Human Variability in Susceptibility to Toxic Chemicals— A Preliminary Analysis of Pharmacokinetic Data from Normal Volunteers

The tenfold "uncertainty" factor traditionally used to guard against human interindividual differences in susceptibility to toxicity is not based on human observations. To begin to build a basis for quantifying an important component of overall variability in susceptibility to toxicity, a data base has been constructed of individual measurements of key pharmacokinetic parameters for specific substances (mostly drugs) in groups of at least five healthy adults. 72 of the 101 data sets studied were positively skewed, indicating that the distributions are generally closer to expectations for log-normal distributions than for normal distributions. Measurements of interindividual variability in elimination half-lives, maximal blood concentrations, and AUC (area under the curve of blood concentration by time) have median values of log10 geometric standard deviations in the range of 0.11-0.145. For the median chemical, therefore, a tenfold difference in these pharmacokinetic parameters would correspond to 7-9 standard deviations in populations of normal healthy adults. For one relatively lipophilic chemical, however, interindividual variability in maximal blood concentration and AUC was 0.4--implying that a tenfold difference would correspond to only about 2.5 standard deviations for those parameters in the human population. The parameters studied to date are only components of overall susceptibility to toxic agents, and do not include contributions from variability in exposure- and response-determining parameters. The current study also implicitly excludes most human interindividual variability from age and illness. When these other sources of variability are included in an overall analysis of variability in susceptibility, it is likely that a tenfold difference will correspond to fewer standard deviations in the overall population, and correspondingly greater numbers of people at risk of toxicity.     

Lognormal Distributions for Water Intake by Children and Adults

We fit lognormal distributions to data collected in a national survey for both total water intake and tap water intake by children and adults for these age groups in years: 0 less than age less than 1; 1 less than or equal to age less than 11; 11 less than or equal to age less than 20; 20 less than or equal to age less than 65; 65 less than or equal to age; and all people in the survey taken as a single group. These distributions are suitable for use in public health risk assessments.     

Identifying Childhood Age Groups for Exposure Assessments and Monitoring

The purpose of this article is to describe a standard set of age groups for exposure assessors to consider when assessing childhood exposure and potential dose to environmental contaminants. In addition, this article presents examples to show how the age groups can be applied in children's exposure assessments. A consistent set of childhood age groups, supported by an underlying scientific rationale, will improve the accuracy and comparability of exposure and risk assessments for children. The effort was undertaken in part to aid the U.S. Environmental Protection Agency (EPA) in implementing such regulatory initiatives as the 1997 Presidential Executive Order 13,045, which required all federal agencies to ensure that their standards take into account special risks to children. The standard age groups include: birth to <1 month; 1 to <3 months; 3 to <6 months; 6 to <12 months; 1 to <2 years; 2 to <3 years; 3 to <6 years; 6 to <11 years; 11 to <16 years; and 16 to <21 years. These age groups reflect a consideration of developmental changes in various behavioral, anatomical, and physiological characteristics that impact exposure and potential dose. It is expected that the availability of a standard set of early-life age groups will inform future analyses of exposure factors data as well as guide new research and data collection efforts to fill knowledge gaps.     

Estimation of Dietary Exposure to Chemicals: A Case Study Illustrating Methods of Distributional Analyses for Food Consumption Data

There are a number of sources of variability in food consumption patterns and residue levels of a particular chemical (e.g., pesticide, food additive) in commodities that lead to an expected high level of variability in dietary exposures across a population. This paper focuses on examples of consumption pattern survey data for specific commodities, namely that for wine and grape juice, and demonstrates how such data might be analyzed in preparation for performing stochastic analyses of dietary exposure. Data from the NIAAA/NHIS wine consumption survey were subset for gender and age group and, with matched body weight data from the survey database, were used to define empirically-based percentile estimates for wine intake (μl wine/kg body weight) for the strata of interest. The data for these two subpopulations were analyzed to estimate 14-day consumption distributional statistics and distributions for only those days on which wine was consumed. Data subsets for all wine-consuming adults and wine-consuming females ages 18 through 45, were determined to fit a lognormal distribution ( R ²= 0.99 for both datasets). Market share data were incorporated into estimation of chronic exposures to hypothetical chemical residues in imported table wine. As a separate example, treatment of grape juice consumption data for females, ages 18–40, as a simple lognormal distribution resulted in a significant underestimation of intake, and thus exposure, because the actual distribution is a mixture (i.e., multiple subpopulations of grape juice consumers exist in the parent distribution). Thus, deriving dietary intake statistics from food consumption survey data requires careful analysis of the underlying empirical distributions.     

