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Population Pharmacokinetic Investigation of Actinomycin-D in Children and Young Adults

From the Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania (Dr. Mondick, Dr. Skolnik, Dr. Adamson, Dr. Barrett); Metrum Institute, Tariffville, Connecticut (Dr. Gibiansky, Dr. Gastonguay); and Northern Institute for Cancer Research, University of Newcastle upon Tyne, Newcastle upon Tyne, United Kingdom (Mr. Cole, Dr. Veal, Dr. Boddy).

Address for reprints: John T. Mondick, Abramson Research Center, Rm 918, 3516 Civic Center Blvd, Philadelphia, PA 19104.

	ABSTRACT

TOP ABSTRACT METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Actinomycin-D is an antineoplastic agent that inhibits RNA synthesis by binding to guanine residues and inhibiting DNA-dependent RNA polymerase. Although actinomycin-D has been used to treat rhabdomyosarcoma and Wilms tumor for more than 40 years, the dose/exposure relationship is not well characterized. The objective of this study was to develop an initial population pharmacokinetic model to describe actinomycin-D disposition in children and young adults from which a prospective study could be designed. A total of 165 actinomycin-D plasma concentration measurements from 33 patients, aged 1.6 to 20.3 years, were used for the analysis. The data were analyzed using nonlinear mixed-effects modeling with the NONMEM software system. Age, weight, and gender were examined as covariates for the ability to explain interindividual variability in actinomycin-D pharmacokinetics. The final model was qualified via predictive check and nonparametric bootstrap procedures. A 3-compartment model with first-order elimination was chosen as the structural model. Allometric expressions incorporating weight were used to describe the effects of body size on actinomycin-D pharmacokinetics. Age and gender had no discernible effects on actinomycin-D pharmacokinetics in the population studied. The predictive check showed that the developed model was able to simulate data in close agreement with the actual study observations. The availability of an initial population pharmacokinetic model to describe actinomycin-D pharmacokinetics will facilitate the development of a large-scale clinical trial to study the actinomycin-D dose/exposure relationship in pediatric patients with rhabdomyosarcoma and Wilms tumor. The covariate analysis described by the current data set suggests that indices of body size captured via allometric expressions improve the partition of variation in actinomycin-D pharmacokinetics from this pilot data set. Relationships between pharmacokinetics and toxicity will be examined in future prospective studies in which children less than 1 year old will be enrolled.

Key Words: Actinomycin-D • pharmacokinetics • pediatrics

Since the 1970s, AMD dosage regimens have been modified in an effort to maximize efficacy and limit toxicity.^2-10 Although the overall cure rates for both childhood rhabdomyosarcoma and Wilms tumor have increased dramatically over the past decades,^11-14 the relationships between AMD exposure, efficacy, and toxicity are not understood. AMD dosing often results in unpredictable and untoward toxicity, including myelosuppression, hepatotoxicity, and mucositis.¹⁵ Results from both the National Wilms Tumor Studies (NWTS) and International Society of Pediatric Oncology (SIOP) studies showed that both AMD dose amount and schedule may affect toxicity risk.^10,16 However, the exact nature of the AMD dose and schedule interaction is unclear. Moreover, the contribution of radiation and other chemotherapy agents to AMD toxicity risk is not clear. These difficulties are further complicated by issues of reporting bias caused by awareness of these toxicities and progressively more rigorous toxicity monitoring.³ The need for additional clinical research to define AMD dosing regimens that maximize therapeutic effects and minimize toxicities based on objective outcomes remains apparent.

When treated with equivalent body-weight-adjusted doses of AMD, infants and young children have higher toxicity rates. Children with rhabdomyosarcoma younger than 36 months who receive vincristine, AMD, and cyclophosphamide have a 15% risk of hepatopathy compared with older children, who have a 4% risk.¹ The reasons for this are not well understood, but there are several possible explanations. Rapidly growing organs in the infant may be more sensitive to the effects of chemotherapy, thus magnifying the toxic effects of the drugs. Immature renal and hepatic function in the infant may result in a decreased drug clearance from systemic circulation, thereby producing elevated and prolonged drug exposure. Also, lower body fat composition in infants compared with older children could lead to a smaller volume of distribution in this population. Because no information is available on the PK of these agents in this young population, it is not known whether this increased incidence of toxicity is attributable to differences in drug disposition or to pharmacological differences.

