}
`
`(e-1% NIH Public Access
`Author ManuscriptP
`
`0'cHEAV
`
`Published in final edited form as:
`J Clin Psychopharinacol. 2010 August ; 30(4): 404 -410. doi :10.1097/JCP.0b013e3181e66a62.
`
`First -Dose Pharmacokinetics of Lithium Carbonate in Children
`and Adolescents
`
`Robert L Findling, MD
`University Hospitals Case Medical Center /Case Western Reserve University
`Cornelia B Landersdorfer, PhD
`University at Buffalo, Department of Pharmaceutical Sciences
`Vivian Kafantaris, MD
`The Feinstein Institute for Medical Research of the North Shore -Long Island Jewish Health System
`
`Mani Pavuluri, MD, PhD
`University of Illinois at Chicago
`
`Nora K McNamara, MD
`University Hospitals Case Medical Center /Case Western Reserve University
`
`Jon McClellan, MD
`University of Washington
`Jean A Frazier, MD
`University of Massachusetts Medical School
`Linmarie Sikich, MD
`University of North Carolina at Chapel Hill
`Robert Kowatch, MD, PhD
`Cincinnati Children's Hospital
`Jacqui Lingler, BS
`University Hospitals Case Medical Center /Case Western Reserve University
`
`Jon Faber, MA
`University Hospitals Case Medical Center /Case Western Reserve University
`
`Perdita Taylor- Zapata, MD
`Eunice Kennedy Shriver National Institute of Child Health and Human Development
`William J Jusko, PhD
`University at Buffalo, Department of Pharmaceutical Sciences
`
`Corresponding author: Robert L Findling, MD, University Hospitals Case Medical Center, Case Western Reserve University School
`of Medicine, Child and Adolescent Psychiatry, 10524 Euclid Avenue, Suite 1155, Cleveland, OH 44106.
`DISCLOSURES The other authors have no financial ties to disclose.
`This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this
`early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is
`published in its final citable form. Please note that during the production process errors may be discovered which could affect the content,
`and all legal disclaimers that apply to the journal pertain.
`
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`Findling et al.
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`INTRODUCTION
`
`Page 2
`
`Bipolar I disorder (BP -I) in children and adolescents is associated with substantive
`psychosocial dysfunction and human suffering [1,2]. Safe and effective treatments are needed
`to reduce symptomatology and improve quality of life for the vulnerable youngsters and
`families impacted by this illness.
`
`Lithium is a benchmark treatment for adults suffering from bipolar illness [3], with evidence
`of benefit dating back almost 60 years [4]. However, there has been relatively little research
`regarding the use of lithium in the treatment of youths suffering from mania [5]. Given this
`paucity of information, a Written Request (WR) was issued by the Food and Drug
`Administration (FDA) under the auspices of the Best Pharmaceuticals for Children Act (BPCA)
`(FDA 2002) for the study of this agent in youths. In response, a contract was awarded to
`rigorously study lithium in children and adolescents with mania.
`
`A key step in developing evidence -based dosing paradigms for any compound is the
`characterization of the drug's pharmacokinetics (PK) [6]. Therefore, two of the goals of this
`contract were to characterize the pharmacokinetics of lithium and to develop evidence -based
`dosing for lithium in children and adolescents [7].
`
`Although many studies have examined the PK of lithium in adults [8 -10], relatively little is
`known about the PK of lithium in pediatric patients. Vitiello et al. [11] described lithium PK
`in nine children (aged 9 -12 years) with a DSM -III -R primary diagnosis of conduct disorder or
`adjustment disorder. Subjects received one single 300 mg dose of lithium carbonate. The
`disposition of lithium in these children appeared to generally be similar to that seen in adults.
`However, their elimination half -life and greater total lithium clearance were shorter than
`reported in adult studies.
`
`The present study was performed in order to describe the first dose PK of lithium in children
`and adolescents. In addition to characterizing the disposition of lithium in children and
`adolescents with BP -I, we also explored whether patient- specific characteristics (e.g. age,
`gender and weight) influence the PK of lithium in this patient population.
