Estimacion farmacocinetica comparativa de Mercurio en lactantes de Estados Unidos después de exposiciones anuales a vacunas inactivada deInfluenza que contienen timerosal

  • 212 views
Uploaded on

Estimacion farmacocinetica que soporta la inocuidad del timerosal en vacunas de influenza

Estimacion farmacocinetica que soporta la inocuidad del timerosal en vacunas de influenza

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
212
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
0
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Risk Analysis DOI: 10.1111/risa.12124 A Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants Following Yearly Exposures to Inactivated Influenza Vaccines Containing Thimerosal Robert J. Mitkus,1,∗ David B. King,1,2 Mark O. Walderhaug,1 and Richard A. Forshee1 The use of thimerosal preservative in childhood vaccines has been largely eliminated over the past decade in the United States because vaccines have been reformulated in single-dose vials that do not require preservative. An exception is the inactivated influenza vaccines, which are formulated in both multidose vials requiring preservative and preservative-free single-dose vials. As part of an ongoing evaluation by USFDA of the safety of biologics throughout their lifecycle, the infant body burden of mercury following scheduled exposures to thimerosal preservative in inactivated influenza vaccines in the United States was estimated and compared to the infant body burden of mercury following daily exposures to dietary methylmercury at the reference dose established by the USEPA. Body burdens were estimated using kinetic parameters derived from experiments conducted in infant monkeys that were exposed episodically to thimerosal or MeHg at identical doses. We found that the body burden of mercury (AUC) in infants (including low birth weight) over the first 4.5 years of life following yearly exposures to thimerosal was two orders of magnitude lower than that estimated for exposures to the lowest regulatory threshold for MeHg over the same time period. In addition, peak body burdens of mercury following episodic exposures to thimerosal in this worst-case analysis did not exceed the corresponding safe body burden of mercury from methylmercury at any time, even for low-birth-weight infants. Our pharmacokinetic analysis supports the acknowledged safety of thimerosal when used as a preservative at current levels in certain multidose infant vaccines in the United States. KEY WORDS: MeHg; modeling; pharmacokinetics; safety; thimerosal 1. INTRODUCTION tally contaminated, as might occur with repeated puncture of multidose vials. Since the 1930s, thimerosal has been used as the preservative of choice for vaccines formulated in multidose vials because of its effective antimicrobial activity and nonreactivity with vaccine components that could lead to a loss of potency. In the late 1990s, as the number of thimerosal-containing childhood vaccines increased, concern arose that the higher levels of exposure of infants and children to the thimerosal metabolite, ethylmercury, might lead to neurotoxicity, based on comparisons with the related organomercurial, methylmercury.(1,2) While recognizing the absence of evidence of harm from exposure to low levels of thimerosal used in vaccines in the late 1990s, as a The U.S. Code of Federal Regulations (CFR) generally requires the addition of a preservative to multidose vials of vaccines to prevent microbial growth in the event that the vaccine is acciden1 Office of Biostatistics and Epidemiology, USFDA Center for Biologics Evaluation and Research, Rockville, MD, USA. 2 Co-first author; current address: Office of New Animal Drug Evaluation, Center for Veterinary Medicine, Rockville, MD, USA. ∗ Address correspondence to Robert J. Mitkus, Office of Biostatistics and Epidemiology, USFDA Center for Biologics Evaluation and Research, 1401 Rockville Pike, HFM-210, Rockville, MD 20852, USA; tel: 301-827-6083; robert.mitkus@fda.hhs.gov. 1 0272-4332/13/0100-0001$22.00/1 Published 2013. This article is a U.S. Government work and is in the public domain in the USA.
  • 2. 2 precautionary measure, the American Academy of Pediatrics (AAP), in a joint statement with the Public Health Service (PHS), in July 1999 and later agreed to by the American Association of Family Physicians (AAFP), established the goal of removing thimerosal as soon as possible from vaccines routinely recommended for infants.(3) Within a few years, vaccine manufacturers voluntarily reformulated childhood vaccines in single-dose vials, which do not contain preservative. However, subsequent evaluation by the National Academy of Sciences Institute of Medicine (IOM), which reviewed epidemiological evidence from the United States, Denmark, Sweden, and the United Kingdom, as well as studies of biological mechanisms related to vaccines and autism, indicated that that body of scientific evidence did not support a causal relationship between thimerosal-containing vaccines and autism.(4) The more recent epidemiological literature has also failed to support a causal relationship between prenatal, neonatal, or postnatal exposure to thimerosal in vaccines and a host of neuropsychological outcomes, including autism.(5–11) In 2006, the American College of Medical Toxicology (ACMT) ratified the conclusions of the IOM,(12) and in that same year, the World Health Organization issued a statement on thimerosal through the Global Advisory Committee on Vaccine Safety (GACVS), which concluded that there was no evidence of toxicity in infants, children, or adults exposed to thimerosal in vaccines.(13) That statement applied to the full suite of recommended infant and childhood vaccines containing thimerosal, which are used extensively outside the United States starting at birth. In 2012, following a more recent, comprehensive assessment of the safety database for thimerosal, the World Health Organization’s GACVS reaffirmed its previous statement and concluded that a link between thimerosal at current levels in vaccines and neurotoxicity was “biologically implausible.”(14) That conclusion was based on an assessment of the best and most relevant epidemiological, toxicological, and pharmacokinetic data in humans and experimental animals. As mentioned, the decision to remove thimerosal from routine infant vaccines in the United States over a decade ago was a precautionary one. It was based on a comparison of cumulative, nominal doses of intramuscular vaccine thimerosal to safe dietary doses of MeHg, but did not take into account the differences in pharmacokinetics between those two distinct organomercurials, especially their distribution, metabolism, and excretion following Mitkus et al. absorption.(1,15) Since 2001, a number of studies have been performed that have specifically investigated the pharmacokinetics of thimerosal either alone or in comparison to MeHg within the same study. Taken together, these studies have indicated that thimerosal is cleared much more rapidly from the bodies of infant or adult humans, nonhuman primates, and rodents than is MeHg.(16–26) As a result, comparisons of nominal doses of thimerosal with nominal doses of MeHg will overestimate the risk of thimerosal toxicity, while calculations of cumulative external exposures to either compound are misleading.(27) Despite the reasonableness of those observations, however, no one has applied this new pharmacokinetic information to estimate internal mercury exposures from thimerosal at levels currently found in infant vaccines in the United States. This would be essential, because internal exposures are more predictive of the potential biological effects of xenobiotics, whether beneficial or adverse, than nominal or external dose.(28,29) Currently in the United States, only certain inactivated influenza vaccines contain thimerosal preservative. All other U.S. vaccines routinely recommended for children six years of age or under do not contain thimerosal preservative or contain thimerosal only in trace amounts (≤1 μg mercury/dose). Inactivated influenza vaccines continue to be marketed in both single-dose, preservative-free and multidose, thimerosal-preservative-containing (0.01%, w/v; 0.005% mercury, w/v) formulations in the United States because the amount of available preservative-free inactivated influenza vaccine is below the amount needed to immunize all infants and children each season. Because some children may be exposed to low levels of thimerosal from influenza vaccines packaged in multidose vials, we have developed a quantitative pharmacokinetic model that estimates internal exposures to thimerosal at current U.S. levels and compares them to predicted internal levels of mercury from dietary MeHg exposures at the USEPA reference dose (RfD). 2. METHODS 2.1. Mercury Exposures from Either Influenza Vaccine or Diet 2.1.1. Vaccine Thimerosal The most recent recommended immunization schedule for persons aged 0–18 years(30) was utilized
