Subdivision of Uncertainty Factors to Allow for Toxicokinetics and Toxicodynamics

Tenfold uncertainty factors have been used in risk assessment for about 40 years to allow for species differences and inter-individual variability. Each factor has to allow for toxicokinetic and toxicodynamic differences. Subdividing the 10-fold factors into kinetic and dynamic defaults, which when multiplied give a product of 10, offers a number of advantages. A major advantage is that chemical-specific data can be introduced to replace one or more of the default subfactors, hence contributing to a chemical-related overall factor. Subdivision of the 10-fold factors also facilitates analysis of the appropriateness of the overall 10-fold defaults, and the development of a more refined approach to the use of uncertainty factors.     

Boron tolerable intake

Boron, which is ubiquitous in the environment, causes developmental and reproductive effects in experimental animals. This observation has led to efforts to establish a Tolerable Intake value for boron. Although risk assessors agree on the use of fetal weight decreases observed in rats as an appropriate critical effect, consensus on the adequacy of toxicokinetic data as a basis for replacement of default uncertainty factors remains to be reached. A critical analysis of the existing data on boron toxicokinetics was conducted to clarify the appropriateness of replacing default uncertainty factors (10-fold for interspecies differences and 10-fold for intraspecies differences) with data-derived values. The default uncertainty factor for variability in response from animals to humans of 10-fold (default values of 4-fold for kinetics and 2.5-fold for dynamics) was recommended, since clearance of boron is 3-to 4-fold higher in rats than in humans and data on dynamic differences—in order to modify the default value—are unavailable. A data-derived adjustment of 6-fold (1.8 for kinetics and 3.1 for dynamics) rather than the default uncertainty factor of 10-fold was considered appropriate for intrahuman variability, based on variability in glomerular filtration rate during pregnancy in humans and the lack of available data on dynamic differences. Additional studies to investigate the toxicokinetics of boron in rats would be useful to provide a stronger basis for replacement of default uncertainty factors for interspecies variation.     

Establishing Data-Derived Adjustment Factors from Published Pharmaceutical Clinical Trial Data

In non-cancer risk assessment the goal traditionally has been to protect the majority of people by setting limits that account for interindividual variability in the human population. The Environmental Protection Agency (EPA) has assigned a default uncertainty factor (?UF) of 10 to account for interindividual variability in response to toxic agents in the general population. Previous studies have suggested that it is appropriate to equally divide this factor into sub-factors of 3.2 each for variability in human pharmacokinetics (PK) and pharmacodynamics (PD). As an extension of this model, one can envision using scientific data from the literature to modify the default sub-factors with compound-specific adjustment factors (AFs) and to create new and more scientifically based defaults. In this paper, data from published clinical trials on six pharmaceutical compounds were used to further illustrate how to calculate and interpret data-derived AFs. The clinical trial data were analyzed for content and the reported mean and standard deviation values for two key PK parameters, area under the curve of blood concentration by time (AUC) and peak plasma concentration (C_max), were evaluated. The mean PK values for each study were subsequently analyzed for variability within the population (unimodal distributions) and for the presence of potentially susceptible sub-populations (bimodal distributions). A method based on the proportion of the population covered was applied and data-derived AFs were calculated for these six compounds. Our results showed that, of the 15 possible data-derived AFs calculated using unimodal and bimodal distributions, only three exceeded a value of 3.2. This study further illustrates the value of calculating data-derived values when sufficient PK data are available.     

Intra-Species Extrapolation in Risk Assessment of Different Classes of Antimicrobials

The risk assessment process for non-carcinogens incorporates all available scientific information, including toxicokinetic and toxicodynamic data. A 10-fold uncertainty factor (UF) is most commonly used to account for underlying variability within the human species. The purposes of this investigation are to evaluate whether the magnitude of the 10X-UF can be reduced when pharmacokinetic and pharma-codynamic data are incorporated to characterize interindividual variability and whether another UF is needed for the children group. An extensive literature search was conducted on seven antimicrobials in order to incorporate information on kinetics and dynamics to allow extrapolation among susceptible humans. The drugs are cefaclor, cefuroxime, erythromycin, clarithromycin, ampicillin, gentamicin and amikacin. The composite factor was calculated using the highest ratio for appropriate parameters and default subfactor. According to the data, we concluded that when relevant kinetic and dynamic data are available, replacing the default factors with actual data-derived values was possible for the antimicrobials evaluated and that there is no need to add another UF to the children group.     

