Risk Analysis DOI: 10.1111/risa.12124

A Comparative Pharmacokinetic Estimate of Mercury inU.S. Infants Following Yearly Exposures to InactivatedInfluenza 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 thepast decade in the United States because vaccines have been reformulated in single-dose vialsthat do not require preservative. An exception is the inactivated influenza vaccines, which areformulated in both multidose vials requiring preservative and preservative-free single-dosevials. As part of an ongoing evaluation by USFDA of the safety of biologics throughout theirlifecycle, the infant body burden of mercury following scheduled exposures to thimerosalpreservative in inactivated influenza vaccines in the United States was estimated and com-pared to the infant body burden of mercury following daily exposures to dietary methylmer-cury at the reference dose established by the USEPA. Body burdens were estimated using ki-netic parameters derived from experiments conducted in infant monkeys that were exposedepisodically to thimerosal or MeHg at identical doses. We found that the body burden ofmercury (AUC) in infants (including low birth weight) over the first 4.5 years of life followingyearly exposures to thimerosal was two orders of magnitude lower than that estimated for ex-posures 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-caseanalysis did not exceed the corresponding safe body burden of mercury from methylmercuryat any time, even for low-birth-weight infants. Our pharmacokinetic analysis supports theacknowledged safety of thimerosal when used as a preservative at current levels in certainmultidose infant vaccines in the United States.

KEY WORDS: MeHg; modeling; pharmacokinetics; safety; thimerosal

1. INTRODUCTION

The U.S. Code of Federal Regulations (CFR)generally requires the addition of a preservativeto multidose vials of vaccines to prevent microbialgrowth in the event that the vaccine is acciden-

1Office of Biostatistics and Epidemiology, USFDA Center forBiologics Evaluation and Research, Rockville, MD, USA.

2Co-first author; current address: Office of New Animal DrugEvaluation, Center for Veterinary Medicine, Rockville, MD,USA.

∗Address correspondence to Robert J. Mitkus, Office of Biostatis-tics and Epidemiology, USFDA Center for Biologics Evaluationand Research, 1401 Rockville Pike, HFM-210, Rockville, MD20852, USA; tel: 301-827-6083; robert.mitkus@fda.hhs.gov.

tally contaminated, as might occur with repeatedpuncture of multidose vials. Since the 1930s,thimerosal has been used as the preservative ofchoice for vaccines formulated in multidose vials be-cause of its effective antimicrobial activity and non-reactivity with vaccine components that could lead toa loss of potency. In the late 1990s, as the number ofthimerosal-containing childhood vaccines increased,concern arose that the higher levels of exposureof infants and children to the thimerosal metabo-lite, ethylmercury, might lead to neurotoxicity, basedon comparisons with the related organomercurial,methylmercury.(1,2) While recognizing the absence ofevidence of harm from exposure to low levels ofthimerosal used in vaccines in the late 1990s, as a

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 Mitkus et al.

precautionary measure, the American Academy ofPediatrics (AAP), in a joint statement with the PublicHealth Service (PHS), in July 1999 and later agreedto by the American Association of Family Physicians(AAFP), established the goal of removing thimerosalas soon as possible from vaccines routinely recom-mended for infants.(3) Within a few years, vaccinemanufacturers voluntarily reformulated childhoodvaccines in single-dose vials, which do not containpreservative.

However, subsequent evaluation by the NationalAcademy of Sciences Institute of Medicine (IOM),which reviewed epidemiological evidence from theUnited States, Denmark, Sweden, and the UnitedKingdom, as well as studies of biological mechanismsrelated to vaccines and autism, indicated that thatbody of scientific evidence did not support a causalrelationship between thimerosal-containing vaccinesand autism.(4) The more recent epidemiological liter-ature has also failed to support a causal relationshipbetween prenatal, neonatal, or postnatal exposure tothimerosal in vaccines and a host of neuropsycho-logical outcomes, including autism.(5–11) In 2006, theAmerican College of Medical Toxicology (ACMT)ratified the conclusions of the IOM,(12) and in thatsame year, the World Health Organization issued astatement on thimerosal through the Global Advi-sory Committee on Vaccine Safety (GACVS), whichconcluded that there was no evidence of toxicity ininfants, children, or adults exposed to thimerosal invaccines.(13) That statement applied to the full suiteof recommended infant and childhood vaccines con-taining thimerosal, which are used extensively out-side the United States starting at birth. In 2012, fol-lowing a more recent, comprehensive assessment ofthe safety database for thimerosal, the World HealthOrganization’s GACVS reaffirmed its previous state-ment and concluded that a link between thimerosalat current levels in vaccines and neurotoxicity was“biologically implausible.”(14) That conclusion wasbased on an assessment of the best and most relevantepidemiological, toxicological, and pharmacokineticdata in humans and experimental animals.