  • 3. Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants Table I. Earliest Maximum Mercury Exposures from Seasonal Inactivated Influenza Vaccines Administered to Infants Over the First 4.5 Years of Life According to the 2013 ACIP Vaccination Schedule; Mercury Exposures Increase in Third Year of Life Due to Increased Volume of Fluzone R Vaccine Administered at That Age; Doses Normalized to Body Weight (bw) Are Based on the 5th and 50th Percentiles for Body Weight in U.S. Infants 0–60 Months of Age(35) Postnatal Age of Administration Mercury Exposure (μg) Dose (μg/kg bw) 12.5 (each) 12.5 12.5 25 25 1.5–2.0 Six months (Two doses four weeks apart) One and a half years Two and a half years Three and a half years Four and a half years 1.1–1.4 0.9–1.1 1.6–1.9 1.4–1.7 3 servative) threshold in the range and was chosen for this analysis. Because it is body-weight dependent, acceptable daily dietary levels of mercury in the form of MeHg were calculated by multiplying the RfD by the body weights (lower 5th and 50th percentiles) of infants over the first 4.5 years of life. Infant body weights were estimated using infant body weight data collected from NHANES(35) as described previously.(36) Bootstrapping was used to capture the uncertainty in body weights for infants of both median and lower 5% quantile body weights. Dietary absorption of MeHg was assumed to be 100% based on results in human volunteers.(37) 2.2. Pharmacokinetic Model Construction and Validation to determine the course of influenza vaccination in the United States over the first four to five years of life. The exposures to mercury (Table I) are based on the reported amounts contained in a recent list of FDA-approved seasonal influenza vaccines(31) and are independent of the body weight of the vaccinated infant. Of the 11 licensed, injectable seasonal vaccine products, five are packaged in multidose vials (Afluria , two FluLaval , Fluvirin , Fluzone ) that contain thimerosal (≤25 μg mercury/0.5 mL dose). Of these five products, only Fluzone is licensed for use in infants as young as six months of age. The other products are not indicated for children under three (FluLaval ), four (Fluvirin ), or five (Afluria ) years of age. Therefore, Fluzone administered from a multidose vial according to the 2013 Advisory Committee on Immunization Practices (ACIP) seasonal vaccination schedule would provide the maximum theoretical exposures to mercury in the youngest children (six months to four years of age). These exposures may not be typical, however, given the possibility that a child may receive Fluzone in a single-dose vial or prefilled syringe or the live attenuated, intranasal influenza vaccine FluMist , both of which are thimerosal-free. R R R R R R R R R R R 2.1.2. MeHg at Its RfD Four health-based guidelines have been published for dietary MeHg: 0.1 μg/kg bw/day(32) (bw, body weight); 1.6 μg/kg bw/week (0.23 μg/kg bw/day);(33) 0.3 μg/kg bw/day;(26) and 0.4 μg/kg bw/day.(34) Although these values are quite similar, the USEPA RfD for MeHg is the lowest (most con- 2.2.1. Source and Justification of Data for the Model As mentioned, several pharmacokinetic studies have been performed for thimerosal in humans, nonhuman primates, and rodents. Of these, we considered those performed in either human or nonhuman primates to be the most relevant to humanhealth risk assessment, based on known physiological, phylogenetic, and taxonomic kinship and concordance in previous cross-species pharmacokinetic comparisons.(38,39) This smaller set of studies includes those by Pichichero et al. in human infants,(17–19) the study by Barregard et al. in human adults,(20) and that by Burbacher et al. in infant nonhuman primates.(16) Of this group, the study by Burbacher et al. provided the only direct, intrastudy comparison of the pharmacokinetics of thimerosal with that of MeHg when administered to infant nonhuman primates at identical doses starting at birth. Briefly, Burbacher et al. exposed infant macaques to either thimerosal (IM) or MeHg (PO) at identical, weekly doses of 20 μg/kg bw for four weeks starting from birth and reported mercury levels in blood weekly thereafter. The dosing regimen in that study was designed to mimic the repeat, episodic dosing schedule of human infants (outside the United States) beginning at birth. In the absence of a similar head-to-head study in human infants, it is the most relevant study on which to base a comparative assessment of infant mercury exposure or risk from thimerosal relative to MeHg. In addition, application of the results from that study to human infants was considered warranted given the almost identical blood half-lives of mercury in human infants (three to seven days) and infant nonhuman primates (two
  • 4. 4 Mitkus et al. Fig. 1. Structure of the Bayesian hierarchical model for thimerosal. The statistical model for MeHg was structured analogously. to nine days) following single, episodic exposures to thimerosal.(16–19) At FDA’s request, the individual infant macaque blood concentration data from the study were kindly provided by the study’s authors, Drs. Thomas Burbacher and Danny Shen. The provision of the individual animal data by the original study’s authors at FDA’s request does not constitute their approval of the methods or conclusions of this article. 2.2.2. Model Description and Justification In order to provide an analysis that was independent and that would also provide multiple plausible fits of the data for the purpose of ascertaining the uncertainty in internal exposure of infants to mercury following influenza vaccinations, the nonhuman primate data from Burbacher et al. were reanalyzed using a Bayesian approach (Fig. 1). To fully capture the biological heterogeneity in the data, the Bayesian hierarchical model allowed each monkey to have its own unique set of pharmacokinetic parameters (e.g., rate constants, volume of distribution), which were based on the individual blood concentration–time data. A Bayesian censoring model was created to deal with individual animal data that were missing or below the limit of detection. The Bayesian hierarchical model, much like an equivalent multilevel mixed model, has many advantages. First, because the model is a multilevel model where there are both subject- and populationlevel predictions, this approach generally allows for better fits to the experimental data because the residuals for the subject-level predictions are usually smaller than the residuals associated with the population-level prediction in an equivalent singlelevel model. Second, because the model allows each monkey to have its own unique pharmacokinetic parameters, the intersubject variation of pharmacokinetic parameterization can be studied and
