Pharmacokinetically Based Estimation of Patient Compliance with Oral Anticancer Chemotherapies View Full Text


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Article Info

DATE

2009-06

AUTHORS

Emilie Hénin, Michel Tod, Véronique Trillet-Lenoir, Catherine Rioufol, Brigitte Tranchand, Pascal Girard

ABSTRACT

Background and objectivesMore and more anticancer chemotherapies are now available as oral formulations. This relatively new route of administration in oncology leads to problems with patient education and non-compliance. The aim of this study was to explore the performances of the ‘inverse problem’, namely, estimation of compliance from pharmacokinetics. For this purpose, we developed and evaluated a method to estimate patient compliance with an oral chemotherapy in silico (i) from an a priori population pharmacokinetic model; (ii) with limited optimal pharmacokinetic information collected on day 1; and (iii) from a single pharmacokinetic sample collected after multiple doses.MethodsPopulation pharmacokinetic models, including estimation of all fixed and random effects estimated on a prior dataset, and sparse samples taken after the first dose, were combined to provide the individual POSTHOC Bayesian pharmacokinetic parameter estimates. Sampling times on day 1 were chosen according to a D-optimal design. Individual pharmacokinetic profiles were simulated according to various dose-taking scenarios.To characterize compliance over the n previous dosing times (supposedly known without error), 2n different compliance scenarios of doses taken/not taken were considered. The observed concentration value was compared with concentrations predicted from the model and each compliance scenario. To discriminate between different compliance profiles, we used the Euclidean distance between the observed pharmacokinetic values and the predicted values simulated without residual errors.This approach was evaluated in silico and applied to imatinib and capecitabine, the pharmacokinetics of which are described in the literature, and which have quite different pharmacokinetic characteristics (imatinib has an elimination half-life of 17 hours, and α-fluoro-β-alanine [FBAL], the metabolite of capecitabine, has an elimination half-life of 3 hours). 1000 parameter sets were drawn according to population distributions, and concentration values were simulated at several timepoints under various compliance patterns to compare with the predicted ones. In addition, several simulation scenarios were run in order to explore the impact of the quality of the error model, interoccasion variability (IOV), error in the number of pills taken, and the performance of the compliance estimation method.ResultsThe best compliance estimate was obtained with pharmacokinetic samples taken 5 hours after the last dose. Performance of the method varied between simulation scenarios. In both the imatinib and capecitabine basic simulations, patient compliance was correctly estimated on the two last scheduled doses (with better results for imatinib). The magnitude of the error model also had a great impact on the quality of the compliance estimate.ConclusionsWe highlight the effect of three parameters on the quality of compliance estimates based on limited pharmacokinetic information: the plasma elimination half-life, interdose interval and magnitude of the error model. Nevertheless, the pharmacokinetic method is not informative enough and should be used with electronic monitoring, which provides additional information on compliance. Our method will be used in a future phase IV clinical trial where the relationships between compliance, efficacy and tolerability will be assessed. More... »

PAGES

359-369

References to SciGraph publications

  • 1994-09. Role of Patient Compliance in Clinical Pharmacokinetics in CLINICAL PHARMACOKINETICS
  • 1989-08. Time to stop counting the tablets? in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2002-02. Population Pharmacokinetic Analysis of the Major Metabolites of Capecitabine in JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS
  • 1992-03. Prediction of Diltiazem Plasma Concentration Curves From Limited Measurements Using Compliance Data in CLINICAL PHARMACOKINETICS
  • 2001-02. Clinical Pharmacokinetics of Capecitabine in CLINICAL PHARMACOKINETICS
  • 2003-02. Estimation of Population Pharmacokinetic Parameters in the Presence of Non-compliance in JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS
  • 2008-02-26. An alternative method for population pharmacokinetic data analysis under noncompliance in JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS
  • 1997-08. Discrimination Between Rival Dosing Histories in PHARMACEUTICAL RESEARCH
  • 2006-09-27. Improving Data Reliability Using a Non-Compliance Detection Method versus Using Pharmacokinetic Criteria in JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS
  • 2001-08. Selecting Reliable Pharmacokinetic Data for Explanatory Analyses of Clinical Trials in the Presence of Possible Noncompliance in JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS
  • 1996-06-01. Do we need full compliance data for population pharmacokinetic analysis? in JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS
  • 1997-05. The Electronic Medication Event Monitor in CLINICAL PHARMACOKINETICS
  • 2007-09-25. Clinical evaluation of IDAS II, a new electronic device enabling drug adherence monitoring in EUROPEAN JOURNAL OF CLINICAL PHARMACOLOGY
  • 2004-11. Population one‐compartment pharmacokinetic analysis with missing dosage data in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2006-10-12. A Pharmacokinetic Formalism Explicitly Integrating the Patient Drug Compliance in JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS
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    http://scigraph.springernature.com/pub.10.2165/00003088-200948060-00002

