A minimal PKPD interaction model for evaluating synergy effects of combined NSCLC therapies

This paper introduces a mathematical compartmental formulation of dose-effect synergy modelling for multiple therapies in non small cell lung cancer (NSCLC): antiangiogenic, immuno- and radiotherapy. The model formulates the dose-effect relationship in a unified context, with tumor proliferating rates and necrotic tissue volume progression as a function of therapy management profiles. The model accounts for inter- and intra-response variability by using surface model response terms. Slow acting peripheral compartments such as fat and muscle for drug distribution are not modelled. This minimal pharmacokinetic-pharmacodynamic (PKPD) model is evaluated with reported data in mice from literature. A systematic analysis is performed by varying only radiotherapy profiles, while antiangiogenesis and immunotherapy are fixed to their initial profiles. Three radiotherapy protocols are selected from literature: (1) a single dose 5 Gy once weekly; (2) a dose of 5 Gy x 3 days followed by a 2 Gy x 3 days after two weeks and (3) a dose of 5 Gy + 2 x 0.075 Gy followed after two weeks by a 2 Gy + 2 x 0.075 Gy dose. A reduction of 28% in tumor end-volume after 30 days was observed in Protocol 2 when compared to Protocol 1. No changes in end-volume were observed between Protocol 2 and Protocol 3, this in agreement with other literature studies. Additional analysis on drug interaction suggested that higher synergy among drugs affects up to three-fold the tumor volume (increased synergy leads to significantly lower growth ratio and lower total tumor volume). Similarly, changes in patient response indicated that increased drug resistance leads to lower reduction rates of tumor volumes, with end-volume increased up to 25-30%. In conclusion, the proposed minimal PKPD model has physiological value and can be used to study therapy management protocols and is an aiding tool in the clinical decision making process. Although developed with data from mice studies, the model is scalable to NSCLC patients

Dynamic Modeling of the Angiogenic Switch and Its Inhibition by Bevacizumab

We formulate a dynamic model of vascular tumor growth, in which the interdependence of vascular dynamics with tumor volume is considered. The model describes the angiogenic switch; thus the inhibition of the vascularization process by antiangiogenic drugs may be taken into account explicitly. We validate the model against volume measurement data originating from experiments on mice and analyze the model behavior assuming different inputs corresponding to different therapies. Furthermore, we show that a simple extension of the model is capable of considering cytotoxic and antiangiogenic drugs as inputs simultaneously in qualitatively different ways

MRI in Cancer: Improving Methodology for Measuring Vascular Properties and Assessing Radiation Treatment Effects in Brain

Tumors cannot survive, progress and metastasize without recruiting new blood vessels. Vascular properties, including perfusion and permeability, provide valuable information for characterizing cancers and assessing therapeutic outcomes. Dynamic contrast-enhanced (DCE) MRI is a non-invasive imaging technique that affords quantitative parameters describing the underlying vascular structure of tissue. To date, the clinical application of DCE-MRI has been hampered by the lack of standardized and validated quantitative modeling approaches for data analysis. From a therapeutic perspective, radiation therapy is a central component of the standard treatment for patients with cancer. Besides killing cancer cells, radiation also induces parenchymal and stromal changes in normal tissue, limiting radiation dose and complicating treatment response evaluation. Further, emerging evidence suggest that the radiation-modulated tumor microenvironment may also contribute to the enhanced tumor regrowth and resistance to therapy. Given these clinical problems, the objectives of this dissertation were to: i) improve the DCE MRI-based measurements of vascular properties; and ii) assess the radiation treatment effects on normal tissue (parenchyma) and the interaction between radiation-modulated parenchyma and tumor growth. For the first goal, Bayesian probability theory-based model selection was employed to evaluate four commonly employed DCE-MRI tracer kinetic models against both in silico DCE-MRI data and high-quality clinical data collected from patients with advanced-staged cervical cancer. Further, a constrained local arterial input function (cL-AIF) modeling approach was developed to improve the pharmacokinetic analysis of DCE-MRI data. For the second goal, a novel mouse model of radiation-mediated effects on normal brain was developed. The efficacy of anti-vascular endothelial growth factor (VEGF) antibody treatment of delayed, radiation-induced necrosis (RN) was evaluated. Also, the effects of radiation-modulated brain parenchyma on glioblastoma cell growth were studied. It was found that 1) complex DCE-MRI signal models are more sensitive to noise than simpler models with respect to parameter estimation accuracy and precision. Caution is thus advised when considering application of complex DCE-MRI kinetic models. It follows that data-driven model selection is an important prerequisite to DCE-MRI data analysis; 2) the proposed cL-AIF method, which estimates an unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better measurements vascular properties than the conventional approach employing a single, remotely measured AIF; 3) anti-VEGF antibody decreased MR-derived RN lesion volumes, while large areas of focal calcification formed and the expression of VEGF remained high post-treatment. More effective therapeutic strategies for RN are still needed; 4) the radiation-modulated brain parenchyma promotes aggressive, infiltrative glioma growth. The histologic features of such tumors are consistent with those commonly observed in recurrent high-grade tumors in patients. These findings afford new insights into the highly aggressive tumor regrowth patterns observed following radiotherapy

