Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

Abstract

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer

“…Notably, there are a number of methods for the virtual patient generation with different algorithms to address potential biases. [21][22][23][24][25] Here, we use the methods that are similar to the recently published studies. 53 54 The optimal techniques for virtual patient generation based on the availability of clinical data is an active area of research that is undergoing rapid development.…”

“…While there exist various methodologies in the virtual patient generation, the optimization of these algorithms is under active investigation. [21][22][23][24][25] In this study, we aim to determine the relationship between our QSP platform and the virtual cohort with the patient cohort and results of the clinical trial. We discuss the limitations related to our choice of methodology, which need to be taken into account while interpreting the present numerical results and comparisons.…”

Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities

“…A key component in target validation is building of confidence in the therapeutic hypothesis using quantitative systems pharmacology (QSP) models. Such mechanistic models integrate information on drug pharmacokinetics (PK), target binding, and biological processes of interest and mechanisms of action, resulting from prior knowledge and available preclinical and clinical data, to quantitatively predict efficacy and safety responses over time and translate molecular data to clinical outcomes (24). QSP provides an ideal quantitative framework for integration of diverse big data sources, including omics (i.e., genomics, transcriptomics, proteomics, and metabolomics) and imaging, the dimensionality of which can be reduced by using ML methods.…”

The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

“…Additionally, inter-patient variability requires consideration in clinical response prediction [ 147 ]. To address these challenges, virtual patients (VPs) were generated to conduct virtual clinical trials [ 148 , 149 ]. VPs are typically defined by an array of pathophysiological parameters, such as gender, age, weight, genotype, phenotype, and biomarkers [ 150 , 151 ].…”