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    28 schema:description Background and objectivesMore and more anticancer chemotherapies are now available as oral formulations. This relatively new route of administration in oncology leads to problems with patient education and non-compliance. The aim of this study was to explore the performances of the ‘inverse problem’, namely, estimation of compliance from pharmacokinetics. For this purpose, we developed and evaluated a method to estimate patient compliance with an oral chemotherapy in silico (i) from an a priori population pharmacokinetic model; (ii) with limited optimal pharmacokinetic information collected on day 1; and (iii) from a single pharmacokinetic sample collected after multiple doses.MethodsPopulation pharmacokinetic models, including estimation of all fixed and random effects estimated on a prior dataset, and sparse samples taken after the first dose, were combined to provide the individual POSTHOC Bayesian pharmacokinetic parameter estimates. Sampling times on day 1 were chosen according to a D-optimal design. Individual pharmacokinetic profiles were simulated according to various dose-taking scenarios.To characterize compliance over the n previous dosing times (supposedly known without error), 2n different compliance scenarios of doses taken/not taken were considered. The observed concentration value was compared with concentrations predicted from the model and each compliance scenario. To discriminate between different compliance profiles, we used the Euclidean distance between the observed pharmacokinetic values and the predicted values simulated without residual errors.This approach was evaluated in silico and applied to imatinib and capecitabine, the pharmacokinetics of which are described in the literature, and which have quite different pharmacokinetic characteristics (imatinib has an elimination half-life of 17 hours, and α-fluoro-β-alanine [FBAL], the metabolite of capecitabine, has an elimination half-life of 3 hours). 1000 parameter sets were drawn according to population distributions, and concentration values were simulated at several timepoints under various compliance patterns to compare with the predicted ones. In addition, several simulation scenarios were run in order to explore the impact of the quality of the error model, interoccasion variability (IOV), error in the number of pills taken, and the performance of the compliance estimation method.ResultsThe best compliance estimate was obtained with pharmacokinetic samples taken 5 hours after the last dose. Performance of the method varied between simulation scenarios. In both the imatinib and capecitabine basic simulations, patient compliance was correctly estimated on the two last scheduled doses (with better results for imatinib). The magnitude of the error model also had a great impact on the quality of the compliance estimate.ConclusionsWe highlight the effect of three parameters on the quality of compliance estimates based on limited pharmacokinetic information: the plasma elimination half-life, interdose interval and magnitude of the error model. Nevertheless, the pharmacokinetic method is not informative enough and should be used with electronic monitoring, which provides additional information on compliance. Our method will be used in a future phase IV clinical trial where the relationships between compliance, efficacy and tolerability will be assessed.
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    35 Euclidean distance
    36 ObjectivesMore
    37 Pharmacokinetically
    38 addition
    39 additional information
    40 administration
    41 aim
    42 anticancer chemotherapy
    43 approach
    44 background
    45 basic simulation
    46 capecitabine
    47 characteristics
    48 chemotherapy
    49 clinical trials
    50 compliance
    51 compliance estimates
    52 compliance patterns
    53 compliance profile
    54 compliance scenario
    55 concentration
    56 concentration values
    57 dataset
    58 day 1
    59 design
    60 different pharmacokinetic characteristics
    61 distance
    62 distribution
    63 dose
    64 doses
    65 dosing time
    66 education
    67 effect
    68 efficacy
    69 electronic monitoring
    70 elimination
    71 error
    72 error model
    73 estimates
    74 estimation
    75 estimation method
    76 estimation of compliance
    77 first dose
    78 formulation
    79 great impact
    80 hours
    81 imatinib
    82 impact
    83 individual pharmacokinetic profiles
    84 information
    85 interdose interval
    86 interoccasion variability
    87 interval
    88 inverse problem
    89 last dose
    90 limited pharmacokinetic information
    91 literature
    92 magnitude
    93 method
    94 model
    95 monitoring
    96 multiple doses
    97 new route
    98 number
    99 number of pills
    100 observed concentration values
    101 oncology
    102 one
    103 optimal design
    104 oral chemotherapy
    105 oral formulation
    106 order
    107 parameter estimates
    108 parameter sets
    109 parameters
    110 patient compliance
    111 patient education
    112 patterns
    113 performance
    114 pharmacokinetic characteristics
    115 pharmacokinetic information
    116 pharmacokinetic methods
    117 pharmacokinetic model
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    119 pharmacokinetic profile
    120 pharmacokinetic samples
    121 pharmacokinetic values
    122 pharmacokinetics
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    151 schema:name Pharmacokinetically Based Estimation of Patient Compliance with Oral Anticancer Chemotherapies
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