  • 5. Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants 5 Fig. 2. The population-level prediction (solid black) for mercury concentration in blood following multiple doses of thimerosal (IM) is shown along with the predicted response for monkey 5 (red) and monkey 14 (blue) (colors visible in online version). The dotted lines represent the 2.5% lower and 97.5% upper bounds on prediction. The blue and red dots correspond to the actual blood concentration measurements published by Burbacher et al. differentiated. The model was hierarchical in the sense that each of the monkey-specific parameters (the first level of hierarchy) was itself assumed to be a random draw from some probability density with distributional parameters centered with means and variances common to the population of all monkeys (the second level of hierarchy). The values of the pharmacokinetic parameters for the overall population were, therefore, inferred by first estimating the parameters for each monkey involved in the biological study and then utilizing the collection of these subject-specific parameter estimates as data for inference toward the population-level parameters. The parameter constants at the third and highest level of hierarchy, in turn, established the prior distribution for the population pharmacokinetic parameters; the process of prior elicitation that defined the plausible envelope for the distribution of population-level pharmacokinetic parameters is described in the Appendix. Bayesian posterior inference on the parameters of the model was carried out by Markov Chain Monte Carlo (MCMC) sampling of the posterior distribution using OpenBugs(40) and the JAGS package in R.(41) 2.2.3. Model Validation We conducted at least five validation steps during model development. First, the predicted values of the population model and the monkey-specific models were plotted against observed values to visually assess the fit of the model (representative examples in Figs. 2 and 3). Each figure includes the population prediction and the predictions for two specific monkeys along with the 95% predictive confidence interval (CI). Most data points are within the 95% predictive CI of the monkey-specific model. In addition, there are as many blue and red curves (subjectspecific predictions) above the black curve (population prediction) as below it. Therefore, there was good agreement between the subject-specific predictions and the subject-level data, as well as consistency
  • 6. 6 Mitkus et al. Fig. 3. The population-level prediction (solid black) for mercury concentration in blood following multiple, equivalent doses of MeHg (PO) is shown along with the predicted response for monkey 1 (red) and monkey 10 (blue) (colors visible in online version). The dotted lines represent the 2.5% lower and 97.5% upper bounds on prediction. The blue and red dots correspond to the actual blood concentration measurements published by Burbacher et al. between the population prediction and the animalspecific estimates. Second, we based the posterior inference of pharmacokinetic parameters on a production sample of 10,000 posterior samples following a burn-in period of 10,000. Visual evidence for posterior convergence was verified via trace plots, and quantitative evidence was based upon the evaluation of the Raftery and Lewis diagnostic test statistic.(42,43) Third, the overall quality of model fit was assessed quantitatively by means of posterior predictive checking.(44) Specifically, for each MCMC simulation, we randomly generated a simulated response for each monkey and measurement time and then compared the sum of squared residuals between these simulated responses and the model predictions. These quantities were in turn compared with the sum of squared residuals between the simulated prediction and the actual data. A Bayesian p-value was then computed by measuring the number of times in the simulation that the sum of squared residuals for the posterior predictive distribution was larger than the sum of squared residuals from the data at hand and dividing by the number of simulations. The Bayesian p-value for the one-compartment model applied to the MeHg data was 0.54, and that for the twocompartment model applied to the thimerosal data was 0.562, indicating an acceptable fit to the data for both models. Fourth, the deviance information criterion (DIC(43) ) was applied to compare models with simpler parameterizations to more complex ones, with a model with a lower DIC value being preferred. Fifth, we conducted a Bayesian sensitivity analysis to determine whether the quality of fit would substantially change if we changed the center of our prior distributions for model parameters at the third level of hierarchy. The sensitivity analysis was conducted by running the baseline MCMC simulation and then perturbing the parameters one at a time and then running alternate MCMC simulations with this new prior distribution. For each set of simulations,
  • 7. Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants we compared how changing the prior distribution changed the overall fit of each model by means of computing the log-likelihood value for each simulated MCMC run. Specifically, we investigated 11 different choices for prior distributions and investigated how each change affected the log-likelihood score. Figure A1 and Tables AI and AII demonstrate that the fit is not significantly changed by the choice in prior distribution. Each box plot in the figure was computed by calculating the log-likelihood score for each of the 10,000 MCMC simulations. The box plots are very similar across the scenarios, suggesting that the overall fit of the model is robust to changes in assumptions about the prior distributions. 2.3. Estimation of Mercury Retention in Human Infants Based on the shape of their blood time course curves, we, like the original study authors,(16) modeled the distribution of mercury into one compartment (central) for MeHg and two compartments (central and peripheral) for thimerosal. This classical pharmacokinetic approach was consistent with the use of either one- or two-compartment models to describe the distribution of these organomercurials in the human body previously.(18–20,32,45) Body burden of mercury following exposure to either thimerosal-containing influenza vaccines or dietary levels of MeHg at the RfD was calculated using the pharmacokinetic parameters derived from the Bayesian models in Table II. Specifically, mercury retention was estimated over the first four to five years of life by substituting the respective, relevant exposures (μg) to either thimerosal (IM) or MeHg (PO), along with the rate constants derived from the Bayesian analysis, into Equations (A.1) through (A.3). Differential equations were solved and time course graphs were generated using R(46) or Biokmod(47) in conjunction with Mathematica 8 (Wolfram Research, Champaign, IL, USA). The areas under the mercury body burden curves (AUCs) following daily exposure to acceptable daily doses of MeHg or episodic exposures to thimerosal were calculated using Equations (A.14) and (A.15), respectively. Results of multiple Bayesian simulations were used to obtain a predictive Bayesian confidence limit for the AUC following exposure to either MeHg or thimerosal. The body burden for infants of both median and lower 5% body weights was then plotted, and the respective AUCs calculated, along 7 with their respective lower 2.5% and upper 97.5% bounds following 5,000 MCMC simulations. 3. RESULTS 3.1. Body Burden of Mercury Following Repeated Exposures to Thimerosal or MeHg at the RfD Fig. 4 presents levels of mercury in infants following single doses of influenza vaccine that contain thimerosal (up to 2 μg/kg bw; Table I) or daily oral consumption of acceptable doses of MeHg(32) over the course of the first 4.5 years of life. It is clear from the graph that there is no long-term accumulation of mercury in the body following single, yearly exposures to thimerosal. Integrating under the concentration–time curve (from time zero to 1,700 days, or 4.5 years) yielded a median AUC for thimerosal of 700 μg Hg-days in infants (Table III). On the other hand, integrating under the median curve for this time period for MeHg exposures at the RfD in the lowest-weight infants yielded an AUC of 100,000 μg Hg-days (120,000 for median-weight infants). Comparing the AUC of 700 μg Hg-days for thimerosal mercury to those from MeHg at the RfD (100,000 or 120,000) over the first 4.5 years of life yields a margin of exposure (safety) of 143 or 171. This indicates that the body burden of mercury from vaccine thimerosal in infants over the first 4.5 years of life is at least 140-fold lower than the body burden of mercury from acceptable daily levels of dietary MeHg over the same time period. Fig. 4 also indicates that an infant’s peak body burden of mercury at the time of his or her first influenza vaccination at 180 days of life is two- to threefold less than the median acceptable body burden of MeHg in either median- or low-weight infants. 3.2. Uncertainty in Body Burden Estimates Over Time (AUC) Table III contains a Bayesian posterior summary of the uncertainty (upper and lower 2.5% quantiles and standard deviation) surrounding the AUC values corresponding to the median time course curves for both thimerosal and MeHg. As mentioned, AUC estimates for MeHg took into account both the variability in the pharmacokinetic parameters in infant monkeys (Table II) and the variability of infant body weights(35) as modeled by Mitkus et al. Therefore, this uncertainty analysis provides an estimate of the