AMD PK studies have been limited primarily to preclinical investigations. Distribution studies of [³H]-AMD in rat, monkey, and dog have shown that equivalent doses based on body surface area of 0.6 mg/m² result in nearly equal tissue exposures in the 3 species.¹⁷ This work was further expanded to characterize the distribution and kinetics of [³H]-AMD in the beagle dog using a flow-limited physiologically based pharmacokinetic model.¹⁸ Published human AMD PK studies consist of [³H]-AMD in 3 adult cancer patients at a dose of 15 µg/kg¹⁹ and 2 doses in children of 0.75 mg/m² (n = 1) and 1.5 mg/m² (n = 1).²⁰ More recently, Veal et al²¹ published the preliminary results from a noncompartmental analysis of PK in children receiving AMD. Results from 7 patients with sampling to 24 hours yielded clearance values ranging from 68 to 203 mL/min/m² and a median terminal half-life of 24 hours (range, 14-43 hours).

The objective of our investigation was to develop a provisional population pharmacokinetic (PPK) model to explore AMD PK in children and young adults. This model will be used as a simulation tool for the design and evaluation of a large-scale, prospective PK study of AMD in pediatric patients with cancer.

	METHODS

TOP ABSTRACT METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Clinical Evaluation
The AMD PPK model was constructed using data collected in 2 clinical studies. The majority of the data were obtained from a United Kingdom Children's Cancer Study Group study of AMD PK resulting in 144 plasma concentrations collected from 31 pediatric patients treated with 0.70 to 1.50 mg/m² of AMD. The design and results from this study were described in detail by Veal et al.²¹ Briefly, patients 21 years or younger who received AMD as part of their standard chemotherapy treatment were eligible for participation. Blood samples for the determination of AMD concentrations were collected prior to administration and at 15 minutes, 30 minutes, and 1, 2, 4, 6, and 24 hours postadministration. Seven patients provided samples at all 8 time points, with the remaining patients supplying between 2 and 7 samples each. Samples were assayed using a validated liquid chromatography-mass spectrometry (LC-MS) assay, with a limit of quantitation of 1.0 ng/mL.²⁰ Written informed consent was obtained from all patients or parents. The study protocol was approved by the UK Trent Multicentre Ethics Committee, and participating study centers obtained approval from their institutional Internal Review Board (IRB).

The remaining 21 plasma concentrations were obtained from 2 pediatric patients administered 0.05 mg/kg AMD who participated in an ongoing pilot study at the Children's Hospital of Philadelphia (CHOP). The study protocol was approved by the CHOP IRB, and written informed consent, with patient assent when appropriate, was obtained from all parents/patients enrolled. Patients between the ages of 6 months and 18 years who were receiving AMD chemotherapy as part of their standard clinical treatment were eligible. AMD was administered by bolus intravenous infusion. Blood samples for measurement of AMD concentration were obtained from a peripheral line prior to administration and at 5, 10, 30, 60, 90, and 150 minutes and 4 to 6, 8 to 12, 20 to 24, and 44 to 48 hours postadministration. Blood samples were collected in heparinized collection tubes, vortexed, and placed on ice within 30 minutes from the collection time. Plasma was stored at –80°C until assayed. Actinomycin was measured in plasma using a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay, with a limit of quantitation of 0.5 ng/mL.²²

Model Development
The PPK data were analyzed using nonlinear mixed-effects modeling with the NONMEM software system, version V, level 1.1 (GloboMax LLC, Hanover, Maryland) with the PREDPP model library and NMTRAN subroutines.²³ The first-order conditional estimation method (FOCE) was used for all model runs. The initial step in the modeling process was the definition of the base PPK model. This was accomplished by identifying (1) the appropriate number of compartments that would best describe observed drug disposition and (2) the appropriate error structures that would describe the random error arising from differences between subjects (interindividual) and from unexplained (residual) sources. Fixed-effect parameters, representing the typical population estimates, were represented by basic structural PK parameters and parameters for the magnitude of covariate effects. Random effects parameters were included to describe the intersubject variation in fixed effects parameters and to account for residual variability.

