Simulations of Combination Therapy in Hepatocellular Carcinoma Using a Quantitative Systems Pharmacology Model

Abstract

Hepatocellular carcinoma (HCC) is a major health concern and despite efforts to screen at‐risk individuals, diagnosis often occurs at late stages when patients are no longer candidates for potentially curative therapies (i.e. surgical resection, ablation, or transplantation). For patients with advanced HCC, tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors may provide clinical benefit, but overall survival remains low. One strategy that has shown promise in on‐going clinical trials is to administer a combination of TKIs and immune checkpoint inhibitors. Despite the increased number of patients that respond to combination therapy compared to individual therapies, there is still a large fraction of patients who remain unresponsive. Therefore, to improve therapy outcomes for patients with HCC, there are two broad questions which need to be addressed. First, through which mechanisms are the therapies interacting in HCC, and secondly, why are they effective in only a subset of patients?To address these questions, we developed a mechanistic quantitative systems pharmacology (QSP) modeling platform for immuno‐oncology (IO) that can be used to predict the progression of HCC in response to molecular and immune therapies. This model integrates cancer growth, immune response, and intracellular signaling in four compartments (tumor, tumor‐draining lymph node, central and peripheral), and considers the interactions and transport of various immune cells (e.g. effector T cells, T regulatory cells and antigen presenting cells) as well as the growth of tumor cells which are partially dictated by interactions with immune cells in the tumor compartment. Additionally, the model includes the binding kinetics of immune checkpoint proteins. Further, pharmacokinetic models are used to model the uptake, transport and clearance of immune checkpoint inhibitors, and the antibodies in the model by bind the immune checkpoint proteins. In this work, the model was applied to an on‐going clinical trial (ClinicalTrials.gov Identifier: NCT03299946) of the combination of TKI, cabozantinib and immune checkpoint inhibitor, nivolumab.Overall, this work presents a modular platform for the development of QSP models in IO that integrate features at the molecular, cellular, tumor and whole patient scales. This framework can then be used to predict therapeutic response to the treatment of HCC, and is the first to predict the outcome of combination therapy for liver cancer. Additionally, it enables the analysis and interpretation of the complex interactions in HCC that may ultimately lead to the understanding of the drug interactions in cancer and enable the prediction of patient response to therapies, facilitating planning for patient cohorts and even individual patients.Support or Funding InformationSupported by NIH grant U01CA212007.