Statistical Distributions of Daily Breathing Rates for Narrow Age Groups of Infants and Children

Children may be more susceptible to toxicity from some environmental chemicals than adults. This susceptibility may occur during narrow age periods (windows), which can last from days to years depending on the toxicant. Breathing rates specific to narrow age periods are useful to assess inhalation dose during suspected windows of susceptibility. Because existing breathing rates used in risk assessment are typically for broad age ranges or are based on data not representative of the population, we derived daily breathing rates for narrow age ranges of children designed to be more representative of the current U.S. children's population. These rates were derived using the metabolic conversion method of Layton (1993) and energy intake data adjusted to represent the U.S. population from a relatively recent dietary survey (CSFII 1994–1996, 1998). We calculated conversion factors more specific to children than those previously used. Both nonnormalized (L/day) and normalized (L/kg-day) breathing rates were derived and found comparable to rates derived using energy estimates that are accurate for the individuals sampled but not representative of the population. Estimates of breathing rate variability within a population can be used with stochastic techniques to characterize the range of risk in the population from inhalation exposures. For each age and age-gender group, we present the mean, standard error of the mean, percentiles (50th, 90th, and 95th), geometric mean, standard deviation, 95th percentile, and best-fit parametric models of the breathing rate distributions. The standard errors characterize uncertainty in the parameter estimate, while the percentiles describe the combined interindividual and intra-individual variability of the sampled population. These breathing rates can be used for risk assessment of subchronic and chronic inhalation exposures of narrow age groups of children.     

Use of Markov Chain Monte Carlo Analysis with a Physiologically-Based Pharmacokinetic Model of Methylmercury to Estimate Exposures in U.S. Women of Childbearing Age

A Bayesian approach, implemented using Markov Chain Monte Carlo (MCMC) analysis, was applied with a physiologically‐based pharmacokinetic (PBPK) model of methylmercury (MeHg) to evaluate the variability of MeHg exposure in women of childbearing age in the U.S. population. The analysis made use of the newly available National Health and Nutrition Survey (NHANES) blood and hair mercury concentration data for women of age 16–49 years (sample size, 1,582). Bayesian analysis was performed to estimate the population variability in MeHg exposure (daily ingestion rate) implied by the variation in blood and hair concentrations of mercury in the NHANES database. The measured variability in the NHANES blood and hair data represents the result of a process that includes interindividual variation in exposure to MeHg and interindividual variation in the pharmacokinetics (distribution, clearance) of MeHg. The PBPK model includes a number of pharmacokinetic parameters (e.g., tissue volumes, partition coefficients, rate constants for metabolism and elimination) that can vary from individual to individual within the subpopulation of interest. Using MCMC analysis, it was possible to combine prior distributions of the PBPK model parameters with the NHANES blood and hair data, as well as with kinetic data from controlled human exposures to MeHg, to derive posterior distributions that refine the estimates of both the population exposure distribution and the pharmacokinetic parameters. In general, based on the populations surveyed by NHANES, the results of the MCMC analysis indicate that a small fraction, less than 1%, of the U.S. population of women of childbearing age may have mercury exposures greater than the EPA RfD for MeHg of 0.1 μg/kgg/day, and that there are few, if any, exposures greater than the ATSDR MRL of 0.3 μgg/kgg/day. The analysis also indicates that typical exposures may be greater than previously estimated from food consumption surveys, but that the variability in exposure within the population of U.S. women of childbearing age may be less than previously assumed.     

A Meta-Analysis of Children's Hand-to-Mouth Frequency Data for Estimating Nondietary Ingestion Exposure

