Relative Potency of Chemical Carcinogens in Rodents

Extensive carcinogenesis data compiled by Gold et al. for 770 compounds tested in 2944 chronic bioassays in animals provided an opportunity to compare cancer rates across animal species for a wide variety of compounds administered by various routes of exposure. The comparisons in this paper are restricted to the most frequently tested species: rats, mice, and hamsters. When sufficient experimental data exist, Gold et al. provide estimates of the TD50 (the chronic dose rate expressed in mg/kg body weight/day which halves the actuarially adjusted percentage of tumor-free animals at the end of a standard lifetime experiment). Since the current practice generally is to base risk assessments upon the data set producing the highest cancer risk, the ratio of the minimum TD50's provides a measure of the relative potency between two species for each compound administered to animals by the same route. The geometric means of the ratios of minimum TD50's for rats:mice are 1/2.2 and 1/1.3 for diet and gavage, respectively. A mean ratio for rats: mice of 1/1.48 is obtained for compounds administered in the diet when the tumor site is the liver for both species. In general the minimum TD50 is lowest for the rat and highest for the hamster. Although limited data are available for inhalation studies, this route of administration resulted in the poorest agreement between rats and mice. In general, comparisons of minimum TD50's across the three rodent species are generally within a factor of 100 for a wide variety of compounds.     

Monte Carlo Simulation of Rodent Carcinogenicity Bioassays

In this paper we describe a simulation, by Monte Carlo methods, of the results of rodent carcinogenicity bioassays. Our aim is to study how the observed correlation between carcinogenic potency (beta or 1n2/TD50) and maximum tolerated dose (MTD) arises, and whether the existence of this correlation leads to an artificial correlation between carcinogenic potencies in rats and mice. The validity of the bioassay results depends upon, among other things, certain biases in the experimental design of the bioassays. These include selection of chemicals for bioassay and details of the experimental protocol, including dose levels. We use as variables in our simulation the following factors: (1) dose group size, (2) number of dose groups, (3) tumor rate in the control (zero-dose) group, (4) distribution of the MTD values of the group of chemicals as specified by the mean and standard deviation, (5) the degree of correlation between beta and the MTD, as given by the standard deviation of the random error term in the linear regression of log beta on log (1/MTD), and (6) an upper limit on the number of animals with tumors. Monte Carlo simulation can show whether the information present in the existing rodent bioassay database is sufficient to reject the validity of the proposed interspecies correlations at a given level of stringency. We hope that such analysis will be useful for future bioassay design, and more importantly, for discussion of the whole NCI/NTP program.     

On the Correlation Coefficient Between the TD50 and the MTD

The existence of correlation between the carcinogenic potency and the maximum tolerated dose has been the subject of many investigations in recent years. Several attempts have been made to quantify this correlation in different bioassay experiments. By using some distributional assumptions, Krewski et al .⁽¹⁾ derive an analytic expression for the coefficient of correlation between the carcinogenic potency TD₅₀ and the maximum tolerated dose. Here, we discuss the deviation that may result in using their analytical expression. By taking a more general approach we derive an expression for the correlation coefficient which includes the result of Krewski et al .⁽¹⁾ as a special case, and show that their expression may overestimate the correlation in some instances and yet underestimate the correlation in other instances. The proposed method is illustrated by application to a real dataset.     

An Overview of the Report: Correlation Between Carcinogenic Potency and the Maximum Tolerated Dose: Implications for Risk Assessment

Current practice in carcinogen bioassay calls for exposure of experimental animals at doses up to and including the maximum tolerated dose (MTD). Such studies have been used to compute measures of carcinogenic potency such as the TD₅₀ as well as unit risk factors such as q ₁ * for predicting low-dose risks. Recent studies have indicated that these measures of carcinogenic potency are highly correlated with the MTD. Carcinogenic potency has also been shown to be correlated with indicators of mutagenicity and toxicity. Correlation of the MTDs for rats and mice implies a corresponding correlation in TD₅₀ values for these two species. The implications of these results for cancer risk assessment are examined in light of the large variation in potency among chemicals known to induce tumors in rodents.     