  • 8. 8 Mitkus et al. Table II. Posterior Summary of the Population-Level Parameters for the Two-Compartment Pharmacokinetic Model for Thimerosal and the One-Compartment Pharmacokinetic Model for MeHg Quantiles Compound Population-Level Parameter Mean Std. Dev. 2.5% 25.0% 50.0% 75.0% 97.5% Thimerosal Thimerosal Thimerosal Thimerosal Thimerosal MeHg MeHg MeHg Vc = exp(μV ) K12 = exp(μk12 ) K21 = exp(μk21 ) Ka = exp(μka ) Ke = exp(μke ) Vc = exp(μV ) Ka = exp(μka ) Ke = exp(μke ) 1.62 0.20 1.08 3.92 0.18 1.76 19.42 0.017 0.24 0.20 1.26 4.96 0.04 0.09 37.86 0.005 0.98 0.03 0.10 0.68 0.13 1.59 2.16 0.007 1.54 0.08 0.40 1.43 0.16 1.69 5.42 0.014 1.66 0.13 0.73 2.37 0.17 1.75 10.21 0.018 1.78 0.23 1.32 4.34 0.20 1.82 19.76 0.021 2.01 0.80 4.16 16.51 0.30 1.95 94.52 0.027 Fig. 4. Simulated kinetics of mercury following exposures to thimerosal or MeHg in the body over the first 4.5 years of life. Thimerosal: mercury retention in central (purple) and peripheral (magenta) compartments following single ACIP-recommended exposures (up to 25 μg) (colors visible in online version). MeHg: body burden of mercury following daily ingestion of USEPA safe levels by infants of median (orange: median curve) or lower fifth percentile (red: median curve; blue: lower 5% bound) BW. MeHg time course curves were generated by simultaneously drawing samples from both the simulated body weight distributions and the pharmacokinetic parameter distributions. internal exposure to mercury in infants who may be at the tails of the population distribution for body weight and pharmacokinetic clearance. AUCs for thimerosal and MeHg in low- or median-birth-weight infants with the fastest mercury kinetics (2.5% quantile, Table III) are 550 and 66,000 or 80,000 μg Hgdays, respectively. That equates to a margin of exposure of 120–145. AUCs for thimerosal and MeHg in lower- or median-birth-weight infants with the slowest mercury kinetics (97.5% quantile, Table III) are much higher: 1,500 and 250,000 or 300,000 μg Hgdays, respectively; and the margin of exposure for that comparison is 167–200. AUCs for internal mer- cury exposure from thimerosal, as predicted by our Bayesian models, therefore, are always at least 120fold lower than the respective AUCs for MeHg, including the most conservative comparison using the lowest-weight babies with the fastest mercury kinetics. Peak body burdens of mercury following episodic exposures to thimerosal were also always lower than the corresponding level of acceptable mercury from methylmercury in low-birth-weight infants with the fastest kinetics (Fig. 4). Taken together, these results indicate that mercury in the body following maximal theoretical exposures to thimerosal preservative in inactivated influenza vaccine packaged in multidose
  • 9. Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants 9 Table III. Posterior Summary of the Bayesian Estimates for the Area Under the Body Burden Curve (with Confidence Limits) from Birth to 1,700 Days of Age (4.5 Years) Following Episodic Exposures to Thimerosal or Daily Safe Exposures to MeHg Posterior Statistics—AUC (μg Hg · Day) Kinetics (Fast → Slow): Thimerosal Safe MeHg—infants at the lowest 5% quantile for body weight Safe MeHg—infants at the median body weight vials does not at any time exceed the body burden of mercury that is based on a highly conservative threshold for dietary MeHg. In addition, a potentially sensitive subpopulation (low-birth-weight infants) will not be exposed to unsafe levels of mercury from vaccine thimerosal following single or repeated exposures in the United States. 4. DISCUSSION AND CONCLUSION In 2001, Ball and colleagues at the Center for Biologics Evaluation at the FDA published an assessment of thimerosal exposures from infant vaccines and reported that some infants could be exposed to cumulative levels of mercury from vaccines that were higher than the EPA threshold for MeHg.(1) Because the clinical significance of that apparent risk was unknown, the authors noted that lowering exposures to thimerosal in vaccines was consistent with the larger aim of reducing exposures to mercury in general. Today, the inactivated influenza vaccine packaged in multidose vials is the only U.S. infant vaccine that contains thimerosal at other than residual amounts. Since the original analysis of Ball et al., it has become increasingly clear that there are significant differences in pharmacokinetics between the organomercurials, thimerosal and MeHg, and that these differences govern their respective toxicities. It has been shown, for example, that mercury is cleared from the blood and brain much faster following exposure to thimerosal than to MeHg in infant nonhuman primates.(16) Peak blood or brain mercury levels have also been shown in several species to be lower following exposures to thimerosal (or EtHg) relative to an equivalent dose of MeHg administered via the same (IM/IM) or real-world (IM/PO) routes of exposure,(16,21–24) and those studies illustrate the acknowledged differences in disposition and tissue uptake between the two compounds.(48–51) In addi- 2.5% Quantile Median Mean 97.5% Quantile Std. Dev. 550 66,000 80,000 700 100,000 120,000 780 110,000 140,000 1500 250,000 300,000 300 57,000 70,000 tion, thimerosal is more quickly and extensively metabolized to inorganic mercury in the brain than is MeHg,(16,48) and that process of dealkylation may be a detoxification step.(49,52) Taking together these three major pharmacokinetic differences, one would expect, therefore, greater toxicity from MeHg than from thimerosal when administered at equivalent doses, given the higher and longer bioavailability of the former. These established differences in pharmacokinetics between thimerosal and MeHg imply that comparing nominal exposures to mercury from thimerosal in infant vaccines directly to nominal exposures at the MeHg RfD will overestimate thimerosal risk.(27) This is the case because, fundamentally, comparisons of nominal (external) exposures do not take into account differences in physiological ADME processes (absorption, distribution, metabolism, excretion), which normally serve to reduce internal exposures to xenobiotics in the body following exposure. Therefore, we expanded the original comparison of Ball et al. in order to provide a more reliable (i.e., internal) estimate of thimerosal exposures. First, we utilized body burden, not nominal dose, as our metric for internal exposures to either thimerosal or MeHg, and we performed our comparison accordingly. Second, in the models that we built to estimate the body burden of either thimerosal or MeHg, we explicitly took into account the major pharmacokinetic difference between these two organomercurials, which is the faster clearance of thimerosal from the body. And, third, in the absence of comparative repeat-dose pharmacokinetic data for thimerosal and MeHg in human infants, we utilized results from the only published study that (1) compared the clearance of thimerosal with that of MeHg; (2) when administered at equivalent doses; (3) in an infant model; (4) according to an episodic, repeat-dosing regimen; and (5) that started at birth: the infant nonhuman primate study by Burbacher et al.
