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Systematic reviews provide a structured framework for summarizing the available evidence in a comprehensive, objective, and transparent manner. They inform evidence-based guidelines in medicine, public policy, and more recently, in environmental health and toxicology. Many regulatory agencies have extended and adapted the well-established systematic review methods, initially developed for clinical studies, for their assessment needs. The use of systematic reviews to summarize evidence from existing human, animal, and mechanistic studies can reduce reliance on animal test data in risk assessment and can help avoid unnecessary duplication of animal experiments that have already been conducted. As alternative test methods can be expected to play an increasing role in human health risk assessment in the future, systematic reviews can be particularly helpful in validating these alternatives. The field of evidence-based toxicology has undergone extensive development since its first meeting in 2007 as a result of collaborative efforts among international experts and public health agencies, particularly with respect to the use of mechanistic data and evidence integration. The continued development and wider adoption of systematic review methodology can lead to better 3R implementation. As undertaking a systematic review can be a complex and lengthy process, it is important to understand the main steps involved. Key steps, along with current best practices, are described with references to guidance from organizations with expertise in evidence synthesis. Applications of systematic reviews in clinical, observational, and experimental studies are presented. Finally, software tools available to facilitate and increase the efficiency of completing a systematic review are described.

Assessment of potential human health risks associated with environmental and other agents requires careful evaluation of all available and relevant evidence for the agent of interest, including both data-rich and data-poor agents. With the advent of new approach methodologies in toxicological risk assessment, guidance on integrating evidence from mul-tiple evidence streams is needed to ensure that all available data is given due consideration in both qualitative and quantitative risk assessment. The present report summarizes the discussions among academic, government, and private sector participants from North America and Europe in an international workshop convened to explore the development of an evidence-based risk assessment framework, taking into account all available evidence in an appropriate manner in order to arrive at the best possible characterization of potential human health risks and associated uncertainty. Although consensus among workshop participants was not a specific goal, there was general agreement on the key consider-ations involved in evidence-based risk assessment incorporating 21st century science into human health risk assessment. These considerations have been embodied into an overarching prototype framework for evidence integration that will be explored in more depth in a follow-up meeting.

Advances in the biological sciences have led to an ongoing paradigm shift in toxicity testing based on expanded application of high-throughput in vitro screening and in silico methods to assess potential health risks of environmental agents. This review examines progress on the vision for toxicity testing elaborated by the US National Research Council (NRC) during the decade that has passed since the 2007 NRC report on Toxicity Testing in the 21st Century (TT21C). Concomitant advances in exposure assessment, including computational approaches and high-throughput exposomics, are also documented. A vision for the next generation of risk science, incorporating risk assessment methodologies suitable for the analysis of new toxicological and exposure data, resulting in human exposure guidelines is described. Case study prototypes indicating how these new approaches to toxicity testing, exposure measurement, and risk assessment are beginning to be applied in practice are presented. Overall, progress on the 20-year transition plan laid out by the US NRC in 2007 has been substantial. Importantly, government agencies within the United States and internationally are beginning to incorporate the new approach methodologies envisaged in the original TT21C vision into regulatory practice. Future perspectives on the continued evolution of toxicity testing to strengthen regulatory risk assessment are provided.

Many epidemiological studies assessing the relationship between exposure and disease are carried out without data on individual exposures. When this barrier is encountered in occupational studies, the subject exposures are often evaluated with a job-exposure matrix (JEM), which consists of mean exposure for occupational categories measured on a comparable group of workers. One of the objectives of the seven-country case-control study of occupational exposure and brain cancer risk, INTEROCC, was to investigate the relationship of occupational exposure to electromagnetic fields (EMF) in different frequency ranges and brain cancer risk. In this paper, we use the Canadian data from INTEROCC to estimate the odds of developing brain tumours due to occupational exposure to EMF. The first step was to find the best EMF exposure surrogate among the arithmetic mean, the geometric mean, and the mean of log-normal exposure distribution for each occupation in the JEM, in comparison to Berkson error adjustments via numerical approximation of the likelihood function. Contrary to previous studies of Berkson errors in JEMs, we found that the geometric mean was the best exposure surrogate. This analysis provided no evidence that cumulative lifetime exposure to extremely low frequency magnetic fields increases brain cancer risk, a finding consistent with other recent epidemiological studies.

