Mechanistic modelling of immuno-modulators in oncology drug development

Abstract

Over the past decade, the development of immunotherapy has become one of the most exciting breakthroughs in cancer research. However, 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, some of these challenges include: the broad number of potential drug targets, the expanding array of immuno-modulatory molecules, the emergence of exploratory biomarkers, and the unleashed potential of combining different treatments. In the clinical setting, the qualification of predictive surrogate biomarkers of treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major obstacles. All these challenges necessitate the development of quantitative, mechanistic-oriented systems models incorporating key biological and pathophysiological aspects of IO and the pharmacokinetics of immuno-modulators. This thesis focuses on the development of model-based approaches in the area of IO, including pharmacokinetics (PK), pharmacodynamics (PD), and disease progression models, using data from different stages of the drug development process. These models aim to characterize the tumor growth behavior and its interaction with the immune system in order to better understand the efficacy of multiple immunotherapies and therefore support IO drug development. The first section of the thesis, the Introduction, provides a general overview of the mechanisms involved in the interplay between cancer cells and the immune system, and highlights the most promising immunotherapies. Additionally, this section summarizes the main concepts of Pharmacometrics and Systems Pharmacology (PSP), and introduces the different modeling strategies that will be further discussed along the thesis. Chapter 1 first reviews the recently approved immunotherapies and the multiple targets for these treatment approaches. Subsequently, the different mathematical approaches applied to IO and integrating the cancer immunity cycle are presented, grouped in top-down, middle-out and bottom-up. Chapter 2 introduces a semi-mechanistic model incorporating key processes of the cancer immunity cycle to account for the pharmacodynamic effects of three different immunotherapeutic agents in monotherapy or combination in cold tumors. Chapter 3 investigates the non-monotonic tumor growth dynamics in untreated animals and proposes a mechanistic framework able to describe this particular behavior and untangling the possible underlying mechanisms. Chapter 4 describes the development of a physiologically-based model for oncolytic viral therapy. The model accounts for viral kinetics, distribution to the tumor, viral dynamics at tumor level, and the elicited response after two different routes of administration. Chapter 5 further expands the model developed in Chapter 4 to include the pharmacokinetics of pembrolizumab and describe the efficacy of both drugs administered in combination. Finally, the General Discussion integrates and highlights the most relevant aspects of the five chapters, to end with the Conclusions where a summary of the most main findings of the thesis are presented.