Abstract
Integrated pharmacokinetic (PK) and pharmacodynamic (PD) models are essential for the understanding of quantitative relationship between drug exposure and response towards the identification of optimal dosing regimens in drug development and clinical therapy. This article summarizes the common PK-PD models being established in oncology, with a focus on combination therapies. Among them, the PK models include those used for practical non-compartmental and compartmental analyses, as well as those for physiologically based modeling that describe and predict exposure to various chemotherapy, targeted therapy, and immunotherapy drugs. Built on proper natural disease progression models, such as the empirical logistic growth curve, the Gompertzian growth model, and their modifications, the integrated PK-PD models recapitulate and predict antitumor drug efficacy, in which the PD models include practical indirect response model and various tumor growth inhibition models, as driven by the mechanistic actions of the drugs administered. Since anticancer drugs are usually co-administered, PK-PD modeling has been extended from monotherapy to combination therapy. However, relying on a single interaction factor or parameter to capitulate complex drug interactions, predict outcomes of different combinations, and determine possible synergism is problematic. Considering the apparent contributions from individual drugs following mutual interactions, a new PK-PD model has been developed for combination therapy, which may be integrated with proper algorism (e.g., the Combination Index method) to critically define combination effects, synergism, additivity, or antagonism. As drug combinations become more complex and individual drug actions are variable, these models should be optimized further to advance the understanding of PK-PD relationships and facilitate the development of improved therapies.