A 3-compartment model with first-order elimination was chosen as the structural model. The estimated parameters were the volume of distribution in the central compartment (V1), volume of distribution in the peripheral compartments (V2, V3), total systemic clearance (CL), and the intercompartmental clearance terms (Q1, Q2). Random effects to describe the intersubject variability were included for V1 and CL. A full covariance block was incorporated to estimate the covariance between the random effects. Intersubject variability in V1 and CL was expressed using an exponential error model:

(1)

where P_i is the estimated parameter value for individual I, is the typical population value (geometric mean) of the parameter, _Pi are individual-specific interindividual random effects for individual i and parameter P and are assumed to be distributed N(0, ²), with covariances defined by the interindividual covariance matrix .

The residual error was described by a proportional error model:

(2)

where C_ij is the jth measured observation in individual I, _ij is the jth model predicted value in individual I, _ij is the proportional residual random error for individual i, and measurement j and is assumed to be independently and identically distributed: NID(0, ²).

The second step in the model construction was to examine the impact of potential covariate effects on PK parameters. Given the small number of subjects from whom the model was developed, a covariate modeling approach emphasizing parameter estimation rather than stepwise hypothesis testing was implemented. Predefined covariate–parameter relationships were identified based on exploratory graphics, scientific interest, and mechanistic plausibility based on prior knowledge. A full model was constructed with care to avoid correlation or collinearity in predictors. Population parameters, including fixed effects parameters (covariate coefficients and structural model parameters), and random effects parameters were estimated. An exploratory assessment of any remaining trends was conducted by graphic inspection of MAP Bayes estimates of individual random effects from the full model versus covariates. Inferences about clinical relevance of parameters were based on the resulting parameter estimates of the full model and measures of estimation precision.

An attempt was made to incorporate known physiologic relationships into the covariate-parameter models. For example, the change in physiologic parameters as a function of body size is both theoretically and empirically described by an allometric model (Equation 3).^24,25 All PK parameters were scaled according to standard allometric equations:

(3)

where TVP is the typical value of a model parameter, described as a function of individual body weight; WT_i, is an individual patient's body weight; WT_ref is the reference weight, which was 70 kg for this analysis; _TVP is an estimated parameter describing the typical PK parameter value for an individual with weight equal to the reference weight; and _allo is a fixed allometric power parameter, which was assigned a value of 0.75 for clearances and a value of 1 for volumes.

The goodness-of-fit of each NONMEM analysis was assessed by the examination diagnostic scatter plots, the plausibility of parameter estimates, the precision of the parameter estimates, successful convergence of the minimization routine with at least 3 significant digits in parameter estimates, and changes in the estimates of interindividual and residual variability for the specified model.

Model Evaluation
The final model and parameter estimates were investigated with a predictive check method.²⁶ Five hundred Monte Carlo simulation replicates of the original data set were generated using the final PPK model. Distributions of the median concentration across all data points for each individual (C_med) and the maximum concentration for each individual (C_max) of the simulated data were compared with the distributions of the same parameter in the observed AMD data set. These characteristics were summarized across all simulation replicates as the population first quartile, median, and third quartile values. A predictive check P value was then calculated for each summary statistic as the proportion of simulated values that were greater than the observed value for that statistic. This P value defined the probability that the simulated data (under the model and parameter point estimates) could be more extreme than the observed data.

The precision of model parameters was investigated by performing a stratified nonparametric bootstrap procedure.^27,28 One thousand replicate data sets were generated by random sampling with replacement and were stratified by weight to ensure a representative weight distribution above and below the median weight (10.6 kg), using the individual as the sampling unit. Population parameters for each data set were subsequently estimated using NONMEM. This resulted in a distribution of approximately 1000 estimates for each population model parameter. Empirical 95% confidence intervals (CI) were constructed by observing the 2.5 and 97.5 quantiles of the resulting parameter distributions for those bootstrap runs with successful convergence.