Essential elements such as copper and manganese may demonstrate U-shaped exposure-response relationships due to toxic responses occurring as a result of both excess and deficiency. Previous work on a copper toxicity database employed CatReg, a software program for categorical regression developed by the U.S. Environmental Protection Agency, to model copper excess and deficiency exposure-response relationships separately. This analysis involved the use of a severity scoring system to place diverse toxic responses on a common severity scale, thereby allowing their inclusion in the same CatReg model. In this article, we present methods for simultaneously fitting excess and deficiency data in the form of a single U-shaped exposure-response curve, the minimum of which occurs at the exposure level that minimizes the probability of an adverse outcome due to either excess or deficiency (or both). We also present a closed-form expression for the point at which the exposure-response curves for excess and deficiency cross, corresponding to the exposure level at which the risk of an adverse outcome due to excess is equal to that for deficiency. The application of these methods is illustrated using the same copper toxicity database noted above. The use of these methods permits the analysis of all available exposure-response data from multiple studies expressing multiple endpoints due to both excess and deficiency. The exposure level corresponding to the minimum of this U-shaped curve, and the confidence limits around this exposure level, may be useful in establishing an acceptable range of exposures that minimize the overall risk associated with the agent of interest., (© 2016 Society for Risk Analysis.)

Background: The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions., Objective: Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures., Methods: New data and methods were applied and evaluated for use in hazard identification and dose-response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling., Discussion: NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure-response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams., Conclusions: While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study-highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671-1682; http://dx.doi.org/10.1289/EHP233., Competing Interests: K.B. is an occasional expert consultant for chemical and pharmaceutical companies, including Boehringer Ingelheim and Sangamo, and owns stock in and is a consultant for CytoSolv, an early stage biotechnology company. D.K. (D. Krewski) and G.M.P. are subcontractors to ICF International, Durham, NC, USA. G.P. is employed by Grant Consulting Group, Washington, DC, USA. D.K. (D. Krewski) is a Principal at Risk Sciences International (RSI), a Canadian company established in partnership with the University of Ottawa in 2006. RSI contributed to the development of the U.S. EPA NexGen framework under a subcontract with ICF International. M.T.S. has received consulting and expert testimony fees from lawyers representing both plaintiffs and defendants in cases involving claims related to exposure to benzene. These activities are unrelated to the current work. The remaining authors declare they have no actual or potential competing financial interests.

Toxicogenomics promises to be an important part of future human health risk assessment of environmental chemicals. The application of gene expression profiles (e.g., for hazard identification, chemical prioritization, chemical grouping, mode of action discovery, and quantitative analysis of response) is growing in the literature, but their use in formal risk assessment by regulatory agencies is relatively infrequent. Although additional validations for specific applications are required, gene expression data can be of immediate use for increasing confidence in chemical evaluations. We believe that a primary reason for the current lack of integration is the limited practical guidance available for risk assessment specialists with limited experience in genomics. The present manuscript provides basic information on gene expression profiling, along with guidance on evaluating the quality of genomic experiments and data, and interpretation of results presented in the form of heat maps, pathway analyses and other common approaches. Moreover, potential ways to integrate information from gene expression experiments into current risk assessment are presented using published studies as examples. The primary objective of this work is to facilitate integration of gene expression data into human health risk assessments of environmental chemicals., (Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.)

Occupational exposure limits (OELs) serve as health-based benchmarks against which measured or estimated workplace exposures can be compared. In the years since the introduction of OELs to public health practice, both developed and developing countries have established processes for deriving, setting, and using OELs to protect workers exposed to hazardous chemicals. These processes vary widely, however, and have thus resulted in a confusing international landscape for identifying and applying such limits in workplaces. The occupational hygienist will encounter significant overlap in coverage among organizations for many chemicals, while other important chemicals have OELs developed by few, if any, organizations. Where multiple organizations have published an OEL, the derived value often varies considerably-reflecting differences in both risk policy and risk assessment methodology as well as access to available pertinent data. This article explores the underlying reasons for variability in OELs, and recommends the harmonization of risk-based methods used by OEL-deriving organizations. A framework is also proposed for the identification and systematic evaluation of OEL resources, which occupational hygienists can use to support risk characterization and risk management decisions in situations where multiple potentially relevant OELs exist.

When the bovine spongiform encephalopathy (BSE) epidemic first emerged in the United Kingdom in the mid 1980s, the etiology of animal prion diseases was largely unknown. Risk management efforts to control the disease were also subject to uncertainties regarding the extent of BSE infections and future course of the epidemic. As understanding of BSE increased, mathematical models were developed to estimate risk of BSE infection and to predict reductions in risk in response to BSE control measures. Risk models of BSE-transmission dynamics determined disease persistence in cattle herds and relative infectivity of cattle prior to onset of clinical disease. These BSE models helped in understanding key epidemiological features of BSE transmission and dynamics, such as incubation period distribution and age-dependent infection susceptibility to infection with the BSE agent. This review summarizes different mathematical models and methods that have been used to estimate risk of BSE, and discusses how such risk projection models have informed risk assessment and management of BSE. This review also provides some general insights on how mathematical models of the type discussed here may be used to estimate risks of emerging zoonotic diseases when biological data on transmission of the etiological agent are limited.

