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Clinical Cancer Research Vol. 12, 5481-5490, September 15, 2006
© 2006 American Association for Cancer Research

Cancer Therapy: Clinical

Population Pharmacokinetic-Pharmacodynamic Model for Neutropenia with Patient Subgroup Identification: Comparison across Anticancer Drugs

Charlotte Kloft1,2, Johan Wallin3, Anja Henningsson3, Etienne Chatelut4 and Mats O. Karlsson3

Authors' Affiliations: 1 Department of Clinical Pharmacy, Institute of Pharmacy, Freie Universitaet Berlin, Germany; 2 Department of Clinical Pharmacy, Faculty of Pharmacy, Martin-Luther-Universitaet Halle-Wittenberg, Germany; 3 Department of Pharmaceutical Biosciences, Uppsala University, Sweden; and 4 Institut Claudius-Regaud, Toulouse, France

Requests for reprints: Charlotte Kloft, Department of Clinical Pharmacy, Institute of Pharmacy, Freie Universitaet Berlin, Kelchstrasse 31, 12169 Berlin, Germany. Phone: 49-30-83850628; Fax: 49-30-83850711; E-mail: ckloft{at}zedat.fu-berlin.de.


   Abstract
Top
 Abstract
Materials and Methods
 Results
 Discussion
 References
 
Purpose: Cancer chemotherapy, although based on body surface area, often causes unpredictable myelosuppression, especially severe neutropenia. The aim of this study was to evaluate qualitatively and quantitatively the influence of patient-specific characteristics on the neutrophil concentration-time course, to identify patient subgroups, and to compare covariates on system-related pharmacodynamic variable between drugs.

Experimental Design: Drug and neutrophil concentration, demographic, and clinical chemistry data of several trials with docetaxel (637 patients), paclitaxel (45 patients), etoposide (71 patients), or topotecan (191 patients) were included in the covariate analysis of a physiology-based pharmacokinetic-pharmacodynamic neutropenia model. Comparisons of covariate relations across drugs were made.

Results: A population model incorporating four to five relevant patient factors for each drug to explain variability in the degree and duration of neutropenia has been developed. Sex, previous anticancer therapy, performance status, height, binding partners, or liver enzymes influenced system-related variables and {alpha}1-acid glycoprotein, albumin, bilirubin, concomitant cytotoxic agents, or administration route changed drug-specific variables. Overall, female and pretreated patients had a lower baseline neutrophil concentration. Across-drug comparison revealed that several covariates (e.g., age) had minor (clinically irrelevant) influences but consistently shifted the pharmacodynamic variable in the same direction.

Conclusions: These mechanistic models, including patient characteristics that influence drug-specific parameters, form the rationale basis for more tailored dosing of individual patients or subgroups to minimize the risk of infection and thus might contribute to a more successful therapy. In addition, nonsignificant or clinically irrelevant relations on system-related parameters suggest that these covariates could be negligible in clinical trails and daily use.

In cancer chemotherapy, despite dose adaptation to body surface area, the degree of interpatient variability in effects is large (1, 2). Some patients fail to respond, whereas others experience unacceptable toxicity (3). Myelosuppression is the most common, often dose-limiting toxicity. Neutropenia makes patients highly susceptible to pathogens resulting in life-threatening infections or even death.

Empirical pharmacodynamic models accounting for the entire concentration-time profile of neutrophils have been developed (4, 5). Recently, also (semi-)mechanistic models (6, 7) and a physiology-based pharmacokinetic-pharmacodynamic model describing neutropenia for several drugs (8) have been introduced. Mechanistic models have the great advantage that estimated variables may be attributed and compared with physiologic values.

The contribution of pharmacokinetic or pharmacodynamic variability to the variable clinical outcome has clearly been shown (9, 10). A more rational approach for optimal dosing is based on elucidating the sources of variability (i.e., identifying patient characteristics responsible for variations between patients; refs. 11–15). Mechanistic models allow to incorporate patient characteristics, which may improve patient predictions and help to identify therapeutic subgroups.

The aim of this study was to develop a pharmacokinetic-pharmacodynamic model, including the characteristics responsible for variability in neutropenia following four cytotoxic drugs, and to quantify their relations to the pharmacodynamic parameters. In addition, an across-drug comparison could reveal whether there were common factors for system-related pharmacodynamic variables, such as baseline neutrophil concentrations or mean transition time (MTT), for the maturation of progenitor cells to circulating neutrophils to rationally individualize cancer chemotherapy.


