Quantitative insight in utilizing circulating angiogenic factors as biomarkers for antiangiogenic therapy: systems pharmacology approach

Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment

Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published in vivo measurements of xenograft tumor volume, producing a model that accurately predicts the tumor’s response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical in vivo mouse studies.

One hallmark of cancer is angiogenesis, the formation of new blood capillaries from pre-existing vessels. Angiogenesis promotes tumor growth by enabling the tumor to obtain oxygen and nutrients from the surrounding microenvironment. Cancer drugs that inhibit angiogenesis ("anti-angiogenic therapies") have focused on inhibiting proteins that promote the growth of new blood vessels. The response to anti-angiogenic therapy is highly variable, and some tumors do not respond at all. Therefore, identifying a biomarker that predicts how specific tumors will respond would be extremely valuable. This work uses a computational model of tumor-bearing mice to investigate the response to anti-angiogenic treatment that targets the potent promoter of angiogenesis, vascular endothelial growth factor (VEGF), and how the response is influenced by tumor growth kinetics. We show that certain properties of tumor growth can be used to predict how much the tumor volume will be reduced upon administration of an anti-VEGF drug. This work identifies tumor growth parameters that may be reliable biomarkers for predicting how tumors will respond to anti-VEGF therapy. Our computational model generates novel, testable hypotheses and nicely complements pre-clinical studies of anti-angiogenic therapeutics.

Quantitative modeling of the dynamics and intracellular trafficking of far-red light-activatable prodrugs: implications in stimuli-responsive drug delivery system

The combination of photodynamic therapy (PDT) with anti-tumor agents is a complimentary strategy to treat local cancers. We developed a unique photosensitizer (PS)-conjugated paclitaxel (PTX) prodrug in which a PS is excited by near-infrared wavelength light to site-specifically release PTX while generating singlet oxygen (SO) to effectively kill cancer cells with both PTX and SO. The aim of the present study was to identify the determinants influencing the combined efficacy of this light-activatable prodrug, especially the bystander killing effects from released PTX. Using PS-conjugated PTX as a model system, we developed a quantitative mathematical model describing the intracellular trafficking. Dynamics of the prodrug and the model predictions were verified with experimental data using human cancer cells in vitro. The sensitivity analysis suggested that parameters related to extracellular concentration of released PTX, prodrug uptake, target engagement, and target abundance are critical in determining the combined killing efficacy of the prodrug. We found that released PTX cytotoxicity was most sensitive to the retention time of the drug in extracellular space. Modulating drug internalization and conjugating the agents targeted to abundant receptors may provide a new strategy for maximizing the killing capacity of the far-red light-activatable prodrug system. These results provide guidance for the design of the PDT combination study in vivo and have implications for other stimuli-responsive drug delivery systems.

Predictive model of thrombospondin-1 and vascular endothelial growth factor in breast tumor tissue

Angiogenesis, the formation of new blood capillaries from pre-existing vessels, is a hallmark of cancer. Thus far, strategies for reducing tumor angiogenesis have focused on inhibiting pro-angiogenic factors, and less is known about the therapeutic effects of mimicking the actions of angiogenesis inhibitors. Thrombospondin-1 (TSP1) is an important endogenous inhibitor of angiogenesis that has been investigated as an anti-angiogenic agent. TSP1 impedes the growth of new blood vessels in many ways, including crosstalk with pro-angiogenic factors. Owing to the complexity of TSP1 signaling, a predictive systems biology model would provide quantitative understanding of the angiogenic balance in tumor tissue. Therefore, we have developed a molecular-detailed, mechanistic model of TSP1 and vascular endothelial growth factor (VEGF), a promoter of angiogenesis, in breast tumor tissue. The model predicts the distribution of the angiogenic factors in tumor tissue, revealing that TSP1 is primarily in an inactive, cleaved form owing to the action of proteases, rather than bound to its cellular receptors or to VEGF. The model also predicts the effects of enhancing TSP1’s interactions with its receptors and with VEGF. To provide additional predictions that can guide the development of new anti-angiogenic drugs, we simulate administration of exogenous TSP1 mimetics that bind specific targets. The model predicts that the CD47-binding TSP1 mimetic markedly decreases the ratio of receptor-bound VEGF to receptor-bound TSP1, in favor of anti-angiogenesis. Thus, we have established a model that provides a quantitative framework to study the response to TSP1 mimetics.

The promises of quantitative systems pharmacology modelling for drug development

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

Systems pharmacology approaches for optimization of antiangiogenic therapies: challenges and opportunities

Targeted therapies have become an important therapeutic paradigm for multiple malignancies. The rapid development of resistance to these therapies impedes the successful management of advanced cancer. Due to the redundancy in angiogenic signaling, alternative proangiogenic factors are activated upon treatment with anti-VEGF agents. Higher doses of the agents lead to greater stimulation of compensatory proangiogenic pathways that limit the therapeutic efficacy of VEGF-targeted drugs and produce escape mechanisms for tumor. Evidence suggests that dose intensity and schedules affect the dynamics of the development of this resistance. Thus, an optimal dosing regimen is crucial to maximizing the therapeutic benefit of antiangiogenic agents and limiting treatment resistance. A systems pharmacology approach using multiscale computational modeling can facilitate a mechanistic understanding of these dynamics of angiogenic biomarkers and their impacts on tumor reduction and resistance. Herein, we discuss a systems pharmacology approach integrating the biology of VEGF-targeted therapy resistance, including circulating biomarkers, and pharmacodynamics to enable the optimization of antiangiogenic therapy for therapeutic gains.