Because of their mouthing behaviors, children have a higher potential for exposure to available chemicals through the nondietary ingestion route; thus, frequency of hand-to-mouth activity is an important variable for exposure assessments. Such data are limited and difficult to collect. Few published studies report such information, and the studies that have been conducted used different data collection approaches (e.g., videography versus real-time observation), data analysis and reporting methods, ages of children, locations, and even definitions of "mouthing." For this article, hand-to-mouth frequency data were gathered from 9 available studies representing 429 subjects and more than 2,000 hours of behavior observation. A meta-analysis was conducted to study differences in hand-to-mouth frequency based on study, age group, gender, and location (indoor vs. outdoor), to fit variability and uncertainty distributions that can be used in probabilistic exposure assessments, and to identify any data gaps. Results of this analysis indicate that age and location are important for hand-to-mouth frequency, but study and gender are not. As age increases, both indoor and outdoor hand-to-mouth frequencies decrease. Hand-to-mouth behavior is significantly greater indoors than outdoors. For both indoor and outdoor hand-to-mouth frequencies, interpersonal, and intra-personal variability are approximately 60% and approximately 30%, respectively. The variance difference among different studies is much bigger than its mean, indicating that different studies with different methodologies have similar central values. Weibull distributions best fit the observed data for the different variables considered and are presented in this article by study, age group, and location. Average indoor hand-to-mouth behavior ranged from 6.7 to 28.0 contacts/hour, with the lowest value corresponding to the 6 to <11 year olds and the highest value corresponding to the 3 to <6 month olds. Average outdoor hand-to-mouth frequency ranged from 2.9 to 14.5 contacts/hour, with the lowest value corresponding to the 6 to <11 year olds and the highest value corresponding to the 6 to <12 month olds. The analysis highlights the need for additional hand-to-mouth data for the <3 months, 3 to <6 months, and 3 to <6 year age groups using standardized collection and analysis because of lack of data or high uncertainty in available data. This is the first publication to report Weibull distributions as the best fitting distribution for hand-to-mouth frequency; using the best fitting exposure factor distribution will help improve estimates of exposure. The analyses also represent a first comprehensive effort to fit hand-to-mouth frequency variability and uncertainty distributions by indoor/outdoor location and by age groups, using the new standard set of age groups recommended by the U.S. Environmental Protection Agency for assessing childhood exposures. Thus, the data presented in this article can be used to update the U.S. EPA's Child-Specific Exposure Factors Handbook and to improve estimates of nondietary ingestion in probabilistic exposure modeling.     

Human Interindividual Variability in Susceptibility to Airborne Particles

Part of the explanation for the persistent epidemiological findings of associations between mortality and morbidity with relatively modest ambient exposures to airborne particles may be that some people are much more susceptible to particle-induced responses than others. This study assembled a database of quantitative observations of interindividual variability in pharmacokinetic and pharmacodynamic parameters likely to affect particle response. The pharmacodynamic responses studied included data drawn from epidemiologic studies of doses of methacholine, flour dust, and other agents that induce acute changes in lung function. In general, the amount of interindividual variability in several of these pharmacodynamic response parameters was greater than the variability in pharmacokinetic (breathing rate, deposition, and clearance) parameters. Quantitatively the results indicated that human interindividual variability of breathing rates and major pharmacokinetic parameters-total deposition and tracheobronchial clearance-were in the region of Log(GSD) = 0.1 to 0.2 (corresponding to geometric standard deviations of 10(.1)-10(.2) or 1.26-1.58). Deposition to the deep lung (alveolar region) appeared to be somewhat more variable: Log(GSD) of about 0.3 (GSD of about 2). Among pharmacodynamic parameters, changes in FEV1 in response to ozone and metabisulfite (an agent that is said to act primarily on neural receptors in the lung) were in the region of Log(GSD) of 0.2 to 0.4. However, similar responses to methacholine, an agent that acts on smooth muscle, seemed to have still more variability (0.4 to somewhat over 1.0, depending on the type of population studied). Similarly high values were suggested for particulate allergens. Central estimates of this kind of variability, and the close correspondence of the data to lognormal distributions, indicate that 99.9th percentile individuals are likely to respond at doses that are 150 to 450-fold less than would be needed in median individuals. It seems plausible that acute responses with this amount of variability could form part of the mechanistic basis for epidemiological observations of enhanced mortality in relation to ambient exposures to fine particles.     

