Covariate Pharmacokinetic Model Building in Oncology and its Potential Clinical Relevance

Abstract

When modeling pharmacokinetic (PK) data, identifying covariates is important in explaining interindividual variability, and thus increasing the predictive value of the model. Nonlinear mixed-effects modeling with stepwise covariate modeling is frequently used to build structural covariate models, and the most commonly used software-NONMEM-provides estimations for the fixed-effect parameters (e.g., drug clearance), interindividual and residual unidentified random effects. The aim of covariate modeling is not only to find covariates that significantly influence the population PK parameters, but also to provide dosing recommendations for a certain drug under different conditions, e.g., organ dysfunction, combination chemotherapy. A true covariate is usually seen as one that carries unique information on a structural model parameter. Covariate models have improved our understanding of the pharmacology of many anticancer drugs, including busulfan or melphalan that are part of high-dose pretransplant treatments, the antifolate methotrexate whose elimination is strongly dependent on GFR and comedication, the taxanes and tyrosine kinase inhibitors, the latter being subject of cytochrome p450 3A4 (CYP3A4) associated metabolism. The purpose of this review article is to provide a tool to help understand population covariate analysis and their potential implications for the clinic. Accordingly, several population covariate models are listed, and their clinical relevance is discussed. The target audience of this article are clinical oncologists with a special interest in clinical and mathematical pharmacology.

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.…”

Vertical Integration of Pharmacogenetics in Population PK/PD Modeling: A Novel Information Theoretic Method

“…Previous work from our group suggests that testing saturated parametric interaction models containing as few as 10 predictors may not be feasible on desktop computers 14 . Minimizing bias in covariate effect estimates and model parameters has been previously assessed in population modeling 15 , 16 , 17 , 18 , 19 . Algorithms for building covariate models have also been proposed 15 , 17 .…”

Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

“…In practice, pharmacometrics often employs nonlinear mixed effects modeling techniques 57 that combine structural models (algebraic or differential equations) that are nonlinear in their parameters with nested variability in the clinical observations (i.e., variation among and within patients) and trial execution components (i.e., patient adherence and dropout rate). There have been many applications of nonlinear mixed effects models to clinical oncology PK/PD 58,59 . In general, nonlinear mixed effects modeling of PK and PD benefited from the early availability of computer software 60,61 and frequent application to situations where other techniques would have been difficult to deploy, such as clinical studies.…”