`
`MATERIALS & METHODS
`
`The data presented herein were collected as part of an open -label clinical trial [7]. All
`procedures were approved by each participating investigator's Institutional Review Board for
`Human Investigation. The parents /guardians of all study subjects provided written informed
`consent and all youths provided written assent before participation.
`
`Study Subjects
`Youths aged 7 to 17 years who met DSM -IV (APA 1994) criteria for BP -I in a current manic
`or mixed state without active psychotic symptoms were eligible to participate. Subjects
`underwent a psychiatric interview by a child and adolescent psychiatrist. In addition, the
`Schedule for Affective Disorders and Schizophrenia for School -Age Children- Present and
`Lifetime version (KSADS -PL) [12] was administered by a trained interviewer to confirm the
`clinician's diagnosis. Subjects also needed to receive a score of 20 or greater on the Young
`Mania Rating Scale (YMRS) [13] both at screening and at the initiation of lithium dosing.
`Subjects were required to be in good physical health and capable of swallowing study
`medication as whole lithium carbonate capsules.
`
`Subjects with a current diagnosis of schizophrenia, schizoaffective disorder, a pervasive
`developmental disorder, anorexia nervosa, bulimia nervosa, substance dependence, or
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`Findling et al.
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`obsessive -compulsive disorder were excluded. Also, subjects with an intelligence quotient less
`than 70 based on the results of the Wechsler Abbreviated Scales of Intelligence (WASI)
`Vocabulary and Matrix Reasoning Subscales [14] were not included. Other exclusion criteria
`included positive screens for drugs of abuse during screening and at retest 1 to 3 weeks later,
`current general medical conditions and unstable medical illnesses that might be affected
`adversely by lithium or could influence the efficacy or safety of lithium, a previous trial with
`lithium lasting at least 4 weeks with trough serum concentrations between 0.8 -1.2 mEq /L, or
`a history of an allergy or adverse reaction to lithium. Furthermore, subjects could not be taking:
`psychotropic agents other than stimulants within the preceding 2 weeks, stimulants within the
`preceding week, or fluoxetine or depot antipsychotics within the past month.
`
`Potential subjects that had a psychiatric hospitalization within 1 month of screening for
`psychosis or serious homicidal/serious suicidal ideation or who were currently experiencing
`active hallucinations or delusions were also excluded. Youth with symptoms of mania that may
`be attributable to a general medical condition or secondary to use of medications were not
`eligible. Sexually active females who were not using adequate forms of birth control were not
`eligible. Additionally, female subjects were ineligible if they were currently pregnant or
`lactating.
`
`The screening period to determine subject eligibility was 3 -28 days in duration. Subjects
`currently receiving fluoxetine at the screen were able to have an extended screening period
`lasting up to, but not exceeding, 6 weeks.
`
`Medication Dosing and Sample Collection
`Prior to their first dose, subjects were required to fast for at least 8 hours. Eligibility criteria
`were reviewed and confirmed prior to receiving the first dose of medication. Subjects weighing
`less than 20 kg were to receive a single dose of 300 mg (arm I). Subjects weighing 20 kg or
`more were randomly assigned to receive either a single 600 mg (arm I) or 900 mg (arm II) dose
`of lithium. Randomization assignments were stratified by age (children: 7 -11 years vs.
`adolescents: 12 -17 years) and sex.
`
`Blood samples for lithium serum levels were obtained pre -single dose, and at 0.5, 1, 1.5, 2, 4,
`8, 12, and 24 hours post -dose. The patients were admitted to the clinical research center for
`the first 24 hours of the study. In addition, subjects were randomly assigned in a 1:1 ratio to
`return to the study site for collection of a single blood sample to assess lithium serum
`concentration at either 48 or 72 hours post -dose.
`
`Safety Assessments
`Prior to the subject receiving lithium, an electrocardiogram (ECG), a physical examination,
`and a determination of sexual maturation [ 15,16] was performed. Blood pressure and pulse
`were measured prior to receiving lithium and at 2, 8, and 24 hours after the single dose.