Objectives: In 2011, the U.S. Environmental Protection Agency initiated the NexGen project to develop a new paradigm for the next generation of risk science., Methods: The NexGen framework was built on three cornerstones: the availability of new data on toxicity pathways made possible by fundamental advances in basic biology and toxicological science, the incorporation of a population health perspective that recognizes that most adverse health outcomes involve multiple determinants, and a renewed focus on new risk assessment methodologies designed to better inform risk management decision making., Results: The NexGen framework has three phases. Phase I (objectives) focuses on problem formulation and scoping, taking into account the risk context and the range of available risk management decision-making options. Phase II (risk assessment) seeks to identify critical toxicity pathway perturbations using new toxicity testing tools and technologies, and to better characterize risks and uncertainties using advanced risk assessment methodologies. Phase III (risk management) involves the development of evidence-based population health risk management strategies of a regulatory, economic, advisory, community-based, or technological nature, using sound principles of risk management decision making., Conclusions: Analysis of a series of case study prototypes indicated that many aspects of the NexGen framework are already beginning to be adopted in practice.

Background: The U.S. National Toxicology Program (NTP) cancer bioassay database provides an opportunity to compare both existing and new approaches to determining points of departure (PoDs) for establishing reference doses (RfDs)., Objectives: The aims of this study were a) to investigate the risk associated with the traditional PoD used in human health risk assessment [the no observed adverse effect level (NOAEL)]; b) to present a new approach based on the signal-to-noise crossover dose (SNCD); and c) to compare the SNCD and SNCD-based RfD with PoDs and RfDs based on the NOAEL and benchmark dose (BMD) approaches., Methods: The complete NTP database was used as the basis for these analyses, which were performed using the Hill model. We determined NOAELs and estimated corresponding extra risks. Lower 95% confidence bounds on the BMD (BMDLs) corresponding to extra risks of 1%, 5%, and 10% (BMDL01, BMDL05, and BMDL10, respectively) were also estimated. We introduce the SNCD as a new PoD, defined as the dose where the additional risk is equal to the "background noise" (the difference between the upper and lower bounds of the two-sided 90% confidence interval on absolute risk) or a specified fraction thereof., Results: The median risk at the NOAEL was approximately 10%, and the default uncertainty factor (UF = 100) was considered most applicable to the BMDL10. Therefore, we chose a target risk of 1/1,000 (0.1/100) to derive an SNCD-based RfD by linear extrapolation. At the median, this approach provided the same RfD as the BMDL10 divided by the default UF., Conclusions: Under a standard BMD approach, the BMDL10 is considered to be the most appropriate PoD. The SNCD approach, which is based on the lowest dose at which the signal can be reliably detected, warrants further development as a PoD for human health risk assessment.

This paper presents a graded approach to the testing of crop-derived genetically modified (GM) foods based on concern levels in a proposed risk-based classification scheme (RBCS) and currently available testing methods. A graded approach offers the potential for more efficient use of testing resources by focusing less on lower concern GM foods, and more on higher concern foods. In this proposed approach to graded testing, products that are classified as Level I would have met baseline testing requirements that are comparable to what is widely applied to premarket assessment of GM foods at present. In most cases, Level I products would require no further testing, or very limited confirmatory analyses. For products classified as Level II or higher, additional testing would be required, depending on the type of the substance, prior dietary history, estimated exposure level, prior knowledge of toxicity of the substance, and the nature of the concern related to unintended changes in the modified food. Level III testing applies only to the assessment of toxic and antinutritional effects from intended changes and is tailored to the nature of the substance in question. Since appropriate test methods are not currently available for all effects of concern, future research to strengthen the testing of GM foods is discussed.

The predominant paradigm for the premarket assessment of genetically modified (GM) foods reflects heightened public concern by focusing on foods modified by recombinant deoxyribonucleic acid (rDNA) techniques, while foods modified by other methods of genetic modification are generally not assessed for safety. To determine whether a GM product requires less or more regulatory oversight and testing, we developed and evaluated a risk-based classification scheme (RBCS) for crop-derived GM foods. The results of this research are presented in three papers. This paper describes the conceptual development of the proposed RBCS that focuses on two categories of adverse health effects: (1) toxic and antinutritional effects, and (2) allergenic effects. The factors that may affect the level of potential health risks of GM foods are identified. For each factor identified, criteria for differentiating health risk potential are developed. The extent to which a GM food satisfies applicable criteria for each factor is rated separately. A concern level for each category of health effects is then determined by aggregating the ratings for the factors using predetermined aggregation rules. An overview of the proposed scheme is presented, as well as the application of the scheme to a hypothetical GM food.