As mentioned, the decision to removethimerosal from routine infant vaccines in theUnited States over a decade ago was a precautionaryone. It was based on a comparison of cumulative,nominal doses of intramuscular vaccine thimerosalto safe dietary doses of MeHg, but did not take intoaccount the differences in pharmacokinetics betweenthose two distinct organomercurials, especially theirdistribution, metabolism, and excretion following

absorption.(1,15) Since 2001, a number of studieshave been performed that have specifically inves-tigated the pharmacokinetics of thimerosal eitheralone or in comparison to MeHg within the samestudy. Taken together, these studies have indicatedthat thimerosal is cleared much more rapidly fromthe bodies of infant or adult humans, nonhumanprimates, and rodents than is MeHg.(16–26) As aresult, comparisons of nominal doses of thimerosalwith nominal doses of MeHg will overestimate therisk of thimerosal toxicity, while calculations ofcumulative external exposures to either compoundare misleading.(27) Despite the reasonableness ofthose observations, however, no one has appliedthis new pharmacokinetic information to estimateinternal mercury exposures from thimerosal at levelscurrently found in infant vaccines in the UnitedStates. This would be essential, because internalexposures are more predictive of the potentialbiological effects of xenobiotics, whether beneficialor adverse, than nominal or external dose.(28,29)

Currently in the United States, only certaininactivated influenza vaccines contain thimerosalpreservative. All other U.S. vaccines routinelyrecommended for children six years of age or un-der do not contain thimerosal preservative or con-tain thimerosal only in trace amounts (≤1 μg mer-cury/dose). Inactivated influenza vaccines continueto be marketed in both single-dose, preservative-freeand multidose, thimerosal-preservative-containing(0.01%, w/v; 0.005% mercury, w/v) formulations inthe United States because the amount of availablepreservative-free inactivated influenza vaccine is be-low the amount needed to immunize all infants andchildren each season. Because some children may beexposed to low levels of thimerosal from influenzavaccines packaged in multidose vials, we have devel-oped a quantitative pharmacokinetic model that es-timates internal exposures to thimerosal at currentU.S. levels and compares them to predicted internallevels of mercury from dietary MeHg exposures atthe USEPA reference dose (RfD).

2. METHODS

2.1. Mercury Exposures from Either InfluenzaVaccine or Diet

2.1.1. Vaccine Thimerosal

The most recent recommended immunizationschedule for persons aged 0–18 years(30) was utilized

Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants 3

Table I. Earliest Maximum Mercury Exposures from SeasonalInactivated Influenza Vaccines Administered to Infants Over theFirst 4.5 Years of Life According to the 2013 ACIP Vaccination

Schedule; Mercury Exposures Increase in Third Year of Life Dueto Increased Volume of Fluzone R© Vaccine Administered at ThatAge; Doses Normalized to Body Weight (bw) Are Based on the5th and 50th Percentiles for Body Weight in U.S. Infants 0–60

Months of Age(35)

Postnatal Age Mercury Doseof Administration Exposure (μg) (μg/kg bw)

Six months 12.5 1.5–2.0(Two doses four weeks apart) (each)One and a half years 12.5 1.1–1.4Two and a half years 12.5 0.9–1.1Three and a half years 25 1.6–1.9Four and a half years 25 1.4–1.7

to determine the course of influenza vaccination inthe United States over the first four to five years oflife. The exposures to mercury (Table I) are basedon the reported amounts contained in a recent listof FDA-approved seasonal influenza vaccines(31) andare independent of the body weight of the vacci-nated infant. Of the 11 licensed, injectable seasonalvaccine products, five are packaged in multidosevials (Afluria R©, two FluLaval R©, Fluvirin R©, Fluzone R©)that contain thimerosal (≤25 μg mercury/0.5 mLdose). Of these five products, only Fluzone R© is li-censed for use in infants as young as six months ofage. The other products are not indicated for chil-dren under three (FluLaval R©), four (Fluvirin R©), orfive (Afluria R©) years of age. Therefore, Fluzone R© ad-ministered from a multidose vial according to the2013 Advisory Committee on Immunization Prac-tices (ACIP) seasonal vaccination schedule wouldprovide the maximum theoretical exposures to mer-cury in the youngest children (six months to fouryears of age). These exposures may not be typical,however, given the possibility that a child may re-ceive Fluzone R© in a single-dose vial or prefilled sy-ringe or the live attenuated, intranasal influenza vac-cine FluMist R©, both of which are thimerosal-free.

2.1.2. MeHg at Its RfD

Four health-based guidelines have been pub-lished for dietary MeHg: 0.1 μg/kg bw/day(32)

(bw, body weight); 1.6 μg/kg bw/week (0.23 μg/kgbw/day);(33) 0.3 μg/kg bw/day;(26) and 0.4 μg/kgbw/day.(34) Although these values are quite similar,the USEPA RfD for MeHg is the lowest (most con-

servative) threshold in the range and was chosenfor this analysis. Because it is body-weight depen-dent, acceptable daily dietary levels of mercury inthe form of MeHg were calculated by multiplying theRfD by the body weights (lower 5th and 50th per-centiles) of infants over the first 4.5 years of life. In-fant body weights were estimated using infant bodyweight data collected from NHANES(35) as describedpreviously.(36) Bootstrapping was used to capture theuncertainty in body weights for infants of both me-dian and lower 5% quantile body weights. Dietaryabsorption of MeHg was assumed to be 100% basedon results in human volunteers.(37)

2.2. Pharmacokinetic Model Constructionand Validation

2.2.1. Source and Justification of Data for the Model

As mentioned, several pharmacokinetic stud-ies have been performed for thimerosal in humans,nonhuman primates, and rodents. Of these, weconsidered those performed in either human or non-human primates to be the most relevant to human-health risk assessment, based on known physio-logical, phylogenetic, and taxonomic kinship andconcordance in previous cross-species pharmacoki-netic comparisons.(38,39) This smaller set of stud-ies includes those by Pichichero et al. in humaninfants,(17–19) the study by Barregard et al. in humanadults,(20) and that by Burbacher et al. in infant non-human primates.(16) Of this group, the study by Bur-bacher et al. provided the only direct, intrastudy com-parison of the pharmacokinetics of thimerosal withthat of MeHg when administered to infant nonhu-man primates at identical doses starting at birth.