View larger version (16K): [in this window] [in a new window]   Figure 1. Actinomycin-D (AMD) plasma concentration–time profiles from 2 pilot studies used for population pharmacokinetic model development. Open circles represent 31 patients from the UK study. Closed circles represent patients from the Children's Hospital of Philadelphia study. Plot depicts the AMD time course from time of administration to 24 hours. Inset depicts complete time course.

	RESULTS

TOP ABSTRACT METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

Initially, the effect of body size alone was investigated as a potential predictor for PK parameters. After investigation of several measures of body size (weight, body surface area), body weight proved to be the most useful covariate to explain variability in PK parameters. Plots of random effects for V1 and CL versus body weight showed a strong correlation (Figure 2). After incorporation of the allometric relationships, the objective function value decreased from 671 in the base model to a value of 468 in the new model. When the exponents for the allometric expressions were estimated, the exponent for V1 was less than 1 (0.8). Because the model will be used to simulate clinical trials and the population is extrapolated beyond the covariate space of the current model (children less than 1 year old), it was decided to fix the allometric exponent to a value of 1.0 rather than 0.8. This was implemented so that the simulation model was representative of what is physiologically reasonable rather than what could be estimated from a limited population.^24,25 Sex and age were also investigated as potential covariates, but their inclusion did not improve the PPK model. Thus, the 3-compartment model with allometric weight on all parameters constituted the final model.

View larger version (7K): [in this window] [in a new window]   Figure 2. Exploratory graphics from the base 3 compartment model for covariate relationships. Values are the individual random effects () estimates. Solid line is a loess smooth fit. (A) V1 versus total body weight; (B) CL versus total body weight

Parameter estimates resulting from the final model are shown in Table II. Fixed and random parameters were generally well estimated with the percentage coefficient of variation (%CV) of the estimates falling under 35%, with the exception of Q3 (50.2%). This result can be expected given the paucity of data after 24 hours. The estimate for V1 of 55.4 mL/kg was approximately equal to plasma volume. Variability in CL and V1 was moderate, with %CVs of 57.2% and 54.4%, respectively. Goodness-of-fit plots for the final model are shown in Figure 3. Population-predicted concentrations versus the observed values show little bias. The individual concentrations were generally well predicted, with the exception of 2 points at higher concentrations (>80 ng/mL). These 2 concentrations were somewhat difficult to characterize, likely because of the inability to estimate intersubject variance components for distribution parameters.

View larger version (10K): [in this window] [in a new window]   Figure 3. Diagnostic plots for the final 3 compartment actinomycin-D (AMD) model. (A) Population-predicted concentrations versus observed concentrations; (B) individual predicted concentrations versus observed concentrations.

Given that the majority of patient data were produced from the United Kingdom Children's Cancer Study Group study and only 2 subjects were included from the CHOP study, we were not able to make inferences concerning the impact of the assay on PK parameter estimates. Post hoc estimates from these 2 subjects did not appear to differ from the UK study population. The impact of the different assays was explored using separate residual error models. No improvement in model fit was observed with separate residual error models.

The PK model evaluation, which included the results of a predictive check and a nonparametric bootstrap, revealed that the final model provided a reliable description of the data. The predictive performances of the final PPK model for C_max and C_med are shown in Figure 4. The model-derived simulations were in agreement with the observed data for both statistics, as judged by visual inspection of the histograms and by the predictive check P values of 0.12, 0.22, and 0.4 for first quartile, mean, and third quartile C_max values, respectively. The calculated P values for C_med were 0.59, 0.25, and 0.58 for the first quartile, mean, and third quartile.

View larger version (10K): [in this window] [in a new window]   Figure 4. Predictive check results for final actinomycin-D (AMD) population pharmacokinetic model: (A) histograms showing distributions of Cave for 500 replicate simulated data sets, (B) histograms showing Cmed for 500 replicate simulated data sets