Case Studies of Categorical Data-Derived Adjustment Factors

Investigations were performed on representative compounds from five different therapeutic classes to evaluate the use of categorical data-derived adjustment factors to account for interindividual variability. The five classes included antidepressants, angiotensin converting enzyme (ACE) inhibitors, nonsteroidal anti-inflammatory drugs (NSAIDS), cholesterol lowering agents, and antibiotics. Each of the case studies summarized the mode of action of the class responsible for both the therapeutic and adverse effects and the key pharmacodynamic (PD) and pharmacokinetic (PK) parameters that determine the likelihood of these responses for individual compounds in the class. For each class, an attempt was made to identify the key factors that determine interindividual variability and whether there was a common basis to establish a categorical default adjustment factor that could be applied across the class (or at least across specific subclasses within the class). Linking the PK and PD parameters to the critical endpoint used to establish a safe level of exposure was an important underlying theme throughout the investigations. Despite the wealth of PK and PD information in the published literature on the surrogate compounds representing these classes, it was difficult to derive a categorical adjustment factor that could be applied broadly within each class. The amount of information available may have hindered rather than helped the evaluations. Derivation of categorical defaults for different classes of “common” chemicals may be more straightforward if sufficient data are available. In a few cases (e.g., tricyclic antibiotics, ACE inhibitors and selected antiinflammatory agents) categorical defaults could be proposed, although it is unclear whether the reduction in uncertainty resulting from their application would be offset by the additional uncertainties that may have resulted from their application. Residual uncertainties may remain depending on the level of confidence in the underlying assumptions used to support the categorical defaults. Regardless of the conclusions on the utility of categorical defaults, these investigations provided further support for the use of data-derived adjustment factors on a compound-specific basis.     

Default Factors for Interspecies Differences in the Major Routes of Xenobiotic Elimination

For the risk to human health posed by chemicals that show threshold toxicity there is an increasing need to move away from using the default approaches, which inherently incorporate uncertainty, towards more biologically defensible risk assessments. However, most chemical databases do not contain data of sufficient quantity or quality that can be used to replace either the interspecies or interindividual aspects of toxicokinetic and toxicodynamic uncertainty. The purpose of the current analysis was to evaluate the use of alternative, species-specific, pathway-related, “categorical” default values to replace the current interspecies toxicokinetic default uncertainty factor of 4.0. The extent of the difference in the internal dose of a compound, for each test species, could then be related to the specific route of metabolism in humans. This refinement would allow for different categories of defaults to be used, providing that the metabolic fate of a toxicant was known in humans. Interspecies differences in metabolism, excretion, and bioavailability have been compared for probe substrates for four different human xenobiotic-metabolizing enzymes: CYP1A2 (caffeine, paraxanthine, theobromine, and theophylline), CYP3A4 (lidocaine), UDP-glucuronyltransferase (AZT), and esterases (aspirin). The results of this analysis showed that there are significant differences between humans and the four test species in the metabolic fate of the probe compounds, the enzymes involved, the route of excretion and oral bioavailability — all of which are factors that can influence the extent of the difference between humans and a test species in the internal dose of a toxicant. The wide variability between both compounds and the individual species suggests that the categorical approach for species differences may be of limited use in refining the current default approach. However, future work to incorporate a wider database of compounds that are metabolized extensively by any pathway in humans to provide more information on the extent to which the different test species are not covered by the default of 4.0. Ultimately this work supports the necessity to remove the uncertainty from the risk assessment process by the generation and use of compound-specific data.     

The Use of Kinetic and Dynamic Data in Risk Assessment of Drugs

The risk assessment process for non-carcinogens must incorporate all available scientific information, including toxicokinetic and toxicodynamic data. The framework for exposure limit setting proposed by Renwick and the International Programme on Chemical Safety (IPCS) subdivides traditional 10X uncertainty factors (UFs) into separate partial-log default values based on kinetic and dynamic considerations and allows for incorporation of compound-specific data when available. In this investigation, an extensive literature search was conducted on nine pharmaceuticals in order to incorporate information on kinetics and dynamics to allow extrapolation across species and among susceptible humans. The drugs are diazepam, oxazepam, midazolam, buspirone, fluoxetine, venlafaxine, amlodipine, felodipine, and nifedipine. The composite factors were calculated using the highest ratio or the average ratio for appropriate parameters and default subfactor. For the drugs examined, most of the subfactors for kinetics and dynamics were less than the proposed values by Renwick and IPCS, and the composite factors were far less than 100. From this study, it was concluded that relevant compound-specific kinetic and dynamic data can reduce uncertainties associated with interspecies differences and interindividual variability.     

