Dataset Information

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.

SUBMITTER: Joerger M

PROVIDER: S-EPMC3291194 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Covariate pharmacokinetic model building in oncology and its potential clinical relevance.

Joerger Markus M

The AAPS journal 20120125 1

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 mod ...[more]

PMID: 22274748

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Project description:: In oncology, an increasing number of targeted anticancer agents and immunotherapies are of biological origin. These biological drugs may trigger immune responses that lead to the formation of antidrug antibodies (ADAs). ADAs are directed against immunogenic parts of the drug and may affect efficacy and safety. In other medical fields, such as rheumatology and hematology, the relevance of ADA formation is well established. However, the relevance of ADAs in oncology is just starting to be recognized, and literature on this topic is scarce. In an attempt to fill this gap in the literature, we provide an up-to-date status of ADA formation in oncology. In this focused review, data on ADAs was extracted from 81 clinical trials with biological anticancer agents. We found that most biological anticancer drugs in these trials are immunogenic and induce ADAs (63%). However, it is difficult to establish the clinical relevance of these ADAs. In order to determine this relevance, the possible effects of ADAs on pharmacokinetics, efficacy, and safety parameters need to be investigated. Our data show that this was done in fewer than 50% of the trials. In addition, we describe the incidence and consequences of ADAs for registered agents. We highlight the challenges in ADA detection and argue for the importance of validating, standardizing, and describing well the used assays. Finally, we discuss prevention strategies such as immunosuppression and regimen adaptations. We encourage the launch of clinical trials that explore these strategies in oncology.Implications for practiceBecause of the increasing use of biologicals in oncology, many patients are at risk of developing antidrug antibodies (ADAs) during therapy. Although clinical consequences are uncertain, ADAs may affect pharmacokinetics, patient safety, and treatment efficacy. ADA detection and reporting is currently highly inconsistent, which makes it difficult to evaluate the clinical consequences. Standardized reporting of ADA investigations in the context of the aforementioned parameters is critical to understanding the relevance of ADA formation for each drug. Furthermore, the development of trials that specifically aim to investigate clinical prevention strategies in oncology is needed.

Project description:A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q? = 4.9 % and q? = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

Dataset Information

Covariate pharmacokinetic model building in oncology and its potential clinical relevance.

Publications

Covariate pharmacokinetic model building in oncology and its potential clinical relevance.

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