Briefly, Burbacher et al. exposed infant mac-aques to either thimerosal (IM) or MeHg (PO)at identical, weekly doses of 20 μg/kg bw for fourweeks starting from birth and reported mercury lev-els in blood weekly thereafter. The dosing regimen inthat study was designed to mimic the repeat, episodicdosing schedule of human infants (outside the UnitedStates) beginning at birth. In the absence of a sim-ilar head-to-head study in human infants, it is themost relevant study on which to base a comparativeassessment of infant mercury exposure or risk fromthimerosal relative to MeHg. In addition, applica-tion of the results from that study to human infantswas considered warranted given the almost identicalblood half-lives of mercury in human infants (threeto seven days) and infant nonhuman primates (two

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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 tothimerosal.(16–19) At FDA’s request, the individualinfant macaque blood concentration data from thestudy were kindly provided by the study’s authors,Drs. Thomas Burbacher and Danny Shen. The pro-vision of the individual animal data by the originalstudy’s authors at FDA’s request does not constitutetheir approval of the methods or conclusions of thisarticle.

2.2.2. Model Description and Justification

In order to provide an analysis that was indepen-dent and that would also provide multiple plausiblefits of the data for the purpose of ascertaining theuncertainty in internal exposure of infants to mer-cury following influenza vaccinations, the nonhumanprimate data from Burbacher et al. were reanalyzedusing a Bayesian approach (Fig. 1). To fully capturethe biological heterogeneity in the data, the Bayesian

hierarchical model allowed each monkey to have itsown unique set of pharmacokinetic parameters (e.g.,rate constants, volume of distribution), which werebased on the individual blood concentration–timedata. A Bayesian censoring model was created todeal with individual animal data that were missing orbelow the limit of detection.

The Bayesian hierarchical model, much like anequivalent multilevel mixed model, has many ad-vantages. First, because the model is a multilevelmodel where there are both subject- and population-level predictions, this approach generally allows forbetter fits to the experimental data because theresiduals for the subject-level predictions are usu-ally smaller than the residuals associated with thepopulation-level prediction in an equivalent single-level model. Second, because the model allowseach monkey to have its own unique pharmacoki-netic parameters, the intersubject variation of phar-macokinetic parameterization can be studied and

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) isshown along with the predicted response for monkey 5 (red) and monkey 14 (blue) (colors visible in online version). The dotted linesrepresent the 2.5% lower and 97.5% upper bounds on prediction. The blue and red dots correspond to the actual blood concentrationmeasurements published by Burbacher et al.

differentiated. The model was hierarchical in thesense that each of the monkey-specific parameters(the first level of hierarchy) was itself assumed to bea random draw from some probability density withdistributional parameters centered with means andvariances common to the population of all monkeys(the second level of hierarchy). The values of thepharmacokinetic parameters for the overall popula-tion were, therefore, inferred by first estimating theparameters for each monkey involved in the biolog-ical study and then utilizing the collection of thesesubject-specific parameter estimates as data for in-ference toward the population-level parameters. Theparameter constants at the third and highest level ofhierarchy, in turn, established the prior distributionfor the population pharmacokinetic parameters; theprocess of prior elicitation that defined the plausi-ble envelope for the distribution of population-levelpharmacokinetic parameters is described in the Ap-pendix. Bayesian posterior inference on the parame-ters of the model was carried out by Markov Chain

Monte Carlo (MCMC) sampling of the posterior dis-tribution using OpenBugs(40) and the JAGS packagein R.(41)

2.2.3. Model Validation

We conducted at least five validation steps dur-ing model development. First, the predicted values ofthe population model and the monkey-specific mod-els were plotted against observed values to visuallyassess the fit of the model (representative examplesin Figs. 2 and 3). Each figure includes the popula-tion prediction and the predictions for two specificmonkeys along with the 95% predictive confidenceinterval (CI). Most data points are within the 95%predictive CI of the monkey-specific model. In addi-tion, there are as many blue and red curves (subject-specific predictions) above the black curve (popu-lation prediction) as below it. Therefore, there wasgood agreement between the subject-specific predic-tions and the subject-level data, as well as consistency

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 linesrepresent the 2.5% lower and 97.5% upper bounds on prediction. The blue and red dots correspond to the actual blood concentrationmeasurements published by Burbacher et al.

between the population prediction and the animal-specific estimates. Second, we based the posterior in-ference of pharmacokinetic parameters on a produc-tion sample of 10,000 posterior samples following aburn-in period of 10,000. Visual evidence for pos-terior convergence was verified via trace plots, andquantitative evidence was based upon the evaluationof the Raftery and Lewis diagnostic test statistic.(42,43)

Third, the overall quality of model fit was as-sessed quantitatively by means of posterior predic-tive checking.(44) Specifically, for each MCMC simu-lation, we randomly generated a simulated responsefor each monkey and measurement time and thencompared the sum of squared residuals betweenthese simulated responses and the model predictions.These quantities were in turn compared with the sumof squared residuals between the simulated predic-tion and the actual data. A Bayesian p-value was thencomputed by measuring the number of times in thesimulation that the sum of squared residuals for the

posterior predictive distribution was larger than thesum of squared residuals from the data at hand anddividing by the number of simulations. The Bayesianp-value for the one-compartment model applied tothe MeHg data was 0.54, and that for the two-compartment model applied to the thimerosal datawas 0.562, indicating an acceptable fit to the data forboth models. Fourth, the deviance information crite-rion (DIC(43)) was applied to compare models withsimpler parameterizations to more complex ones,with a model with a lower DIC value being preferred.