Rationale for the Chemical-Specific Adjustment Factors Used to Derive an Occupational Exposure Limit for Timolol Maleate

Timolol maleate is a non-selective beta-adrenergic blocking agent currently used primarily to reduce intraocular pressure in the treatment of glaucoma. It also produces effects on the heart and bronchial smooth muscle and all of these effects are of potential concern in workers handling this active pharmaceutical ingredient. The disposition of timolol maleate is influenced by a polymorphism in oxidative metabolism by CYP2D6 and two distinct phenotypes have been identified (i.e., poor and extensive metabolizers). These properties of timolol maleate provided an opportunity to use the compound as a case study to demonstrate the derivation of chemical-specific adjustment factors for pharmacokinetics and pharmacodynamics to replace the default uncertainty factor for interindividual variability. Overall, the available data on the pharmacodynamic endpoints showed very little variability and most pharmacokinetic studies failed to discern significant differences in relatively small groups of healthy volunteers or patients. Reports of bradycardia and bronchoconstriction in patients receiving therapeutic doses are relatively rare. In one study, there was a significant reduction in heart rate 24 hours post-dose that was associated with elevated area under the curve (AUC) values. A chemical-specific adjustment factor (CSAF) for kinetics of 9.8 based on these AUC data was combined with a CSAF for dynamics of 1.2 and applied to the extrapolated no-effect level for clinically significant cardiovascular effects (with correction for oral bioavailability) to establish an occupational exposure limit (OEL) for timolol maleate which is expected to be protective of workers that may be poor metabolizers or asthmatics.     

Scientific basis for uncertainty factors used to establish occupational exposure limits for pharmaceutical active ingredients

The traditional “safety factor”; method has been used for years to establish occupational exposure limits (OELs) for active ingredients used in drugs. In the past, a single safety factor was used to address all sources of uncertainty in the limit setting process. The traditional 100‐fold safety factor commonly used to derive an acceptable daily intake value incorporates a default factor of 10 each to account for interindividual variability and interspecies extrapolation. Use of these defaults can lead to overly conservative health‐based limits, especially when they are combined with other (up to 10‐fold) factors to adjust for inadequacies in the available database. In recent years, attempts have been made to quantitate individual sources of uncertainty and variability to improve the scientific basis for OELs. In this paper we discuss the science supporting reductions in the traditional default uncertainty factors. A number of workplace‐specific factors also support reductions in these factors. Recently proposed alternative methodologies provide a framework to make maximum use of preclinical and clinical information, e.g., toxicokinetic and toxicodynamic data, to reduce uncertainties when establishing OELs for pharmaceutical active ingredients.     

Categorical Default Uncertainty Factors-Interspecies Variation and Adequacy of Database

The potential application of categorical (i.e., species, pathway, or group specific) defaults for several components of uncertainty relevant to development of tolerable or reference concentrations/doses is considered-namely, interspecies variation and adequacy of database. For the former, the adequacy of allometric scaling by body surface area as a species-specific default for oral tolerable or reference doses is considered. For the latter, the extent to which data from analyses of subchronic:chronic effect levels, LOAELs/NOAELs, and critical effect levels for complete versus incomplete datasets informs selection of defaults is examined. The relative role of categorical defaults for these aspects is considered in the context of the continuum of increasingly data-informed approaches to characterization of uncertainty and variability that range from default (“presumed protective”) to “biologically based predictive”.     

Uncertainty/Safety Factors in Health Risk Assessment: Opportunities for Improvement

This paper presents the results of deliberations from participants who met on the second day of the Fourth Annual Workshop on the Evaluation of Uncertainty/Safety Factors in Health Risk Assessment. The group reviewed the previous day's presentations and implications for improvement in risk assessment. After much discussion, the group concluded that, in the short term, significant improvements could be made in the pharmacokinetic component of the inter-species uncertainty factor and developed a series of default options for this factor. These defaults consider route of exposure (oral or inhalation), and the form of the active compound (parent, metabolite, or very reactive metabolite). Several assumptions are key to this approach, such as a similar oral or inhalation bioavailability across species. We believe this method represents a useful default approach until more compound-specific information is available.     