Physiologically-Based Pharmacokinetic Modeling of Benzene in Humans: A Bayesian Approach

Benzene is myelotoxic and leukemogenic in humans exposed at high doses (>1 ppm, more definitely above 10 ppm) for extended periods. However, leukemia risks at lower exposures are uncertain. Benzene occurs widely in the work environment and also indoor air, but mostly below 1 ppm, so assessing the leukemia risks at these low concentrations is important. Here, we describe a human physiologically-based pharmacokinetic (PBPK) model that quantifies tissue doses of benzene and its key metabolites, benzene oxide, phenol, and hydroquinone after inhalation and oral exposures. The model was integrated into a statistical framework that acknowledges sources of variation due to inherent intra- and interindividual variation, measurement error, and other data collection issues. A primary contribution of this work is the estimation of population distributions of key PBPK model parameters. We hypothesized that observed interindividual variability in the dosimetry of benzene and its metabolites resulted primarily from known or estimated variability in key metabolic parameters and that a statistical PBPK model that explicitly included variability in only those metabolic parameters would sufficiently describe the observed variability. We then identified parameter distributions for the PBPK model to characterize observed variability through the use of Markov chain Monte Carlo analysis applied to two data sets. The identified parameter distributions described most of the observed variability, but variability in physiological parameters such as organ weights may also be helpful to faithfully predict the observed human-population variability in benzene dosimetry.     

Evaluation of the Uncertainty in an Oral Reference Dose for Methylmercury Due to Interindividual Variability in Pharmacokinetics

An analysis of the uncertainty in guidelines for the ingestion of methylmercury (MeHg) due to human pharmacokinetic variability was conducted using a physiologically based pharmacokinetic (PBPK) model that describes MeHg kinetics in the pregnant human and fetus. Two alternative derivations of an ingestion guideline for MeHg were considered: the U.S. Environmental Protection Agency reference dose (RfD) of 0.1 g/kg/day derived from studies of an Iraqi grain poisoning episode, and the Agency for Toxic Substances and Disease Registry chronic oral minimal risk level (MRL) of 0.5 g/kg/day based on studies of a fish-eating population in the Seychelles Islands. Calculation of an ingestion guideline for MeHg from either of these epidemiological studies requires calculation of a dose conversion factor (DCF) relating a hair mercury concentration to a chronic MeHg ingestion rate. To evaluate the uncertainty in this DCF across the population of U.S. women of child-bearing age, Monte Carlo analyses were performed in which distributions for each of the parameters in the PBPK model were randomly sampled 1000 times. The 1st and 5th percentiles of the resulting distribution of DCFs were a factor of 1.8 and 1.5 below the median, respectively. This estimate of variability is consistent with, but somewhat less than, previous analyses performed with empirical, one-compartment pharmacokinetic models. The use of a consistent factor in both guidelines of 1.5 for pharmacokinetic variability in the DCF, and keeping all other aspects of the derivations unchanged, would result in an RfD of 0.2 g/kg/day and an MRL of 0.3 g/kg/day.     

Lognormal Distributions for Fish Consumption by the General U.S. Population

The rate of fish consumption is a critical variable in the assessment of human health risk from water bodies affected by chemical contamination and in the establishment of federal and state Ambient Water Quality Criteria (AWQC). For 1973 and 1974, the National Marine Fisheries Service (NMFS) analyzed data on the consumption of salt-water finfish, shellfish, and freshwater finfish from all sources in 10 regions of the United States for three age groups in the general population: children (ages 1 through 11 years), teenagers (ages 12 through 18 years), and adults (ages 19 through 98 years). Even though the NMFS data reported in Ref. 14 are 20 years old, they remain the most complete data on the overall consumption of all fish by the general U.S. population and they have been widely used to select point values for consumption. Using three methods, we fit lognormal distributions to the results of the survey as analyzed and published in Ref. 14. Strong lognormal fits were obtained for most of the 90 separate data sets. These results cannot necessarily be used to model the consumption of fish by sport or subsistence anglers from specific sites or from single water bodies.     

Lognormal Distributions for Total Water Intake and Tap Water Intake by Pregnant and Lactating Women in the United States

Using probability plots and Maximum Likelihood Estimation (MLE), we fit lognormal distributions to data compiled by Ershow et al. for daily intake of total water and tap water by three groups of women (controls, pregnant, and lactating; all between 15–49 years of age) in the United States. We also develop bivariate lognormal distributions for the joint distribution of water ingestion and body weight for these three groups. Overall, we recommend the marginal distributions for water intake as fit by MLE for use in human health risk assessments.     

