Assessing Toxicity of Mixtures: The Search for Economical Study Designs

Toxicity screening and testing of chemical mixtures for interaction effects is a potentially onerous task due to the sheer volume of combinations that may be of interest. We propose an economical approach for assessing the interaction effects of chemical mixtures that is guided by risk-based considerations. We describe the statistical underpinnings of the approach and use examples from the published literature to illustrate concepts of local versus global mixture assessment. Our approach employs a sequential testing procedure to find the dose combinations that define the dose boundary for a specified acceptable risk level. The first test is conducted for a dose combination consisting of the acceptable doses of each individual chemical in the mixture. The outcome of this first test indicates the dose combination that should be tested next. Continuing in this manner, the boundary of dose combinations for the specified acceptable risk level can be approximated based on measurements for relatively few dose combinations. Dose combinations on one side of the boundary would have responses less than the response associated with the acceptable risk level, and dose combinations on the boundary would be acceptable levels of exposure for the mixture.     

A statistical simulation model for field testing of non‐target organisms in environmental risk assessment of genetically modified plants

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non‐target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess‐zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.     

Framework for validation and implementation of in vitro toxicity tests

Summary The development and application of in vitro alternatives designed to reduce or replace the use of animals, or to lessen the distress and discomfort of laboratory animals, is a rapidly developing trend in toxicology. However, at present there is no formal administrative process to organize, coordinate, or evaluate validation activities. A framework capable of fostering the validation of new methods is essential for the effective transfer of new technologic developments from the research laboratory into practical use. This committee has identified four essential validation resources: chemical bank(s), cell and tissue banks, a data bank, and reference laboratories. The creation of a Scientific Advisory Board composed of experts in the various aspects and endpoints of toxicity testing, and representing the academic, industrial, and regulatory communities, is recommended. Test validation acceptance is contingent on broad buy-in by disparate groups in the scientific community—academics, industry, and government. This is best achieved by early and frequent communication among parties and agreement on common goals. It is hoped that the creation of a validation infrastructure composed of the elements described in this report will facilitate scientific acceptance and utilization of alternative methodologies and speed implementation of replacement, reduction, and refinement alternatives in toxicity testing.     

The one‐inflated positive Poisson mixture model for use in population size estimation

The one‐inflated positive Poisson mixture model (OIPPMM) is presented, for use as the truncated count model in Horvitz–Thompson estimation of an unknown population size. The OIPPMM offers a way to address two important features of some capture–recapture data: one‐inflation and unobserved heterogeneity. The OIPPMM provides markedly different results than some other popular estimators, and these other estimators can appear to be quite biased, or utterly fail due to the boundary problem, when the OIPPMM is the true data‐generating process. In addition, the OIPPMM provides a solution to the boundary problem, by labelling any mixture components on the boundary instead as one‐inflation.     

Dichroic statistical model for prediction and analysis of peptide helicity

Traditional statistical models for the prediction of peptide helicity are written in terms of the mean fractional helicity of the peptide residues. Far ultraviolet circular dichroic measurements of peptide solutions are converted to mean fractional helicity by partitioning the observed ellipticity between that of a perfect helix and a random coil. This partition does not adequately represent the ensemble of peptide molecules present in solution that populate imperfect helical conformations of quite variable lengths. A new dichroic statistical model has been written in terms of ellipticity rather than fractional helical content that recognizes (1) the source of ellipticity, peptide bond adsorption; (2) the differential ellipticity of peptide bonds in the terminal and interior helical turns; and (3) the contributions of each participant in a conformational ensemble to the observed ellipticity. Comparative analyses of host/guest peptides indicates that significant differences are obtained between residue w and n weights and ellipticity values using the traditional and dichroic statistical models. Proteins 28:467–480, 1997. © 1997 Wiley-Liss, Inc.     

Evaluating Chemical Interaction Studies for Mixture Risk Assessment

We describe a set of criteria to evaluate the quality of data and interpretations in chemical interaction studies. These criteria reflect the consensus of the literature on interaction analysis developed over decades of research in pharmacology, toxicology, and biometry; address common pitfalls in published interaction studies; and can be easily applied to common methods of interaction analysis. The criteria apply broadly to interaction data for drugs, pesticides, industrial chemicals, food additives, and natural products and are intended to assist risk assessors who must evaluate interaction studies for use in component-based mixture risk assessments. The criteria may also assist researchers interested in conducting interaction studies to inform mixture risk assessment. The criteria are also intended to serve larger scientific goals, including increasing the repeatability of results obtained in chemical interaction studies, enhancing the reliability of conclusions drawn from interaction data, providing greater consistency of interpretations among various analysts, and decreasing uncertainty in using interaction data in risk assessments. We describe the basis for each criterion and demonstrate their utility by using them to evaluate interaction studies from the recent toxicological and pharmacological literature, which serve as examples of different types of data sets that the risk assessor may encounter.     