`
`Additionally, laboratory examinations, including a chemistry profile, complete blood count
`with differential, urine toxicology screen, urinalysis, thyroid stimulating hormone (TSH),
`triiodothyronin (T3), serum thyroxine (T4), anti -thyroid antibody, urine (on the day of dosing)
`and a serum (during the screening process) pregnancy test for females was obtained prior to
`subjects receiving their first dose of lithium carbonate. The collection of a 24 hour urine sample
`was also initiated immediately prior to the first dose of lithium in order to determine creatinine
`clearance. Spontaneously reported adverse events were recorded during the blood sample
`collection period.
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`Findling et al.
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`Lithium Assays
`Lithium concentrations in serum were measured using standard clinical chemistry methods
`available at each site. These included: the LI Flex reagent cartridge (Dade Behring) used on
`the Dimension Clinical Chemistry System, a lithium Ion -Specific Electrode in a DuPont Na/
`K/Li Analyzer, the colorimetric Vitros Li Slide method on a Vitros chemistry system at 3 sites,
`and a spectrophotometric method on the Beckman Coulter Synchron chemistry analyzer at 2
`sites. Concentrations were reported in a format with 2 significant digits after the decimal point
`from centers 1, 2, and 4, and with one such significant digit from centers 3, 6, 7, and 8. The
`lower limit of quantification was 0.20 mEq/L for samples which were analyzed at centers 1,
`3, 4, 7 and 8, and was 0.25 mEq/L at centers 2 and 6.
`
`Pharmacokinetic Analyses
`The creatinine clearance was both measured directly and calculated using the Schwartz method
`[17,18] in subjects under 12 years of age. The Cockcroft -Gault method [19] was used for all
`other subjects.
`
`Statistical Analyses
`Nominal data are reported as frequencies and percents and continuous data are reported as
`means and standard deviations unless otherwise noted.
`
`Population Pharmacokinetic Analyses
`One -, two -, and three- compaitment disposition models with first - order, zero -order, sequential
`zero- and first- order, and mixed -order absorption, with or without lag -time of oral absorption
`were considered. First -order, mixed - order, and parallel first -order and mixed -order elimination
`were assessed. Competing models were evaluated by their predictive performance assessed via
`visual predictive checks, NONMEM's objective function, and residual plots.
`
`A model with two disposition compartments and first -order absorption and elimination was
`chosen as the structural model (base model). The amount of lithium in the absorption
`compartment (Agut) and initial condition is
`
`dAgut
`dt
`
`= - ka ' Agnt
`
`Agut (0) =Dose
`
`where ka (h -1) is the absorption rate constant for lithium. The amount of lithium in the central
`compartment (Ac) is
`
`dt` -ka Agut -
`
`CL+CLic
`Vc
`
`CLic
`
`Ac+
`
`P
`
`Ap
`
`Ac (0) =0
`
`where CL (L/h) is the apparent elimination clearance of lithium, CLic (L/h) is the apparent
`intercompartmental clearance, and Vc (L) and Vp (L) are the apparent volumes of distribution
`of the central and peripheral compartments. The total volume of distribution is described by
`Vc + Vp. The amount in the peripheral compartment (AO is
`
`dAp CLic
`dt
`Vc
`
`Ac
`
`CLic
`Vp
`
`Ap (0) =0
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`For the visual predictive check, the plasma concentration profiles were simulated for 20,000
`subjects for each competing model and assessed for the whole dataset and for each dose
`separately. From these data the median, the nonparametric 80% prediction interval (10% to
`90% percentile), and the nonparametric 50% prediction interval (25% to 75% percentile) were
`calculated for the predicted plasma concentrations. These prediction interval lines were then
`over -laid on the original raw data. If the model described the data adequately, then 20% of the
`observed data points should fall outside the 80% prediction interval at each time point and 50%
`of the data should fall outside the interquartile range. The median predicted concentrations and
`the prediction intervals were compared with the observed data. It was assessed whether the
`median and the prediction intervals mirrored the central tendency and the variability of the
`observed data for the various models.
`
`The between subject variability (BSV) was estimated for all PK parameters with an exponential
`parameter variability model. The residual unidentified variability was described by a combined
`additive and proportional error model.