Issues Concerning the Estimation of the TD50

The TD50 (or tumorigenic dose rate 50) is a generally accepted measure of the carcinogenic potency of a chemical in a particular strain of animal. This paper discusses error in the estimation of the TD50 caused by intercurrent mortality and error resulting from consideration of only significant TD50's. Using computer simulations, we found that treatment-related toxicity had only a small effect on estimating the TD50, with errors seldom exceeding 5%. The TD50 is sensitive to changes in tumor lethality with errors ranging to as high as 50%. Many of these errors were significantly different from zero and the results suggest that potency estimation could be improved by basing the estimates upon the tumor incidence rate rather than upon the tumor death rate when an estimate of tumor lethality is obtainable.     

Characterizing Dose-Response Relationships in Multiple Cancer Bioassays

In the evaluation of chemical compounds for carcinogenic risk, regulatory agencies such as the U.S. Environmental Protection Agency and National Toxicology Program (NTP) have traditionally fit a dose-response model to data from rodent bioassays, and then used the fitted model to estimate a Virtually Safe Dose or the dose corresponding to a very small increase (usually 10(-6)) in risk over background. Much recent interest has been directed at incorporating additional scientific information regarding the properties of the specific chemical under investigation into the risk assessment process, including biological mechanisms of cancer induction, metabolic pathways, and chemical structure and activity. Despite the fact that regulatory agencies are currently poised to allow use of nonlinear dose-response models based on the concept of an underlying threshold for nongenotoxic chemicals, there have been few attempts to investigate the overall relationship between the shape of dose-response curves and mutagenicity. Using data from an historical database of NTP cancer bioassays, the authors conducted a repeated-measures Analysis of the estimated shape from fitting extended Weibull dose-response curves. It was concluded that genotoxic chemicals have dose-response curves that are closer to linear than those for nongenotoxic chemicals, though on average, both types of compounds have dose-response curves that are convex and the effect of genotoxicity is small.     

Developmental Disruption of Thyroid Hormone: Correlations with Hearing Dysfunction in Rats

A wide variety of environmental contaminants adversely affect thyroid hormone (TH) homeostasis. Hypothyroidism and/or hypothyroxinemia during the early postnatal period in the rat leads to permanent structural damage and loss of function in the cochlea. A major uncertainty in assessing the risks of developmental exposure to thyroid-disrupting chemicals (TDCs) is the lack of a clear characterization of the dose-response relationship, especially in the lower region, between disruption of hormones and adverse consequences. The current work correlated early postnatal hypothyroxinemia with hearing loss in the adult rat. Linear regression was performed on the log transform for total serum thyroxine (T4) concentrations on postnatal day 14 or 21 versus dB(SPL) of hearing loss in adult animals developmentally exposed to TDCs. Regression analyses revealed a highly significant correlation between T4 concentration and hearing loss. In the rat, a 50-60% decrease in circulating T4 was needed to significantly impact hearing function. This correlation suggests that T4 serum concentrations at 14 or 21 days of postnatal age may be a good predictive biomarker in rodents of the adverse consequence of developmental exposure to TDCs.     

A {{10}}-Approximation Algorithm for a Generalization of the Weighted Edge-Dominating Set Problem

We study the approximability of the weighted edge-dominating set problem. Although even the unweighted case is NP-Complete, in this case a solution of size at most twice the minimum can be efficiently computed due to its close relationship with minimum maximal matching; however, in the weighted case such a nice relationship is not known to exist. In this paper, after showing that weighted edge domination is as hard to approximate as the well studied weighted vertex cover problem, we consider a natural strategy, reducing edge-dominating set to edge cover. Our main result is a simple -approximation algorithm for the weighted edge-dominating set problem, improving the existing ratio, due to a simple reduction to weighted vertex cover, of 2r _WVC, where r _WVC is the approximation guarantee of any polynomial-time weighted vertex cover algorithm. The best value of r _WVC currently stands at . Furthermore we establish that the factor of is tight in the sense that it coincides with the integrality gap incurred by a natural linear programming relaxation of the problem.     

