Pathway-Related Factors: The Potential for Human Data to Improve the Scientific Basis of Risk Assessment

The default uncertainty factors used for risk assessment are applied either to allow for different aspects of extrapolation of the dose-response curve or to allow for database deficiencies. Replacement of toxicokinetic or toxicodynamics defaults by chemical-specific data allows the calculation of a chemical-specific “data-derived factor”, which is the product of chemical-specific values and default uncertainty factors. Such chemical-specific composite values will improve the scientific basis of the risk assessment of that chemical, but the necessary chemical-specific data are rarely available. Categorical defaults related to pathways of elimination and mechanisms of toxicity could be used when the overall fate or mechanism is known, but there are no chemical-specific data sufficient to allow replacement of the default, and the development of an overall data-derived factor. The development of pathway-related categorical defaults is being undertaken using data on selected probe substrates for which adequate data are available. The concept and difficulties of this approach are illustrated using data for CYP1A2.     

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.     

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.     

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.     

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.     

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.     

Studies on the use of uncertainty factors in deriving RfDs

Risk assessment of exposure to chemicals having a toxic end point routinely uses the reference dose (RfD) approach based on uncertainty factors of 10. RfD model can be used with widely different databases. However, the quality of individual risk assessment is unequal among chemicals, often resulting in either an over‐ or underestimation of adverse health risk. The purpose of this investigation was to evaluate whether the magnitude of the 10X uncertainty factors has scientific merit against data from recent human and animal experimental studies. Although we assessed the use of uncertainty factors for representative chemicals from various classes of compounds, such as volatile organics, alcohols, gasoline components, and pesticides, we are presenting our findings for 24 chemicals.

A compilation and comparison of ratios between LOAEL/NOAEL (Lowest Observed Adverse Effect Level/No Observed Adverse Effect Level), and subchronic/chronic values were made. Although a 10X uncertainty factor is most commonly used in the risk assessment processes, an examination of the datasets which have been used to calculate RfDs suggests different values which are scientifically justifiable.     

Determining “safe” levels of exposure: The validity of the use of 10X safety factors

The current guideline for risk assessment of chemicals having a toxic end point routinely uses the reference dose (RfD) approach based on uncertainty factors of 10. With this method the quality of individual risk assessment varies among chemicals, often resulting in either an over‐ or under‐estimation of adverse health risk. The purpose of this investigation is to evaluate whether the magnitude of the 10X uncertainty factors have scientific merit against data from published experimental studies. A compilation and comparison of ratios between LOAEL/NOAEL (Lowest Observed Adverse Effect Level/No Observed Adverse Effect Level), subchronic/chronic, and animal/human values were made. The results of the present investigation revealed that the use of default factors could be over‐conservative or unprotective. More reasonable estimates of the risk to human health would result in a reduction of unnecessary, and expensive over‐regulation. In addition to the LOAEL to NOAEL, and subchronic to chronic ratios, the adequacy of uncertainty factors for animal to human extrapolations were examined. Although a 10‐fold uncertainty factor (UF) is most commonly used in the risk assessment process, an examination of the literature for the compounds presented here suggests that the use of different values is scientifically justifiable.     

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.     

Uncertainty in life cycle impact assessment of toxic releases practical experiences - arguments for a reductionalistic approach?

This paper describes the experience with impact assessment of toxic releases in a Substance Flow Analysis (SFA) for PVC in Sweden. For this system, all emissions related to the PVC-chain were inventoried. They have been evaluated making use of the Life Cycle Impact Assessment (LCIA) step from the CMI.-guide, including the new toxicity equivalency factors calculated with the Uniform System for Evaluation of Substances (USES). The application of this method led to the conclusion that I.CA Impact Assessment of toxic releases is still a major bottleneck: the USES-equivalency factors are not to he trusted due to outdated data, inappropriate defaults, etc. in the USES’ substance properties database. Therefore, a second USES-ser of factors was calculated that differed up to factors of 1,000 or more from the old ones. Even these factors probably suffer from unacceptable high structural, in practice not reducible uncertainties. In conclusion, we warn the LCA community not to overestimate the possibility of LCA Impact Assessment to obtain a meaningfull priority setting with regard to toxicity problems. Instead, we propose developing indicator systems for LCIA of toxic releases that genuinely deal with all relevant types of uncertainty: data uncertainty, modelling uncertainty and particularly paradigmatic uncertainty.     

