From Cold to Hot: Changing Perceptions and Future Opportunities for Quantitative Systems Pharmacology Modeling in Cancer Immunotherapy.

Immuno-oncology (IO) is a fast-expanding field due to recent success using IO therapies in treating cancer. As IO therapies do not directly kill tumor cells but rather act upon the patients' own immune cells either systemically or in the tumor microenvironment, new and innovative approaches are required to inform IO therapy research and development. Quantitative systems pharmacology (QSP) modeling describes the biological mechanisms of disease and the mode of action of drugs with mathematical equations, which has significant potential to address the big challenges in the IO field, from identifying patient populations that respond to different therapies to guiding the selection, dosing, and scheduling of combination therapy. To assess the perspectives of the community on the impact of QSP modeling in IO drug development and to understand current applications and challenges, the IO QSP working group-under the QSP Special Interest Group (SIG) of the International Society of Pharmacometrics (ISoP)-conducted a survey among QSP modelers, non-QSP modelers, and non-modeling IO program stakeholders. The survey results are presented here with discussions on how to address some of the findings. One of the findings is the differences in perception among these groups. To help bridge this perception gap, we present several case studies demonstrating the impact of QSP modeling in IO and suggest actions that can be taken in the future to increase the real and perceived impact of QSP modeling in IO drug research and development.

Mathematical model of hypoxia and tumor signaling interplay reveals the importance of hypoxia and cell-to-cell variability in tumor growth inhibition.

BACKGROUND: Human tumor is a complex tissue with multiple heterogeneous hypoxic regions and significant cell-to-cell variability. Due to the complexity of the disease, the explanation of why anticancer therapies fail cannot be attributed to intrinsic or acquired drug resistance alone. Furthermore, there are inconsistent reports of hypoxia-induced kinase activities in different cancer cell-lines, where increase, decreases, or no change has been observed. Thus, we asked, why are there widely contrasting results in kinase activity under hypoxia in different cancer cell-lines and how does hypoxia play a role in anti-cancer drug sensitivity? RESULTS: We took a modeling approach to address these questions by analyzing the model simulation to explain why hypoxia driven signals can have dissimilar impact on tumor growth and alter the efficacy of anti-cancer drugs. Repeated simulations with varying concentrations of biomolecules followed by decision tree analysis reveal that the highly differential effects among heterogeneous subpopulation of tumor cells could be governed by varying concentrations of just a few key biomolecules. These biomolecules include activated serine/threonine-specific protein kinases (pRAF), mitogen-activated protein kinase kinase (pMEK), protein kinase B (pAkt), or phosphoinositide-4,5-bisphosphate 3-kinase (pPI3K). Additionally, the ratio of activated extracellular signal-regulated kinases (pERK) or pAkt to its respective total was a key factor in determining the sensitivity of pERK or pAkt to hypoxia. CONCLUSION: This work offers a mechanistic insight into how hypoxia can affect the efficacy of anti-cancer drug that targets tumor signaling and provides a framework to identify the types of tumor cells that are either sensitive or resistant to anti-cancer therapy.

Cross-regulation of Phosphodiesterase 1 and Phosphodiesterase 2 Activities Controls Dopamine-mediated Striatal α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid (AMPA) Receptor Trafficking.

Dopamine, a key striatal neuromodulator, increases synaptic strength by promoting surface insertion and/or retention of AMPA receptors (AMPARs). This process is mediated by the phosphorylation of the GluA1 subunit of AMPAR by cyclic nucleotide-dependent kinases, making cyclic nucleotide phosphodiesterases (PDEs) potential regulators of synaptic strength. In this study, we examined the role of phosphodiesterase 2 (PDE2), a medium spiny neuron-enriched and cGMP-activated PDE, in AMPAR trafficking. We found that inhibiting PDE2 resulted in enhancement of dopamine-induced surface GluA1 expression in dopamine receptor 1-expressing medium spiny neurons. Using pharmacological and genetic approaches, we found that inhibition of PDE1 resulted in a decrease in surface AMPAR levels because of the allosteric activation of PDE2. The cross-regulation of PDE1 and PDE2 activities results in counterintuitive control of surface AMPAR expression, making it possible to regulate the directionality and magnitude of AMPAR trafficking.

Development of computational models of cAMP signaling.

Despite the growing evidence defining the cAMP signaling network as a master regulator of cellular function in a number of tissues, regulatory feedback loops, signal compartmentalization, as well as cross-talk with other signaling pathways make understanding the emergent properties of cAMP cellular action a daunting task. Dynamical models of signaling that combine quantitative rigor with molecular details can contribute valuable mechanistic insight into the complexity of intracellular cAMP signaling by complementing and guiding experimental efforts. In this chapter, we review the development of cAMP computational models. We describe how features of the cAMP network can be represented and review the types of experimental data useful in modeling cAMP signaling. We also compile a list of published cAMP models that can aid in the development of novel dynamical models of cAMP signaling.

ERK regulation of phosphodiesterase 4 enhances dopamine-stimulated AMPA receptor membrane insertion.

AMPA-type glutamate receptor (AMPAR) trafficking is essential for modulating synaptic transmission strength. Prior studies that have characterized signaling pathways underlying AMPAR trafficking have identified the cAMP/PKA-mediated phosphorylation of GluA1, an AMPAR subunit, as a key step in the membrane insertion of AMPAR. Inhibition of ERK impairs AMPAR membrane insertion, but the mechanism by which ERK exerts its effect is unknown. Dopamine, an activator of both PKA and ERK, induces AMPAR insertion, but the relationship between the two protein kinases in the process is not understood. We used a combination of computational modeling and live cell imaging to determine the relationship between ERK and PKA in AMPAR insertion. We developed a dynamical model to study the effects of phosphodiesterase 4 (PDE4), a cAMP phosphodiesterase that is phosphorylated and inhibited by ERK, on the membrane insertion of AMPAR. The model predicted that PKA could be a downstream effector of ERK in regulating AMPAR insertion. We experimentally tested the model predictions and found that dopamine-induced ERK phosphorylates and inhibits PDE4. This regulation results in increased cAMP levels and PKA-mediated phosphorylation of DARPP-32 and GluA1, leading to increased GluA1 trafficking to the membrane. These findings provide unique insight into an unanticipated network topology in which ERK uses PDE4 to regulate PKA output during dopamine signaling. The combination of dynamical models and experiments has helped us unravel the complex interactions between two protein kinase pathways in regulating a fundamental molecular process underlying synaptic plasticity.