT107-induced disruption of α5β1 promotes clustering of Tie2 at junctions and converts Ang2 into a strong agonist, similar to responses observed when Ang1 levels greatly exceed those of Ang2. The potentiation of Tie2 activation by Ang2 even extended to mouse models in which AXT107 induced Tie2 phosphorylation in a model of hypoxia and inhibited vascular leakage in an Ang2-overexpression transgenic model and an LPS-induced inflammation model. Because Ang2 levels are very high in ischemic diseases, such as diabetic macular edema, neovascular age-related macular degeneration, uveitis, and cancer, targeting α5β1 with AXT107 provides a potentially more effective approach to treat these diseases.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1172/jci.insight.122043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478425PMC
February 2019

Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors.

Front Oncol 2018 7;8:649. Epub 2019 Jan 7.

Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.

Quantitative characterization of the tumor microenvironment, including its immuno-architecture, is important for developing quantitative diagnostic and predictive biomarkers, matching patients to the most appropriate treatments for precision medicine, and for providing quantitative data for building systems biology computational models able to predict tumor dynamics in the context of immune checkpoint blockade therapies. The intra- and inter-tumoral spatial heterogeneities are potentially key to the understanding of the dose-response relationships, but they also bring challenges to properly parameterizing and validating such models. In this study, we developed a workflow to detect CD8+ T cells from whole slide imaging data, and quantify the spatial heterogeneity using multiple metrics by applying spatial point pattern analysis and morphometric analysis. The results indicate a higher intra-tumoral heterogeneity compared with the heterogeneity across patients. By comparing the baseline metrics with PD-1 blockade treatment outcome, our results indicate that the number of high-density T cell clusters of both circular and elongated shapes are higher in patients who responded to the treatment. This methodology can be applied to quantitatively characterize the tumor microenvironment, including immuno-architecture, and its heterogeneity for different cancer types.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fonc.2018.00649DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6330341PMC
January 2019

Three-Dimensional Transport Model for Intravitreal and Suprachoroidal Drug Injection.

Invest Ophthalmol Vis Sci 2018 10;59(12):5266-5276

Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States.

Purpose: Quantitative understanding of the transport of therapeutic macromolecules following intraocular injections is critical for the design of efficient strategies in treating eye diseases, such as neovascular (wet) age-related macular degeneration (AMD) and macular edema (ME). Antiangiogenic treatments, such as neutralizing antibodies against VEGF or recently characterized antiangiogenic peptides, have shown promise in slowing disease progression.

Methods: We developed a comprehensive three-dimensional (3D) transport model for intraocular injections using published data on drug distribution in rabbit eyes following intravitreal and suprachoroidal (SC) injection of sodium fluorescein (SF), bevacizumab, and ranibizumab. The model then was applied to evaluate the distribution of small molecules and antiangiogenic proteins following intravitreal and SC injections in human eyes.

Results: The model predicts that intravitreally administered molecules are substantially mixed within the vitreous following injection, and that the long-term behavior of the injected drug does not depend on the initial mixing. Ocular pharmacokinetics of different drugs is sensitive to different clearance mechanisms. Effective retinal drug delivery is impacted by RPE permeability. For VEGF antibody, intravitreal injection provides sustained delivery to the retina, whereas SC injection provides more efficient, but short-lived, retinal delivery for smaller-sized molecules. Long-term suppression of neovascularization through SC administration of antiangiogenic drugs necessitates frequent injection or sustained delivery, such as microparticle-based delivery of antiangiogenic peptides.

Conclusions: A comprehensive 3D model for intravitreal and SC drug injection is developed to provide a framework and platform for testing drug delivery routes and sustained delivery devices for new and existing drugs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1167/iovs.17-23632DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6207998PMC
October 2018

Author Correction: Deciphering microvascular changes after myocardial infarction through 3D fully automated image analysis.

Sci Rep 2018 Sep 25;8(1):14563. Epub 2018 Sep 25.

Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, 28029, Spain.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-018-32598-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156410PMC
September 2018

Computational modeling of synergistic interaction between αVβ3 integrin and VEGFR2 in endothelial cells: Implications for the mechanism of action of angiogenesis-modulating integrin-binding peptides.

J Theor Biol 2018 10 20;455:212-221. Epub 2018 Jul 20.

Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, United States.

Cooperation between VEGFR2 and integrin αVβ3 is critical for neovascularization in wound healing, cardiovascular ischemic diseases, ocular diseases, and tumor angiogenesis. In the present study, we developed a rule-based computational model to investigate the potential mechanism by which the Src-induced integrin association with VEGFR2 enhances VEGFR2 activation. Simulations demonstrated that the main function of integrin is to reduce the degradation of VEGFR2 and hence stabilize the activation signal. In addition, receptor synthesis rate and recruitment from internal compartment were found to be sensitive determinants of the activation state of VEGFR2. The model was then applied to simulate the effect of integrin-binding peptides such as tumstatin and cilengitide on VEGFR2 signaling. Further, computational modeling proposed potential molecular mechanisms for the angiogenesis-modulating activity of other integrin-binding peptides. The model highlights the complexity of the crosstalk between αVβ3 integrin and VEGFR2 and the necessity of utilizing models to elucidate potential mechanisms in angiogenesis-modulating peptide therapy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jtbi.2018.06.029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156791PMC
October 2018

Computer Simulation of TSP1 Inhibition of VEGF-Akt-eNOS: An Angiogenesis Triple Threat.

Front Physiol 2018 30;9:644. Epub 2018 May 30.

Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.

The matricellular protein thrombospondin-1 (TSP1) is a potent inhibitor of angiogenesis. Specifically, TSP1 has been experimentally shown to inhibit signaling downstream of vascular endothelial growth factor (VEGF). The molecular mechanism of this inhibition is not entirely clear. We developed a detailed computational model of VEGF signaling to Akt-endothelial nitric oxide synthase (eNOS) to investigate the quantitative molecular mechanism of TSP1 inhibition. The model demonstrated that TSP1 acceleration of VEGFR2 degradation is sufficient to explain the inhibition of VEGFR2 and eNOS phosphorylation. However, Akt inhibition requires TSP1-induced phosphatase recruitment to VEGFR2. The model was then utilized to test various strategies for the rescue of VEGF signaling to Akt and eNOS. Inhibiting TSP1 was predicted to be not as effective as CD47 depletion in rescuing signaling to Akt. The model further predicts that combination strategy involving depletion of CD47 and inhibition of TSP1 binding to CD47 is necessary for effective recovery of signaling to eNOS. In all, computational modeling offers insight to molecular mechanisms involving TSP1 interaction with VEGF signaling and provides strategies for rescuing angiogenesis by targeting TSP1-CD47 axis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fphys.2018.00644DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5988849PMC
May 2018