PKPD and Disease Modeling: Concepts and Applications to Oncology

Abstract

Despite the transition from chemotherapy with cytotoxic and cytostatic drugs to targeted therapy with monoclonal antibodies, clinical decisions regarding the benefit of pharmacological interventions still rely on the concept of a maximum tolerated dose (MTD). In this chapter, it is shown that a model-based approach can be used in oncology to optimize dose selection and characterize drug effect on tumor growth, overall survival and safety. Furthermore, modeling and simulation also provides insight into the underlying mechanisms of action, enabling translation of the differences in pharmacology, safety and disease processes from preclinical experiments to clinical trials. A paradigm shift is proposed to bring the benefits of model-based drug development to cancer patients, in which biomarkers of safety and prognostic markers of overall survival are assessed to predict treatment outcome and disease progression. General Concepts and History of Model-Based Research in OncologyModels aim to represent and simplify complex systems. The accurate representation and simplification of the complexity and heterogeneity of biological systems have characterized oncology modeling efforts in clinical pharmacology and translational medicine research (Rew 2000a;Quaranta et al. 2005). Although clinical oncologists can appreciate the value and potential implications of experimental models, the justification for the use of mathematical models remains unclear to most of them. Mathematical modeling can be a powerful tool for analyzing

Impact of disease progression on individual IPSS trajectories and consequences of immediate versus delayed start of treatment in patients with moderate or severe LUTS associated with BPH

“…Moreover, they often focus on mean parameter estimates, yielding results that ignore underlying covariates that may modify the treatment effect. By contrast, the application of longitudinal modelling and CTS at individual patient level allows investigation of a range of design characteristics on the power to detect treatment effects, without confounding or practical restrictions, prior to exposing patients to an experimental intervention [16,17]. CTS can be performed not only to evaluate scenarios that have not been previously investigated in clinical trials, but also to explore hypothetical scenarios which cannot be implemented in real-life conditions.…”

Population pharmacokinetic–pharmacodynamic modelling in oncology: a tool for predicting clinical response

“…Shown in Figure 1 is a proposed modelling framework, expanded from Bruno & Claret [5], which encapsulates this review and illustrates a methodology towards establishing quantitative relationships between model-based metrics and treatment outcome. We refer the reader to reviews on population PK [6,7], PKPD [8][9][10][11] and model-based drug development [12][13][14][15] which have nicely presented these concepts. The similarity among measurements and endpoints for oncology, regardless of cancer type, makes this an applicable framework for clinical drug development programmes.…”

Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine

“…Since classical microdialysis already allowed insights into tissue-specific drug [ 28 ] and cytokine levels [ 29 ], the automated determination of pharmacokinetic profiles will enable patient-specific analyses in higher throughput. PK-PD modelling already improved dose selection and characterization of drug effects on tumor growth, overall survival and safety [ 30 ], but requires relevant data for the patient and his/her tumor. Nonclinical testing together with pharmacometrics may provide a more detailed insight by testing drugs in patient-specific models and extrapolating drug concentrations in tumors to adapt dose regimen for patients.…”