This paper presents an exploratory evaluation of four functional components of a proposed risk-based classification scheme (RBCS) for crop-derived genetically modified (GM) foods in a concordance study. Two independent raters assigned concern levels to 20 reference GM foods using a rating form based on the proposed RBCS. The four components of evaluation were: (1) degree of concordance, (2) distribution across concern levels, (3) discriminating ability of the scheme, and (4) ease of use. At least one of the 20 reference foods was assigned to each of the possible concern levels, demonstrating the ability of the scheme to identify GM foods of different concern with respect to potential health risk. There was reasonably good concordance between the two raters for the three separate parts of the RBCS. The raters agreed that the criteria in the scheme were sufficiently clear in discriminating reference foods into different concern levels, and that with some experience, the scheme was reasonably easy to use. Specific issues and suggestions for improvements identified in the concordance study are discussed.

Managing risks to human health and the environment produced by endocrine-active chemicals (EAC) is dependent on sound principles of risk assessment and risk management, which need to be adapted to address the uncertainties in the state of the science of EAC. Quantifying EAC hazard identification, mechanisms of action, and dose-response curves is complicated by a range of chemical structure/toxicology classes, receptors and receptor subtypes, and nonlinear dose-response curves with low-dose effects. Advances in risk science including toxicogenomics and quantitative structure-activity relationships (QSAR) along with a return to the biological process of hormesis are proposed to complement existing risk assessment strategies, including that of the Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC 1998). EAC represents a policy issue that has captured the public's fears and concerns about environmental health. This overview describes the process of EAC risk assessment and risk management in the context of traditional risk management frameworks, with emphasis on the National Research Council Framework (1983), taking into consideration the strategies for EAC management in Canada, the United States, and the European Union.

Evidence in the disaster mental health literature indicates that psychosocial consequences of terrorism are a critical component of chemical, biological, radiological, and nuclear (CBRN) events, both at the clinical level and the normal behavioral and emotional levels. Planning for such psychosocial aspects should be an integral part of emergency preparedness. As Canada and other countries build the capacity to prevent, mitigate, and manage CBRN threats and events, it is important to recognize the range of social, psychological, emotional, spiritual, behavioral, and cognitive factors that may affect victims and their families, communities, children, the elderly, responders, decision makers, and others at all phases of terrorism, from threat to post-impact recovery. A structured process to assist CBRN emergency planners, decision makers, and responders in identifying psychosocial risks, vulnerable populations, resources, and interventions at various phases of a CBRN event to limit negative psychosocial impacts and promote resilience and adaptive responses is the essence of our psychosocial risk assessment and management (P-RAM) framework. This article presents the evidence base and conceptual underpinnings of the framework, the principles underlying its design, its key elements, and its use in the development of decision tools for responders, planners, decision makers, and the general public to better assess and manage psychosocial aspects of CBRN threats or attacks.