  • 10. 10 Specifically, we generated (Bayesian) statistical models for thimerosal and MeHg that were based on the individual animal blood mercury concentration data from that study. Several visual and quantitative validation tests indicated that our Bayesian models were robust and fit the experimental data well. Pharmacokinetic rate constants were then derived from our two data-based models and applied to a real-life human exposure scenario, in order to estimate the body burden of mercury in U.S. infants following exposures to thimerosal in vaccines or dietary MeHg at its RfD. Direct cross-species application of the parameters was considered reasonable based on the essentially identical blood half-lives of thimerosal (average four to five days) in infant human and nonhuman primates.(16–19) As far as we are aware, this is the first time such an estimate of mercury body burden from thimerosal has been made for human infants. The major outcome of the modeling efforts reported here has been the estimation of the mercury body burden in infants from either thimerosal at U.S. exposure levels or MeHg at its oral RfD. This is a significant advance over previous estimates that calculated (only) external exposures to these compounds.(1) Although Pichichero et al. measured blood levels of mercury in human infants following single or repeat doses of thimerosal,(17–19) those studies, which are still highly relevant, did not assess the effect of controlled dietary MeHg exposures on blood levels of mercury or conduct additional pharmacokinetic analyses to estimate parameters such as rate constants that could then be applied to estimate a more comprehensive metric of internal exposure (body burden) over several years, as we have done here. Although we view our study to be an advance over previous efforts, one of its limitations is its comparison to the regulatory threshold for MeHg. That comparison is highly conservative because the RfD for MeHg is designed to be protective of possible adverse effects following daily exposure for a lifetime, while exposure to thimerosal is small and episodic, hence the conservatism. In addition, regulatory thresholds for short-term exposures to xenobiotics are normally above those for long-term exposures because toxicity thresholds trend lower as length of exposure becomes higher. However, because an acute or short-term threshold (e.g., “RfD,” “minimal risk level”) for either MeHg or thimerosal does not exist, the RfD for MeHg was the next best, although imperfect, comparator. This means that the margins of exposure reported here (i.e., ≥120) ac- Mitkus et al. tually underestimate the true margin of safety for thimerosal by some factor. For that reason, we support future assessments of thimerosal safety that move away from comparisons with MeHg. In summary, using pharmacokinetic parameters (rate constants) derived from the most relevant available study of exposure of infant nonhuman primates to either thimerosal or MeHg, we have estimated the body burden of both thimerosal and MeHg in human infants. We have demonstrated that children, including those of low birth weight, who receive the recommended schedule of annual inactivated influenza vaccines formulated with thimerosal as a preservative in multidose vials in the United States will receive over 100 times less internal exposure to mercury on a time-integrated basis over the first 4.5 years of life as compared to the USEPA regulatory threshold for dietary MeHg over a similar interval. Peak mercury levels from thimerosal in the body were also always below the regulatory threshold for MeHg over the same time period. By taking into account the significant pharmacokinetic differences between thimerosal and MeHg that have been elucidated over the past decade and by not relying on nominal dose as the exposure metric, our study significantly improves upon the previous estimates of Ball et al. Together with the robust human epidemiological data for thimerosal, our results, which are based on a “worst-case” comparison, support the continued safety of this preservative at levels currently used in multidose inactivated influenza vaccines in the United States. ACKNOWLEDGMENTS We thank Drs. Burbacher and Shen for kindly providing the individual infant macaque blood concentration data from their paper.(16) The provision of the individual animal data by the original study’s authors at FDA’s request does not constitute approval of the methods or conclusions of the article by the original study’s authors. APPENDIX: MATHEMATICS OF THE BAYESIAN PHARMACOKINETIC MODELS In the one-compartment pharmacokinetic model for MeHg, mercury is absorbed into the blood from the GI (rate constant ka ) and removed from the blood at a rate proportional to the elimination rate constant ke . The rate of movement of MeHg into and out of a single, central compartment is described by
  • 11. Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants A= the following equation: dX1 /dt = ka XGI (t) − ke X1 (t), dX1 /dt = k21 X2 (t) + ka XM (t) − (ke + k12 )X1 (t), (A.2) (A.3) where dX1 /dt and dX2 /dt refer to the rates of change of thimerosal mercury within the central and peripheral compartments, respectively, and XM refers to the amount of mercury at the site of intramuscular injection. ˜ j=1 When K doses {D(t j )} K are administered at the K ˜ dosing times {t j } j=1 , the superposition principle in differential equations ensures that the blood concentration in the blood is given by f1 (t) = ˜ j:t j <t ˜ FD(t j )ka ˜ exp (−ke (t − t j )) Vc (ka − k) (A.4) ˜ ˜ −exp (−ka (t − t j )) I[t − t j ] for the one-compartment model, and f2 (t) = ˜ j:t j <t ˜ FD(t j ) ˜ Aexp (−ka (t − t j )) Vc ˜ +B exp (−α(t − t j )) ˜ ˜ +C exp (−β(t − t j )) I[t − t j ] (A.5) for the two-compartment model, where Vc is the effective volume of the central compartment, F is the bioavailability, I[•] is an indicator function that has a value of 1 whenever the argument (t − t0 ) > 0 and is 0 otherwise, and the parameters {α, β, A, B, C} are given by: α, β = (k12 + k21 + ke ) ± (k12 + k21 + ke )2 − 4k21 ke 2 ka (k21 − α) ka (k21 − ka ) ,B= , (α − ka )(β − ka ) (ka − α)(β − α) C= ka (k21 − β) (Ref. 53). (ka − β)(α − β) (A.1) where dX1 /dt refers to the rate of change of MeHg within the central compartment; and XGI refers to the amount of MeHg at the port of entry following oral, dietary exposure (GI). In the two-compartment model for thimerosal, mercury is absorbed from the site of IM injection (rate constant ka ) into the central compartment (well-perfused tissues) and distributes between the central compartment and the peripheral compartment (less well-perfused tissues; rate constants k12 and k21 ). Elimination takes place from the central compartment (rate constant ke ). The differential equations describing the time course of mercury in either compartment are described as follows: dX2 /dt = k12 X1 (t) − k21 X2 (t), 11 Often, when dealing with data for a single method of administration, the bioavailability F is statistically unidentifiable; accordingly, we estimate the effective central-compartment volume Vc = Vc /F in all the results that follow. Let yik denote the kth blood concentration