A primal-dual approximation algorithm for the Asymmetric Prize-Collecting TSP

We present a primal-dual ?log(n)?-approximation algorithm for the version of the asymmetric prize collecting traveling salesman problem, where the objective is to find a directed tour that visits a subset of vertices such that the length of the tour plus the sum of penalties associated with vertices not in the tour is as small as possible. The previous algorithm for the problem (V.H. Nguyen and T.T Nguyen in Int. J. Math. Oper. Res. 4(3):294–301, 2012) which is not combinatorial, is based on the Held-Karp relaxation and heuristic methods such as the Frieze et al.’s heuristic (Frieze et al. in Networks 12:23–39, 1982) or the recent Asadpour et al.’s heuristic for the ATSP (Asadpour et al. in 21st ACM-SIAM symposium on discrete algorithms, 2010). Depending on which of the two heuristics is used, it gives respectively 1+?log(n)? and $3+ 8\frac{\log(n)}{\log(\log(n))}$ as an approximation ratio. Our algorithm achieves an approximation ratio of ?log(n)? which is weaker than $3+ 8\frac{\log(n)}{\log(\log(n))}$ but represents the first combinatorial approximation algorithm for the Asymmetric Prize-Collecting TSP.     

Improving Cancer Dose–Response Characterization by Using Physiologically Based Pharmacokinetic Modeling: An Analysis of Pooled Data for Acrylonitrile-Induced Brain Tumors to Assess Cancer Potency in the Rat

Historically, U.S. regulators have derived cancer slope factors by using applied dose and tumor response data from a single key bioassay or by averaging the cancer slope factors of several key bioassays. Recent changes in U.S. Environmental Protection Agency (EPA) guidelines for cancer risk assessment have acknowledged the value of better use of mechanistic data and better dose–response characterization. However, agency guidelines may benefit from additional considerations presented in this paper. An exploratory study was conducted by using rat brain tumor data for acrylonitrile (AN) to investigate the use of physiologically based pharmacokinetic (PBPK) modeling along with pooling of dose–response data across routes of exposure as a means for improving carcinogen risk assessment methods. In this study, two contrasting assessments were conducted for AN-induced brain tumors in the rat on the basis of (1) the EPA's approach, the dose–response relationship was characterized by using administered dose/concentration for each of the key studies assessed individually; and (2) an analysis of the pooled data, the dose–response relationship was characterized by using PBPK-derived internal dose measures for a combined database of ten bioassays. The cancer potencies predicted for AN by the contrasting assessments are remarkably different (i.e., risk-specific doses differ by as much as two to four orders of magnitude), with the pooled data assessments yielding lower values. This result suggests that current carcinogen risk assessment practices overestimate AN cancer potency. This methodology should be equally applicable to other data-rich chemicals in identifying (1) a useful dose measure, (2) an appropriate dose–response model, (3) an acceptable point of departure, and (4) an appropriate method of extrapolation from the range of observation to the range of prediction when a chemical's mode of action remains uncertain.     

Improving cancer dose-response characterization by using physiologically based pharmacokinetic modeling: an analysis of pooled data for acrylonitrile-induced brain tumors to assess cancer potency in the rat.