   Materials and Methods
Top
 Abstract
 Materials and Methods
Results
 Discussion
 References
 
Patient population. Docetaxel (n = 9,771) and neutrophil (n = 3,549; range, 1-26/patient) plasma concentrations were collected from 637 carcinoma, melanoma, and sarcoma patients included in 24 open uncontrolled trials done at >50 different sites (16). Docetaxel was applied as monotherapeutic agent at a dosage of 75 or 100 mg/m2 (1-hour i.v. infusion every 3 weeks). Due to very few patients receiving two to three infusions for the modeling, only data of the first cycle were considered. Depending on the tumor entity [breast (36%), non–small cell lung cancer (30%), and 7 other carcinomas (34%)] or stage of disease, the patients had received different previous chemotherapy regimens.

Paclitaxel (n = 243; on cycles 1 and 3) and neutrophil (n = 530; range, 1-39/patient) concentrations were collected from 45 patients with different types of solid tumors (breast, colorectal, gastric, gall bladder, uterine, ovarian, and pancreas; ref. 17). The initial paclitaxel dose was 175 mg/m2 (3-hour i.v. infusion every 3 weeks) and based on toxicity adjusted to 110 to 232 mg/m2. Although four patients with implausible profiles had to be excluded, in total, data analysis was based on 196 cycles (median, 3/patient; range, 1-18/patient).

Etoposide (n = 236) and neutrophil (n = 682; range, 1-22/patient) concentrations were available from 71 patients with various confirmed malignancies of two clinical trials (18, 19). The total dose of etoposide was either 375 mg/m2 in the standard group or ranged from 225 to 789 mg/m2 in individualized groups. In total, 118 cycles of etoposide were given as a 3-day continuous infusion with 47 patients receiving 2 cycles at least 4 weeks after first treatment. As the start of the second cycle day was not exactly known, it was assumed that the neutrophil concentrations had returned to baseline before the start of the second cycle (i.e., no carry-over effect from the previous dose was considered).

Topotecan (n = 2,064) and neutrophil (n = 1,210; range, 1-22/patient) concentrations originated from 191 patients with various solid tumor types, mainly gynecologic and colorectal, of 5 clinical trials or off-protocol monitoring. One hundred twenty-six patients were treated with topotecan monotherapy and 65 patients were treated in combination with cisplatin. Seventy-one patients received topotecan as daily 30-minute infusions for 5 to 13 days at a dose range from 0.2 to 2.4 mg/m2/d and the remaining 120 patients received topotecan orally at a dose range from 0.15 to 2.7 mg/m2/d for 5 to 21 days (20). Topotecan data from the first treatment cycle were included in the analysis.

The patient characteristics for all data sets are summarized in Table 1 . The median baseline concentrations of neutrophils were 4.9·109, 5.5·109, 4.9·109, and 5.0·109 cells/L before docetaxel, paclitaxel, etoposide, and topotecan treatment, respectively. Granulocyte colony-stimulating factor was not administered to any patient according to protocol. All trial protocols were approved by the respective institutional review boards and patients gave their written informed consent.


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  		Table 1. Summary of patient characteristics

 
Pharmacokinetic-pharmacodynamic modeling. Data analysis was done using the nonlinear mixed effects modeling approach implemented in NONMEM, version VIß (double precision, level 1.0; ref. 21). The population analysis was based on the pharmacokinetic-pharmacodynamic model by Friberg et al. (8). For docetaxel (22) and topotecan (20), individual pharmacokinetic variables were generated from a previous population pharmacokinetic model. For paclitaxel, individual unbound concentration-time courses were generated based on dosing information and empirical Bayesian estimates or the population pharmacokinetic estimates if drug concentrations were missing (in five patients; ref. 17). For etoposide, individual concentration-time profiles were generated as described previously (8).

Covariate pharmacokinetic-pharmacodynamic model development was based on the semimechanistic structural model (8) that described the cytotoxic effect on susceptible mitotic progenitor cells. Drug effect (Edrug) was defined as an inhibitory linear slope model (f = 1 – SLOPE·Cdrug) on the proliferation rate constant (kprol) of the progenitor cells. An Emax model was applied to docetaxel, where most data were available, but did not do better in describing the data and was therefore not considered any further. The pharmacodynamic model composed of five compartments mimicking the maturation chain of progenitor cells in bone marrow to differentiated neutrophils (NEU) in the systemic circulation. Three consecutive transit compartments representing cell intermediates accounted for the time delay between Cdrug and the observed effect. The transition rate constant (ktr) between the compartments was assumed to be equal and determined by the number of transitions divided by the MTT (i.e., the average time for a progenitor cell in the bone marrow to mature to a circulating neutrophil). kprol was also influenced by a feedback mechanism determined as ratio between the baseline concentration (NEUt0) and the concentration at any time (NEUti) raised to the power {gamma}.

