Dynamic Multiscale Regulation of Perfusion Recovery in Experimental Peripheral Arterial Disease: A Mechanistic Computational Model

Promoting vascular stability through Src inhibition and Tie2 activation: A model-based analysis

Dysregulated angiogenesis signaling leads to pathological vascular growth and leakage, and is a hallmark of many diseases including cancer and ocular diseases. In peripheral arterial disease, the concomitant increase in vascular permeability presents significant challenges in therapeutic efforts to improve perfusion by stimulating vascular growth. Building a mechanistic understanding of the endothelial control of vascular growth and permeability signaling is crucial to guide our efforts to identify therapeutic strategies that permit blood vessel growth while maintaining vascular stability. We develop a mechanistic systems biology model of the endothelial signaling network formed by the vascular endothelial growth factor (VEGF) and angiopoietin (Ang)-Tie pathways, two major signaling pathways regulating vascular growth and stability. Our model, calibrated and validated against experimental data, reveals the mechanisms through which chronic Ang1 stimulation protects endothelial cells from VEGF-induced hyperpermeability, and predicts that combining Src inhibition with Tie2 activation can inhibit vascular leakage without disturbing angiogenesis signaling.

A mechanistic quantitative systems pharmacology model platform for translational efficacy evaluation and checkpoint combination design of bispecific immuno-modulatory antibodies

Over the past 2 decades, tumor immunotherapies have witnessed remarkable advancements, especially with the emergence of immune checkpoint-targeting bispecific antibodies. However, a quantitative understanding of the dynamic cross-talking mechanisms underlying different immune checkpoints as well as the optimal dosing and target design of checkpoint-targeting bispecific antibodies still remain challenging to researchers. To address this challenge, we have here developed a multi-scale quantitative systems pharmacology (QSP) model platform that integrates a diverse array of immune checkpoints and their interactive functions. The model has been calibrated and validated against an extensive collection of multiscale experimental datasets covering 20+ different monoclonal and bispecific antibody treatments at over 60 administered dose levels. Based on high-throughput simulations, the QSP model platform comprehensively screened and characterized the potential efficacy of different bispecific antibody target combination designs, and model-based preclinical population-level simulations revealed target-specific dose-response relationships as well as alternative dosing strategies that can maintain anti-tumor treatment efficacy while reducing dosing frequencies. Model simulations also pointed out that combining checkpoint-targeting bispecific antibodies with monoclonal antibodies can lead to significantly enhanced anti-tumor efficacy. Our mechanistic QSP model can serve as an integrated precision medicine simulation platform to guide the translational research and clinical development of checkpoint-targeting immuno-modulatory bispecific antibodies.

A mechanistic systems biology model of brain microvascular endothelial cell signaling reveals dynamic pathway-based therapeutic targets for brain ischemia

Ischemic stroke is a significant threat to human health. Currently, there is a lack of effective treatments for stroke, and progress in new neuron-centered drug target development is relatively slow. On the other hand, studies have demonstrated that brain microvascular endothelial cells (BMECs) are crucial components of the neurovascular unit and play pivotal roles in ischemic stroke progression. To better understand the complex multifaceted roles of BMECs in the regulation of ischemic stroke pathophysiology and facilitate BMEC-based drug target discovery, we utilized a transcriptomics-informed systems biology modeling approach and constructed a mechanism-based computational multipathway model to systematically investigate BMEC function and its modulatory potential. Extensive multilevel data regarding complex BMEC pathway signal transduction and biomarker expression under various pathophysiological conditions were used for quantitative model calibration and validation, and we generated dynamic BMEC phenotype maps in response to various stroke-related stimuli to identify potential determinants of BMEC fate under stress conditions. Through high-throughput model sensitivity analyses and virtual target perturbations in model-based single cells, our model predicted that targeting succinate could effectively reverse the detrimental cell phenotype of BMECs under oxygen and glucose deprivation/reoxygenation, a condition that mimics stroke pathogenesis, and we experimentally validated the utility of this new target in terms of regulating inflammatory factor production, free radical generation and tight junction protection in vitro and in vivo. Our work is the first that complementarily couples transcriptomic analysis with mechanistic systems-level pathway modeling in the study of BMEC function and endothelium-based therapeutic targets in ischemic stroke.

