Human Interindividual Variability in Susceptibility to Airborne Particles

Part of the explanation for the persistent epidemiological findings of associations between mortality and morbidity with relatively modest ambient exposures to airborne particles may be that some people are much more susceptible to particle-induced responses than others. This study assembled a database of quantitative observations of interindividual variability in pharmacokinetic and pharmacodynamic parameters likely to affect particle response. The pharmacodynamic responses studied included data drawn from epidemiologic studies of doses of methacholine, flour dust, and other agents that induce acute changes in lung function. In general, the amount of interindividual variability in several of these pharmacodynamic response parameters was greater than the variability in pharmacokinetic (breathing rate, deposition, and clearance) parameters. Quantitatively the results indicated that human interindividual variability of breathing rates and major pharmacokinetic parameters-total deposition and tracheobronchial clearance-were in the region of Log(GSD) = 0.1 to 0.2 (corresponding to geometric standard deviations of 10(.1)-10(.2) or 1.26-1.58). Deposition to the deep lung (alveolar region) appeared to be somewhat more variable: Log(GSD) of about 0.3 (GSD of about 2). Among pharmacodynamic parameters, changes in FEV1 in response to ozone and metabisulfite (an agent that is said to act primarily on neural receptors in the lung) were in the region of Log(GSD) of 0.2 to 0.4. However, similar responses to methacholine, an agent that acts on smooth muscle, seemed to have still more variability (0.4 to somewhat over 1.0, depending on the type of population studied). Similarly high values were suggested for particulate allergens. Central estimates of this kind of variability, and the close correspondence of the data to lognormal distributions, indicate that 99.9th percentile individuals are likely to respond at doses that are 150 to 450-fold less than would be needed in median individuals. It seems plausible that acute responses with this amount of variability could form part of the mechanistic basis for epidemiological observations of enhanced mortality in relation to ambient exposures to fine particles.     

Estimating time to steady state using the effective rate of drug accumulation

Unless all of a drug is eliminated during each dosing interval, the plasma concentrations within a dosing interval will increase until the time course of change in plasma concentrations becomes invariant from one dosing interval to the next, resulting in steady state. A simple method for estimating drug concentration time to steady state based on multiple dose area under the plasma concentration–time curve and effective rate of drug accumulation is presented. Several point estimates and confidence intervals for time to 90% of steady state are compared, and a recommendation is made on how to summarize and present the results. Copyright © 2009 John Wiley & Sons, Ltd.     

Dioxin–An Analysis of the Major Human Studies: Comparison with Animal-Based Cancer Risks

Several major epidemiological studies have reported significant mortality rates (SMRs) for both rare cancers (soft tissue sarcoma, non-Hodgkin's, lymphoma, liver) and the more common cancers (lung, colon, etc), all allegedly caused by TCDD. In this paper, we use the potency of TCDD in animals to establish a plausible worst case cancer risk and ask whether its likely that TCDD is responsible for the epidemiological findings assuming the animal carcinogenic potency is applicable to the conditions of human exposure. Two new features of the technique are the use of measured TCDD blood levels in both animals and humans for dose scale-up and the calculation of an integrated life-time exposure for the exposed workers using measured blood levels. On the basis of the stated assumptions it appears unlikely that any of the major epidemiological studies, with the possible exception of the NIOSH study⁽¹⁾have adequate power to detect the common cancers potentially caused by TCDD.     

双黄连粉针不同剂量给药后黄芩苷的药动学及唾液中的分布

目的明确双黄连粉针剂不同剂量给药后有效成分黄芩苷在大鼠体内的动态变化规律及其在唾液中的分布情况。方法 大鼠静脉注射双黄连粉针后,反相高效液相色谱法测定黄芩苷的血液、唾液浓度,采用药动学软件MULTI97V10程序进行数据处理,确定药动学参数。结果 双黄连粉针剂高、中、低剂量（740,555,370 mg·kg-1）给药后有效成分黄芩苷在大鼠体内符合二室模型分布,其高、中、低剂量主要药动学参数分别如下：t1/2＝（0.79±0.12）h,AUC＝（1.30±0.13）mg·h·mL-1,Vc＝（0.21±0.036）L,CL＝（0.19±0.019）L·h-1;t1/2＝1.52±0.14 h,AUC＝（1.22±0.20）mg·h·mL-1,Vc＝（0.36±0.04）L,CL＝（0.16±0.030）L·h-1;t1/2＝（0.34±0.021）h,AUC＝（0.22±0.004）mg·h·mL-1,Vc＝（0.33±0.027）L,CL＝（0.66±0.014）L·h-1。高剂量给药后黄芩苷唾液浓度范围为：0.11～0.78 μg·mL-1,中、低剂量给药后唾液样品中未检测到黄芩苷。结论 通过高、中、低剂量及相应的AUC对比,AUC随剂量增加不成比例增加,说明双黄连粉针给药后黄芩苷在体内呈非线性药动学消除过程。另外,黄芩苷向唾液中分布的浓度较血液低的多。     

Carcinogenic Mixtures

Human populations are generally exposed simultaneously to a number of toxicants present in the environment, including complex mixtures of unknown and variable origin. While scientific methods for evaluating the potential carcinogenic risks of pure compounds are relatively well established, methods for assessing the risks of complex mixtures are somewhat less developed. This article provides a report of a recent workshop on carcinogenic mixtures sponsored by the Committee on Toxicology of the U.S. National Research Council, in which toxicological, epidemiological, and statistical approaches to carcinogenic risk assessment for mixtures were discussed. Complex mixtures, such as diesel emissions and tobacco smoke, have been shown to have carcinogenic potential. Bioassay-directed fractionation based on short-term screening test for genotoxicity has also been used in identifying carcinogenic components of mixtures. Both toxicological and epidemiological studies have identified clear interactions between chemical carcinogens, including synergistic effects at moderate to high doses. To date, laboratory studies have demonstrated over 900 interactions involving nearly 200 chemical carcinogens. At lower doses, theoretical arguments suggest that risks may be near additive. Thus, additivity at low doses has been invoked as as a working hypothesis by regulatory authorities in the absence of evidence to the contrary. Future studies of the joint effects of carcinogenic agents may serve to elucidate the mechanisms by which interactions occur at higher doses.     