First order conditional estimation method with INTERACTION or GLS was tried to be established; observed and model predicted cell concentrations were log transformed. For the interindividual variability ({eta}), a log-normal distribution of the variables was assumed (21). Residual variability ({varepsilon}) was explored with different models (21). All random effects variables ({eta} and {varepsilon}) were assumed to be symmetrically distributed with zero mean and variance {omega}2 or {sigma}2, respectively.

The model building process was guided by goodness-of-fit plots (Xpose, versions 3.009-3.011; ref. 23), precision of estimates, and the objective function value supplied by NONMEM. Objective function value was used for discrimination between nested models (21). Generally, the addition of a parameter was considered significant if the decrease in objective function value was >10.83 (degrees of freedom = 1; P = 0.001); for the covariate analysis, a different strategy was followed (see below).

The covariate model building was a stepwise process. Patient-specific characteristics were analyzed for influence on the estimated pharmacodynamic parameters of the basic model (system related: NEUt0, MTT, and {gamma}; drug specific: SLOPE). Missing baseline values of continuous variables (only in the paclitaxel data set: 9% of patients lacked age and 13% lacked albumin and lactate dehydrogenase) were replaced by the median of the population. If baseline data of categorical variables were missing (docetaxel data set: 0.8% of patients lacked performance status; paclitaxel data set: 2% lacked sex) the mode was used. Missing values within one patient were replaced by carrying the last value forward. Overall, the number of missing covariates was low. Performance status was grouped as "unrestricted" performance (corresponding to performance status of 0 according to WHO; ref. 24) and "restricted" performance (corresponding to performance status of >1 according to WHO). Categorical dichotomous relations were expressed as percentage change of the typical parameter from one to the other covariate (COV) characteristic:

Formula(eq.1)
where {theta}P is the typical population estimate for one, {theta}P, COV for the other covariate characteristic, differing by {theta}P-COV, the quantitative relation estimate. As an example, covariate was coded as 0 for males and 1 for females. Thus, {theta}P represents the typical population parameter estimate for males and {theta}P, COV for females, differing {theta}P-COV percentage from {theta}P. Continuous covariates relations were modeled as percentage change from typical parameter value per unit deviation from median covariate:

Formula(eq.2)
with medianCOV being the median value of the covariate in the data set.

Covariates were investigated in an automated procedure in NONMEM (25). In the covariate analysis, the significance level was P < 0.05 during forward inclusion and P < 0.001 during backward deletion. Ninety-five percent confidence intervals (95% CI) were calculated from the SEs estimated by NONMEM or by log-likelihood profiling (26, 27).

Across-drug comparison. For the covariate effects on system-related parameter, joint weighted means and SEs for the estimates from all four drug analyses were calculated using the inverse variance method (28, 29):

Formula(eq.3)

Formula(eq.4)
where SEi is the SE for the covariate effect {theta}P-COVi of each drug. Joint estimates were calculated for those relations available in at least two drug analyses. The results were recalculated to show the percentage difference from the typical value of the parameters with the alternative dichotomous covariate characteristic or with the alteration in covariate units (continuous covariate).


   Results
Top
 Abstract
 Materials and Methods
 Results
Discussion
 References
 
Basic pharmacokinetic-pharmacodynamic models with first order conditional estimation (with INTERACTION: paclitaxel and etoposide; with GLS: topotecan) were developed and the population parameters are listed in Table 2 . Interindividual variability was estimated for the variables NEUt0, SLOPE, and MTT and residual variability as an additive error on the log-transformed scale or plus a proportional part on that scale if this further improved the goodness of fit.