Relevance of the 10X Uncertainty Factor to the Risk Assessment of Drugs Used by Children and Geriatrics

Conventional risk assessment practices utilize a tenfold uncertainty factor (UF) to extrapolate from the general human population to sensitive subgroups, such as children and geriatrics. This study evaluated whether the tenfold UF can be reduced when pharmacokinetic and pharmacodynamic data for pharmaceuticals used by children and geriatrics are incorporated into the risk assessment for human sensitivity. Composite factors (kinetics X dynamics) were calculated from data-derived values for bumetanide, furosemide, metoprolol, atenolol, naproxen, and ibuprofen. For the compounds examined, all of the composite factors were lower than 10. Furthermore, 8 of the 12 composite factors were less than 5.5. Incorporation of human kinetic and dynamic data into risk assessment can aid in reducing the uncertainties associated with sensitive subgroups and further study is encouraged.     

Data-Derived Uncertainty Factors: Boric Acid (BA) as a Case Study

There is growing support for the use of data-derived uncertainty factors. In recent years, risk assessments of boric acid have been performed by several well-respected organizations, including IEHR, ECETOC, IPCS, and WHO. For each, the pivotal study was a developmental toxicity study in rats with a no-observed-adverse-effect level (NOAEL) of 55 mg BA/kg/day. These risk assessments employed reduced uncertainty factors in the range of 25 to 60 for boric acid, because available pharmacokinetic data for boric acid reduced uncertainty in evaluating the overall data base with this compound. However, a limitation of previous risk assessments was the absence of specific data on the renal clearance of boric acid in pregnant rats and pregnant women. New data has demonstrated that when renal clearance was normalized to body weight (ml/min/kg), pregnant rats cleared boric acid at a rate roughly three times greater than pregnant women. In addition, the boric acid specific allometric relationship was determined from the log-log plot of clearance vs. body weight. Based on the new renal clearance data, it was estimated that pregnant women and rats would have the same AUC when pregnant women are given 30% of the boric acid dose given to pregnant rats. In addition, the renal clearance of boric acid among pregnant women varied by a factor of about 2. Therefore, boric acid-specific data on renal clearance in pregnant women and rats supports reduced interspecies and intraspecies pharmacokinetic uncertainty factors of approximately 3 and 2, respectively. Further, growing evidence of the essentiality in animals, combined with consistency of effects among species in toxicity studies, suggests a reduced pharmacodynamic uncertainty factor is appropriate for boric acid. Total uncertainty factors in the range of 22 to 44 are scientifically justified for this compound. An acceptable daily intake of 1.25 to 2.5 mg BA/kg/day is estimated by applying an uncertainty factor of 22 to 44 to the NOAEL of 55 mg BA/kg/day. Data-derived uncertainty factors should be used whenever possible, and they should be determined and applied in a consistent manner. Ultimately, estimates based on target tissue dose-adjusted relationships should offer a better approach to risk assessment.     

Evaluating the Relationship Between Variance in Enzyme Expression and Toxicant Concentration in Health Risk Assessment

The replacement of default uncertainty factors with those based on chemical-specific data is a topic of interest to a growing number of government-based organizations and those in affiliated professional societies. The division of the uncertainty factors for animal-to-human extrapolation and human interindividual variance (UF_A and UF_H, respectively) into their pharmacodynamic (PD) and pharmacokinetic (PK) components invites additional and specific considerations. Where data are available, or substantiated PK models have been developed, the animal-to-human chemical-specific differences have been quantified and utilized to replace the PK component of the uncertainty factor. The increasing degree to which the genome is being characterized has stimulated additional interest in describing the impact of genetic polymorphisms on susceptibility. Frequently, proteins for which the genes are being evaluated are the group of xenobiotic metabolizing enzymes. In-depth understanding of the genetic polymorphisms of genes coding for Aldehyde dehydrogenase, glucuronyl transferase and cytochrome P450 enzyme forms has been combined with information on the bioactivation or detoxication of environmental contaminants. The preliminary conclusion of some of these considerations is that alterations in enzyme content or enzyme activity result in a de facto alteration of risk. While this may be true of the “all-or-none” genetic alterations, the impact of more subtle changes in enzyme content and/or activity are more difficult to predict. The hepatotoxicity of trichloroethylene (TCE) is dependent upon an initial, cytochrome P450 2E1 (CYP2E1)-mediated oxidative step. Variance of CYP2E1 content of human liver has been characterized from a bank of tissues from human organ donors and combined with data describing the in vitro Michaelis-Menten kinetic parameters in order to extrapolate the metabolic capacity (and variance thereof) from in vitro to in vivo and assess its impact on PK through incorporation in a physiologically based pharmacokinetic (PBPK) model. This presentation summarizes that work, and demonstrates and discusses why extremes of CYP2E1-mediated metabolic capacity in adult humans has virtually no impact on the PK metric most closely related to hepatotoxic injury from TCE exposure.     