A comprehensive analytical review of the risk assessment, risk management, and risk communication approaches currently being undertaken by key national, provincial/state, territorial, and international agencies was conducted. The information acquired for review was used to identify the differences, commonalities, strengths, and weaknesses among the various approaches, and to identify elements that should be included in an effective, current, and comprehensive approach applicable to environmental, human health and occupational health risks. More than 80 agencies, organizations, and advisory councils, encompassing more than 100 risk documents, were examined during the period from February 2000 until November 2002. An overview was made of the most important general frameworks for risk assessment, risk management, and risk communication for human health and ecological risk, and for occupational health risk. In addition, frameworks for specific applications were reviewed and summarized, including those for (1)contaminated sites; (2) northern contaminants; (3) priority substances; (4) standards development; (5) food safety; (6) medical devices; (7) prescription drug use; (8) emergency response; (9) transportation; (10) risk communication. Twelve frameworks were selected for more extensive review on the basis of representation of the areas of human health, ecological, and occupational health risk; relevance to Canadian risk management needs; representation of comprehensive and well-defined approaches; generalizability with their risk areas; representation of "state of the art" in Canada, the United States, and/or internationally; and extent of usage of potential usage within Canada. These 12 frameworks were: 1. Framework for Environmental Health Risk Management (US Presidential/Congressional Commission on Risk Assessment and Risk Management, 1997). 2. Health Risk Determination: The Challenge of Health Protection (Health and Welfare Canada, 1990). 3. Health Canada Decision-Making Framework for Identifying, Assessing and Managing Health Risks (Health Canada, 2000). 4. Canadian Environmental Protection Act: Human Health Risk Assessment of Priority Substances(Health Canada, 1994). 5. CSA-Q8550 Risk Management: Guidelines for Decision-Makers (Canada Standards Association, 1997). 6. Risk Assessment in the Federal Government: Managing the Process (US National Research Council, 1983). 7. Understanding Risk: Informing Decisions in a Democratic Society (US National Research Council, 1996). 8. Environmental Health Risk Assessment (enHealth Council of Australia, 2002). 9. A Framework for Ecological Risk Assessment (CCME, 1996). 10. Ecological Risk Assessments of Priority Substances Under the Canadian Environmental Protection Act (Environment Canada, 1996).11. Guidelines for Ecological Risk Assessment (US EPA, 1998b). 12. Proposed Model for Occupational Health Risk Assessment and Management (Rampal & Sadhra, 1999). Based on the extensive review of these frameworks, seven key elements that should be included in a comprehensive framework for human health, ecological, and occupational risk assessment and management were identified: 1. Problem formulation stage. 2. Stakeholder involvement. 3. Communication. 4. Quantitative risk assessment components. 5. Iteration and evaluation. 6. Informed decision making. 7. Flexibility. On the basis of this overarching approach to risk management, the following "checklist" to ensure a good risk management decision is proposed: - Make sure you're solving the right problem. - Consider the problem and the risk within the full context of the situation, using a broad perspective. - Acknowledge, incorporate, and balance the multiple dimensions of risk. - Ensure the highest degree of reliability for all components of the risk management process. - Involve interested and effected parties from the outset of the process. - Commit to honest and open communication between all parties. - Employ continuous evaluation throughout the process (formative, process, and outcome evaluation), and be prepared to change the decision if new information becomes available. Comprehensive and sound principles are critical to providing structure and integrity to risk management frameworks. Guiding principles are intended to provide an ethical grounding for considering the many factors involved in risk management decision making. Ten principles are proposed to guide risk management decision making. The first four principles were adapted and modified from Hattis (1996) along with the addition of two more principles by Hrudey (2000). These have been supplemented by another four principles to make the 10 presented. The principles are based in fundamental ethical principles and values. These principles are intended to be aspirational rather than prescriptive--their application requires flexibility and practical judgement. Risk management is inherently a process in search of balance among competing interests and concerns. Each risk management decision will be "balancing act" of competing priorities, and trade-offs may sometimes have to be made between seemingly conflicting principles. The 10 decision-making principles, with the corresponding ethical principle in italics are: 1. Do more good than harm (beneficence, nonmalificence).- The ultimate goal of good risk management is to prevent or minimize risk, or to "do good" as much as possible. 2. Fair process of decision making (fairness, natural justice). - Risk management must be just, equitable, impartial, unbiased, dispassionate, and objective as far as possible given the circumstances of each situation. 3. Ensure an equitable distribution of risk (equity). - An equitable process of risk management would ensure fair outcomes and equal treatment of all concerned through an equal distribution of benefits and burdens (includes the concept of distributive justice, i.e., equal opportunities for all individuals). 4. Seek optimal use of limited risk management resources (utility). - Optimal risk management demands using limited resources where they will achieve the most risk reduction of overall benefit. 5. Promise no more risk management that can be delivered (honesty).- Unrealistic expectations of risk management can be avoided with honest and candid public accounting of what we know and don't know, and what we can and can't do using risk assessment and risk management. 6. Impose no more risk that you would tolerate yourself (the Golden Rule). - The Golden Rule is important in risk management because it forces decision makers to abandon complete detachment from their decisions so they may understand the perspectives of those affected. 7. Be cautious in the face of uncertainty ("better safe than sorry"). - Risk management must adopt a cautious approach when faced with a potentially serous risk, even if the evidence is uncertain. 8. Foster informed risk decision making for all stakeholders (autonomy). - Fostering autonomous decision making involves both providing people with the opportunity to participate, and full and honest disclosure of all the information required for informed decisions. 9. Risk management processes must be flexible and evolutionary to be open to new knowledge and understanding (evolution, evaluation, iterative process). - The incorporation of new evidence requires that risk management be a flexible, evolutionary, and iterative process, and that evaluation is employed at the beginning and througthout the process. 10. the complete elimination fo risk is not possible (life is not risk free).- Risk is pervasive in our society, and cannot be totally eliminated despite an oft-expressed public desire for "zero risk". However, the level of risk that may ve tolerable by any individual is dependent on values of beliefs, as well as scientific information. Each agency must continue to employ a process that meets the needs of their specific application of risk management. A single approach cannot satisfy the diverse areas to which risk decisions are being applied. However, with increasing experience in the application of the approaches, we are evolving to a common understanding of the essential elements and principles required for successful risk assessment, risk management, and risk communication. Risk management will continue to be a balancing act of competing priorities and needs. Flexibility and good judgement are ultimately the key to successfully making appropriate risk decisions.