Systems-level computational modeling in ischemic stroke: from cells to patients

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

Quantitative systems pharmacology modeling of HER2-positive metastatic breast cancer for translational efficacy evaluation and combination assessment across therapeutic modalities

HER2-positive (HER2+) metastatic breast cancer (mBC) is highly aggressive and a major threat to human health. Despite the significant improvement in patients' prognosis given the drug development efforts during the past several decades, many clinical questions still remain to be addressed such as efficacy when combining different therapeutic modalities, best treatment sequences, interindividual variability as well as resistance and potential coping strategies. To better answer these questions, we developed a mechanistic quantitative systems pharmacology model of the pathophysiology of HER2+ mBC that was extensively calibrated and validated against multiscale data to quantitatively predict and characterize the signal transduction and preclinical tumor growth kinetics under different therapeutic interventions. Focusing on the second-line treatment for HER2+ mBC, e.g., antibody-drug conjugates (ADC), small molecule inhibitors/TKI and chemotherapy, the model accurately predicted the efficacy of various drug combinations and dosing regimens at the in vitro and in vivo levels. Sensitivity analyses and subsequent heterogeneous phenotype simulations revealed important insights into the design of new drug combinations to effectively overcome various resistance scenarios in HER2+ mBC treatments. In addition, the model predicted a better efficacy of the new TKI plus ADC combination which can potentially reduce drug dosage and toxicity, while it also shed light on the optimal treatment ordering of ADC versus TKI plus capecitabine regimens, and these findings were validated by new in vivo experiments. Our model is the first that mechanistically integrates multiple key drug modalities in HER2+ mBC research and it can serve as a high-throughput computational platform to guide future model-informed drug development and clinical translation.

An In Silico Platform to Predict Cardiotoxicity Risk of Anti-tumor Drug Combination with hiPSC-CMs Based In Vitro Study

OBJECTIVE

Antineoplastic agent-induced systolic dysfunction is a major reason for interruption of anticancer treatment. Although targeted anticancer agents infrequently cause systolic dysfunction, their combinations with chemotherapies remarkably increase the incidence. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide a potent in vitro model to assess cardiovascular safety. However, quantitatively predicting the reduction of ejection fraction based on hiPSC-CMs is challenging due to the absence of the body's regulatory response to cardiomyocyte injury.

METHODS

Here, we developed and validated an in vitro-in vivo translational platform to assess the reduction of ejection fraction induced by antineoplastic drugs based on hiPSC-CMs. The translational platform integrates drug exposure, drug-cardiomyocyte interaction, and systemic response. The drug-cardiomyocyte interaction was implemented as a mechanism-based toxicodynamic (TD) model, which was then integrated into a quantitative system pharmacology-physiological-based pharmacokinetics (QSP-PBPK) model to form a complete translational platform. The platform was validated by comparing the model-predicted and clinically observed incidence of doxorubicin and trastuzumab-induced systolic dysfunction.

RESULTS

A total of 33,418 virtual patients were incorporated to receive doxorubicin and trastuzumab alone or in combination. For doxorubicin, the QSP-PBPK-TD model successfully captured the overall trend of systolic dysfunction incidences against the cumulative doses. For trastuzumab, the predicted incidence interval was 0.31-2.7% for single-agent treatment and 0.15-10% for trastuzumab-doxorubicin sequential treatment, covering the observations in clinical reports (0.50-1.0% and 1.5-8.3%, respectively).

CONCLUSIONS

In conclusion, the in vitro-in vivo translational platform is capable of predicting systolic dysfunction incidence almost merely depend on hiPSC-CMs, which could facilitate optimizing the treatment protocol of antineoplastic agents.

Signaling-biophysical modeling unravels mechanistic control of red blood cell phagocytosis by macrophages in sickle cell disease

Red blood cell (RBC) aging manifests through progressive changes in cell morphology, rigidity, and expression of membrane proteins. To maintain the quality of circulating blood, splenic macrophages detect the biochemical signals and biophysical changes of RBCs and selectively clear them through erythrophagocytosis. In sickle cell disease (SCD), RBCs display alterations affecting their interaction with macrophages, leading to aberrant phagocytosis that may cause life-threatening spleen sequestration crises. To illuminate the mechanistic control of RBC engulfment by macrophages in SCD, we integrate a system biology model of RBC-macrophage signaling interactions with a biophysical model of macrophage engulfment, as well as in vitro phagocytosis experiments using the spleen-on-a-chip technology. Our modeling framework accurately predicts the phagocytosis dynamics of RBCs under different disease conditions, reveals patterns distinguishing normal and sickle RBCs, and identifies molecular targets including Src homology 2 domain-containing protein tyrosine phosphatase-1 (SHP1) and cluster of differentiation 47 (CD47)/signal regulatory protein α (SIRPα) as therapeutic targets to facilitate the controlled clearance of sickle RBCs in the spleen.

