Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology.

Kirill Peskov,Ivan Azarov,Lulu Chu,Veronika Voronova,Gabriel Helmlinger,Yuri Kosinsky

doi:10.3389/fimmu.2019.00924

Kirill Peskov, Ivan Azarov + Show 4 more

Open Access

https://doi.org/10.3389/fimmu.2019.00924

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Abstract

Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. 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, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment—with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges.

Highlights

Immunotherapy of cancer has had a long history of development, starting from pioneering efforts in using coley toxins to treat patients—a therapeutic approach named after Dr William Coley [1]
Due to the simple description of tumor vs. immune system interactions, pharmacological interventions and limited validation with experimental data, the model cannot be generalized to other mechanisms of action (MoA) nor used for clinically relevant simulations 2
The observed imbalance, between the amount of biological and clinical data being generated vs. probability of trial success is not uncommon in biomedical disciplines, and calls for the development and updating of a companion, integrative, quantitative modeling framework with predictive value for MoAs and simulation value for study design purposes

Summary

INTRODUCTION

Immunotherapy of cancer has had a long history of development, starting from pioneering efforts in using coley toxins to treat patients—a therapeutic approach named after Dr William Coley [1]. With the explosive growth of experimental data surrounding the complexity of tumor vs immune system interplay, “two-ODE” models experienced a further evolution with additional biological entities and mechanisms being taken into mathematical consideration At this point and looking forward, many biological candidates were tested as the “third modeling variable,” representing either specific immune cells or cytokines that modulate cytotoxic T lymphocyte (CTL) function [28]. Adding components of tumor vs immune system interactions into such PKPD models may well support the addressing of questions around pharmacologically-modulated IO biology, a topic of paramount importance in, for example, the search for therapeutic IO drug combinations [90] Such a systems approach may become an indispensable quantitative tool supporting “go/no-go” decisions in development programs, especially if sufficient biological knowledge for viable generalization is considered in the model [91].

Model is based on preclinical data only

Limited validation with clinical data was performed

CONCLUDING REMARKS