Historically, U.S. regulators have derived cancer slope factors by using applied dose and tumor response data from a single key bioassay or by averaging the cancer slope factors of several key bioassays. Recent changes in U.S. Environmental Protection Agency (EPA) guidelines for cancer risk assessment have acknowledged the value of better use of mechanistic data and better dose-response characterization. However, agency guidelines may benefit from additional considerations presented in this paper. An exploratory study was conducted by using rat brain tumor data for acrylonitrile (AN) to investigate the use of physiologically based pharmacokinetic (PBPK) modeling along with pooling of dose-response data across routes of exposure as a means for improving carcinogen risk assessment methods. In this study, two contrasting assessments were conducted for AN-induced brain tumors in the rat on the basis of (1) the EPA's approach, the dose-response relationship was characterized by using administered dose/concentration for each of the key studies assessed individually; and (2) an analysis of the pooled data, the dose-response relationship was characterized by using PBPK-derived internal dose measures for a combined database of ten bioassays. The cancer potencies predicted for AN by the contrasting assessments are remarkably different (i.e., risk-specific doses differ by as much as two to four orders of magnitude), with the pooled data assessments yielding lower values. This result suggests that current carcinogen risk assessment practices overestimate AN cancer potency. This methodology should be equally applicable to other data-rich chemicals in identifying (1) a useful dose measure, (2) an appropriate dose-response model, (3) an acceptable point of departure, and (4) an appropriate method of extrapolation from the range of observation to the range of prediction when a chemical's mode of action remains uncertain.     

On the Relationship Between Carcinogenicity and Acute Toxicity

Parodi et al. ⁽¹⁾ and Zeise et al. ⁽²⁾ found a surprising statistical correlation (or association) between acute toxicity and carcinogenic potency. In order to shed light on the questions of whether or not it is a causal correlation, and whether or not it is a statistical or tautological artifact, we have compared the correlations for the NCI/NTP data set with those for chemicals not in this set. Carcinogenic potencies were taken from the Gold et al. database. We find a weak correlation with an average value of TD₅₀/LD₅₀= 0.04 for the non-NCI data set, compared with TD₅₀/LD₅₀= 0.15 for the NCI data set. We conclude that it is not easy to distinguish types of carcinogens on the basis of whether or not they are acutely toxic.     

员工目标内容对突破性创造力和渐进性创造力的影响:基于代际比较视角

基于目标内容理论的观点,探讨了企业员工的内部目标和外部目标追求对突破性创造力和渐进性创造力的影响,并检验了代际差异对上述路径的调节效应。基于不同企业的247对上下级匹配数据的研究结果表明,外部目标促进员工的渐进性创造力,内部目标促进员工的突破性创造力;员工的目标内容对突破性和渐进性创造力的影响存在代际差异,相比新生代员工,外部目标对老一代员工的渐进性创造力产生更显著的影响,相比新生代员工,内部目标对老一代员工的突破性创造力产生更显著的影响。针对上述结论,进一步讨论了本文的理论与实践意义。     

A faster strongly polynomial time algorithm to solve the minimum cost tension problem

This paper presents a strongly polynomial time algorithm for the minimum cost tension problem, which runs in \(O(\max \{m^3n, m^2 \log n(m+n \log n)\})\) time, where n and m denote the number of nodes and number of arcs, respectively. Our algorithm improves upon the previous strongly polynomial time of \(O(n^4 m^3 \log n)\) due to Hadjiat and Maurras (Discret Math 165(166):377–394, 1997).     

基于SVM求解不均衡数据集分类的主观权重约束方法

支持向量机(SVM)的二类分问题中针对不平衡数据集可以通过减少样本信息的不对称性和改进算法来解决。本文针对中小企业中有财务风险与无财务风险样本的不平衡性问题,使用一种带有主观权重约束条件的支持向量机新模型对样本进行分类。实验表明新模型确实提高了财务风险企业即少类样本的识别性能,是一种类别不均衡学习(class imbalance learning)的新方法。     

The Fallacy of Ranking Possible Carcinogen Hazards Using the TD50

Ames et al. have proposed a new model for evaluating carcinogenic hazards in the environment. They advocate ranking possible carcinogens on the basis of the TD50, the estimated dose at which 50% of the test animals would get tumors, and extrapolating that ranking to all other doses. We argue that implicit in this methodology is a simplistic and inappropriate statistical model. All carcinogens are assumed to act similarly and to have dose-response curves of the same shape that differ only in the value of one parameter. We show by counterexample that the rank order of cancer potencies for two chemicals can change over a reasonable range of doses. Ames et al.'s use of these TD50 ranks to compare the hazards from low level exposures to contaminants in our food and environment is wholly inappropriate and inaccurate. Their dismissal of public health concern for environmental exposures, in general, based on these comparisons, is not supported by the data.     

