Characterizing uncertainty and population variability in the toxicokinetics of trichloroethylene and metabolites in mice, rats, and humans using an updated database, physiologically based pharmacokinetic (PBPK) model, and Bayesian approach

Chiu, Weihsueh A.; Okino, Miles S.; Evans, Marina V.

doi:10.1016/j.taap.2009.07.032

Cited by 71 publications

(64 citation statements)

References 107 publications

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“…Although traditionally used for environmental toxicants, PBPK models are increasingly used for the prediction of absorption, distribution, metabolism, and excretion (ADME) of various drugs (45)(46)(47), including antibiotics (9,10,16,32). A relatively recent advance in PBPK modeling has been the incorporation of approaches for accounting for interindividual variabilities in anatomy, physiology, biochemistry, and chemical exposure (1,6,8,12,36,39). Among other things, these approaches allow a rigorous incorporation of uncertainties, as well as predictions of chemical ADME in susceptible subpopulations.…”

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confidence: 99%

A Physiologically Based Pharmacokinetic Model for Capreomycin

Reisfeld

Metzler

Lyons

et al. 2012

Antimicrob Agents Chemother

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The emergence of multidrug-resistant tuberculosis (MDR-TB) has led to a renewed interest in the use of second-line antibiotic agents. Unfortunately, there are currently dearths of information, data, and computational models that can be used to help design rational regimens for administration of these drugs. To help fill this knowledge gap, an exploratory physiologically based pharmacokinetic (PBPK) model, supported by targeted experimental data, was developed to predict the absorption, distribution, metabolism, and excretion (ADME) of the second-line agent capreomycin, a cyclic peptide antibiotic often grouped with the aminoglycoside antibiotics. To account for interindividual variability, Bayesian inference and Monte Carlo methods were used for model calibration, validation, and testing. Along with the predictive PBPK model, the first for an antituberculosis agent, this study provides estimates of various key pharmacokinetic parameter distributions and supports a hypothesized mechanism for capreomycin transport into the kidney.

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confidence: 99%

A Physiologically Based Pharmacokinetic Model for Capreomycin

Reisfeld

Metzler

Lyons

et al. 2012

Antimicrob Agents Chemother

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“…To include the effects of data uncertainty and interstudy variability on model outputs, unknown parameters were estimated within a Bayesian hierarchical context (26)(27)(28). Within this context, parameters were estimated by first computing partition coefficients and other relevant parameters for the R-PBPK and then using these parameter distributions as "priors" in the estimation of the human-specific parameters.…”

Section: Methodsmentioning

confidence: 99%

Physiologically Based Pharmacokinetic Model of Rifapentine and 25-Desacetyl Rifapentine Disposition in Humans

Zurlinden

Eppers

Reisfeld

2016

Antimicrob Agents Chemother

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b Rifapentine (RPT) is a rifamycin antimycobacterial and, as part of a combination therapy, is indicated for the treatment of pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis. Although the results from a number of studies indicate that rifapentine has the potential to shorten treatment duration and enhance completion rates compared to other rifamycin agents utilized in antituberculosis drug regimens (i.e., regimens 1 to 4), its optimal dose and exposure in humans are unknown. To help inform such an optimization, a physiologically based pharmacokinetic (PBPK) model was developed to predict time course, tissue-specific concentrations of RPT and its active metabolite, 25-desacetyl rifapentine (dRPT), in humans after specified administration schedules for RPT. Starting with the development and verification of a PBPK model for rats, the model was extrapolated and then tested using human pharmacokinetic data. Testing and verification of the models included comparisons of predictions to experimental data in several rat tissues and time course RPT and dRPT plasma concentrations in humans from several singleand repeated-dosing studies. Finally, the model was used to predict RPT concentrations in the lung during the intensive and continuation phases of a current recommended TB treatment regimen. Based on these results, it is anticipated that the PBPK model developed in this study will be useful in evaluating dosing regimens for RPT and for characterizing tissue-level doses that could be predictors of problems related to efficacy or safety. R ifapentine (RPT) is a rifamycin-class antibiotic indicated for the treatment of pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis and in the treatment of latent TB infection in patients at high risk of progression to TB disease. RPT has a longer half-life, increased affinity to serum protein binding (1), and a lower MIC against M. tuberculosis than rifampin, which is currently used as part of several first-line TB treatment regimens (2, 3). Moreover, the primary metabolite for RPT, 25-desacetyl rifapentine (dRPT), has also been found to be active against M. tuberculosis, although at markedly lower MICs (1, 3, 4). Because of these characteristics, RPT has been the subject of a number of clinical pharmacology studies aimed at evaluating pharmacokinetics and developing effective therapies (5-15). Although data from these investigations are valuable in their own right, mathematical modeling offers a way to complement these studies, synthesize their disparate data, and provide the clinician an additional tool to characterize and predict the absorption, distribution, metabolism, and excretion (ADME) of RPT under dosing conditions of interest.One of the very few such mathematical models was developed by Savic et al. (16), who used a classical compartmental modeling approach to assess human population pharmacokinetics of both RPT and dRPT. This model described the absorption, metabolism, and clearance of these two species and accurately predicted their time cour...