`
`Possible relationships between patient specific covariates such as body size, age, gender, sexual
`maturation and renal function, and the individual pharmacokinetic parameter estimates were
`first explored by graphical analysis. The individual estimates for eta (deviation of the individual
`estimate from the population mean) of the respective pharmacokinetic parameters were plotted
`against the individual values of the covariate (eta -plots). Several body size descriptors such as
`total body weight, body mass index (BMI), and fat -free mass (FFM) [20] were tested. The
`effect of body size on the pharmacokinetic parameters was predicated on allometric scaling
`based on FFM as follows:
`
`1
`
`V'=V
`
`FFM'
`PP FFMstd
`FFMi
`CLi=CLpop(FF1Vhtd
`
`0.75
`
`)
`
`where Vi and Vpop are the group and populaton estimates of volume of distribution for all
`subjects with the same FFM, FFMi is the individual FFM, and FFMstd is a standard FFM chosen
`at 53 kg to enable comparisons with adult subjects. The CLi and CLpop are the group and
`population estimates of clearance for all subjects with the same FFM. Similar equations were
`used for allometric scaling based on a standard body weight of 70 kg.
`
`After accounting for body size and body composition, the potential effect of other covariates
`was assessed. Covariates were introduced into the model in a stepwise fashion. Inclusion of a
`specific covariate in the final model was based on visual analysis of eta -plots, change in
`NONMEM's objective function, and the reduction in BSV.
`
`Computation
`The Laplacian estimation method with the interaction estimation option in NONMEM version
`VI level 1.1 (NONMEM Project Group, University of California, San Francisco, CA, USA)
`was used for population PK modeling and simulation. The Beal M3 method [21] was
`implemented with the F -FLAG option in NONMEM in order to consider concentrations below
`the quantification limit. The individual limits of quantification for each site were included.
`
`RESULTS
`Demographics
`Thirty -nine subjects were enrolled into treatment arms I and II across seven study sites and
`received the single dose of study medication. No subjects who received study medication
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`discontinued before the 48/72 hour visit. These 39 subjects (20 males and 19 females) received
`study medication, had evaluable pharmacokinetic data, and were included in this analysis.
`
`The average age of the subjects was 11.8 years. None of the subjects received a dose of 300
`mg of lithium, because no subject weighed less than 20 kg. Seventeen (9 children and 8
`adolescents) received a starting dose of 600 mg of lithium and 22 (11 children and 11
`adolescents) received a starting dose of 900 mg of lithium. Subject demographics are shown
`in Table 1.
`
`Safety and Tolerability
`No subjects discontinued subsequent prospective open -label lithium treatment due to any
`adverse event that occurred during this portion of the study. Twelve subjects reported adverse
`events through either 48 or 72 hours after the first dose. The most frequently reported adverse
`events were headache (33 %), abdominal pain (17 %), initial insomnia (17 %), dizziness (17 %)
`and nausea (17 %).
`
`Concentration -time data
`The individual lithium concentration -time curves for each dose level are shown in Figure 1.
`The concentration -time curves were typically bi- exponential. Lithium concentrations were
`below the quantification limit for one subject in the 600 mg dose group at 8 h, for five subjects
`in the 600 mg and four subjects in the 900 mg group at 12 h and for seven subjects in the 600
`mg and eleven subjects in the 900 mg group at 24 h after the dose.
`
`Population PK analysis
`Figure 2 shows the array of lithium plasma concentrations for all subjects in the two dosing
`groups along with the results of the optimum population model predictions. A two -
`compartment model with first -order absorption provided optimum fitting based on the
`objective function, visual predictive checks, and residual plots. The visual predictive check
`shown in Figure 2 indicates suitable recapture of the central tendency in the data as
`approximately 50% of the observed data points fall below and 50% above the median prediction
`(solid line) at each time point. The variability was adequately predicted for the 600 mg dose.
`For the 900 mg dose at the later time points the variability in the observed data was slightly
`over -predicted, as ideally 20% of the observed data should fall outside the 80% prediction
`interval. Overall suitable predictive performance was achieved with the final model. Therefore
`this model qualified for general assessment of the pharmacokinetics in these subjects.