Fifth, we conducted a Bayesian sensitivity anal-ysis to determine whether the quality of fit wouldsubstantially change if we changed the center of ourprior distributions for model parameters at the thirdlevel of hierarchy. The sensitivity analysis was con-ducted by running the baseline MCMC simulationand then perturbing the parameters one at a time andthen running alternate MCMC simulations with thisnew prior distribution. For each set of simulations,

Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants 7

we compared how changing the prior distributionchanged the overall fit of each model by means ofcomputing the log-likelihood value for each simu-lated MCMC run. Specifically, we investigated 11 dif-ferent choices for prior distributions and investigatedhow each change affected the log-likelihood score.Figure A1 and Tables AI and AII demonstrate thatthe fit is not significantly changed by the choice inprior distribution. Each box plot in the figure wascomputed by calculating the log-likelihood score foreach of the 10,000 MCMC simulations. The box plotsare very similar across the scenarios, suggesting thatthe overall fit of the model is robust to changes in as-sumptions about the prior distributions.

2.3. Estimation of Mercury Retention inHuman Infants

Based on the shape of their blood time coursecurves, we, like the original study authors,(16) mod-eled the distribution of mercury into one compart-ment (central) for MeHg and two compartments(central and peripheral) for thimerosal. This classi-cal pharmacokinetic approach was consistent withthe use of either one- or two-compartment modelsto describe the distribution of these organomercu-rials in the human body previously.(18–20,32,45) Bodyburden of mercury following exposure to eitherthimerosal-containing influenza vaccines or dietarylevels of MeHg at the RfD was calculated us-ing the pharmacokinetic parameters derived fromthe Bayesian models in Table II. Specifically, mer-cury retention was estimated over the first fourto five years of life by substituting the respec-tive, relevant exposures (μg) to either thimerosal(IM) or MeHg (PO), along with the rate con-stants derived from the Bayesian analysis, into Equa-tions (A.1) through (A.3). Differential equationswere solved and time course graphs were gener-ated using R(46) or Biokmod(47) in conjunction withMathematica 8 (Wolfram Research, Champaign, IL,USA).

The areas under the mercury body burden curves(AUCs) following daily exposure to acceptable dailydoses of MeHg or episodic exposures to thimerosalwere calculated using Equations (A.14) and (A.15),respectively. Results of multiple Bayesian simula-tions were used to obtain a predictive Bayesian confi-dence limit for the AUC following exposure to eitherMeHg or thimerosal. The body burden for infants ofboth median and lower 5% body weights was thenplotted, and the respective AUCs calculated, along

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 RepeatedExposures to Thimerosal or MeHg at the RfD

Fig. 4 presents levels of mercury in infants fol-lowing single doses of influenza vaccine that con-tain thimerosal (up to 2 μg/kg bw; Table I) or dailyoral consumption of acceptable doses of MeHg(32)

over the course of the first 4.5 years of life. It isclear from the graph that there is no long-term ac-cumulation of mercury in the body following sin-gle, yearly exposures to thimerosal. Integrating un-der the concentration–time curve (from time zero to1,700 days, or 4.5 years) yielded a median AUC forthimerosal of 700 μg Hg-days in infants (Table III).On the other hand, integrating under the mediancurve for this time period for MeHg exposures at theRfD in the lowest-weight infants yielded an AUC of100,000 μg Hg-days (120,000 for median-weight in-fants). Comparing the AUC of 700 μg Hg-days forthimerosal mercury to those from MeHg at the RfD(100,000 or 120,000) over the first 4.5 years of lifeyields a margin of exposure (safety) of 143 or 171.This indicates that the body burden of mercury fromvaccine thimerosal in infants over the first 4.5 yearsof life is at least 140-fold lower than the body bur-den of mercury from acceptable daily levels of di-etary MeHg over the same time period. Fig. 4 alsoindicates that an infant’s peak body burden of mer-cury at the time of his or her first influenza vaccina-tion at 180 days of life is two- to threefold less thanthe median acceptable body burden of MeHg in ei-ther median- or low-weight infants.

3.2. Uncertainty in Body Burden Estimates OverTime (AUC)

Table III contains a Bayesian posterior summaryof the uncertainty (upper and lower 2.5% quantilesand standard deviation) surrounding the AUC val-ues corresponding to the median time course curvesfor both thimerosal and MeHg. As mentioned, AUCestimates for MeHg took into account both the vari-ability in the pharmacokinetic parameters in infantmonkeys (Table II) and the variability of infant bodyweights(35) as modeled by Mitkus et al. Therefore,this uncertainty analysis provides an estimate of the

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Table II. Posterior Summary of the Population-Level Parameters for the Two-Compartment Pharmacokinetic Model for Thimerosal andthe 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 Vc = exp(μV) 1.62 0.24 0.98 1.54 1.66 1.78 2.01Thimerosal K12 = exp(μk12) 0.20 0.20 0.03 0.08 0.13 0.23 0.80Thimerosal K21 = exp(μk21) 1.08 1.26 0.10 0.40 0.73 1.32 4.16Thimerosal Ka = exp(μka) 3.92 4.96 0.68 1.43 2.37 4.34 16.51Thimerosal Ke = exp(μke) 0.18 0.04 0.13 0.16 0.17 0.20 0.30MeHg Vc = exp(μV) 1.76 0.09 1.59 1.69 1.75 1.82 1.95MeHg Ka = exp(μka) 19.42 37.86 2.16 5.42 10.21 19.76 94.52MeHg Ke = exp(μke) 0.017 0.005 0.007 0.014 0.018 0.021 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 25μg) (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 generatedby simultaneously drawing samples from both the simulated body weight distributions and the pharmacokinetic parameter distributions.