Biologically based risk projection models for radiation carcinogenesis seek to describe the fundamental biological processes involved in neoplastic transformation of somatic cells into malignant cancer cells. A validated biologically based model, whose parameters have a direct biological interpretation, can also be used to extrapolate cancer risks to different exposure conditions with some confidence. In this article biologically based models for radiation carcinogenesis, including the two-stage clonal expansion (TSCE) model and its extensions, are reviewed. The biological and mathematical bases for such models are described, and the implications of key model parameters for cancer risk assessment examined. Specific applications of versions of the TSCE model to important epidemiological datasets are discussed, including the Colorado uranium miners' cohort; a cohort of Chinese tin miners; the lifespan cohort of atomic bomb survivors in Hiroshima and Nagasaki; and a cohort of over 200,000 workers included in the National Dose Registry (NDR) of Canada.

The management of potential health risks from electromagnetic (EM) fields presents both scientific and nonscientific challenges. When the scientific evidence is ambiguous, as is the case with EM fields, expert judgment of this evidence becomes particularly important. This article provides biomedical researchers with a comprehensive assessment of the status of EM health risk based on our two previous articles [Parts I and II, Critical Reviews in Biomedical Engineering, Volume 31, Issue 3]. Ambiguous evidence also necessitates rigorous public debate. This article also discusses effective risk communication approaches that play a key role in the EM risk issue. Because of uncertainty about health risks associated with EMF exposure, the public is more likely to experience difficulty in evaluating the available information and rely more on perceptions than facts when drawing conclusions. Even the most effective risk communication approaches are not likely to clarify all of the subtleties surrounding EM fields as a population health issue. Thus it is essential that all stakeholders involved in this issue participate in developing consensus solutions.

The association between daily fluctuations in ambient particulate matter and daily variations in nonaccidental mortality have been extensively investigated. Although it is now widely recognized that such an association exists, the form of the concentration-response model is still in question. Linear, no threshold and linear threshold models have been most commonly examined. In this paper we considered methods to detect and estimate threshold concentrations using time series data of daily mortality rates and air pollution concentrations. Because exposure is measured with error, we also considered the influence of measurement error in distinguishing between these two completing model specifications. The methods were illustrated on a 15-year daily time series of nonaccidental mortality and particulate air pollution data in Toronto, Canada. Nonparametric smoothed representations of the association between mortality and air pollution were adequate to graphically distinguish between these two forms. Weighted nonlinear regression methods for relative risk models were adequate to give nearly unbiased estimates of threshold concentrations even under conditions of extreme exposure measurement error. The uncertainty in the threshold estimates increased with the degree of exposure error. Regression models incorporating threshold concentrations could be clearly distinguished from linear relative risk models in the presence of exposure measurement error. The assumption of a linear model given that a threshold model was the correct form usually resulted in overestimates in the number of averted premature deaths, except for low threshold concentrations and large measurement error.

A fundamental goal of toxicology is to determine safe levels of human exposure to toxic substances. In the absence of information to establish dose-response relationships at low exposure levels generally experienced by humans, high-dose to low-dose linear extrapolation is generally used for estimating carcinogenic risks and the no-observed-adverse-effect-level divided by uncertainty (safety) factors is widely used for establishing human exposure guidelines for noncancer effects. The basis and impact of this dichotomy is examined and questioned. It is proposed that a unified approach be adopted for establishing human exposure guidelines for both cancer and noncancer endpoints. It is suggested that a lower confidence limit on the dose estimated to produce an excess incidence of adverse health effects in 10% of the individuals in a human study or 10% of the animals in laboratory experiments be used as a point-of-departure. This dose would be divided by appropriate uncertainty factors to establish human exposure guidelines. For severe irreversible adverse health effects we suggest a total default uncertainty factor (divisor) for animal data on the order of 10,000, which is comparable to current guidelines. For reversible biological effects a smaller default uncertainty factor on the order of 1000 may be employed. This is comparable to the divisor often used currently when the point-of-departure is the lowest-observed-adverse-effect-level. It is asserted that the toxicological information generally available does not warrant numerical estimates of risk at low levels of human exposure. Rather, we support a unified approach for all adverse health effects of dividing a benchmark dose by appropriate uncertainty factors to establish guidelines for human exposures to toxic substances., (Copyright 1999 Academic Press.)