measurement made on the ith monkey subject and let tik denote the time of the blood measurement. A basic statistical model for the blood concentration of MeHg under the one-compartment model would be yik = f1 (tik|ka , ke , V) + εik, (A.6) where f1 (t|ka , k, V) is given by Equation (A.4), the residuals εik are independent and identically distributed (iid) with εik ∼ N(0, σ 2 ), and the variance σ 2 denotes the residual variance or the lack of fit error. The structure of the two-compartment model statistically utilized to fit the concentration of EtHg is given by: yik = f2 (tik|ka , ke , k12 , k21 , V) + εik, (A.7) which has identical assumptions as Equation (A.5). The problem with the statistical models in Equations (A.6) and (A.7) is that: (1) They assume that every monkey has exactly the same set of pharmacokinetic parameters and so does not account for biological heterogeneity between monkeys. (2) The models assume that the residuals εik are caused by a simple and unexplainable source of error with uniform variance σ 2 . Because of this, the model does not distinguish between variation caused by monkey-to-monkey variation (biodiversity) and other unexplainable errors. To more accurately model the biological heterogeneity that is present among animal subjects and more faithfully account for the hierarchical data structure, a more appropriate model would assume that each of the monkeys involved in the Burbacher et al. experiment is endowed with its own monkey-specific set of pharmacokinetic parame17 ters. Let ka [i], ke [i], k12 [i], k21 [i], V[i] i=1 denote the monkey-specific rate constants and effective volumes
  • 12. 12 Mitkus et al. for the ith monkey. The index i will be utilized to represent the data and parameters particular to the ith monkey in all that follows. The one-compartment model for the blood concentration of mercury from MeHg is: ˜ yik = f1 (tik|ka [i], ke [i], V[i]) + εik, ˜ (A.8) and the equivalent two-compartment model for the blood concentration of thimerosal mercury has the form: yik = f2 (tik|ka [i], ke [i], k12 [i], k21 [i], V[i]) + εik, ˜ (A.9) with the residuals εik in Equations (A.8) and (A.9) ˜ assumed to be independent and normally distributed having a monkey-specific residual variance σ 2 [i] ˜ (εik ∼ N(0, σ 2 [i])). We note that because the sta˜ tistical models in Equations (A.8) and (A.9) fit each monkey with its own separate monkey-specific set of pharmacokinetic parameters, the residuals εik ˜ in Equations (A.8) and (A.9) will necessarily be smaller, on average, than the residuals εik in Equations (A.6) and (A.7). Moreover, the lack of fit, or residual variance specific to each monkey σ 2 [i] = ˜ Var(εik) in Equations (A.8) and (A.9) will similarly ˜ be smaller than σ 2 as well. Because we allow different pharmacokinetic parameters for each monkey, we assume that each monkey-specific parameter is itself a random draw from an overall population distribution. In our onecompartment Bayesian model for MeHg we assume that the monkey-specific pharmacokinetic parameters are distributed by: 2 ka [i] ∼ LogNormal(μka , σka ), 2 ke [i] ∼ LogNormal(μke , σke ), 2 V[i] ∼ LogNormal(μV , σV ), (A.10) and for the two-compartment pharmacokinetic model for EtHg we similarly assume that: ka [i] ke [i] k12 [i] k21 [i] V[i] ∼ ∼ ∼ ∼ ∼ 2 LogNormal(μka , σka ), 2 LogNormal(μke , σke ), 2 LogNormal(μ12 , σ12 ), 2 LogNormal(μ21 , σ21 ), 2 LogNormal(μV , σV ), (A.11) where {μka , μke , μk12 , μk21 , μV } are the means of the logarithm for the population of the respective phar2 2 2 2 2 macokinetic parameters, and σka , σke , σk12 , σk12 , σV are the respective population variances of the logarithms of each parameter. In our specification we utilize a log normal distribution in order to ensure that each of the pharmacokinetic parameters is posi- tive. The population-level parameters themselves are unknown and are of primary interest for estimation, so following Bayesian methodology we introduce yet another level of hierarchy and let the populationlevel parameter means and variances be specified by 2 2 μka ∼ N(μka0 , σka0 ) σka ∼ Unif(0, θa ), 2 2 μk ∼ N(μk0 , σk0 ) σk ∼ Unif(0, θk), 2 2 μV ∼ N(μV0 , σV0 ) σV ∼ Unif(0, θV ), (A.12) for the one-compartment model of MeHg and similarly let μka μke μk12 μk21 μV ∼ ∼ ∼ ∼ ∼ 2 2 N(μka0 , σka0 ) σka ∼ Unif(0, θa ), 2 2 N(μke0 , σke0 ) σke ∼ Unif(0, θe ), 2 2 N(μk120 , σk120 ) σk12 ∼ Unif(0, θ12 ), 2 2 N(μk210 , σk210 ) σk21 ∼ Unif(0, θ21 ), 2 2 N(μV0 , σV0 ) σV ∼ Unif(0, θV ), (A.13) be the analogous prior population-level parameters for the two-compartment model for thimerosal mercury. In both models, we specify the lack-of-fit error with a vague Gelman prior σ 2 [i] ∼ Unif(0, θ ) and ˜ then the Bayesian model is completely specified.(43) The Bayesian model is an empirical Bayesian model (“objective Bayesian”) in the sense that the distributional parameters of the prior were specified by setting them equal to the maximum likelihood estimators from Tables III and IV in Burbacher et al. Bayesian posterior inference on the parameters of the model was then carried out by MCMC sampling utilizing the Metropolis-Hastings algorithm to simulate 10,000 samples from the posterior distribution.(43) To ensure that the MCMC algorithm had converged, a burn-in of 1,000 posterior simulations was utilized and posterior convergence was verified via trace plots. The posterior summary of the two models mentioned above generally concur with the parameter estimates and corresponding standard errors given by Burbacher et al. As stated, we obtained quantitative evidence for the quality of fit of our statistical model by means of posterior predictive checking.(44) To establish the priors for the population vari2 2 2 2 2 ˜ ances {σka , σke , σk12 , σk21 , σV , σ 2 }, we utilized an analysis by Renwick and Lazarus to inform our decision making.(54) In that paper, the pharmacokinetics of 60 different compounds were studied, and the interindividual variation was found to be roughly 3.2 (that study currently serves as a basis for a default threefold uncertainty factor for pharmacokinetic variability in human-health risk assessment). On the log scale, this variation translates to 1.16. We padded