internal exposure to mercury in infants who may beat the tails of the population distribution for bodyweight and pharmacokinetic clearance. AUCs forthimerosal and MeHg in low- or median-birth-weightinfants with the fastest mercury kinetics (2.5% quan-tile, Table III) are 550 and 66,000 or 80,000 μg Hg-days, respectively. That equates to a margin of expo-sure of 120–145. AUCs for thimerosal and MeHg inlower- or median-birth-weight infants with the slow-est mercury kinetics (97.5% quantile, Table III) aremuch higher: 1,500 and 250,000 or 300,000 μg Hg-days, respectively; and the margin of exposure forthat comparison is 167–200. AUCs for internal mer-

cury exposure from thimerosal, as predicted by ourBayesian models, therefore, are always at least 120-fold lower than the respective AUCs for MeHg, in-cluding the most conservative comparison using thelowest-weight babies with the fastest mercury kinet-ics. Peak body burdens of mercury following episodicexposures to thimerosal were also always lower thanthe corresponding level of acceptable mercury frommethylmercury in low-birth-weight infants with thefastest kinetics (Fig. 4). Taken together, these resultsindicate that mercury in the body following maximaltheoretical exposures to thimerosal preservative ininactivated influenza vaccine packaged in multidose

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 Birthto 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): 2.5% Quantile Median Mean 97.5% Quantile Std. Dev.

Thimerosal 550 700 780 1500 300Safe MeHg—infants at the lowest 5% quantile for body weight 66,000 100,000 110,000 250,000 57,000Safe MeHg—infants at the median body weight 80,000 120,000 140,000 300,000 70,000

vials does not at any time exceed the body burdenof mercury that is based on a highly conservativethreshold for dietary MeHg. In addition, a poten-tially sensitive subpopulation (low-birth-weight in-fants) will not be exposed to unsafe levels of mercuryfrom vaccine thimerosal following single or repeatedexposures in the United States.

4. DISCUSSION AND CONCLUSION

In 2001, Ball and colleagues at the Center for Bi-ologics Evaluation at the FDA published an assess-ment of thimerosal exposures from infant vaccinesand reported that some infants could be exposed tocumulative levels of mercury from vaccines that werehigher than the EPA threshold for MeHg.(1) Becausethe clinical significance of that apparent risk was un-known, the authors noted that lowering exposuresto thimerosal in vaccines was consistent with thelarger aim of reducing exposures to mercury in gen-eral. Today, the inactivated influenza vaccine pack-aged in multidose vials is the only U.S. infant vac-cine that contains thimerosal at other than residualamounts.

Since the original analysis of Ball et al., it hasbecome increasingly clear that there are signifi-cant differences in pharmacokinetics between theorganomercurials, thimerosal and MeHg, and thatthese differences govern their respective toxicities. Ithas been shown, for example, that mercury is clearedfrom the blood and brain much faster following ex-posure to thimerosal than to MeHg in infant nonhu-man primates.(16) Peak blood or brain mercury lev-els have also been shown in several species to belower following exposures to thimerosal (or EtHg)relative to an equivalent dose of MeHg administeredvia the same (IM/IM) or real-world (IM/PO) routesof exposure,(16,21–24) and those studies illustrate theacknowledged differences in disposition and tissueuptake between the two compounds.(48–51) In addi-

tion, thimerosal is more quickly and extensively me-tabolized to inorganic mercury in the brain than isMeHg,(16,48) and that process of dealkylation maybe a detoxification step.(49,52) Taking together thesethree major pharmacokinetic differences, one wouldexpect, therefore, greater toxicity from MeHg thanfrom thimerosal when administered at equivalentdoses, given the higher and longer bioavailability ofthe former.

These established differences in pharmacoki-netics between thimerosal and MeHg imply thatcomparing nominal exposures to mercury fromthimerosal in infant vaccines directly to nominalexposures at the MeHg RfD will overestimatethimerosal risk.(27) This is the case because, funda-mentally, comparisons of nominal (external) expo-sures do not take into account differences in physi-ological ADME processes (absorption, distribution,metabolism, excretion), which normally serve to re-duce internal exposures to xenobiotics in the bodyfollowing exposure. Therefore, we expanded theoriginal comparison of Ball et al. in order to providea more reliable (i.e., internal) estimate of thimerosalexposures. First, we utilized body burden, not nom-inal dose, as our metric for internal exposures toeither thimerosal or MeHg, and we performed ourcomparison accordingly. Second, in the models thatwe built to estimate the body burden of eitherthimerosal or MeHg, we explicitly took into accountthe major pharmacokinetic difference between thesetwo organomercurials, which is the faster clearanceof thimerosal from the body. And, third, in the ab-sence of comparative repeat-dose pharmacokineticdata for thimerosal and MeHg in human infants, weutilized results from the only published study that(1) compared the clearance of thimerosal with thatof 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 Burbacheret al.

