Quantitative systems pharmacology modeling of biotherapeutics in oncology

In this thesis, mathematical modeling and simulation was applied as a tool to inform quantitative decision making in oncology drug discovery and development. Modeling based approaches were shown to be useful to understand the mechanism of action and deconvolve the complexities of novel biotherapeutic modalities being used to treat cancer, including monospecific and bispecific monoclonal antibodies and antibody drug conjugates. Several key observations and learnings were made. For example, modeling was shown to be a useful method to reduce animal experimentation, by enabling in vitro to in vivo correlations or use of simulation to replace experimental methodologies. Mechanism based modeling and simulation was found to be a useful means to translate from preclinical studies to the clinic to ensure progression of the best drug to clinical trials. These models could then be used to optimize design of clinical studies from selection of starting doses to recommended efficacious doses for pivotal trials. Modeling was shown to be beneficial to understand variability in the clinic and to identify factors impacting drug response in individual patients, paving the way for precision medicine strategies, informing clinical diagnostics, biomarkers, and doses for different oncology indications.