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Gayrard V, Moreau J, Picard-Hagen N, Helies V, Marchand P, Antignac JP, Toutain PL, Leandri R. Use of Mixture Dosing and Nonlinear Mixed Effect Modeling of Eight Environmental Contaminants in Rabbits to Improve Extrapolation Value of Toxicokinetic Data. ENVIRONMENTAL HEALTH PERSPECTIVES 2021; 129:117006. [PMID: 34786950 PMCID: PMC8597046 DOI: 10.1289/ehp8957] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 10/05/2021] [Accepted: 10/20/2021] [Indexed: 06/01/2023]
Abstract
BACKGROUND Although in vivo studies of internal exposure to hazardous substances have been carried out for many years, there is room for progress to improve their informative value while adhering to the four R's: replacement, reduction, refinement, and responsibility rule. OBJECTIVES The objective of the study was to illustrate how toxicokinetic (TK) study design and data analysis can be implemented under the 4R rule to plan a chronic dosage regimen for investigating TK/toxicodynamic (TD) relationships. METHODS The intravenous (IV) and oral serum concentrations of eight hazardous environmental contaminants including 1,1-Dichloro-2,2-bis(p-chlorophenyl)ethylene (pp'DDE), ß-Hexachlorocyclohexane (β-HCH), hexachlorobenzene (HCB), 2,2'4,4'-tetrabromodiphenyl ether (BDE-47), perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), di(2ethylhexyl)phthalate (DEHP), and bisphenol S (BPS) were obtained after mixture dosing in rabbits using a sparse sampling design. Data were comprehensively analyzed using nonlinear mixed effect (NLME) modeling. RESULTS The short persistence of BPS and of the DEHP metabolite (mono-2-ethylhexyl phthalate), reflected by their mean residence times (MRT) of a few hours, was due to their efficient clearance (CL, 3.2 and 0.47L/kg/h). The longer MRT of the other compounds (1-48 d) resulted either from their extremely low clearance (lower than 0.01L/kg/h for PFOA and PFOS) or from their very large volume of distribution (VSS) ranging from 33 to 45L/kg. Estimates of CL, VSS, and bioavailability were used to compute the oral loading and daily maintenance doses required to attain a nominal steady-state serum concentration of 1 ng/mL. Simulations with the NLME model were applied to predict the serum concentration profile and to contrast the differential rates of accumulation in the central vs. peripheral compartments. CONCLUSION NLME modeling of the IV and oral TK of hazardous environmental contaminants, in rabbits while fulfilling the 4R rule, was able to provide the physiological basis for interspecies extrapolation of exposure rates in a TK/TD approach to risk assessment. https://doi.org/10.1289/EHP8957.
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Affiliation(s)
- Véronique Gayrard
- ToxAlim (Research Center in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Jessika Moreau
- ToxAlim (Research Center in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
- Médecine de la Reproduction, Hôpital Paule de Viguier, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Nicole Picard-Hagen
- ToxAlim (Research Center in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Virginie Helies
- GenPhySE, INRA, Université de Toulouse, INPT, ENVT, Castanet Tolosan, France
| | | | | | - Pierre-Louis Toutain
- INTHERES, Université de Toulouse, INRA, ENVT, Toulouse, France
- The Royal Veterinary College, University of London, London, UK
| | - Roger Leandri
- ToxAlim (Research Center in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
- Médecine de la Reproduction, Hôpital Paule de Viguier, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
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Evaluation of Omeprazole Limited Sampling Strategies to Estimate Constitutive Cytochrome P450 2C19 Activity in Healthy Adults. Ther Drug Monit 2018; 40:754-758. [PMID: 30045358 DOI: 10.1097/ftd.0000000000000554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Limited sampling strategy (LSS) is a validated method to estimate pharmacokinetic (PK) parameters from a reduced number of samples. Omeprazole is used to phenotype in vivo cytochrome P450 (CYP) 2C19 activity. This study examined an LSS using 2 estimation methods to determine apparent oral clearance (CL/F) and thus CYP2C19 activity. METHODS Data from 7 previously published studies included healthy subjects receiving a single, oral dose of omeprazole with intensive PK sampling. CL/F was estimated using noncompartmental analysis (NCA) and population PK modeling. LSS was simulated by selecting the 1, 2, 4, and/or 6-hour postdose time points. Linear regression was performed to assess whether CL/F estimated from limited sampling could accurately predict CL/F from the full PK profile. RESULTS Median CL/F was 23.7 L/h by NCA and 19.3 L/h by population PK modeling. In comparing the LSS NCA estimated versus observed CL/F, all evaluated linear regression models had unacceptable coefficients of determination (r, range: 0.14-0.81). With the population PK approach, 737 plasma concentrations (n = 71) and CYP2C19 genotype data were described with a 1-compartment structural model with mixed zero and first-order absorption and lag time. In comparing the population PK LSS estimated versus observed CL/F, all evaluated linear regression models had unacceptable r (range: 0.02-0.74). Post hoc comparison of CYP2C19 poor metabolizers versus CYP2C19 extensive metabolizers resulted in significantly lower CL/F in poor metabolizers versus extensive metabolizers. CONCLUSIONS Omeprazole LSS performed poorly in estimating CL/F using 2 separate estimation approaches and does not seem to be a suitable method for determining CYP2C19 activity.
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Yang J, Patel M, Nikanjam M, Capparelli EV, Tsunoda SM, Greenberg HE, Penzak SR, Aubrey Stoch S, Bertino JS, Nafziger AN, Ma JD. Midazolam Single Time Point Concentrations to Estimate Exposure and Cytochrome P450 (CYP) 3A Constitutive Activity Utilizing Limited Sampling Strategy With a Population Pharmacokinetic Approach. J Clin Pharmacol 2018; 58:1205-1213. [DOI: 10.1002/jcph.1125] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 02/27/2018] [Indexed: 12/16/2022]
Affiliation(s)
- Jincheng Yang
- University of California (UC); San Diego La Jolla CA USA
| | - Maulik Patel
- University of California (UC); San Diego La Jolla CA USA
| | - Mina Nikanjam
- University of California (UC); San Diego La Jolla CA USA
| | | | | | | | | | | | | | | | - Joseph D. Ma
- University of California (UC); San Diego La Jolla CA USA
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Dumont C, Lestini G, Le Nagard H, Mentré F, Comets E, Nguyen TT. PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 156:217-229. [PMID: 29428073 DOI: 10.1016/j.cmpb.2018.01.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 12/22/2017] [Accepted: 01/10/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. METHODS Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. RESULTS The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. CONCLUSION PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr.
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Affiliation(s)
- Cyrielle Dumont
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France; University of Lille, EA 2694, Public Health: Epidemiology and Healthcare Quality, ILIS, Lille, F-59000, France
| | - Giulia Lestini
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France
| | - Hervé Le Nagard
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France
| | - France Mentré
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France
| | - Emmanuelle Comets
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France
| | - Thu Thuy Nguyen
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France.
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