10 Mitkus et al.

Specifically, we generated (Bayesian) statisticalmodels for thimerosal and MeHg that were based onthe individual animal blood mercury concentrationdata from that study. Several visual and quantitativevalidation tests indicated that our Bayesian modelswere robust and fit the experimental data well. Phar-macokinetic rate constants were then derived fromour two data-based models and applied to a real-lifehuman exposure scenario, in order to estimate thebody burden of mercury in U.S. infants followingexposures to thimerosal in vaccines or dietary MeHgat its RfD. Direct cross-species application of the pa-rameters was considered reasonable based on the es-sentially identical blood half-lives of thimerosal (av-erage four to five days) in infant human and nonhu-man primates.(16–19) As far as we are aware, this is thefirst time such an estimate of mercury body burdenfrom thimerosal has been made for human infants.

The major outcome of the modeling efforts re-ported here has been the estimation of the mer-cury body burden in infants from either thimerosalat U.S. exposure levels or MeHg at its oral RfD.This is a significant advance over previous estimatesthat calculated (only) external exposures to thesecompounds.(1) Although Pichichero et al. measuredblood levels of mercury in human infants follow-ing single or repeat doses of thimerosal,(17–19) thosestudies, which are still highly relevant, did not assessthe effect of controlled dietary MeHg exposures onblood levels of mercury or conduct additional phar-macokinetic analyses to estimate parameters such asrate constants that could then be applied to estimatea more comprehensive metric of internal exposure(body burden) over several years, as we have donehere.

Although we view our study to be an advanceover previous efforts, one of its limitations is itscomparison to the regulatory threshold for MeHg.That comparison is highly conservative because theRfD for MeHg is designed to be protective of pos-sible adverse effects following daily exposure for alifetime, while exposure to thimerosal is small andepisodic, hence the conservatism. In addition, regu-latory thresholds for short-term exposures to xeno-biotics are normally above those for long-term ex-posures because toxicity thresholds trend lower aslength of exposure becomes higher. However, be-cause an acute or short-term threshold (e.g., “RfD,”“minimal risk level”) for either MeHg or thimerosaldoes not exist, the RfD for MeHg was the next best,although imperfect, comparator. This means that themargins of exposure reported here (i.e., ≥120) ac-

tually underestimate the true margin of safety forthimerosal by some factor. For that reason, we sup-port future assessments of thimerosal safety thatmove away from comparisons with MeHg.

In summary, using pharmacokinetic parameters(rate constants) derived from the most relevant avail-able study of exposure of infant nonhuman primatesto either thimerosal or MeHg, we have estimated thebody burden of both thimerosal and MeHg in humaninfants. We have demonstrated that children, includ-ing those of low birth weight, who receive the rec-ommended schedule of annual inactivated influenzavaccines formulated with thimerosal as a preser-vative in multidose vials in the United States willreceive over 100 times less internal exposure to mer-cury on a time-integrated basis over the first 4.5years of life as compared to the USEPA regula-tory threshold for dietary MeHg over a similar in-terval. Peak mercury levels from thimerosal in thebody were also always below the regulatory thresh-old for MeHg over the same time period. By takinginto account the significant pharmacokinetic differ-ences between thimerosal and MeHg that have beenelucidated over the past decade and by not relyingon nominal dose as the exposure metric, our studysignificantly improves upon the previous estimatesof Ball et al. Together with the robust human epi-demiological data for thimerosal, our results, whichare based on a “worst-case” comparison, support thecontinued safety of this preservative at levels cur-rently used in multidose inactivated influenza vac-cines in the United States.

ACKNOWLEDGMENTS

We thank Drs. Burbacher and Shen for kindlyproviding the individual infant macaque blood con-centration data from their paper.(16) The provision ofthe individual animal data by the original study’s au-thors at FDA’s request does not constitute approvalof the methods or conclusions of the article by theoriginal study’s authors.

APPENDIX: MATHEMATICS OF THEBAYESIAN PHARMACOKINETIC MODELS

In the one-compartment pharmacokineticmodel for MeHg, mercury is absorbed into the bloodfrom the GI (rate constant ka) and removed from theblood at a rate proportional to the elimination rateconstant ke. The rate of movement of MeHg into andout of a single, central compartment is described by

Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants 11

the following equation:

dX1/dt = ka XGI(t) − ke X1(t), (A.1)

where dX1/dt refers to the rate of change of MeHgwithin the central compartment; and XGI refers tothe amount of MeHg at the port of entry followingoral, dietary exposure (GI). In the two-compartmentmodel for thimerosal, mercury is absorbed from thesite of IM injection (rate constant ka) into the centralcompartment (well-perfused tissues) and distributesbetween the central compartment and the peripheralcompartment (less well-perfused tissues; rate con-stants k12 and k21). Elimination takes place from thecentral compartment (rate constant ke). The differen-tial equations describing the time course of mercuryin either compartment are described as follows:

dX1/dt = k21 X2(t) + ka XM(t) − (ke + k12)X1(t),(A.2)

dX2/dt = k12 X1(t) − k21 X2(t), (A.3)

where dX1/dt and dX2/dt refer to the rates of changeof thimerosal mercury within the central and periph-eral compartments, respectively, and XM refers tothe amount of mercury at the site of intramuscularinjection.

When K doses {D(t j )}Kj=1 are administered at the

dosing times {t j }Kj=1, the superposition principle in

differential equations ensures that the blood concen-tration in the blood is given by

f1(t) =∑j :t j <t

FD(t j )ka

Vc(ka − k)

[exp (−ke(t − t j ))

−exp (−ka(t − t j ))]I[t − t j ]

(A.4)

for the one-compartment model, and

f2(t) =∑j :t j <t

FD(t j )Vc

[Aexp (−ka(t − t j ))

+Bexp (−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 ef-fective volume of the central compartment, F is thebioavailability, I[•] is an indicator function that hasa value of 1 whenever the argument (t − t0) > 0 andis 0 otherwise, and the parameters {α, β, A, B, C} aregiven by:

α, β =(k12 + k21 + ke) ±

√(k12 + k21 + ke)2 − 4k21ke

2

A = ka(k21 − ka)(α − ka)(β − ka)

, B = ka(k21 − α)(ka − α)(β − α)

,

C = ka(k21 − β)(ka − β)(α − β)

(Ref. 53).