`
`The population parameter estimates and BSV are listed in Table 2. According to the population
`analysis (eta -plots), clearance and volume of distribution terms were positively correlated with
`total body weight, FFM, BMI and height. Within the range of creatinine clearances present in
`the studied subjects, creatinine clearance based on 24 h urine collection or calculated by the
`Cockcroft and Gault and the Schwartz equations did not show correlation with the estimated
`lithium plasma clearance (Figure 3). The allometrically scaled estimated lithium plasma
`clearance was similar between the two doses, males and females, and children and adolescents
`(Figure 4). Inspection of the eta -plots did not reveal any relationships between PK parameters
`and age, race, gender, or sexual maturation state; however the sample size in this study was
`relatively small for detecting such possible effects. The inclusion of infants or age extended
`over decades is usually needed to detect an age factor.
`
`Inclusion of fat free mass, calculated according to Janmahasatian et al. [20], as a covariate for
`clearance and volume of distribution terms based on allometric scaling resulted in a decrease
`in BSV of 9.4% on CL, 8.4% on Vc, and 6.7% on CLic, and no decrease in BSV of Vp. Inclusion
`of total body weight as a covariate on clearance and volume of distribution terms based on
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`Findling et al.
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`allometric scaling resulted in a decrease in BSV of 7.1% on CL, 39% on CLie, and no decrease
`in BSV of Ve and Vp. Predictive performance according to visual predictive checks was
`considerably improved by including FFM or WT as a covariate compared to the base model.
`Also after inclusion of a measure of body size as covariate, no correlation between the estimated
`lithium clearance and creatinine clearance was identified. Figure 3 shows the estimated lithium
`clearances in most subjects in relation to measured creatinine clearances. The graph depicts
`considerable variability in lithium clearance in this group of young patients with mania.
`
`The parameter estimates for the base model and the models including total body weight and
`FFM as covariates are shown in Table 2. Due to the large decrease in BSV of CL and Ve, and
`based on physiological considerations, the model including FFM as a covariate was chosen as
`the final model. Visual predictive checks for the model including FFM as covariate are shown
`in Figure 2. The predictive performance was similar for the model including WT. The estimates
`for the a- and f3-half-life calculated from population pharmacokinetic parameter estimates
`(Table 2) described a fast initial decline of the concentrations with a half -life averaging 2.4
`hours followed by a long terminal phase of about 27 hours.
`
`DISCUSSION
`The present report includes a population analysis which has the advantage of 'borrowing'
`information for individual subjects from the entire group of profiles, consideration of BQL
`values, and simultaneously addresses issues such as the optimum structural model, the
`influence of covariates which might account for the variability in drug exposures among the
`subjects, and utilizes several statistical measures of optimum fitting and predictive performance
`such as visual predictive checks (Figure 2).
`
`The population analysis revealed two phases in the disposition of lithium with an initial half -
`life of 2.4 hours and a later half -life of 27 hours. These phases are evident in Figures 1 and 2.
`It is important to note that neither of these phases determines the multiple -dose accumulation
`of lithium. Sahin and Benet [22] have recently pointed out that the 'operational multiple -dose
`half -life' is a composite of various phases for drugs with absorption and polyexponential
`disposition and dependent on the dosing interval (i) according to:
`
`T 1 /2op=1n 2. z/ln [ C max 55 /C min 55 ]
`
`Multiple -dose simulations indicate that the T1/20p for lithium is 13.1 hr for the QD, 14.0 hr for
`the BID, and 15.1 hr for the TID (q8h) dosing regimens. The terminal phase in the single -dose
`profiles accounts for only a part of the dose of drug and thus does not dominate in controlling
`elimination of lithium. Further, the multiple dose simulations suggested that a starting dose of
`300 mg twice or three times daily for youths weighing 30 kg or more and a starting dose of
`300 mg once daily for those weighing less than 30 kg appear to be appropriate based on safety
`margins for trough concentrations (data available on request).
`
`The pharmacokinetic parameters of lithium reflect realistic clinical conditions as the
`measurements were obtained at the time of first dosing in manic children and adolescent
`patients. Thus the variability in the time -course profiles (Figure 1) and the 48% CV in apparent
`CL (Figure 4) is not unexpected. These findings argue for careful selection of initial dosages
`of the drug based on body weights and continued practice of therapeutic drug monitoring to
`assure a range of effective and non -toxic drug concentrations.