Diffusion basis spectrum imaging as an adjunct to conventional MRI leads to earlier diagnosis of high-grade glioma tumor progression versus treatment effect

BACKGROUND: Following chemoradiotherapy for high-grade glioma (HGG), it is often challenging to distinguish treatment changes from true tumor progression using conventional MRI. The diffusion basis spectrum imaging (DBSI) hindered fraction is associated with tissue edema or necrosis, which are common treatment-related changes. We hypothesized that DBSI hindered fraction may augment conventional imaging for earlier diagnosis of progression versus treatment effect. METHODS: Adult patients were prospectively recruited if they had a known histologic diagnosis of HGG and completed standard-of-care chemoradiotherapy. DBSI and conventional MRI data were acquired longitudinally beginning 4 weeks post-radiation. Conventional MRI and DBSI metrics were compared with respect to their ability to diagnose progression versus treatment effect. RESULTS: Twelve HGG patients were enrolled between August 2019 and February 2020, and 9 were ultimately analyzed (5 progression, 4 treatment effect). Within new or enlarging contrast-enhancing regions, DBSI hindered fraction was significantly higher in the treatment effect group compared to progression group ( CONCLUSIONS: In the first longitudinal prospective study of DBSI in adult HGG patients, we found that in new or enlarging contrast-enhancing regions following therapy, DBSI hindered fraction is elevated in cases of treatment effect compared to those with progression. Hindered fraction map may be a valuable adjunct to conventional MRI to distinguish tumor progression from treatment effect

Pharmacodynamic therapeutic drug monitoring for cancer: challenges, advances, and future opportunities

In the modern era of cancer treatment, with targeted agents superseding more traditional cytotoxic chemotherapeutics, it is becoming increasingly important to use stratified medicine approaches to ensure that patients receive the most appropriate drugs and treatment schedules. In this context, there is significant potential for the use of pharmacodynamic biomarkers to provide pharmacological information, which could be used in a therapeutic drug monitoring setting. This review focuses on discussing some of the challenges faced to date in translating preclinical pharmacodynamic biomarker approaches to a clinical setting. Recent advances in important areas including circulating biomarkers and pharmacokinetic/pharmacodynamic modeling approaches are discussed, and selected examples of anticancer drugs where there is existing evidence to potentially advance pharmacodynamic therapeutic drug monitoring approaches to deliver more effective treatment are discussed. Although we may not yet be in a position to systematically implement therapeutic drug monitoring approaches based on pharmacodynamic information in a cancer patient setting, such approaches are likely to become more commonplace in the coming years. Based on ever-increasing levels of pharmacodynamic information being generated on newer anticancer drugs, facilitated by increasingly advanced and accessible experimental approaches available to researchers to collect these data, we can now look forward optimistically to significant advances being made in this area

Cost-Effectiveness Of Bevacizumab Concomitant With The Standard Of Therapy For Newly Diagnosed Glioblastoma

Glioblastoma Multiforme (GBM), a glioma – cancer of the brain’s glial cells – is the most common and deadly malignant primary central nervous system tumor in developed countries. Two recently completed clinical trials investigating the use of bevacizumab (BEV), a monoclonal antibody, to treat newly diagnosed GBM concomitant with the standard-of-care (SOC) showed mixed survival and quality of life outcomes. In this study, a cost utility study was conducted to investigate if BEV should be used to treat newly diagnosed GBM. A three stage time-dependent Markov model was built using survival estimates from the two clinical trials, costs from Ontario residents diagnosed with GBM between 2003 and 2011, and literature utility values. The expected incremental cost utility ratio for BEV plus the SOC compared to the SOC alone is 8,393,212 $/quality adjusted life year over a six year period. Therefore, BEV plus the SOC is not cost effective as a first-line therapy

The Immunology and Biology of Brain Tumors

Immunotherapy has become a viable treatment modality for a variety of cancers (and referred to as Science Magazine’s “Breakthrough of the Year” in 2013, as well as ASCO’s “Advance of the Year” in both 2016 and 2017). This Special Issue is focused on the relevance of immunobiology in brain tumors, touching on elements of immune suppression, immune stimulation, and the immune microenvironment, with culminations in translational immunotherapy