Developing a statistical support system for environmental hazard evaluation

Estimating the hazard or risk to both human health and the environment has been based almost exclusively on single species toxicity tests low in environmental realism and without validation of their accuracy in more complex systems. While this may be quite appropriate for humans in a large variety of circumstances, there is no substantive body of direct experimental evidence indicating that precise predictions of harm from hazardous materials can be extrapolated from single species laboratory tests (or even multispecies laboratory tests) to the more complex highly variable natural systems. Now added to the hazardous chemical assessment problem is the accidental or deliberate release of genetically engineered microorganisms into the environment that have the additional capability of multiplying and expanding their numbers and also transferring genetic information to other organisms. This paper focuses entirely on hazard evaluation for organisms other than humans, namely predicting the potential risk or probability of harm to natural systems based on laboratory toxicity testing using single species. Not only will the basic risk assessment strategy itself be examined but also the question of determining the statistical reliability of various extrapolations from one level of biological organization to another. ‘For every complex problem, there is a simple, direct solution ... and it is invariably wrong!’ H. L. Mencken     

一种模型模拟结果的统计检验方法

模拟结果的准确性是衡量生态学模型是否成功的关键,但采用统计学方法判别模型模拟结果与观察值相符程度的报道较少。根据两个直线回归方程能否合并为一个方程的统计学检验方法,提出了通过检验观察值与模拟值直线回归方程和1∶1直线方程截距与斜率是否相同,进而在统计显著水平上判断生态学模型模拟值与观察值一致性的统计学检验方法。数据检验表明,此方法可以较好解决判断生态学模型模拟结果准确性的问题。     

Linear mixture model approach for selecting fuzzy exponent value in fuzzy c-means algorithm

The implementations of both the supervised and unsupervised fuzzy c-means classification algorithms require a priori selection of the fuzzy exponent parameter. This parameter is a weighting exponent and it determines the degree of fuzziness of the membership grades. The determination of an optimal value for this parameter in a fuzzy classification process is problematic and remains an open problem. This paper presents a new and efficient procedure for determining a local optimal value for the fuzzy exponent in the implementation of fuzzy classification technique. Numerical results using simulated image and real data sets are used to illustrate the simplicity and effectiveness of the proposed method.     

Improving Risk Assessment: Priorities for Epidemiologic Research

The Epidemiology Work Group at the Workshop on Future Research for Improving Risk Assessment Methods, Of Mice, Men, and Models, held August 16 to 18, 2000, at Snowmass Village, Aspen, Colorado, concluded that in order to improve the utility of epidemiologic studies for risk assessment, methodologic research is needed in the following areas: (1) aspects of epidemiologic study designs that affect doseresponse estimation; (2) alternative methods for estimating dose in human studies; and (3) refined methods for dose-response modeling for epidemiologic data. Needed research in aspects of epidemiologic study design includes recognition and control of study biases, identification of susceptible subpopulations, choice of exposure metrics, and choice of epidemiologic risk parameters. Much of this research can be done with existing data. Research needed to improve determinants of dose in human studies includes additional individual-level data (e.g., diet, co-morbidity), development of more extensive human data for physiologically based pharmacokinetic (PBPK) dose modeling, tissue registries to increase the availability of tissue for studies of exposure/dose and susceptibility biomarkers, and biomarker data to assess exposures in humans and animals. Research needed on dose-response modeling of human studies includes more widespread application of flexible statistical methods (e.g., general additive models), development of methods to compensate for epidemiologic bias in dose-response models, improved biological models using human data, and evaluation of the benchmark dose using human data. There was consensus among the Work Group that, whereas most prior risk assessments have focused on cancer, there is a growing need for applications to other health outcomes. Developmental and reproductive effects, injuries, respiratory disease, and cardiovascular disease were identified as especially high priorities for research. It was also a consensus view that epidemiologists, industrial hygienists, and other scientists focusing on human data need to play a stronger role throughout the risk assessment process. Finally, the group agreed that there was a need to improve risk communication, particularly on uncertainty inherent in risk assessments that use epidemiologic data.     