`
`The use of FFM provided the best accounting for variability in CL and volumes in these
`patients. Lithium is a simple ion which largely distributes into body water spaces, which helps
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`Findling et al.
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`explain these results. However, lithium is appreciably cleared by the kidneys and the lack of
`correlation of lithium clearance with creatinine clearance was not expected (Figure 3). The
`reason or reasons for this are not clear. Incomplete urine collections do not account for these
`observations as both directly measured and calculated (from plasma values) creatinine
`clearances yielded such variability. The range of creatinine clearances observed in this study
`was limited as all patients had normal renal function. In this case observing a relationship
`between creatinine clearance and lithium clearance is more difficult. Also the number of
`patients in this study was substantially higher than in the previously published pediatric lithium
`study; however it is still relatively low for detecting covariate effects. The expectation of
`lithium clearance relating to GFR may serve best to anticipate disposition of the drug in older
`patients and those with renal failure.
`
`Several studies explored lithium PK in adults [23 -32]. The average reported clearance was
`between 1.32 and 2.15 L/h and half -life between 17.1 and 27.1 h (parameters allometrically
`scaled to 70 kg body weight where possible). The majority of these studies were analyzed by
`non- compartmental analysis (NCA).
`
`Prior to our study, lithium pharmacokinetics had only been examined in 9 children, ages
`ranging from 10 to 12 years, weighing between 27 and 56 kg [11]. In this study, the average
`apparent lithium clearance from NCA was 1.58 L/h, which corresponds to 2.5 L/h when scaled
`allometrically to 70 kg body weight. Their average [3-half-life from least square regression was
`17.9 h. It was concluded that children had a shorter elimination half -life and greater clearance
`compared to adults. An NCA performed on their graphed average concentrations revealed a
`large extrapolated fraction of the AUC of 22 %. Also the lowest measured lithium
`concentrations in this study were 0.03 mEq /L. NCA does not consider concentrations below
`the quantification limit, which might be a reason for the reported lower terminal half -life and
`higher clearance compared to reports from adults.
`
`Our study also found that the terminal half -life from NCA appears shorter and the clearance
`scaled to 70 kg body weight higher (average 2.66 L/h) than those values previously reported
`in adults. The NCA provides similar results for clearance for our study and the Vitiello study
`[11]. Due to truncation of the concentration -time profiles, NCA leads to biased results towards
`shorter elimination half -lives and higher clearances, and to the misleading conclusion that
`lithium clearance would be higher in children than in adults. When taking into account the
`concentration time profiles of all subjects simultaneously and considering concentrations
`below the quantification limit by population PK analysis, the allometrically scaled clearance
`is within the range of values reported for adults. This suggests that the differences in lithium
`PK parameters between children and adults can be explained by including the effect of body
`weight. Therefore population PK modeling was an essential tool for this analysis.
`
`Lithium PK in plasma and urine was modeled previously by population analysis in adults,
`utilizing a two compartment model. The estimate for renal clearance was 1.53 L/h [25]. In a
`population PK analysis in 79 adult patients [32], a clearance of 1.36 L/h and volume of 32.8
`L was reported based on a one compartment model. In this study only trough concentrations
`were measured, and neither data nor fittings were shown. Lean body weight and creatinine
`clearance were identified as covariates for lithium clearance.
`
`In a study in obese patients [33] a greater clearance compared to normal weight adults which
`correlated with total body weight but not creatinine clearance was reported. Volume of
`distribution correlated with fat -free mass, but volume per kg total body weight was lower in
`obese compared to normal weight subjects. These results agree with our conclusions that
`clearance and volume are correlated with total body weight and even better with fat -free mass.
`Our study also included obese patients (BMI greater than 24 in 12 out of 39 patients). For our
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`study population including both lean and obese subjects, FFM explained even more of the
`variability than total body weight. Therefore it is important to also take into account body
`composition, especially in obese subjects. As obesity is commonly encountered in patients
`taking antipsychotic drugs, it is a useful finding that lithium clearance correlates well with fat -
`free mass.
