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.

Modeling the receptor pharmacology, pharmacokinetics, and pharmacodynamics of NKTR-214, a kinetically-controlled interleukin-2 (IL2) receptor agonist for cancer immunotherapy.

Cytokines are potent immune modulating agents but are not ideal medicines in their natural form due to their short half-life and pleiotropic systemic effects. NKTR-214 is a clinical-stage biologic that comprises interleukin-2 (IL2) protein bound by multiple releasable polyethylene glycol (PEG) chains. In this highly PEG-bound form, the IL2 is inactive; therefore, NKTR-214 is a biologic prodrug. When administered in vivo, the PEG chains slowly release, creating a cascade of increasingly active IL2 protein conjugates bound by fewer PEG chains. The 1-PEG-IL2 and 2-PEG-IL2 species derived from NKTR-214 are the most active conjugated-IL2 species. Free-IL2 protein is undetectable in vivo as it is eliminated faster than formed. The PEG chains on NKTR-214 are located at the region of IL2 that contacts the alpha (α) subunit of the heterotrimeric IL2 receptor complex, IL2Rαßγ, reducing its ability to bind and activate the heterotrimer. The IL2Rαßγ complex is constitutively expressed on regulatory T cells (Tregs). Therefore, without the use of mutations, PEGylation reduces the affinity for IL2Rαßγ to a greater extent than for IL2Rßγ, the receptor complex predominant on CD8 T cells. NKTR-214 treatment in vivo favors activation of CD8 T cells over Tregs in the tumor microenvironment to provide anti-tumor efficacy in multiple syngeneic models. Mechanistic modeling based on in vitro and in vivo kinetic data provides insight into the mechanism of NKTR-214 pharmacology. The model reveals that conjugated-IL2 protein derived from NKTR-214 occupy IL-2Rßγ to a greater extent compared to free-IL2 protein. The model accurately describes the sustained in vivo signaling observed after a single dose of NKTR-214 and explains how the properties of NKTR-214 impart a unique kinetically-controlled immunological mechanism of action.

Dual effects of eugenol on the neuronal excitability: An in vitro study.

Besides its well-known actions on sensory afferents, eugenol also affects general excitability of the nervous system, but the mechanisms involved in the recent effect, especially through modulation of ion channels, have received much less attention. In this study, we studied the effects of eugenol on the excitability of central neurons of land snail Caucasotachea atrolabiata and tried to elucidate the underlying ionic mechanisms. The lower concentration of eugenol (0.5mM) reversibly reduced the frequency of spontaneous action potentials that was associated with elevation of threshold, reduction of maximum slope of rising phase and prolongation of actin potentials. These effects were mimicked by riluzole, suggesting that they might be mediated by inhibition of Na+ channels. Eugenol also prolonged the single-spike afterhyperpolarization and post stimulus inhibitory period, but these effects seemed to be consequent to action potential prolongation that indirectly augment Ca2+ inward currents and Ca2+-activated K+ currents. This concentration of eugenol was also able to prevent or abolish pentylenetetrazole-induced epileptiform activity. On the other hand, a higher concentration of eugenol (2mM) reversibly increased the frequency of action potentials and then induced epileptiform activity in majority of treated neurons. Several criteria suggest that the inhibition of K+ channels by higher concentration of eugenol and indirect augmentation of Ca2+ currents are central to the hyperexcitability and epileptiform activity induced by eugenol. Our findings indicate that while low concentration of eugenol could have antiepileptic properties, at higher concentration it induces epileptiform activity. It seems that does dependent inhibition of the ionic currents underlying rising and falling phases of action potential is relevant to the eugenol suppressant and excitatory actions, respectively.

Development of a stochastic model for the efficacy of NRTIs using known mechanisms of action.

We analyze the mechanisms by which nucleoside-analogue reverse transcriptase inhibitors, the most common class of drugs used in the treatment of HIV-1, exert their antiviral effects. We then seek to identify ways in which those known mechanisms can be employed to generate mathematical models for drug efficacy in terms of measurable physical values. We demonstrate that the probability a NRTI instead of a natural nucleotide is included can be expressed in terms of intracellular drug concentrations, natural nucleotide concentrations, and relevant rate constants derived from reverse transcriptase's mechanism of nucleotide addition. In order to determine the ultimate effect, the resistance of the NRTI to removal from the genome must be considered, which is achieved via stochastic modeling. We employ this model to determine the relationship between efficacy and drug concentration, as well as other drug characteristics like half life. We also investigate the effect of drug administration time on the overall efficacy. The model is employed for four different drugs and a sensitivity analysis on mutation and resistance is performed.