Replacing the Default Values of 10 With Data-Derived Values: A Comparison of Two Different Data-Derived Uncertainty Factors for Boron

A “safe” or sub-threshold dose is often estimated for oral toxicity of substances in order to protect humans from adverse health effects. This dose is referred to by several terms: reference dose (RfD), tolerable daily intake (TDI), and acceptable daily intake (ADI). Similarly, tolerable concentration (TC), and reference concentration (RfC) are commonly used terms for a “safe” concentration for inhalation. The process of deriving these doses generally involves identifying a no observed, or lowest observed adverse effect level (NOAEL or LOAEL) in animals, or humans, and application of uncertainty factors to account for the extrapolation from laboratory animals to humans and/or from an average human to a sensitive human. Public health agencies have begun to consider using a data derived approach, which uses available toxicokinetic and toxicodynamic data in the determination of uncertainty factors, rather than relying on the standard default values. Recently two different tolerable daily intake risk values were derived by two different World Health Organization (WHO) work groups. The International Programme on Chemical Safety, and the Working Group on Chemical Substances in Drinking Water both used the approach developed by Renwick (1993); however, the two groups interpreted and used the available data differently. The result was a difference of over twofold in the total uncertainty factor used. This review compares and contrasts the two approaches used by these WHO work groups.     

Pesticide ecotoxicological effect factors and their uncertainties for freshwater ecosystems

Background, aim, and scope  Characterization factors for ecotoxicity in the Life Cycle Impact Assessment (LCIA) are used to convert emissions into ecotoxicological impacts. Deriving them involves a fate and an effect analysis step. The fate factor quantifies the change in environmental concentration per unit of emission, while the effect factor quantifies the change in impact on the ecosystem per unit of environmental concentration. This paper calculates freshwater ecotoxicological effect factors for 397 pesticides belonging to 11 pesticide-specific toxic modes of action (TMoA), such as acetylcholinesterase inhibition and photosynthesis inhibition. Moreover, uncertainties in the effect factors due to uncertain background concentrations and due to limited toxicity data are quantified. Methods  To calculate median ecotoxicological effect factors (EEFs), toxic pressure assessments were made, based on the species sensitivity distribution—and the multisubstance potentially affected fraction—concept. The EEF quantifies an estimate of the fraction of species that is probably affected due to a marginal change in concentration of a pesticide. EEFs were divided into a TMoA-specific and a chemical-specific part, which were calculated on the basis of physicochemical properties, emissions, and toxicity data. Propagation of parameter uncertainty in the EEFs and the TMoA- and chemical-specific parts was quantified by Monte Carlo simulation and results were reported as 90% confidence intervals. Results  Median EEFs range from 2·10⁻³ to 7·10⁶ l/g. Uncertainty in the TMoA-specific part is dominated by uncertainty in the TMoA-specific spread in species sensitivity and by uncertainty in the effective toxicity of a TMoA. Uncertainty in the chemical-specific part of the EEFs depends on the number of species for which toxicity data are available to calculate average toxicity (n _s) and ranges from a median uncertainty of 2.6 orders of magnitude for n _s = 2 to one order of magnitude for n _s ≥ 4. The TMoA-specific effect factor for systemic fungicides shows the largest uncertainty range. For seven TMoAs, uncertainty ranges of the TMoA-specific effect factor are less than two orders of magnitude. For the other four TMoAs, the EEF uncertainty range is between two and eight orders of magnitude. For the chemical-specific part of the EEFs, we found that variation in uncertainty readily decreases for pesticides for which toxicity data are available for at least three species. Discussion  The same parameters that contributed most to uncertainty were found for pesticides as were found before for high-production-volume chemicals. However, uncertainty in concentrations of pesticides was lower. TMoA-specific factors obtained with the applied nonlinear method differ up to nine orders of magnitude from the factor of 0.5, which is used in the linear method. With the applied method, a distinction in EEFs can be made among different TMoAs. Conclusions   Ecotoxicological effect factors are presented, including overviews of their uncertainty ranges and the main contributors to uncertainty. The applied nonlinear method provides the possibility to quantify parameter uncertainty in the TMoA-specific part of the ecotoxicological effect factor, which is helpful to get more insight in how uncertainty in ecotoxicological characterization factors can be reduced. Recommendations and perspectives  The calculated uncertainty ranges can be included in life cycle assessment (LCA) case studies, which allows for better interpretation of LCA results obtained with the EEFs. To put the uncertainty in effect factors into perspective within LCIA, more information on the uncertainty in fate factors should be derived. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Comprehensive Realism's Weight-of-Evidence Based Distributional Dose-Response Characterization