Safety assessment for noncancer health effects generally has been based upon dividing a no observed adverse effect (NOAEL) by uncertainty (safety) factors to provide an acceptable daily intake (ADI) or reference dose (RfD). Since the NOAEL does not utilize all of the available dose-response data, allows higher ADI from poorer experiments, and may have an unknown, unacceptable level of risk, the benchmark dose (BD) with a specified, controlled low level of risk has become popular as an adjunct to the NOAEL or the low observed adverse effect level (LOAEL) in the safety assessment process. The purpose of this paper is to summarize statistical procedures available for calculating BDs and their confidence limits for noncancer endpoints. Procedures are presented and illustrated for quantal (binary), quasicontinuous (proportion), and continuous data. Quasicontinuous data arise in developmental studies where the measure of an effect for a fetus is quantal (normal or abnormal) but the experimental unit is the mother (litter) so that results can be expressed as the proportion of abnormal fetuses per litter. However, the correlation of effects among fetuses within a litter poses some additional statistical problems. Also, developmental studies usually include some continuous measures, such as fetal body weight or length. With continuous data there generally is not a clear demarcation between normal and adverse measurements. In such cases, extremely high and/or low measurements at some designated percentile(s) can be considered abnormal. Then the probability (risk) of abnormal individuals can be estimated as a function of dose. The procedure for estimating a BD with continuous data is illustrated using neurotoxicity data. When multiple measures of adverse effects are available, a BD can be estimated based on a selected endpoint or the appearance of any combination of endpoints. Multivariate procedures are illustrated using developmental and reproductive toxicity data., (Copyright 1998 Academic Press.)

Quantitative estimates of human health risk are often based on mathematical models fit to experimental or epidemiological data. Recent years have witnessed a trend towards the use of mechanistic models in risk assessment applications. Such models afford a more biologically based interpretation of the data and a firmer scientific basis for extrapolation beyond the conditions under which the original data were obtained. In this article, we review some recent advances in the development of biologically based models for mutagenesis, carcinogenesis and developmental toxicity. Pharmacokinetic and receptor-binding models and their roles in mechanistic risk assessment are also discussed. The future of mechanistic research in risk assessment is contemplated, including the need for more elaborate experiments to obtain the data necessary for mechanistic modeling.

The use of physiologically based pharmacokinetic (PBPK) models has been proposed as a means of estimating the dose of the reactive metabolites of carcinogenic xenobiotics reaching target tissues, thereby affording an opportunity to base estimates of potential cancer risk on tissue dose rather than external levels of exposure. In this article, we demonstrate how a PBPK model can be constructed by specifying mass-balance equations for each physiological compartment included in the model. In general, this leads to a system of nonlinear partial differential equations with which to characterize the compartment system. These equations then can be solved numerically to determine the concentration of metabolites in each compartment as functions of time. In the special case of a linear pharmacokinetic system, we present simple closed-form expressions for the area under the concentration-time curves (AUC) in individual tissue compartments. A general relationship between the AUC in blood and other tissue compartments is also established. These results are of use in identifying those parameters in the models that characterize the integrated tissue dose, and which should therefore be the primary focus of sensitivity analyses. Applications of PBPK modeling for purposes of tissue dosimetry are reviewed, including models developed for methylene chloride, ethylene oxide, 1,4-dioxane, 1-nitropyrene, as well as polychlorinated biphenyls, dioxins, and furans. Special considerations in PBPK modeling related to aging, topical absorption, pregnancy, and mixed exposures are discussed. The linkage between pharmacokinetic models used for tissue dosimetry and pharmacodynamic models for neoplastic transformation of stem cells in the target tissue is explored.

In this article, the operating characteristics of recently proposed tests for trend in correlated binary data arising in laboratory studies of developmental toxicity are examined using both computer-generated and experimental data. Specifically, we consider adjusted Cochran-Armitge tests based on the Rao-Scott transformation which are of the same general form as that for uncorrelated data. In addition, generalized score tests based on generalized estimating equations allowing for extrabinomial variation in the data are discussed. Specific forms of these statistics demonstrating favorable type I and type II error rates are identified and recommended for use in practice. The application of these tests is illustrated using data from studies of developmental toxicity that have been reported in the literature.

