Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology

Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment—with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges

Combination of immune checkpoint inhibitors with radiation therapy in cancer: A hammer breaking the wall of resistance

Immuno-oncology is an emerging field in the treatment of oncological diseases, that is based on recruitment of the host immune system to attack the tumor. Radiation exposure may help to unlock the potential of the immune activating agents by enhancing the antigen release and presentation, attraction of immunocompetent cells to the inflammation site, and eliminating the tumor cells by phagocytosis, thereby leading to an overall enhancement of the immune response. Numerous preclinical studies in mouse models of glioma, murine melanoma, extracranial cancer, or colorectal cancer have contributed to determination of the optimal radiotherapy fractionation, as well as the radio- and immunotherapy sequencing strategies for maximizing the antitumor activity of the treatment regimen. At the same time, efficacy of combined radio- and immunotherapy has been actively investigated in clinical trials of metastatic melanoma, non-small-cell lung cancer and renal cell carcinoma. The present review summarizes the current advancements and challenges related to the aforementioned treatment approach

Interpretation of metabolic memory phenomenon using a physiological systems model: What drives oxidative stress following glucose normalization?

Hyperglycemia is generally associated with oxidative stress, which plays a key role in diabetes-related complications. A complex, quantitative relationship has been established between glucose levels and oxidative stress, both in vitro and in vivo. For example, oxidative stress is known to persist after glucose normalization, a phenomenon described as metabolic memory. Also, uncontrolled glucose levels appear to be more detrimental to patients with diabetes (non-constant glucose levels) vs. patients with high, constant glucose levels. The objective of the current study was to delineate the mechanisms underlying such behaviors, using a mechanistic physiological systems modeling approach that captures and integrates essential underlying pathophysiological processes. The proposed model was based on a system of ordinary differential equations. It describes the interplay between reactive oxygen species production potential (ROS), ROS-induced cell alterations, and subsequent adaptation mechanisms. Model parameters were calibrated using different sources of experimental information, including ROS production in cell cultures exposed to various concentration profiles of constant and oscillating glucose levels. The model adequately reproduced the ROS excess generation after glucose normalization. Such behavior appeared to be driven by positive feedback regulations between ROS and ROS-induced cell alterations. The further oxidative stress-related detrimental effect as induced by unstable glucose levels can be explained by inability of cells to adapt to dynamic environment. Cell adaptation to instable high glucose declines during glucose normalization phases, and further glucose increase promotes similar or higher oxidative stress. In contrast, gradual ROS production potential decrease, driven by adaptation, is observed in cells exposed to constant high glucose

Predictions of model variables and their dynamics, for typical experimental settings.

<p>(A) CG exposure experiment: Glucose was maintained at 20 mM for 14 days, then was decreased to 5 mM. (B) OG exposure experiment: glucose was allowed to oscillate between 5 mM and 20 mM over 24-hour intervals for 14 days, then was decreased to 5 mM.</p

Model quality in reproducing data.

<p>(A) Observations <i>vs</i>. population model predictions. (B) Observations <i>vs</i>. individual model predictions. The straight line represents a perfect agreement between experimental and calculated values. The magnitude of glucose exposure is coded by color; the type of experiment is coded by dot shape. (C) Population simulations of ROS dynamics in CG experiments. (D) Population simulations of ROS dynamics in OG experiments. Solid line denotes model-predicted median; gray shades correspond to different percentiles of population predictions; the magnitude of glucose exposure is coded by color; the type of experiment is coded by dot shape.</p

Impact of model parameters on ROS levels, during a 2-week, 20 mM CG exposure experiment.

<p>ROS levels were predicted at three different time points: the early ROS response at 24 hours after the start of the experiment (A); ROS levels at 2 weeks after the start of the experiment (B); ROS levels after achieving NG exposure conditions, 12 weeks after the start of the experiment (C). Parameter values were varied ± 50% (brown bar color—for positive change; orange color—for negative change) from the initial estimate (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171781#pone.0171781.t001" target="_blank">Table 1</a>). Bar size and X-axis represent the magnitude of the parameter change effect on the ROS value.</p

Model schematic.

<p>Glucose stimulates ROS production (<b>ROS</b>) and additionally promotes cellular adaptive processes (<b>AD</b>)—the latter then mitigates further glucose-dependent ROS generation and subsequently allows for the development of metabolic memory (<b>MM</b>). ROS and MM positively affect each other, whereas AD is stimulated by glucose excess and negatively influences ROS synthesis. Boxes denote model variables, black arrows denote reaction rates, dotted lines denote positive influences, and dashed lines denote negative influences.</p

Contour plots of model simulations: variables and their dynamics in an experimental setting of CG exposure, with varying glucose amplitude.

<p>(A) Simulations of ROS dynamics. (B) Simulations of cellular adaptive processes. (C) Simulations of metabolic memory dynamics.</p

Contour plots of model simulations: variables and their steady-state levels, after reaching an NG exposure condition.

<p>The duration of cell exposure to high glucose and to glucose levels during the experiment was varied. (A) Simulations of ROS dynamics. (B) Simulations of metabolic memory dynamics.</p