Experiment-based computational model predicts that IL-6 classic and trans-signaling exhibit similar potency in inducing downstream signaling in endothelial cells

Inflammatory cytokine mediated responses are important in the development of many diseases that are associated with angiogenesis. Targeting angiogenesis as a prominent strategy has shown limited effects in many contexts such as cardiovascular diseases and cancer. One potential reason for the unsuccessful outcome is the mutual dependent role between inflammation and angiogenesis. Inflammation-based therapies primarily target inflammatory cytokines such as interleukin-6 (IL-6) in T cells, macrophages, cancer cells, and muscle cells, and there is a limited understanding of how these cytokines act on endothelial cells. Thus, we focus on one of the major inflammatory cytokines, IL-6, mediated intracellular signaling in endothelial cells by developing a detailed computational model. Our model quantitatively characterized the effects of IL-6 classic and trans-signaling in activating the signal transducer and activator of transcription 3 (STAT3), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling to phosphorylate STAT3, extracellular regulated kinase (ERK) and Akt, respectively. We applied the trained and validated experiment-based computational model to characterize the dynamics of phosphorylated STAT3 (pSTAT3), Akt (pAkt), and ERK (pERK) in response to IL-6 classic and/or trans-signaling. The model predicts that IL-6 classic and trans-signaling induced responses are IL-6 and soluble IL-6 receptor (sIL-6R) dose-dependent. Also, IL-6 classic and trans-signaling showed similar potency in inducing downstream signaling; however, trans-signaling induces stronger downstream responses and plays a dominant role in the overall effects from IL-6 due to the in vitro experimental setting of abundant sIL-6R. In addition, both IL-6 and sIL-6R levels regulate signaling strength. Moreover, our model identifies the influential species and kinetic parameters that specifically modulate the downstream inflammatory and/or angiogenic signals, pSTAT3, pAkt, and pERK responses. Overall, the model predicts the effects of IL-6 classic and/or trans-signaling stimulation quantitatively and provides a framework for analyzing and integrating experimental data. More broadly, this model can be utilized to identify potential targets that influence IL-6 mediated signaling in endothelial cells and to study their effects quantitatively in modulating STAT3, Akt, and ERK activation.

Experiment-based Computational Model Predicts that IL-6 Trans-Signaling Plays a Dominant Role in IL-6 mediated signaling in Endothelial Cells

Inflammatory cytokine mediated responses are important in the development of many diseases that are associated with angiogenesis. Targeting angiogenesis as a prominent strategy has shown limited effects in many contexts such as peripheral arterial disease (PAD) and cancer. One potential reason for the unsuccessful outcome is the mutual dependent role between inflammation and angiogenesis. Inflammation-based therapies primarily target inflammatory cytokines such as interleukin-6 (IL-6) in T cells, macrophages, cancer cells, muscle cells, and there is a limited understanding of how these cytokines act on endothelial cells. Thus, we focus on one of the major inflammatory cytokines, IL-6, mediated intracellular signaling in endothelial cells by developing a detailed computational model. Our model quantitatively characterized the effects of IL-6 classic and trans-signaling in activating the signal transducer and activator of transcription 3 (STAT3), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling to phosphorylate STAT3, extracellular regulated kinase (ERK) and Akt, respectively. We applied the trained and validated experiment-based computational model to characterize the dynamics of phosphorylated STAT3 (pSTAT3), Akt (pAkt), and extracellular regulated kinase (pERK) in response to IL-6 classic and/or trans-signaling. The model predicts that IL-6 classic and trans-signaling induced responses are IL-6 and soluble IL-6 receptor (sIL-6R) dose-dependent. Also, IL-6 trans-signaling induces stronger downstream signaling and plays a dominant role in the overall effects from IL-6. In addition, both IL-6 and sIL-6R levels regulate signaling strength. Moreover, our model identifies the influential species and kinetic parameters that specifically modulate the pSTAT3, pAkt, and pERK responses, which represent potential targets for inflammatory cytokine mediated signaling and angiogenesis-based therapies. Overall, the model predicts the effects of IL-6 classic and/or trans-signaling stimulation quantitatively and provides a framework for analyzing and integrating experimental data. More broadly, this model can be utilized to identify targets that influence inflammatory cytokine mediated signaling in endothelial cells and to study the effects of angiogenesis- and inflammation-based therapies.

Systems biology of angiogenesis signaling: Computational models and omics

Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases.

This article is categorized under:

Cardiovascular Diseases > Computational Models

Cancer > Computational Models