How Children Spend Their Time: A Sample Survey for Use in Exposure and Risk Assessments

Children are becoming an increasingly important focus for exposure and risk assessments because they are more sensitive than adults to environmental contaminants. A necessary step in measuring the extent of children's exposure and in calculating risk assessments is to document how and where children spend their time. This 1990-1991 survey of 1000 households was designed for this purpose, targeting children between 5 and 12 years of age, in six states in varied geographic regions. The behavior of children was sampled on both weekdays and weekends over all four seasons of the year using a retrospective time diary to allocate time to activities during the previous 24 h. Information was obtained on the kinds and locations of activities, the nature of the microenvironments of the locations, and the time spent in the different environments. Measures of variability in addition to mean hours per day are reported. Results of this study closely match those of earlier research on California children's activities done by the California Air Resources Board. One important finding of the survey was that 5- to 12-year-old children in all geographic regions spend most of their time indoors at home, indicating that risk assessments should focus on indoor, on-site hazards.     

The Practical Significance of Measurement Error in Pulmonary Function Testing Conducted in Research Settings

Conventional spirometry produces measurement error by using repeatability criteria (RC) to discard acceptable data and terminating tests early when RC are met. These practices also implicitly assume that there is no variation across maneuvers within each test. This has implications for air pollution regulations that rely on pulmonary function tests to determine adverse effects or set standards. We perform a Monte Carlo simulation of 20,902 tests of forced expiratory volume in 1 second (FEV₁), each with eight maneuvers, for an individual with empirically obtained, plausibly normal pulmonary function. Default coefficients of variation for inter‐ and intratest variability (3% and 6%, respectively) are employed. Measurement error is defined as the difference between results from the conventional protocol and an unconstrained, eight‐maneuver alternative. In the default model, average measurement error is shown to be ～5%. The minimum difference necessary for statistical significance at p < 0.05 for a before/after comparison is shown to be 16%. Meanwhile, the U.S. Environmental Protection Agency has deemed single‐digit percentage decrements in FEV₁ sufficient to justify more stringent national ambient air quality standards. Sensitivity analysis reveals that results are insensitive to intertest variability but highly sensitive to intratest variability. Halving the latter to 3% reduces measurement error by 55%. Increasing it to 9% or 12% increases measurement error by 65% or 125%, respectively. Within‐day FEV₁ differences ≤5% among normal subjects are believed to be clinically insignificant. Therefore, many differences reported as statistically significant are likely to be artifactual. Reliable data are needed to estimate intratest variability for the general population, subpopulations of interest, and research samples. Sensitive subpopulations (e.g., chronic obstructive pulmonary disease or COPD patients, asthmatics, children) are likely to have higher intratest variability, making it more difficult to derive valid statistical inferences about differences observed after treatment or exposure.     

Assessing Cancer Risks from Short-Term Exposures in Children

For the vast majority of chemicals that have cancer potency estimates on IRIS, the underlying database is deficient with respect to early-life exposures. This data gap has prevented derivation of cancer potency factors that are relevant to this time period, and so assessments may not fully address children's risks. This article provides a review of juvenile animal bioassay data in comparison to adult animal data for a broad array of carcinogens. This comparison indicates that short-term exposures in early life are likely to yield a greater tumor response than short-term exposures in adults, but similar tumor response when compared to long-term exposures in adults. This evidence is brought into a risk assessment context by proposing an approach that: (1) does not prorate children's exposures over the entire life span or mix them with exposures that occur at other ages; (2) applies the cancer slope factor from adult animal or human epidemiology studies to the children's exposure dose to calculate the cancer risk associated with the early-life period; and (3) adds the cancer risk for young children to that for older children/adults to yield a total lifetime cancer risk. The proposed approach allows for the unique exposure and pharmacokinetic factors associated with young children to be fully weighted in the cancer risk assessment. It is very similar to the approach currently used by U.S. EPA for vinyl chloride. The current analysis finds that the database of early life and adult cancer bioassays supports extension of this approach from vinyl chloride to other carcinogens of diverse mode of action. This approach should be enhanced by early-life data specific to the particular carcinogen under analysis whenever possible.     

Distributions of Total Job Tenure for Men and Women in Selected Industries and Occupations in the United States, February 1996

This article develops and fits probability distributions for the variability in projected (total) job tenure for adult men and women in 31 industries and 22 occupations based on data reported by the U.S. Department of Labor's Bureau of Labor Statistics. It extends previously published results and updates those results from January 1987 to February 1996. The model provides probability distributions for the variability in projected (total) job tenures within the time range of the data, and it extrapolates the distributions beyond the time range of the data, i.e., beyond 25 years.     