  • 13. Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants the interindividual variation somewhat as insurance against compound-specific peculiarities and set the 2 2 2 2 2 prior for each variance σka , σke , σk12 , σk21 , σV , σ 2 ∼ ˜ Unif(0, 3), so θa = θk = θ12 = θ21 = θV = θe = θ = 3. Because these prior distributions were uniformly distributed over the entire plausible range of variances, this prior specification had little influence in the estimation procedure. The specification of priors for the population means {μka , μke , μk12 , μk21 , μV } was a bit trickier as some biologically plausible understanding about the true value of these parameters must be applied in order to ensure algorithm convergence. Accordingly, in order to prevent conflicts between priors and likelihoods, we adopted an empirical Bayesian approach, which substitutes point estimates utilizing the data for certain parameters. To obtain point estimates on pharmacokinetic parameters, we fit a one- or two-compartment model to the data for each monkey utilizing the nonlinear regression algorithm from SAS, PROC NLIN (SAS institute, Cary, NC, USA). Once the nonlinear estimates for each monkey were obtained, we set the expected value of the mean of the prior distribution equal to the sample average from the regression estimates for the logarithms of the parameters (not shown). By doing this, we would ensure that the center of the prior distribution would be roughly in the same neighborhood where the posterior mode would likely be. In order to ensure that the prior distributions for the population means {μka , μke , μk12 , μk21 , μV } were sufficiently diffuse, we were motivated to choose the 2 2 2 2 2 variances {σka0 , σke0 , σk120 , σk210 , σV0 } as large as possible. The logic behind the selection of these variances follows closely from Renwick and Lazarus, in which the mean coefficient of variation in pharmacokinetic parameters for the 60 compounds involved in the study was a maximum of 137%. In all cases, the distributions for the population means had coefficients of variation larger than 137%. The complete prior specification for the population means in the one-compartment model for MeHg was: μka ∼ N(−2.09, 10), μk ∼ N(1.04, 2), μV ∼ N(0.26, 10), and the similar prior specification for the twocompartment model for thimerosal was: μka ∼ N(0.58, 1), μke ∼ N(−0.81, 10), 13 μk12 ∼ N(−0.98, 1.5), μk21 ∼ N(−0.7, 1), μV ∼ N(0.2, 100). To compute the area under the body burden curve (AUC) for mercury, we used appropriate formulas for the integral of the mass (body burden) under both the one- and two-compartment models. Following the exposure to safe daily doses of MeHg, the formula for the integral from 0 to T is: T AUC(T) = V f1 (t)dt 0 = t j ≤T F D(t j ) + ke t j ≤T F D(t j )ka (ka − ke ) (A.14) exp (−ke (T − t j )) exp (−ka (t − t j )) . − ke ka × Following the episodic exposures to thimerosal, the formula for AUC of the body burden is given by: AUC(T) = V fcentral (t) + fperipheral (t) dt+ D(t j ) (A+ D) (B + E) (C + F) + + ka α β D(t j ) = (A+ D) exp (−ka (T − t j )) ka t j ≤T − t j ≤T (B + E) exp (−α(T − t j )) α (C + F) exp (−β(T − t j )) , + β + (A.15) where fcentral (t), fperipheral (t) are the equations for the concentration time-course in the central and peripheral compartments and the constants A through F are given by: A= ka (k21 − α) ka (k21 − ka ) ,B= , (α − ka )(β − ka ) (ka − α)(β − α) C= k12 ka ka (k21 − β) ,D= , (ka − β)(α − β) (α − ka )(β − ka ) E= k12 ka , (ka − α)(β − α) F= k12 ka (Ref. 53). (ka − β)(α − β)
  • 14. 14 Mitkus et al. Fig. A1. The log-likelihood score for each of the 11 models explored during the sensitivity analysis. Each box plot is constructed from 10,000 MCMC iterations of the model. Table AI. Table of Parameter Values Explored in the Sensitivity Analysis Parameter μv0 μka0 μke0 μk120 μk210 Baseline Value Low Value High Value 0.2 0.75 − 0.81 − 1.06 − 0.75 0 0.5 −1 − 1.25 −1 0.4 1 − 0.5 − 0.8 − 0.5 A Bayesian sensitivity analysis was conducted to determine whether the quality of fit would substantially change if we changed the center of our prior distributions for model parameters at the third level of hierarchy. We investigated 11 different choices for prior distributions and investigate how each change affected the log-likelihood score. Figure A1 and Tables AI and AII demonstrate that the fit was not significantly changed by the choice in prior distribution. Table AII. Table of Parameter Values Explored in the Sensitivity Analysis Model Parameter Perturbation 1 2 3 4 5 6 7 8 9 10 11 Baseline case Low μv0 High μv0 Low μka0 High μka0 Low μke0 High μke0 Low μk120 High μk120 Low μk210 High μk210 REFERENCES 1. Ball LK, Ball R, Pratt RD. An assessment of thimerosal use in childhood vaccines. Pediatrics, 2001; 107(5):1147–1154. 2. Hinman AR, Orenstein WA, Schuchat A; Centers for Disease Control and Prevention (CDC). Vaccine-preventable
  • 15. Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. diseases, immunizations, and MMWR: 1961–2011. Morbidity and Mortality Weekly Reports Surveillance Summaries, 2011; 60 Suppl 4:49–57. American Academy of Pediatrics and the Public Health Service (AAP and USPHS). Notice to readers: Thimerosal in vaccines: A joint statement of the American Academy of Pediatrics and the Public Health Service. Morbidity and Mortality Weekly Reports Surveillance Summaries, 1999; 48(26):563– 565. Institute of Medicine (IOM). Immunization Safety Review: Vaccines and Autism. Washington, DC: National Academy Press, 2004. DeStefano F. Vaccines and autism: Evidence does not support a causal association. Clinical Pharmacology & Therapeutics, 2007; 82(6):756–759. Thompson WW, Price C, Goodson B, Shay DK, Benson P, Hinrichsen VL, Lewis E, Eriksen E, Ray P, Marcy SM, Dunn J, Jackson LA, Lieu TA, Black S, Stewart G, Weintraub ES, Davis RL, DeStefano F; Vaccine Safety Datalink Team. Early thimerosal exposure and neuropsychological outcomes at 7 and 10 years. New England Journal of Medicine, 2007; 357:1281–1292. Tozzi AE, Bisiacchi P, Tarantino V, De Mei B, D’Elia L, Chiarotti F, Salmaso S. Neuropsychological performance 10 years after immunization in infancy with thimerosalcontaining vaccines. Pediatrics, 2009; 123(2):475–482. Price CS, Thompson WW, Goodson B, Weintraub ES, Croen LA, Hinrichsen VL, Marcy M, Robertson A, Eriksen E, Lewis E, Bernal P, Shay D, Davis RL, DeStefano F. Prenatal and infant exposure to thimerosal from vaccines and immunoglobulins and risk of autism. Pediatrics, 2010; 126(4):656– 664. Hurley AM, Tadrous M, Miller ES. Thimerosal-containing vaccines and autism: A review of recent epidemiologic studies. Journal of Pediatric Pharmacology and Therapeutics, 2010; 15(3):173–181. Barile JP, Kuperminc GP, Weintraub ES, Mink JW, Thompson WW. Thimerosal exposure in early life and neuropsychological outcomes 7–10 years later. Journal of Pediatric Psychology, 2012; 37(1):106–118. Orenstein WA, Paulson JA, Brady MT, Cooper LZ, Seib K. Global vaccination recommendations and thimerosal. Pediatrics, 2013; 131(1):149–51. Kurt TL. ACMT position statement: The IOM report on thimerosal and autism. Journal of Medical Toxicology, 2006; 2(4):170–171. World Health Organization (WHO). Global Advisory Committee on Vaccine Safety. Statement on Thiomersal, 2006. Available at: http://www.who.int/vaccine safety/ committee/topics/thiomersal/statement jul2006/en/index.html, Accessed on December 1, 2012. World Health Organization (WHO). Global advisory committee on vaccine safety, 2012. Weekly Epidemiological Record, 2012; 87(6):53–59. Brent J. Toxicologists and the assessment of risk: The problem with mercury (commentary). Journal of Toxicology: Clinical Toxicology, 2001; 39(7):707–710. Burbacher TM, Shen DD, Liberato N, Grant KS, Cernichiari E, Clarkson T. Comparison of blood and brain mercury levels in infant monkeys exposed to methylmercury or vaccines containing thimerosal. Environmental Health Perspectives, 2005; 113(8):1015–1021. Pichichero ME, Cernichari E, Lopreiato J, Treanor J. Mercury concentrations and metabolism in infants receiving vaccines containing thiomersal: A descriptive study. Lancet, 2002; 360:1737–1741. Pichichero ME, Gentile A, Giglio