Often, when dealing with data for a single methodof administration, the bioavailability F is statisticallyunidentifiable; accordingly, we estimate the effectivecentral-compartment volume Vc = Vc/F in all the re-sults that follow.

Let yik denote the kth blood concentration mea-surement made on the ith monkey subject and let tikdenote the time of the blood measurement. A ba-sic statistical model for the blood concentration ofMeHg 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), theresiduals εik are independent and identically dis-tributed (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 statisti-cally utilized to fit the concentration of EtHg is givenby:

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 exactlythe same set of pharmacokinetic parametersand so does not account for biological hetero-geneity between monkeys.

(2) The models assume that the residuals εik arecaused by a simple and unexplainable sourceof error with uniform variance σ 2. Because ofthis, the model does not distinguish betweenvariation caused by monkey-to-monkey vari-ation (biodiversity) and other unexplainableerrors.

To more accurately model the biological het-erogeneity that is present among animal subjectsand more faithfully account for the hierarchicaldata structure, a more appropriate model would as-sume that each of the monkeys involved in theBurbacher et al. experiment is endowed with itsown monkey-specific set of pharmacokinetic parame-ters. Let

{ka[i], ke[i], k12[i], k21[i], V[i]

}17i=1 denote the

monkey-specific rate constants and effective volumes

12 Mitkus et al.

for the ith monkey. The index i will be utilized torepresent the data and parameters particular to theith monkey in all that follows. The one-compartmentmodel for the blood concentration of mercury fromMeHg is:

yik = f1(tik|ka[i], ke[i], V[i]) + εik, (A.8)

and the equivalent two-compartment model for theblood concentration of thimerosal mercury has theform:

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 distributedhaving 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) fiteach monkey with its own separate monkey-specificset of pharmacokinetic parameters, the residuals εik

in Equations (A.8) and (A.9) will necessarily besmaller, on average, than the residuals εik in Equa-tions (A.6) and (A.7). Moreover, the lack of fit, orresidual variance specific to each monkey σ 2[i] =Var(εik) in Equations (A.8) and (A.9) will similarlybe smaller than σ 2as well.

Because we allow different pharmacokinetic pa-rameters for each monkey, we assume that eachmonkey-specific parameter is itself a random drawfrom an overall population distribution. In our one-compartment Bayesian model for MeHg we assumethat the monkey-specific pharmacokinetic parame-ters are distributed by:

ka[i] ∼ LogNormal(μka, σ2ka),

ke[i] ∼ LogNormal(μke, σ2ke),

V[i] ∼ LogNormal(μV, σ 2V),

(A.10)

and for the two-compartment pharmacokinetic mod-el for EtHg we similarly assume that:

ka[i] ∼ LogNormal(μka, σ2ka),

ke[i] ∼ LogNormal(μke, σ2ke),

k12[i] ∼ LogNormal(μ12, σ212),

k21[i] ∼ LogNormal(μ21, σ221),

V[i] ∼ LogNormal(μV, σ 2V),

(A.11)

where {μka, μke, μk12, μk21, μV} are the means of thelogarithm for the population of the respective phar-macokinetic parameters, and

{σ 2

ka, σ2ke, σ

2k12, σ

2k12, σ

2V

}are the respective population variances of the log-arithms of each parameter. In our specification weutilize a log normal distribution in order to ensurethat each of the pharmacokinetic parameters is posi-

tive. The population-level parameters themselves areunknown and are of primary interest for estimation,so following Bayesian methodology we introduce yetanother level of hierarchy and let the population-level parameter means and variances be specified by

μka ∼ N(μka0, σ2ka0) σ 2

ka ∼ Unif(0, θa),μk ∼ N(μk0, σ

2k0) σ 2

k ∼ Unif(0, θk),μV ∼ N(μV0, σ

2V0) σ 2

V ∼ Unif(0, θV),(A.12)

for the one-compartment model of MeHg and simi-larly let

μka ∼ N(μka0, σ2ka0) σ 2

ka ∼ Unif(0, θa),μke ∼ N(μke0, σ

2ke0) σ 2

ke ∼ Unif(0, θe),μk12 ∼ N(μk120, σ

2k120) σ 2

k12 ∼ Unif(0, θ12),μk21 ∼ N(μk210, σ

2k210) σ 2

k21 ∼ Unif(0, θ21),μV ∼ N(μV0, σ

2V0) σ 2

V ∼ Unif(0, θV),

(A.13)

be the analogous prior population-level parametersfor the two-compartment model for thimerosal mer-cury. In both models, we specify the lack-of-fit er-ror with a vague Gelman prior σ 2[i] ∼ Unif(0, θ) andthen the Bayesian model is completely specified.(43)