Challenges to low-dose linearity and other default assumptions in cancer risk assessment and the limitations associated with NOAELs, LOAELs, and constant uncertainty factor values in the evaluation of noncancer health effects have stimulated the continued evolution of risk assessment methodologies. The increasing need for more realistic estimates of the dose-response relationship, better uncertainty characterization, and greater utilization of cost-benefit analyses have also contributed to this evolution. “Comprehensive Realism” is an emerging quantitative weight-of-evidence based risk assessment methodology for both cancer and noncancer health effects which utilizes probability distributions and decision analysis techniques to reflect more of the relevant human exposure data, more of the available and pertinent human and animal dose-response data, and the current state of knowledge about the relative plausibility of alternative dose-response analyses. A tree (like a decision tree and a probability tree) is used to decompose the dose-response assessment into component factors, to provide a structure for explicitly considering multiple alternatives for each factor, and to explicitly incorporate the current state of knowledge about the relative plausibility of these alternatives. Groundbreaking work has demonstrated the feasibility of weight-of-evidence based distributional characterizations, and provided initial examples. Computer software implementations are also available.     

Evaluating the 10X Uncertainty Factor for Children and Elderly with Data-Derived Values for Neuromuscular Blocking Agents

Conventional risk assessment process utilizes a 10-fold uncertainty factor (UF) to extrapolate from the general human population to sensitive subgroups, such as children and elderly. The purpose of this investigation was to evaluate whether the magnitude of the 10X-UF can be reduced when pharmacokinetic and pharmacody-namic data are incorporated to characterize human sensitivity. An extensive literature search was conducted on seven neuromuscular blocking agents (mivacurium, atracurium, rocuronium, vecuronium, doxacurium, pancuronium, pipecuronium). Composite factors (kinetics × dynamics) were calculated using the highest data-derived kinetic and dynamic values. For the drugs examined, all of the composite factors for the sensitivity of children were lower than 5. In the elderly, all of the composite factors were lower than 10, and five of seven composite factors were less than 5. From this study, it was concluded that relevant compound-specific kinetic and dynamic data can reduce the uncertainties associated with sensitive subgroups.     

Inter- and Intra-Species Extrapolation in Risk Assessment of Different Classes of Pesticides

Risk assessors routinely use the reference dose (RfD) approach for non-cancer risk assessment. In this approach, No-Observed-Adverse-Effect-Level (NOAEL) is divided by the product of uncertainty factors (UFs) and, occasionally, an additional modifying factor (MF), each usually employed by default as factors of 10. In the present investigation, kinetic and dynamic data have been used in order to reduce uncertainties when establishing exposure guidelines for examples of chemicals representing four classes of pesticides (warfarin, lindane, carbaryl and parathion). An intensive search of databases was conducted for these pesticides, and toxicokinetic and toxicodynamic parameters in inter- and intra-species were evaluated. The kinetic and dynamic subfactors were less than the proposed values of Renwick and the International Programme on Chemical Safety (IPCS). The composite factors for all the examined pesticides were less than 100. The present study indicated that in setting exposure levels it is important to incorporate kinetic and dynamic data, as they become available, rather than rely on default uncertainty factors, which are imprecise in many cases.     

Methods for Identifying a Default Cross-Species Scaling Factor

The need to identify “toxicologically equivalent” doses across different species is a major issue in toxicology and risk assessment. In this article, we describe an approach for establishing default cross-species extrapolation factors used to scale oral doses across species for non-carcinogenic endpoints. This work represents part of an on-going effort to harmonize the way animal data are evaluated for carcinogenic and non-carcinogenic endpoints. In addition to considering default scaling factors, we also discuss how chemical-specific data (e.g., metabolic or mechanistic data) can be incorporated into the dose extrapolation process. After first examining the required properties of a default scaling methodology, we consider scaling approaches based on empirical relationships observed for particular classes of compounds and also more theoretical approaches based on general physiological principles (i.e, allometry). The available data suggest that the empirical and allometric approaches each provide support for the idea that toxicological risks are approximately equal when daily oral doses are proportional to body weight raised to the 3/4-power. We also discuss specific challenges for dose scaling related to different routes of exposure, acute versus chronic toxicity, and extrapolations related to particular life stages (e.g., childhood).