N, Umido V, Clarkson T, Cernichiari E, Zareba G, Gotelli C, Gotelli M, Yan L, Treanor J. Mercury levels in newborns and infants after receipt of 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 15 thimerosal-containing vaccines. Pediatrics, 2008; 121(2):e208– 14. Pichichero ME, Gentile A, Giglio N, Alonso MM, Fernandez Mentaberri MV, Zareba G, Clarkson T, Gotelli C, Gotelli M, Yan L, Treanor J. Mercury levels in premature and low birth weight newborn infants after receipt of thimerosal-containing vaccines. Journal of Pediatrics, 2009; 155(4):495–499. Barregard L, Rekic D, Horvat M, Elmberg L, Lundh T, Zachrisson O. Toxicokinetics of mercury after long-term repeated exposure to thimerosal-containing vaccine. Toxicological Sciences, 2011; 120(2):499–506. Rodrigues JL, Serpeloni JM, Batista BL, Souza SS, Barbosa F, Jr. Identification and distribution of mercury species in rat tissues following administration of thimerosal or methylmercury. Archives of Toxicology, 2010; 84(11):891–896. Zareba G, Cernichiari E, Hojo R, Nitt SM, Weiss B, Mumtaz MM, Jones DE, Clarkson TW. Thimerosal distribution and metabolism in neonatal mice: Comparison with methyl mercury. Journal of Applied Toxicology, 2007; 27(5):511–518. Harry GJ, Harris MW, Burka LT. Mercury concentrations in brain and kidney following ethylmercury, methylmercury and thimerosal administration to neonatal mice. Toxicology Letters, 2004; 154(3):183–189. Suzuki T, Miyama T, Katsunuma H. Comparative study of bodily distribution of mercury in mice after subcutaneous administration of methyl, ethyl and n-propyl mercury acetates. Japanese Journal of Experimental Medicine, 1963; 33:277– 282. Suzuki T, Takemoto T. Metabolic fate of ethylmercury salts in man and animal. Pp. 209–232 in Morton Miller, Thomas Clarkson (eds). Mercury, Mercurials and Mercaptans. Springfield, IL: Charles C Thomas, 1973. Agency for Toxic Substances and Disease Registry (ATSDR). Toxicological Profile for Mercury. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service, 1999. Aschner M, Ceccatelli S. Are neuropathological conditions relevant to ethylmercury exposure? Neurotoxicity Research, 2010; 18(1):59–68. Saghir SA, Mendrala AL, Bartels MJ, Day SJ, Hansen SC, Sushynski JM, Bus JS. Strategies to assess systemic exposure of chemicals in subchronic/chronic diet and drinking water studies. Toxicology and Applied Pharmacology, 2006; 211(3):245–260. Creton S, Billington R, Davies W, Dent MP, Hawksworth GM, Parry S, Travis KZ. Application of toxicokinetics to improve chemical risk assessment: Implications for the use of animals. Regulatory Toxicology and Pharmacology, 2009; 55(3):291–299. Centers for Disease Control and Prevention (CDC). Advisory Committee on Immunization Practices (ACIP) recommended immunization schedules for persons aged 0 through 18 years and adults aged 19 years and older: United States, 2013. Morbidity and Mortality Weekly Reports Surveillance Summaries, 2013; 62 Suppl 1(1):2–8. Food and Drug Administration (FDA), 2013. Available at: http://www.fda.gov/BiologicsBloodVaccines/Vaccines/ ApprovedProducts/ucm093830.htm. Accessed on September 5, 2013. Environmental Protection Agency (EPA). Methylmercury (MeHg) (CASRN 22967–92–6). Integrated Risk Information System, 2001. Available at: http://www.epa.gov/ iris/subst/0073.htm. Accessed on December 1, 2012. World Health Organization (WHO). Methylmercury. Pp. 132– 140 in Safety Evaluation of Certain Food Additives and Contaminants. Report of the 61st Joint FAO/WHO Expert Committee on Food Additives. Geneva: World Health Organization, International Programme on Chemical Safety. WHO Technical Report Series Vol. 922, 2004.
  • 16. 16 34. Food and Drug Administration (FDA). Action level for mercury in fish, shellfish, crustaceans, and other aquatic animals. Federal Register, 1979; 44:3990. 35. NHANES (National Health and Nutrition Examination Survey Data). Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics (NCHS), 2008. Available at: http://www.cdc.gov/nchs/nhanes/nhanes20072008/BMX E.htm. Accessed on March 1, 2011. 36. Mitkus RJ, King DB, Hess MA, Forshee RA, Walderhaug MO. Updated aluminum pharmacokinetics following infant exposures through diet and vaccination. Vaccine, 2011; 29(51):9538–9543. 37. Aberg B, Ekman L, Falk R, Greitz U, Persson G, Snihs J-O. Metabolism of methyl mercury (203 Hg) compounds in man. Archives of Environmental Health, 1969; 19:478–484. 38. Ward KW, Smith BR. A comprehensive quantitative and qualitative evaluation of extrapolation of intravenous pharmacokinetic parameters from rat, dog, and monkey to humans. I. Clearance. Drug Metabolism and Disposition, 2004; 32(6):603–611. 39. Ward KW, Smith BR. A comprehensive quantitative and qualitative evaluation of extrapolation of intravenous pharmacokinetic parameters from rat, dog, and monkey to humans. II. Volume of distribution and mean residence time. Drug Metabolism and Disposition, 2004; 32(6):612–619. 40. Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS: A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing, 2000; 10:325– 337. 41. Plummer M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), Vienna, Austria, March 20–22, 2003. 42. Raftery AE, Lewis SM. How many iterations in the Gibbs sampler? Pp. 763–773 in Bernardo JM, Berger JO, Dawid AP, Smith AFM (eds). Bayesian Statistics 4. Oxford: Oxford University Press, 1992. Mitkus et al. 43. Carlin BP, Louis TA. Bayesian Methods for Data Analysis, 3rd ed. Boca Raton, FL: Chapman & Hall/CRC, 2008. 44. Gelman, Carlin, Stern, Rubin. Bayesian Data Analysis, 2nd ed. Boca Raton, FL: Chapman & Hall/CRC, 2003. ¨ 45. Bjorkman L, Mottet K, Nylander M, Vahter M, Lind B, Friberg L. Selenium concentrations in brain after exposure to methylmercury: Relations between the inorganic mercury fraction and selenium. Archives of Toxicology, 1995; 69(4):228–234. 46. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2008. Available at: http://www.R-project.org. ´ 47. Sanchez G. Fitting bioassay data and performing uncertainty analysis with BIOKMOD. Health Physics, 2007; 92(1):64–72. 48. Magos L. Neurotoxic character of thimerosal and the allometric extrapolation of adult clearance half-time to infants. Journal of Applied Toxicology, 2003; 23(4):263–269. 49. Clarkson TW, Magos L. The toxicology of mercury and its chemical compounds. Critical Reviews in Toxicology, 2006; 36:609–662. 50. Clarkson TW, Vyas JB, Ballatori N. Mechanisms of mercury disposition in the body. American Journal of Industrial Medicine, 2007; 50(10):757–764. 51. Simmons-Willis TA, Koh AS, Clarkson TW, Ballatori N. Transport of a neurotoxicant by molecular mimicry: The methylmercury-L-cysteine complex is a substrate for human L-type large neutral amino acid transporter (LAT) 1 and LAT2. Biochemical Journal, 2002; 367(Pt 1):239–246. 52. World Health Organization (WHO). Environmental Health Criteria 101: Methylmercury. Geneva: International Program on Chemical Safety, 1990. 53. Gibaldi M, Perrier D. Pharmacokinetics. New York: Marcel Dekker, 1982. 54. Renwick AG, Lazarus NR. Human variability and noncancer risk assessment: An analysis of the default uncertainty factor. Regulatory Toxicology and Pharmacology, 1998; 27(1 Pt 1): 3–20.