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“…However, as conjugative metabolites of TCE and PCE can form reactive metabolites that may rapidly bind to cellular macromolecules, specifically in the kidney, there is potential to considerably underestimate the flux of TCE or PCE through conjugative metabolism in humans and laboratory rodents (Chiu et al, 2007Chiu and Ginsberg, 2011). For this reason, considerable uncertainty exists in estimates of conjugation of TCE (Chiu et al, 2009) or PCE (Chiu and Ginsberg, 2011), particularly in humans at environmental exposure levels.…”

Section: Metabolism and Metabolitesmentioning

confidence: 99%

“…Humans are expected to be less sensitive than rodents to hepatic effects of PCE owing to interspecies differences in hepatic oxidative metabolism (Chiu and Ginsberg, 2011). In contrast, interspecies differences in TCE hepatotoxicity are expected to be small because oxidative metabolism is hepatic blood-flow-limited (Chiu et al, 2009).…”

Section: Noncancer Toxicitymentioning

confidence: 99%

“…For both these chemicals, understanding population-level variability in toxicokinetics and toxicodynamics (and the relationship between the two) is critical to understanding and predicting risk. Several computational studies using population PBPK models have examined TCE or PCE toxicokinetic variability by combining data from historical human controlled exposure studies (Chiu and Bois 2006;Chiu et al, 2009). A more recent experimental study used a multistrain panel of inbred mice to examine interindividual variability in susceptibility to TCE-associated toxicity (Bradford et al, 2011).…”

Section: Perspectives and Conclusionmentioning

confidence: 99%

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Target Organ Metabolism, Toxicity, and Mechanisms of Trichloroethylene and Perchloroethylene: Key Similarities, Differences, and Data Gaps

Cichocki

Guyton

Guha

et al. 2016

Journal of Pharmacology and Experimental Therapeutics

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Trichloroethylene (TCE) and perchloroethylene or tetrachloroethylene (PCE) are high-production volume chemicals with numerous industrial applications. As a consequence of their widespread use, these chemicals are ubiquitous environmental contaminants to which the general population is commonly exposed. It is widely assumed that TCE and PCE are toxicologically similar; both are simple olefins with three (TCE) or four (PCE) chlorines. Nonetheless, despite decades of research on the adverse health effects of TCE or PCE, few studies have directly compared these two toxicants. Although the metabolic pathways are qualitatively similar, quantitative differences in the flux and yield of metabolites exist. Recent human health assessments have uncovered some overlap in target organs that are affected by exposure to TCE or PCE, and divergent speciesand sex-specificity with regard to cancer and noncancer hazards. The objective of this minireview is to highlight key similarities, differences, and data gaps in target organ metabolism and mechanism of toxicity. The main anticipated outcome of this review is to encourage research to 1) directly compare the responses to TCE and PCE using more sensitive biochemical techniques and robust statistical comparisons; 2) more closely examine interindividual variability in the relationship between toxicokinetics and toxicodynamics for TCE and PCE; 3) elucidate the effect of coexposure to these two toxicants; and 4) explore new mechanisms for target organ toxicity associated with TCE and/or PCE exposure.

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Characterizing uncertainty and population variability in the toxicokinetics of trichloroethylene and metabolites in mice, rats, and humans using an updated database, physiologically based pharmacokinetic (PBPK) model, and Bayesian approach

Cited by 71 publications

References 107 publications

A Physiologically Based Pharmacokinetic Model for Capreomycin

A Physiologically Based Pharmacokinetic Model for Capreomycin

Physiologically Based Pharmacokinetic Model of Rifapentine and 25-Desacetyl Rifapentine Disposition in Humans

Target Organ Metabolism, Toxicity, and Mechanisms of Trichloroethylene and Perchloroethylene: Key Similarities, Differences, and Data Gaps

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