The statistical analysis of fluctuating asymmetry: REML estimation of a mixed regression model

The unbiased estimation of fluctuating asymmetry (FA) requires independent repeated measurements on both sides. The statistical analysis of such data is currently performed by a two-way mixed ANOVA analysis. Although this approach produces unbiased estimates of FA, many studies do not utilize this method. This may be attributed in part to the fact that the complete analysis of FA is very cumbersome and cannot be performed automatically with standard statistical software. Therefore, further elaboration of the statistical tools to analyse FA should focus on the usefulness of the method, in order for the correct statistical approaches to be applied more regularly. In this paper we propose a mixed regression model with restricted maximum likelihood (REML) parameter estimation to model FA. This routine yields exactly the same estimates of FA as the two-way mixed ANOVA . Yet the advantages of this approach are that it allows (a) testing the statistical significance of FA, (b) modelling and testing heterogeneity in both FA and measurement error (ME) among samples, (c) testing for nonzero directional asymmetry and (d) obtaining unbiased estimates of individual FA levels. The switch from a mixed two-way ANOVA to a mixed regression model was made to avoid overparametrization. Two simulation studies are presented. The first shows that a previously proposed method to test the significance of FA is incorrect, contrary to our mixed regression approach. In the second simulation study we show that a traditionally applied measure of individual FA [abs(left – right)] is biased by ME. The proposed mixed regression method, however, produces unbiased estimates of individual FA after modelling heterogeneity in ME. The applicability of this method is illustrated with two analyses.     

大兴安岭塔河地区林火发生的优势预测模型选择

选取在经济学和社会科学领域广泛应用的零膨胀模型(zero-inflated models)和栅栏模型(Hurdle models)对大兴安岭地区林火发生进行模拟,应用赤池准则(AIC)、似然比检验(LR)和模型残差平方和(SSR)对两类共4个回归模型——零膨胀泊松模型(ZIP)、零膨胀负二项模型(ZINB)、栅栏泊松模型(PH)、栅栏负二项模型(NBH)进行拟合分析,最终选取适合此林火发生特性的预测模型.模型的AIC和SSR值表明,ZINB模型对当地林火数据的拟合度最高.运用LR检验对嵌套模型(ZINB与ZIP,NBH与PH)进行检验,结果显示: ZINB和NBH均优于各自的嵌入模型,说明负二项(NB)模型对数据结构中的过度离散现象可以很好地模拟和解释.根据研究区林火实际发生规律和两类不同模型的应用假设条件判断,零膨胀模型更适合塔河地区的林火特性.     

The cynomolgus monkey as a model for developmental toxicity studies: variability of pregnancy losses,statistical power estimates,and group size considerations

BACKGROUND: This work evaluates pregnancy and infant loss in 1,069 vehicle‐treated cynomolgus monkeys from 78 embryo‐fetal development (EFD) studies and 14 pre‐postnatal development (PPND) studies accrued during 1981–2007. METHODS: Losses were analysed by survival function and hazard ratio using logistic regression for influence of year, study type (e.g., dose duration), and test item route of administration (ig, im, iv, sc). RESULTS: Neither study type nor route of dosing affected pregnancy outcome. Losses were higher pre‐1990 (104 losses/347 pregnancies) compared to 1990 onwards (94 losses/722 pregnancies). Losses were greatest before gestation day 50 and at parturition. Using post‐1989 data, Monte‐Carlo simulations of pregnancy outcomes were created. The power associated with the comparison of vehicle survival curves and simulated adverse survival curves was examined. This showed that EFD studies with initial vehicle group sizes of 16 and 20 have an 80% probability of having 13 and 16 ongoing pregnancies at gestational day 100, respectively. For PPND studies with initial vehicle group sizes of 16, 20, or 28, there is an 80% likelihood of having 9, 11, or 16 infants at day 7 post‐partum, respectively. A PPND study initiated with group size 20 could detect a threefold increase of test item–related pregnancy or infant loss. CONCLUSIONS: For designing and managing primate developmental toxicity studies, this type of analysis provides an objective tool to facilitate decisions either by supplementing groups with additional pregnant animals or stopping a group because an adverse effect on offspring survival has already been adequately revealed. Birth Defects Res (Part B) 89:175–187, 2010.© 2010 Wiley‐Liss, Inc.     