Reproductive and developmental anomalies induced by toxic chemicals may be identified using laboratory experiments with small mammalian species such as rats, mice, and rabbits. In this paper, dose-response models for correlated multinomial data arising in studies of developmental toxicity are discussed. These models provide a joint characterization of dose-response relationships for both embryolethality and teratogenicity. Generalized estimating equations are used for model fitting, incorporating overdispersion relative to the multinomial variation due to correlation among littermates. The fitted dose-response models are used to estimate benchmark doses in a series of experiments conducted by the U.S. National Toxicology Program. Joint analysis of prenatal death and fetal malformation using an extended Dirichlet-trinomial covariance function to characterize overdispersion appears to have statistical and computational advantages over separate analysis of these two end points. Benchmark doses based on overall toxicity are below the minimum of those for prenatal death and fetal malformation and may, thus, be preferred for risk assessment purposes.

Blood and urine are historically the most frequent matrices used for measuring chemical levels in human biomonitoring studies. As biomonitoring programs are refreshed, consideration of specific priority substances and specific population targets provide opportunities for inclusion of alternative non- or minimally invasive matrices. This review describes methods used in health risk assessment to characterize exposure and risk based upon biomarkers from noninvasive matrices other than urine or blood, including human milk, hair, fingernails, toenails, exhaled breath, deciduous teeth, sweat, semen, meconium, and feces. Illustrative examples of these methods relevant to chemical management are provided. This review suggests that, although these alternative noninvasive biomarkers are not frequently used in human health risk assessment at present, these biomarkers may prove useful in (1) characterizing exposure and health risk in vulnerable populations, (2) cumulative risk assessments, and (3) community-based risk assessments, depending upon the substance of concern. To incorporate alternative noninvasive biomarkers into human health risk assessments with confidence, more research is needed to improve our knowledge of the relationships between external dose, internal dose, and biologic consequent effects in matrices other than blood and urine. [ABSTRACT FROM AUTHOR]

Deliberations over chemical safety rely heavily upon the interpretation of toxicological and epidemiological evidence. Dose-response modeling plays an important role, allowing true effects to be more effectively discerned from background processes (qualitative determinations) and playing an even more instrumental role in quantitative judgments (e.g., the estimation of potency or acceptable dose). We survey five relatively distinct topic areas (ranging from the assessment of genotoxicity, to epidemiological studies of respirable particles) where dose-response models have been applied, and explore the varying degrees of complexity used in modeling. We choose a descriptive approach, presenting each topic area as a case study. The survey reveals a wide spectrum of complexity both within and between topic areas. The question of 'what level of model detail is appropriate?' is widely debated in various disciplines. Notably, the policy context of dose-response assessment (with its attendant real-world stakes) involves some added considerations. We therefore try to emphasize the role of each modeling application's outcome in affecting a decision process. The case studies provide a first step in identifying issues related to model parsimony that are unique to the dose-response, and policy realm. [ABSTRACT FROM AUTHOR]

Estimates were made of the numbers of liver carcinogens in 390 long-term bioassays conducted by the National Toxicology Program (NTP). These estimates were obtained from examination of the global pattern ofp-values obtained from statistical tests applied to individual bioassays. Representative estimates of the number of liver carcinogens (90% confidence interval in parentheses) obtained in our analysis compared to NTP's determination are as follows: female rats-49 (23, 76), NTP = 30; male rats-88 (59, 116), NTP = 35; female mice-131 (105, 157),NTP = 81; male mice-100 (73, 126), NTP = 61; overall-166 (135, 197),NTP = 108. The estimator from which these estimates were obtained isbiased low by an unknown amount. Consequently, this study provides persuasive evidence of the existence of more rodent liver carcinogens than were identified by the NTP. [ABSTRACT FROM AUTHOR]

Reproductive and developmental anomalies induced by toxic chemicals may be identified using laboratory experiments with small mammalian species such as rats, mice, and rabbits. In this paper, dose-response models for correlated multinomial data arising in studies of developmental toxicity are discussed. These models provide a joint characterization of dose-response relationships for both embryolethality and teratogenicity. Generalized estimating equations are used for model fitting, incorporating overdispersion relative to the multinomial variation due to correlation among littermates. The fitted dose-response models are used to estimate benchmark doses in a series of experiments conducted by the U.S. National Toxicology Program. Joint analysis of prenatal death and fetal malformation using an extended Dirichlet-trinomial covariance function to characterize overdispersion appears to have statistical and computational advantages over separate analysis of these two end points. Benchmark doses based on overall toxicity are below the minimum of those for prenatal death and fetal malformation and may, thus, be preferred for risk assessment purposes. [ABSTRACT FROM AUTHOR]