Design optimisation for pharmacokinetic modeling of a cocktail of phenotyping drugs

Our paper proposes a methodological strategy to select optimal sampling designs for phenotyping studies including a cocktail of drugs. A cocktail approach is of high interest to determine the simultaneous activity of enzymes responsible for drug metabolism and pharmacokinetics, therefore useful in anticipating drug–drug interactions and in personalized medicine. Phenotyping indexes, which are area under the concentration‐time curves, can be derived from a few samples using nonlinear mixed effect models and maximum a posteriori estimation. Because of clinical constraints in phenotyping studies, the number of samples that can be collected in individuals is limited and the sampling times must be as flexible as possible. Therefore to optimize joint design for several drugs (i.e., to determine a compromise between informative times that best characterize each drug's kinetics), we proposed to use a compound optimality criterion based on the expected population Fisher information matrix in nonlinear mixed effect models. This criterion allows weighting different models, which might be useful to take into account the importance accorded to each target in a phenotyping test. We also computed windows around the optimal times based on recursive random sampling and Monte‐Carlo simulation while maintaining a reasonable level of efficiency for parameter estimation. We illustrated this strategy for two drugs often included in phenotyping cocktails, midazolam (probe for CYP3A) and digoxin (P‐glycoprotein), based on the data of a previous study, and were able to find a sparse and flexible design. The obtained design was evaluated by clinical trial simulations and shown to be efficient for the estimation of population and individual parameters. Copyright © 2015 John Wiley & Sons, Ltd.     

喹诺酮类对氨茶碱药代动力学的影响

本文观察了豚鼠加用环丙沙星或氧氟沙星前后血清茶碱药代动力学的变化,结果表明:环丙沙星对茶碱消除药代动力学有较大的影响,同时用药氨茶碱应个体化给药,加强血药浓度的监测;而氧氟沙星与氨茶碱合用则相对安全。     

Framework for Evaluation of Physiologically-Based Pharmacokinetic Models for Use in Safety or Risk Assessment

Proposed applications of increasingly sophisticated biologically-based computational models, such as physiologically-based pharmacokinetic models, raise the issue of how to evaluate whether the models are adequate for proposed uses, including safety or risk assessment. A six-step process for model evaluation is described. It relies on multidisciplinary expertise to address the biological, toxicological, mathematical, statistical, and risk assessment aspects of the modeling and its application. The first step is to have a clear definition of the purpose(s) of the model in the particular assessment; this provides critical perspectives on all subsequent steps. The second step is to evaluate the biological characterization described by the model structure based on the intended uses of the model and available information on the compound being modeled or related compounds. The next two steps review the mathematical equations used to describe the biology and their implementation in an appropriate computer program. At this point, the values selected for the model parameters (i.e., model calibration) must be evaluated. Thus, the fifth step is a combination of evaluating the model parameterization and calibration against data and evaluating the uncertainty in the model outputs. The final step is to evaluate specialized analyses that were done using the model, such as modeling of population distributions of parameters leading to population estimates for model outcomes or inclusion of early pharmacodynamic events. The process also helps to define the kinds of documentation that would be needed for a model to facilitate its evaluation and implementation.     

Uncertainties in Pharmacokinetic Modeling for Perchloroethylene. I. Comparison of Model Structure, Parameters, and Predictions for Low-Dose Metabolism Rates for Models Derived by Different Authors

In recent years physiologically based pharmacokinetic models have come to play an increasingly important role in risk assessment for carcinogens. The hope is that they can help open the black box between external exposure and carcinogenic effects to experimental observations, and improve both high-dose to low-dose and interspecies projections of risk. However, to date, there have been only relatively preliminary efforts to assess the uncertainties in current modeling results. In this paper we compare the physiologically based pharmacokinetic models (and model predictions of risk-related overall metabolism) that have been produced by seven different sets of authors for perchloroethylene (tetrachloroethylene). The most striking conclusion from the data is that most of the differences in risk-related model predictions are attributable to the choice of the data sets used for calibrating the metabolic parameters. Second, it is clear that the bottom-line differences among the model predictions are appreciable. Overall, the ratios of low-dose human to bioassay rodent metabolism spanned a 30-fold range for the six available human/rat comparisons, and the seven predicted ratios of low-dose human to bioassay mouse metabolism spanned a 13-fold range. (The greater range for the rat/human comparison is attributable to a structural assumption by one author group of competing linear and saturable pathways, and their conclusion that the dangerous saturable pathway constitutes a minor fraction of metabolism in rats.) It is clear that there are a number of opportunities for modelers to make different choices of model structure, interpretive assumptions, and calibrating data in the process of constructing pharmacokinetic models for use in estimating "delivered" or "biologically effective" dose for carcinogenesis risk assessments. We believe that in presenting the results of such modeling studies, it is important for researchers to explore the results of alternative, reasonably likely approaches for interpreting the available data--and either show that any conclusions they make are relatively insensitive to particular interpretive choices, or to acknowledge the differences in conclusions that would result from plausible alternative views of the world.