`
`Limitations of the present study are the relatively high limits of quantification of the clinical
`assays which lead to many samples being below the quantification limit and also the lack of
`measuring lithium amounts excreted in urine for determination of lithium renal clearance.
`Population PK analysis including the Beal M3 method for handling BQL data was applied as
`the most sophisticated method available in order to deal with this limitation [21,34].
`
`Additionally, the use of clinical laboratories, rather than a central laboratory, for lithium assay
`is a limitation of the present study. Despite this limitation, it was not anticipated that the results
`would be substantially affected, as the utilized lithium assays are standard and validated clinical
`methods. Similar commercial techniques for lithium assay were employed across the study
`sites. Specifically, all clinical laboratories used in this study analyzed the lithium samples as
`they were received, rather than batched for group analysis. Additionally, prior to
`implementation, Linearity and Precision were verified for lithium with very low coefficients
`of variation (typically below 6 %). All clinical laboratories subscribe to College of American
`Pathologists (CAP) Linearity and Calibration standards. Furthermore, all clinical laboratories
`used in this study run quality control daily, per CAP standards, in order to ensure accuracy and
`precision between day and within day variation for the analyses.
`
`In conclusion, linear elimination for lithium was found within the studied dosage regimen. Fat
`free mass was identified as the covariate which explained most of the variability in clearance
`and volume of distribution parameters. The difference in body size explains different values
`for the PK parameters in children compared to adults. Possible uses of the developed population
`pharmacokinetic model are to predict other dosage regimens, support scaling from adult to
`pediatric pharmacokinetics and support the design of future clinical trials.
`
`Acknowledgments
`This project has been funded by the Eunice Kennedy Shriver National Institute of Child Health and Human
`Development, National Institutes of Health, Department of Health and Human Services, under Contract No.
`HHSN275200503406C. Additional support for clinical research centers used in this project has been funded by the
`National Institutes of Health, grant #s GCRC MO l RR000080 and RR025014. The authors would like to thank Brieana
`M. Rowles, M.A. for her technical assistance in the drafting of this manuscript.
`
`Dr. Findling receives or has received research support, acted as a consultant and/or served on a speaker's bureau for
`Abbott, Addrenex, AstraZeneca, Biovail, Bristol -Myers Squibb, Forest, GlaxoSmithKline, Johnson & Johnson,
`KemPharm, Lilly, Lundbeck, Neuropharm, Novartis, Noven, Organon, Otsuka, Pfizer, Sanofi -Aventis, Sepracore,
`Shire, Solvay, Supernus Pharmaceuticals, Validus, and Wyeth. Dr. Kafantaris has received research support from
`AstraZeneca, Eli Lilly & Co, Glaxo -Smith Kline, Janssen Pharmaceuticals, and Pfizer. Dr. Pavuluri's work unrelated
`to this manuscript is currently supported by NIMH, NICHD, Dana Foundation, NARSAD, AFSP, and Marshall
`Reynolds Foundation. Dr. Frazier receives or has received research support from Bristol -Myers Squibb, Eli Lilly &
`Co, Johnson & Johnson, Neuropharm, Otsuka America Pharmaceutical, and Pfizer Inc. Dr. Sikich has a current
`financial interest in that she receives research funding or participates in clinical trials with Janssen, Pfizer, Bristol
`Myers- Squibb, Neuropharm, Curemark and Seaside Pharmaceuticals, and received software for a computer
`intervention in schizophrenia from Posit Science; in the past, Dr. Sikich received research funding from Eli Lilly,
`Janssen, Pfizer, Otsuka, and Astra Zeneca, and has served as a consultant for Sanofi Aventis and ABT Associates. Dr.
`Kowatch receives or has received research support, acted as a consultant and/or served on a speaker's bureau for
`AstraZeneca, Forest, Medscale, National Alliance for Research on Schizophrenia and Depression, NICHD, NIMH,
`Physicians Postgraduate Press, and the Stanley Foundation.
`
`J Clin Psychopharmacol. Author manuscript; available in PMC 2011 August 1.
`
`Page 9
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`

`Findling et al.
`
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