The Bayesian model is an empirical Bayesianmodel (“objective Bayesian”) in the sense that thedistributional parameters of the prior were speci-fied by setting them equal to the maximum like-lihood estimators from Tables III and IV in Bur-bacher et al. Bayesian posterior inference on theparameters of the model was then carried out byMCMC sampling utilizing the Metropolis-Hastingsalgorithm to simulate 10,000 samples from the pos-terior distribution.(43) To ensure that the MCMC al-gorithm had converged, a burn-in of 1,000 posteriorsimulations was utilized and posterior convergencewas verified via trace plots. The posterior summaryof the two models mentioned above generally con-cur with the parameter estimates and correspondingstandard errors given by Burbacher et al. As stated,we obtained quantitative evidence for the quality offit of our statistical model by means of posterior pre-dictive checking.(44)

To establish the priors for the population vari-ances {σ 2

ka, σ2ke, σ

2k12, σ

2k21, σ

2V, σ 2}, we utilized an anal-

ysis by Renwick and Lazarus to inform our decisionmaking.(54) In that paper, the pharmacokinetics of 60different compounds were studied, and the interindi-vidual variation was found to be roughly 3.2 (thatstudy currently serves as a basis for a default three-fold uncertainty factor for pharmacokinetic variabil-ity in human-health risk assessment). On the logscale, this variation translates to 1.16. We padded

Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants 13

the interindividual variation somewhat as insuranceagainst compound-specific peculiarities and set theprior for each variance

{σ 2

ka, σ2ke, σ

2k12, σ

2k21, σ

2V, σ 2

} ∼Unif(0, 3), so θa = θk = θ12 = θ21 = θV = θe = θ = 3.Because these prior distributions were uniformly dis-tributed over the entire plausible range of variances,this prior specification had little influence in theestimation procedure. The specification of priors forthe population means {μka, μke, μk12, μk21, μV} wasa bit trickier as some biologically plausible under-standing about the true value of these parametersmust be applied in order to ensure algorithm con-vergence. Accordingly, in order to prevent conflictsbetween priors and likelihoods, we adopted an em-pirical Bayesian approach, which substitutes point es-timates utilizing the data for certain parameters. Toobtain point estimates on pharmacokinetic parame-ters, we fit a one- or two-compartment model to thedata for each monkey utilizing the nonlinear regres-sion algorithm from SAS, PROC NLIN (SAS insti-tute, Cary, NC, USA).

Once the nonlinear estimates for each monkeywere obtained, we set the expected value of themean of the prior distribution equal to the sampleaverage from the regression estimates for the loga-rithms of the parameters (not shown). By doing this,we would ensure that the center of the prior dis-tribution would be roughly in the same neighbor-hood where the posterior mode would likely be. Inorder to ensure that the prior distributions for thepopulation means {μka, μke, μk12, μk21, μV} were suf-ficiently diffuse, we were motivated to choose thevariances {σ 2

ka0, σ2ke0, σ

2k120, σ

2k210, σ

2V0} as large as pos-

sible. The logic behind the selection of these vari-ances follows closely from Renwick and Lazarus, inwhich the mean coefficient of variation in pharma-cokinetic parameters for the 60 compounds involvedin the study was a maximum of 137%. In all cases,the distributions for the population means had coef-ficients of variation larger than 137%. The completeprior specification for the population means in theone-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 two-compartment model for thimerosal was:

μka ∼ N(0.58, 1),

μke ∼ N(−0.81, 10),

μk12 ∼ N(−0.98, 1.5),

μk21 ∼ N(−0.7, 1),

μV ∼ N(0.2, 100).

To compute the area under the body burdencurve (AUC) for mercury, we used appropriate for-mulas for the integral of the mass (body burden) un-der both the one- and two-compartment models. Fol-lowing the exposure to safe daily doses of MeHg, theformula for the integral from 0 to T is:

AUC(T) = V∫ T

0f1(t)dt

=∑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 ))ke

− exp (−ka(t − t j ))ka

].

Following the episodic exposures to thimerosal,the formula for AUC of the body burden is givenby:

AUC(T) = V∫ (

fcentral(t) + fperipheral(t))

dt+

=∑t j ≤T

D(t j )[

(A+ D)ka

+ (B + E)α

+ (C + F)β

]

−∑t j ≤T

D(t j )[

(A+ D) exp (−ka(T − t j ))ka

+ (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 theconcentration time-course in the central and periph-eral compartments and the constants A through F aregiven by:

A = ka(k21 − ka)(α − ka)(β − ka)

, B = ka(k21 − α)(ka − α)(β − α)

,

C = ka(k21 − β)(ka − β)(α − β)

, D = k12ka

(α − ka)(β − ka),

E = k12ka

(ka − α)(β − α),

F = k12ka

(ka − β)(α − β)(Ref. 53).

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 10,000MCMC iterations of the model.

Table AI. Table of Parameter Values Explored in the SensitivityAnalysis

Parameter Baseline Value Low Value High Value

μv0 0.2 0 0.4μka0 0.75 0.5 1μke0 − 0.81 − 1 − 0.5μk120 − 1.06 − 1.25 − 0.8μk210 − 0.75 − 1 − 0.5

A Bayesian sensitivity analysis was conducted todetermine whether the quality of fit would substan-tially change if we changed the center of our priordistributions for model parameters at the third levelof hierarchy. We investigated 11 different choicesfor prior distributions and investigate how eachchange affected the log-likelihood score. Figure A1and Tables AI and AII demonstrate that the fitwas not significantly changed by the choice in priordistribution.

Table AII. Table of Parameter Values Explored in theSensitivity Analysis

Model Parameter Perturbation

1 Baseline case2 Low μv03 High μv04 Low μka05 High μka06 Low μke07 High μke08 Low μk1209 High μk12010 Low μk21011 High μk210

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