Biomarker- versus drug-driven XXXX models: an equivalence analysis.

@entity5 @entity851
The mathematical modeling of @entity5 xenograft experiments following the dosing of antitumor drugs has received much attention in the last decade. Biomarker data can further provide useful insights on the pathological processes and be used for translational purposes in the early clinical development. Therefore, it is of particular interest the development of integrated pharmacokinetic-pharmacodynamic (PK-PD) models encompassing drug, biomarker and @entity5 -size data. This paper investigates the reciprocal consistency of three types of models: @entity5 , such as established drug-driven @entity851 ( @entity851 ) models, drug-to-biomarker, e.g. indirect response models, and biomarker-to- @entity5 , e.g. the more recent biomarker-driven @entity851 models. In particular, this paper derives a mathematical relationship that guarantees the steady-state equivalence of the cascade of drug-to-biomarker and biomarker-to- @entity5 models with a @entity5 @entity851 model. Using the Simeoni @entity851 model as a reference, conditions for steady-state equivalence are worked out and used to derive a new biomarker-driven model. Simulated and real data are used to show that in realistic cases the steady-state equivalence extends also to transient responses. The possibility of predicting the @entity5 potency of a new candidate drug based only on biomarker response is discussed.