An iterative self-refining and self-evaluating approach for protein model quality estimation

Evaluating or predicting the quality of protein models (i.e., predicted protein tertiary structures) without knowing their native structures is important for selecting and appropriately using protein models. We describe an iterative approach that improves the performances of protein Model Quality Assurance Programs (MQAPs). Given the initial quality scores of a list of models assigned by a MQAP, the method iteratively refines the scores until the ranking of the models does not change. We applied the method to the model quality assessment data generated by 30 MQAPs during the Eighth Critical Assessment of Techniques for Protein Structure Prediction. To various degrees, our method increased the average correlation between predicted and real quality scores of 25 out of 30 MQAPs and reduced the average loss (i.e., the difference between the top ranked model and the best model) for 28 MQAPs. Particularly, for MQAPs with low average correlations (<0.4), the correlation can be increased by several times. Similar experiments conducted on the CASP9 MQAPs also demonstrated the effectiveness of the method. Our method is a hybrid method that combines the original method of a MQAP and the pair-wise comparison clustering method. It can achieve a high accuracy similar to a full pair-wise clustering method, but with much less computation time when evaluating hundreds of models. Furthermore, without knowing native structures, the iterative refining method can evaluate the performance of a MQAP by analyzing its model quality predictions.     

Health Risk Assessment of Chemical Mixtures Exposure to Residents in the Lake Taihu Region,China

This study assessed the mixture health risk for the residents of China's Lake Taihu region posed by a Persistent Organic Pollutants (POPs) mixture of dichloro-diphenyl-trichloroethane (DDT) and hexachlorocyclohexane (HCH). Multiple-pathway exposure models were used for exposure assessment in order to estimate the DDT and HCH exposure dose. The DDT and HCH PBPK models were developed and used for consequence assessment in order to analyze the pollutant distribution and accumulation process in human tissues. The tissue dose hazard index (HI) was used to estimate the mixture health risk. The results showed that the total exposure doses for male residents and female residents were 4.01 × 10^{? 4}～ 7.67 × 10^{? 3} mg/kg/day and 3.73 × 10^{? 4}～ 6.75 × 10^{? 3} mg/kg/day for DDT, respectively, and 3.78 × 10^{? 4}～ 5.14 × 10^{? 3} mg/kg/day and 3.53 × 10^{? 4}～ 4.66 × 10^{? 3} mg/kg/day for HCH, respectively. The maximum tissue concentrations in fat for male and female residents reached 110.51 mg/l and 97.21 mg/l for DDT, respectively, and 189.66 mg/l and 171.72 mg/l for HCH, respectively. The tissue dose hazard indexes for male and female residents were 0.1472 ～ 2.4990 and 0.1377 ～ 2.2230, respectively, and the probabilities of the risk exceeding the acceptable risk (HI = 1) for male and female were 24.60% and 16.51%, respectively.     

Statistical potential for assessment and prediction of protein structures

Protein structures in the Protein Data Bank provide a wealth of data about the interactions that determine the native states of proteins. Using the probability theory, we derive an atomic distance-dependent statistical potential from a sample of native structures that does not depend on any adjustable parameters (Discrete Optimized Protein Energy, or DOPE). DOPE is based on an improved reference state that corresponds to noninteracting atoms in a homogeneous sphere with the radius dependent on a sample native structure; it thus accounts for the finite and spherical shape of the native structures. The DOPE potential was extracted from a nonredundant set of 1472 crystallographic structures. We tested DOPE and five other scoring functions by the detection of the native state among six multiple target decoy sets, the correlation between the score and model error, and the identification of the most accurate non-native structure in the decoy set. For all decoy sets, DOPE is the best performing function in terms of all criteria, except for a tie in one criterion for one decoy set. To facilitate its use in various applications, such as model assessment, loop modeling, and fitting into cryo-electron microscopy mass density maps combined with comparative protein structure modeling, DOPE was incorporated into the modeling package MODELLER-8.     

Effect of Shuangjinlian mixture on oral ulcer model in rat

ObjectiveTo observe the effect of common clinical drug Shuangjin Lian mixture on rats with oral ulcer and discuss its mechanism.MethodsSodium carboxymethyl cellulose caused leukocyte aggregation in rats, observed the anti-inflammatory effect of Shuangjin mixture. 3 mm * 3 mm size ulcer surface in the oral cavity of rats was caused by 90% phenol solution, to observe the therapeutic effect and anti-inflammatory effect of Shuangjinlian mixture on rats with oral ulcers.ResultThe low, middle and high dose Shuangjinlian mixture can inhibits the accumulation of white blood cells caused by sodium carboxymethyl cellulose in rats significantly (P < 0.01). And reduce the degree of edema and hyperaemia around the ulcer tissue significantly (P < 0.01), improve the ulcer healing probability, and reduce the level of TNF-α, VEGF levels and increase IL-2 level in the serum of rats with oral ulcers significantly (P < 0.01). Pathological examination showed that the lesion of ulcer tissue in each treatment group was obviously alleviated.ConclusionShuangjinlian mixture had anti-inflammatory effect and was effective for the prevention and treatment of oral ulcer in rats.