Dietary and Inhalation Intake of Lead and Estimation of Blood Lead Levels in Adults and Children in Kanpur, India

This research was initiated to study lead levels in various food items in the city of Kanpur, India, to assess the dietary intake of lead and to estimate blood lead (PbB) levels, a biomarker of lead toxicity. For this purpose, sampling of food products, laboratory analysis, and computational exercises were undertaken. Specifically, six food groups (leafy vegetables, nonleafy vegetables, fruits, pulses, cereals, and milk), drinking water, and lead air concentration were considered for estimating lead intake. Results indicated highest lead content in leafy vegetables followed by pulses. Fruits showed low lead content and drinking water lead levels were always within tolerable limits. It was estimated that average daily lead intake through diet was about 114 microg/day for adults and 50 microg/day in children; tolerable limit is 250 microg/day for adults and 90 microg/day for children. The estimated lead intakes were translated into the resultant PbB concentrations for children and adults using a physiologically-based pharmacokinetic (PBPK) model. Monte Carlo simulation of PbB level variations for adults showed that probability of exceeding the tolerable limit of PbB (i.e.,10 microg/dL) was 0.062 for the pre-unleaded and 0.000328 for the post-unleaded gasoline period. The probability of exceeding tolerable limits in PbB level was reduced by a factor of 189 in the post-unleaded scenario. The study also suggested that in spite of the introduction of unleaded gasoline, children continue to be at a high risk (probability of exceeding 10 microg/dL = 0.39) because of a high intake of lead per unit body weight.     

Lognormal Distributions for Body Weight as a Function of Age for Males and Females in the United States, 1976–1980

Based on results reported from the NHANES II Survey (the National Health and Nutrition Examination Survey II) for people living in the United States during 1976–1980, we use exploratory data analysis, probability plots, and the method of maximum likelihood to fit lognormal distributions to percentiles of body weight for males and females as a function of age from 6 months through 74 years. The results are immediately useful in probabilistic (and deterministic) risk assessments.     

Structure and Parameterization of Pharmacokinetic Models: Their Impact on Model Predictions

There has been an increasing interest in physiologically based pharmacokinetic (PBPK)models in the area of risk assessment. The use of these models raises two important issues: (1)How good are PBPK models for predicting experimental kinetic data? (2)How is the variability in the model output affected by the number of parameters and the structure of the model? To examine these issues, we compared a five-compartment PBPK model, a three-compartment PBPK model, and nonphysiological compartmental models of benzene pharmacokinetics. Monte Carlo simulations were used to take into account the variability of the parameters. The models were fitted to three sets of experimental data and a hypothetical experiment was simulated with each model to provide a uniform basis for comparison. Two main results are presented: (1)the difference is larger between the predictions of the same model fitted to different data se1ts than between the predictions of different models fitted to the dame data; and (2)the type of data used to fit the model has a larger effect on the variability of the predictions than the type of model and the number of parameters.     

Dose-response assessment for influenza A virus based on data sets of infection with its live attenuated reassortants

Reported data sets on infection of volunteers challenged with wild-type influenza A virus at graded doses are few. Alternatively, we aimed at developing a dose-response assessment for this virus based on the data sets for its live attenuated reassortants. Eleven data sets for live attenuated reassortants that were fit to beta-Poisson and exponential dose-response models. Dose-response relationships for those reassortants were characterized by pooling analysis of the data sets with respect to virus subtype (H1N1 or H3N2), attenuation method (cold-adapted or avian-human gene reassortment), and human age (adults or children). Furthermore, by comparing the above data sets to a limited number of reported data sets for wild-type virus, we quantified the degree of attenuation of wild-type virus with gene reassortment and estimated its infectivity. As a result, dose-response relationships of all reassortants were best described by a beta-Poisson model. Virus subtype and human age were significant factors determining the dose-response relationship, whereas attenuation method affected only the relationship of H1N1 virus infection to adults. The data sets for H3N2 wild-type virus could be pooled with those for its reassortants on the assumption that the gene reassortment attenuates wild-type virus by at least 63 times and most likely 1,070 times. Considering this most likely degree of attenuation, 10% infectious dose of H3N2 wild-type virus for adults was estimated at 18 TCID50 (95% CI = 8.8-35 TCID50). The infectivity of wild-type H1N1 virus remains unknown as the data set pooling was unsuccessful.