	DISCUSSION

TOP ABSTRACT METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
The AMD product label is vague on its recommendations for drug dosing. Although a dose of 15 µg/kg/d for 5 days is suggested for the treatment of Wilms tumor, rhabdomyosarcoma, and Ewing sarcoma, current clinical practice does not adhere to this recommendation. This guidance is provided with the caveat that AMD dose will depend on the tolerance of the patient, size and location of the tumor, and the use of concomitant chemotherapy. This underscores the empiricism that pervades AMD dosing rules given that formal relationships of such covariates with dose have not been quantified. The current dosing paradigm for AMD administration to children less than 1 year of age dictates that doses should be reduced by 50% to avoid hepatic toxicity.¹ In older children and young adults, the AMD dose is typically capped at 2.5 mg for rhabdomyosarcoma patients and at 2.3 mg for Wilms tumor patients, but there is no rationale for these dosing modifications. Given the polypharmacy setting in oncology and the empiricism around chemotherapy administration, an obvious need exists for guidance on the potential for drug interactions with AMD. Additionally, no dosing guidance exists for special populations (eg, patients with renal impairment).

This investigation was undertaken to assemble the currently limited AMD data available in children and young adults into a quantitative representation of dose-exposure functionality. Despite the scarcity of PK data, an initial PPK model has been developed to describe the PK of this drug in patients incorporating body size effects over a wide age range. Although our primary interest was to examine AMD disposition in very young children, 3 of the patients included in this study were young adults. It was necessary to use the adult PK data in this study for several reasons: (1) Given the small population of patients, it was appropriate to include all patients to provide as much information as possible for making inferences concerning covariates. (2) The model is being implemented as a simulation tool for the design of a large-scale study. The prospective study is likely to enroll some young adult patients, so inclusion of these subjects in this initial modeling effort was actually desirable. (3) It is appropriate to combine adult and pediatric PK data when available. This makes it possible to delineate dispositional differences between adult and pediatric patients (although in this case the small study design does not permit this). Goodness-of-fit criteria revealed that the final model was consistent with the observed data and no systematic bias remained. The model evaluation results provided evidence that both the fixed and random effects components of the final model reflected the observed data as well.

Pharmacokinetic results from 31 of the 33 patients used for this analysis were previously reported by Veal et al.²¹ In the noncompartmental pharmacokinetic analysis from this study, the median clearance was 114 mL/min in 7 patients with full PK profiles over 24 hours (mean weight = 38.5 kg). The CL estimate from the population analysis was 342 mL/h/kg^0.75. Scaling this value via allometry to a 38.5-kg person yields a CL of 88.1 mL/min. Although this model was developed from limited PK data in a small number of patients, it allows for weight-specific prediction of AMD clearance and will be useful as a simulation tool for the design and evaluation of a large-scale clinical study of AMD in pediatric patients.

It was possible to incorporate allometric relationships in order to scale the model over the range of observed patient weights. Given the aforementioned increased severe toxicity risk in younger patients, a critical population to include in any covariate analysis for AMD would be infants younger than 1 year. It is not known whether this increased risk is attributable to dispositional or pharmacological differences (or both). The current analysis includes no patients younger than 1 year, making it difficult to associate decreased clearance in younger patients with increased risk of toxicity. This will be explored more rigorously in upcoming clinical investigations that will also revisit the model developed herein with respect to consideration of additional covariates that may further improve the variance partition in key pharmacokinetic parameters.

Given the lack of informative dosing guidance for AMD, an obvious need exists for continued evaluation of AMD PK. The overall goal of this investigation is to design and conduct pharmacokinetic/pharmacodynamic-driven clinical trials in order to define AMD dosing guidance in various pediatric subpopulations, particularly for children less than 1 year of age. The design of an upcoming prospective clinical study through the Children's Oncology Group has been refined through PPK simulation work performed using the model reported here. We anticipate that together with this upcoming trial, these results will yield informed dosing guidance, and improved labeling, for AMD in children with cancer.

	ACKNOWLEDGEMENTS

TOP ABSTRACT METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 
Financial disclosure: This work was funded in part by NCI grant U10 CA098543-0251.

	REFERENCES

TOP ABSTRACT METHODS RESULTS DISCUSSION ACKNOWLEDGEMENTS REFERENCES

 

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Request Reprints Citing Articles Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Mondick, J. T. Articles by Barrett, J. S. Search for Related Content PubMed PubMed Citation Articles by Mondick, J. T. Articles by Barrett, J. S. Social Bookmarking                   What's this?