Assessing human health response in life cycle assessment using ED10s and DALYs: part 1--Cancer effects.

Life cycle assessment (LCA) is a framework for comparing products according to their total estimated environmental impact, summed over all chemical emissions and activities associated with a product at all stages in its life cycle (from raw material acquisition, manufacturing, use, to final disposal). For each chemical involved, the exposure associated with the mass released into the environment, integrated over time and space, is multiplied by a toxicological measure to estimate the likelihood of effects and their potential consequences. In this article, we explore the use of quantitative methods drawn from conventional single-chemical regulatory risk assessments to create a procedure for the estimation of the cancer effect measure in the impact phase of LCA. The approach is based on the maximum likelihood estimate of the effect dose inducing a 10% response over background, ED10, and default linear low-dose extrapolation using the slope betaED10 (0.1/ED10). The calculated effects may correspond to residual risks below current regulatory compliance requirements that occur over multiple generations and at multiple locations; but at the very least they represent a "using up" of some portion of the human population's ability to accommodate emissions. Preliminary comparisons are performed with existing measures, such as the U.S. Environmental Protection Agency's (U.S. EPA's) slope factor measure q1*. By analyzing bioassay data for 44 chemicals drawn from the EPA's Integrated Risk Information System (IRIS) database, we explore estimating ED10 from more readily available information such as the median tumor dose rate TD50 and the median single lethal dose LD50. Based on the TD50, we then estimate the ED10 for more than 600 chemicals. Differences in potential consequences, or severity, are addressed by combining betaED10 with the measure disability adjusted life years per affected person, DALYp. Most of the variation among chemicals for cancer effects is found to be due to differences in the slope factors (betaED10) ranging from 10(-4) up to 10(4) (risk of cancer/mg/kg-day).     

RELATIONSHIP LENDING DURING A FINANCIAL CRISIS

This paper studies whether relationship lending mitigates the transmission of the Lehman default shock to the supply of credit in Italy. Exploiting the presence of multiple banking relationships, we control for banks' and firms' unobserved characteristics. Results show that the growth of credit itself is higher and its cost lower the shorter the distance between the bank and the firm, the longer the relationship, and the higher the share of credit held by the bank. Credit growth by relationship lenders is 4.6% higher than that by transactional lenders; the increase in the cost of credit is 50 basis points lower. The positive effect of relationship lending on credit supply increased during the crisis, compared to a pre‐crisis period. The beneficial effect of relationship lending is weaker if the relationship lender is more exposed to the financial crisis, especially when lending to weaker borrowers.     

Approximation and Exact Algorithms for Constructing Minimum Ultrametric Trees from Distance Matrices

An edge-weighted tree is called ultrametric if the distances from the root to all the leaves in the tree are equal. For an n by n distance matrix M, the minimum ultrametric tree for M is an ultrametric tree T = (V, E, w) with leaf set {1,..., n} such that d_T(i, j) M[i, j] for all i, j and is minimum, where d_T(i, j) is the distance between i and j on T. Constructing minimum ultrametric trees from distance matrices is an important problem in computational biology. In this paper, we examine its computational complexity and approximability. When the distances satisfy the triangle inequality, we show that the minimum ultrametric tree problem can be approximated in polynomial time with error ratio 1.5(1 + log n), where n is the number of species. We also develop an efficient branch-and-bound algorithm for constructing the minimum ultrametric tree for both metric and non-metric inputs. The experimental results show that it can find an optimal solution for 25 species within reasonable time, while, to the best of our knowledge, there is no report of algorithms solving the problem even for 12 species.