Integrating Cell-level Kinetic Modeling into the Optimization of Cancer Therapeutics

Cancer therapy benefits today from the availability of new promising classes of drugs such as therapeutic proteins. Due to their ability to specifically bind targets in the body they allow to modulate specific chemical reactions and ultimately to modify the functional response of the cell, such as cell growth or cell division. Targeting receptor systems by competitive inhibition is the objective of various protein drugs in development and on the market. Many targeted receptor systems also constitute a degradation mechanism for the drug via endocytosis and a thorough understanding of the complex interplay between the drug’s pharmacokinetics and its effect, is largely missing. For complex diseases such as cancer, systems biology models of therapeutically relevant cellular processes have proven valuable for identifying potent drug targets. So far, such information about the dynamics of the targeted system is neglected in later stages of the drug development process when pharmacokinetic modeling is used to guide dose finding and analyze preclinical or clinical in vivo data. This is especially critical for therapeutic proteins where, due to the degradation mediated by the targeted receptor, drug effect and pharmacokinetics are inherently interdependent. This thesis combines the points of view of systems biology and pharmacokinetics. We present a detailed mechanistic model of the targeted cellular system that explicitly takes into account receptor binding and trafficking inside the cell and that is used to derive reduced models of drug degradation which retain a mechanistic interpretation. By integrating cell-level models with established pharmacokinetic models, we translate biophysical properties of protein drugs into a transient drug effect in vivo. We illustrate the approach for anti-bodies against the epidermal growth factor receptor used in cancer therapy. The cell-level pharmacokinetic/pharmacodynamic model identifies options and limits for future therapeutic antibodies and links their inhibitory effect with genomic alteration of tumor cells. ii