Selecting a more realistic uncertainty factor: Reducing compounding effects of multiple uncertainties

Available toxicology datasets provide a unique opportunity to validate some of the currently used Uncertainty Factors in the development of acceptable exposure levels for noncancer effects. Toxicity studies from two separate sources, the FAO/WHO database on pesticides (1978–1987) and the Monsanto database (through 1988) were chosen to evaluate three of the five currently used Uncertainty Factors. Interspecies differences in NOELs between the three mammalian species evaluated are equal to or less than a factor of 10 for both the FAO/WHO data and the Monsanto data in greater than 90% of the cases evaluated. Median values for the comparison of interspecies NOELs were 3.0 or less for all comparisons except the comparison between the mouse and rat for the Monsanto dataset where the median value was 7.5. Analyses of the Monsanto toxicity database show that the reprotoxicity NOELs were always equivalent to or higher than the chronic or subchronic NOELs for the same material. Therefore, even without conduct of a specific study to address reproductive effects, reasonable protection from adverse reproductive effects can be afforded by use of either subchronic or chronic study NOELs without application of UFD. The median ratio of subchronic NOELs and chronic NOELs was 4, and for a majority of the studies the difference between the NOELs was within one order of magnitude. Our analysis aids in validating the assumption that the upperbound for individual uncertainties maybe accounted for by use of 10‐fold uncertainty factors. However, the current U.S. Environmental Protection Agency (USEPA) reference doses/concentrations may be overly conservative because upperbounds of each of the uncertainty factors are used and each of the uncertainty factors are considered to be independent variables. Because uncertainties are probably not independent variables, the influence of compounding upperbounds when multiple uncertainty factors are used is generally only considered when four or more areas of uncertainty are outstanding. When multiple uncertainties exist, we recommend upperbound estimates only be used for the first two Uncertainty Factors, and median values be used to account for the remaining uncertainties.     

Separating Pharmacokinetic and Pharmacodynamic Components in Empirical Adjustment Factor Distributions

For a particular chemical, one can treat the chemical-by-chemical variation in relative doses for equal toxicity in experimental animals and humans as a characterization of the likelihoods of extrapolation factors of different magnitudes. An emerging approach to noncancer risk assessment is to use such empirical distributions in place of fixed Uncertainty Factors. This paper discusses dividing the overall variation into two sub-distributions representing pharmacokinetic (PK) and pharmacodynamic (PD) contributions to the variation among chemicals in the animal-to-human toxicologically equivalent dose. If a physiologically based pharmacokinetic model (PBPK model) is used to derive a compound specific adjustment factor (CSAF) for the pharmacokinetic component, the deconvolution of the PK and PD components allows one to remove the PK component (to be replaced with the CSAF), while retaining the uncertainty in pharmacodynamics that PBPK models do not address. One must then add back the uncertainty in the PBPK determination of the CSAF (which may be considerable). A candidate criterion for whether one can use an uncertain PBPK model is whether the generic uncertainty about cross-species pharmacokinetics (reflected in the PK component of the overall empirical distribution) is larger than the chemical-specific uncertainty in the determination of kinetically equivalent doses in experimental animals and humans.     

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”.     

Induction of 5-Deiodinase Activity in Astroglial Cells by 12-O-Tetradecanoylphorbol 13-Acetate and Fibroblast Growth Factors

In the brain, 5'-deiodinase (5'-D) is responsible for the metabolic activation of thyroxine (T4) into 3,5,3'-triiodothyronine (T3) and 5-deiodinase (5-D) deiodinates T4 and T3 into inactive metabolites. This study examines the effects of factors known to induce astroglial 5'-D activity on the 5-D activity in cultured rat astroglial cells. The potencies of these factors were compared after 8 h of incubation, when stimulations by these factors near their maximal effects. 12-O-Tetradecanoylphorbol 13-acetate (TPA) at 10(-7) M was a potent inducer of 5-D activity, producing a 30- to 80-fold increase after 8 h. The maximal effect of TPA was observed after about 14 h. The TPA stimulation of 5-D activity was not dependent on glucocorticoids, unlike 5'-D activity. In comparison with TPA, 8-bromo-cyclic AMP (10(-3) M) was a poor inducer of 5-D activity whereas it is an excellent inducer of 5'-D activity. It produced a 2- to 20-fold increase in 5-D activity after 8 h. Natural acidic fibroblast growth factor (20 ng/ml) produced a degree of stimulation similar to that of TPA after 8 h. The maximal effect of acidic fibroblast growth factor was observed after about 16 h (until a 120-fold increase). Recombinant acidic fibroblast growth factor also induced 5-D activity. Basic fibroblast growth factor was less potent than acidic fibroblast growth factor for increasing 5-D activity (maximal increase by 40- to 50-fold after 8 h).(ABSTRACT TRUNCATED AT 250 WORDS)     

