Abstract
Accurate prediction of tumor growth is critical in modeling the effects of anti-tumor agents. Popular models of tumor growth inhibition (TGI) generally offer empirical description of tumor growth. We propose a lifespan-based tumor growth inhibition (LS TGI) model that describes tumor growth in a xenograft mouse model, on the basis of cellular lifespan T. At the end of the lifespan, cells divide, and to account for tumor burden on growth, we introduce a cell division efficiency function that is negatively affected by tumor size. The LS TGI model capability to describe dynamic growth characteristics is similar to many empirical TGI models. Our model describes anti-cancer drug effect as a dose-dependent shift of proliferating tumor cells into a non-proliferating population that die after an altered lifespan TA. Sensitivity analysis indicated that all model parameters are identifiable. The model was validated through case studies of xenograft mouse tumor growth. Data from paclitaxel mediated tumor inhibition was well described by the LS TGI model, and model parameters were estimated with high precision. A study involving a protein casein kinase 2 inhibitor, AZ968, contained tumor growth data that only exhibited linear growth kinetics. The LS TGI model accurately described the linear growth data and estimated the potency of AZ968 that was very similar to the estimate from an established TGI model. In the case study of AZD1208, a pan-Pim inhibitor, the doubling time was not estimable from the control data. By fixing the parameter to the reported in vitro value of the tumor cell doubling time, the model was still able to fit the data well and estimated the remaining parameters with high precision. We have developed a mechanistic model that describes tumor growth based on cell division and has the flexibility to describe tumor data with diverse growth kinetics.
Highlights
The integration of pharmacokinetic and pharmacodynamic (PK/PD) modeling in drug development has greatly improved the efficacy and safety of anti-cancer treatments
Model Exploration – Unperturbed Tumor Growth The lifespan model of tumor growth inhibition outlined in the model development section accounts for tumor growth through the process of cellular division of tumor cells, and is capable of describing non-cycle-specific anti-cancer drug effects and cyclespecific drug effects
Increasing values of T resulted in slower growth kinetics
Summary
The integration of pharmacokinetic and pharmacodynamic (PK/PD) modeling in drug development has greatly improved the efficacy and safety of anti-cancer treatments. Recent efforts have demonstrated the benefits of applying PK/PD modeling in early stages of drug development. Advancement in PK/PD modeling, the progression of PK/PD modeling from empirical to more mechanistic approaches have greatly increased the predictive power of models [1]. Empirical models are attractive because of their simplicity and parsimony, and for early compound screening based on specific criteria, they are very practical. The major drawback of empirical models is their reliance on drug-specific, rather than systemspecific, parameters. At the opposite end of the spectrum are mechanistic models of tumor growth, which combine drug-specific parameters with system-specific parameters for numerous molecular species. Mechanistic models offer superior prediction accuracy, they often require rich datasets on numerous biomarkers in order to identify the parameters
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