Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model

Abstract

BackgroundNumerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies.MethodsA quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx.ResultsThe model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1.ConclusionsThis study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.Electronic supplementary materialThe online version of this article (10.1186/s40425-018-0327-9) contains supplementary material, which is available to authorized users.

Quantitative Systems Pharmacology: An Exemplar Model‐Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

“…To this end, the availability and functionalities of a semi‐industrialized modeling workflow for trial simulations, with both variability and uncertainty being quantitatively accounted for in the model, are of tremendous help in expanding the number of possible treatment scenarios and their corresponding simulations ( Figure a ). In the exemplar case featured here, multiple preclinical trial simulations could be performed in silico , strongly supporting concurrent schedule treatment scenarios when RT and anti‐PD‐(L)1 are used in combination …”

“…The QSP model adequately reproduced all of these outcome data (tumor‐size dynamics) for these additional experimental conditions, demonstrating the model's ability to predict individual tumor‐size responses to de novo monotherapy and combination treatment regimens. Simultaneously, the model provided corresponding mechanistic insights of the underlying molecular and cellular dynamical interplays in tumor tissue …”

“…), (iii) fractionated 5 × 2 Gy dose of RT, and (iv) RT and anti‐PD‐(L)1 combination treatment using concurrent and sequenced schedules. In such a setting, we determined that the addition of only one random effect on a parameter quantifying the ability of effector T cells to respond against a systemic antigen level is sufficient to adequately describe the interindividual variability observed in both monotherapy (PD‐(L)1 inhibitor or RT alone) and combination experiments …”

“…Interestingly, such antigen exposure, although certainly necessary, may not be sufficient for generating an effective overall immune response. Hence, tumor microenvironment conditions that prevail immediately following RT intervention combined with a more rapid maturation of dendritic cells appear crucial to achieve an overall antitumor effect …”

“…[16][17][18] QSP models are, arguably, most useful when used in quantitative comparative mode, for they provide a common drug-exposure and disease "denominator" to perform fair comparisons. These include comparisons, often not mutually exclusive, of (i) a compound of interest in earlier discovery or development vs. forerunner(s) in later phases of development or on the market 19,20 or (ii) multiple choices in therapeutic modalities for a given target, motivated by the challenge of developing the better modality given desired metrics around efficacy, safety, the target patient population, and/or cost of goods, e.g., a small molecule vs. an engineered protein therapy vs. an a ribonucleic acid (RNA)-based therapy 21 vs. drug combination approaches, where the choices of compounds available and corresponding study designs typically grow exponentially, particularly in oncology and immuno-oncology 9,[22][23][24][25] and in many other disease domains as well. 26,27 QSP models have also found use at the early drug-discovery stage, for example, in optimizing the design of compound pharmacokinetic properties given the desired efficacy and/or safety metrics that can be simulated through the molecular, cellular, or pathophysiological "pharmacodynamic" portion of the QSP model.…”