Application of Kinetic and Dynamic Data for Antihistamines to Risk Characterization in Sensitive Populations

A major goal of risk assessment is to protect the health of individuals who may be more sensitive than the general population. This study compared human phar-macokinetic and pharmacodynamic data in sensitive groups (i.e., children, the elderly, diseased states, and poor metabolizers) versus young, healthy adults for the antihistamines cetirizine, fexofenadine, loratadine, azelastine, ebastine, chlorpheniramine, and diphenhydramine. The default components (3.16 each for kinetic and dynamic aspects) of the intraspecies uncertainty factor were adjusted with compound specific data for the antihistamines. The majority (16 of 18) of the composite factors (kinetics X dynamics) for the sensitive groups were less than 10. Children had the lowest composite factors for antihistamines, ranging from 1.1 to 6.3. Application of kinetic and dynamic data for antihistamines to the Renwick/International Programme on Chemical Safety (IPCS) scheme can aid in characterizing the extent of variability in sensitive populations, thereby reducing the uncertainty associated with the risk assessment of sensitive populations.     

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.     

Amino acid composition, protein content and calculation of nitrogen-to-protein conversion factors for 19 tropical seaweeds

The use of nitrogen‐to‐protein conversion factors (N‐Prot factors) is the most practical way of determining protein content. The accuracy of protein determination by this method depends on the establishment of N‐Prot factors specific to individual species. Experimental data are needed to allow the use of this methodology with seaweeds. The present study was designed to characterize the amino acid composition and to establish specific N‐Prot factors for six green, four brown and nine red marine algae. Mean values for individual amino acids tended to be similar among the three groups, but some differences were found. Green algae tended to show lower percentages of both aspartic acid and glutamic acid than the other two groups of algae. The percentages of both lysine and arginine were higher in red algae, while brown algae tended to show more methionine than green and red algae. The actual protein content of the species, based on the sum of amino acid residues, varied from 10.8% (Chnoospora minima, brown algae) to 23.1% (Aglaothamnion uru‐guayense, red algae) of the dry weight. Nitrogen‐to‐protein conversion factors were established for the species studied, based on the ratio of amino acid residues to total nitrogen, with values ranging from 3.75 (Cryptonemia seminervis, red algae) to 5.72 (Padina gymnospora, brown algae). The relative importance of non‐protein nitrogen is greater in red algae, and consequently lower N‐Prot factors were calculated for these species (average value 4.59). Conversely, protein nitrogen content in both green and brown algae tends to be higher, and average N‐Prot factors were 5.13 and 5.38, respectively. An overall average N‐Prot factor for all species studied of 4.92 ± 0.59 (n = 57) was established. This study confirms that the use of the traditional factor 6.25 is unsuitable for seaweeds, and the use of the N‐Prot factors proposed here is recommended.     

Human mammary tumor cell proliferation: primary role of platelet-derived growth factor and possible synergism with human alpha-fetoprotein.

Human mammary medullary carcinoma cells (passages 16 to 21) were cultured for 2 days to allow for attachment, followed by 6 days of culture in either fetal calf serum, human cord blood, human amniotic fluid, or growth factors in the presence or absence of purified human alpha-fetoprotein (AFP). When growth factors were tested alone, only platelet-derived growth factor produced a significant increase in cell proliferation. Although up to 40% amniotic fluid had no effect on cell proliferation, human cord blood was two-fold more potent than fetal calf serum at similar concentrations. The addition of 10 ng/ml of platelet-derived growth factor increased the proliferative activity of human cord blood 1.5- to 2.5-fold. Ablation of endogenous AFP by affinity chromatography reduced the proliferative activity of cord blood by 75%. Similarly, the mitogenic activity of cord blood plus platelet-derived growth factor was reduced by 56% when AFP was removed. Purified AFP dose-dependently enhanced the proliferative activity of platelet-derived growth factor. This synergistic effect was specific for platelet-derived growth factor. We conclude that platelet-derived growth factor is a major growth factor controlling the proliferation of these tumor cells and that AFP may enhance growth factor proliferative activity and human mammary tumor growth.     

A review of the use of uncertainty factors in the health risk assessment of some metals

Because metals can produce health risks, standards for regulating metal exposure are necessary. The purpose of this chapter is to review the application of uncertainty factors to mercury, arsenic, and cadmium. By the conventional method, uncertainty factors are often applied to animal studies to establish the reference dose (RfD) in humans. However, with the availability of a better database from improved study designs, it was demonstrated that uncertainty factors can be decreased. Incorporation of more pharmacokinetic and mechanistic data into the risk assessment process, as well as discussions between risk assessors and the research community to identify research needs are essential in reducing uncertainty factors.