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  		Table 2. Parameter estimates with RSE for the basic models

 
Final covariate pharmacokinetic-pharmacodynamic model
The estimates of the final models are summarized in Table 3 and ranged for system-related parameters from 5.22 to 6.41·109cells/L (NEUt0), 84.2-141 hours (MTT), and 0.0976 to 0.203 ({gamma}; i.e., differed by 19%, 40%, and 52% between the drugs). In contrast, the drug-specific parameter SLOPE was highly variable, being 15.6, 2.8, 0.162, and 68.1 L/µm (docetaxel, paclitaxel, etoposide, and topotecan). Hence, our data suggest that the progenitor cells in bone marrow were on a molar basis most sensitive to topotecan, then to docetaxel, paclitaxel, and least sensitive to etoposide.


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  		Table 3. Final population model parameter estimates for all drugs

 
Covariate relations
Docetaxel. Five significant relations were found for NEUt0 and SLOPE, both being influenced by the {alpha}1-acid glycoprotein (AAG; Table 3). The highest AAG of the patient population (3.63 g/L) increased NEUt0 by +113% and decreased SLOPE by 80% compared with estimates with median AAG. Sex, restricted performance status (WHO), and previous chemotherapy showed an influence on NEUt0 of approximately 12% to 15%.

Paclitaxel. Statistically significant covariate relations were only found for system-related parameters (Table 3). Previous radiotherapy strongly decreased NEUt0 by ~35%. The liver variables bilirubin, aspartate aminotransferase, or lactate dehydrogenase changed MTT or {gamma}. For very high aspartate aminotransferase values, physiologically implausible profiles were predicted. Because neutrophil observation during the return to baseline were lacking, the relation was excluded from further evaluation. Reestimating the final model without this relation caused only minor estimate changes.

Etoposide. Two covariates, albumin at baseline and bilirubin as difference in bilirubin between actual and baseline value, were included on SLOPE (Table 3). The influence of bilirubin on {gamma} could be separated in two different parts: the variation (a) between patients as influence on baseline values (bilirubin at baseline) and (b) within one patient (difference in bilirubin between actual and baseline value).

Topotecan. Two covariate relations were found significant for SLOPE (Table 3). Oral administration decreased the sensitivity by 58% and concomitant cisplatin caused an increase by 434%. Albumin concentrations were inversely related to NEUt0 and females had 22% lower NEUt0. Body height influenced MTT with a 1.8% change of the estimate per centimeter deviation from median height.

The precision of all parameter estimates in the final model was high with narrow 95% CIs never including zero [corresponding relative SEs (RSE) of {theta}P-COV: <25% docetaxel and topotecan; <29% paclitaxel; and <43% etoposide. Inclusion of the covariates reduced interindividual variability by for example, 32% to 23% (NEUt0, SLOPE; docetaxel), 14% (NEUt0; paclitaxel), 10% (SLOPE; etoposide), and 36% [SLOPE; topotecan; (i.e., these fractions of random variability were explained by the patient-specific characteristics)]. The quality of the covariate pharmacokinetic-pharmacodynamic model is shown in the goodness-of-fit plot in Fig. 1 where observed versus predicted neutrophil concentrations based on empirical Bayes estimates are depicted. The latter were based on the final model, individual pharmacokinetic profiles, covariates, and observed predose baseline neutrophils. For both data sets, (docetaxel and topotecan), where there was a very large number of concentrations in the lower part, an indent of this region on the log-transformed scale is also included. Predictive performance of the models by posterior predictive check or simulation was not evaluated due to the many and very different dosing regimens used for each drug and due to the very long run times of the models. Overall, the models developed described the data sets sufficiently well.


Figure 1
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  		Fig. 1. Observed versus predicted neutrophil concentration (·109/L) after docetaxel (A), paclitaxel (B), etoposide (C), and topotecan (D) with indents on the log-transformed scale for docetaxel and topotecan where there was a large number of data in the lower region. The predictions are empirical Bayes estimates based on the final model, covariates, individual pharmacokinetic profiles, and observed baseline neutrophil concentration; regression (- - -) and line of identity (—) are included.

 
Clinical implication of covariate sets
To assess the clinical implication of covariates combinations, neutrophil concentration-time profiles of different covariates sets were generated (Fig. 2 ). These predictions were based on each final model and all observed covariate sets of the patient population. To evaluate the effect in clinical practice, standard doses (docetaxel, 100 mg/m2; paclitaxel, 175 mg/m2; topotecan; 1.5 mg/m2/d i.v., 5 days) with typical pharmacokinetic variables or a median concentration-time profile were considered (etoposide, 4.8 mg/L average concentration; 7.5 hours half-life). Covariates influencing pharmacodynamic and pharmacokinetic were included in clearance (AAG: docetaxel; bilirubin: paclitaxel) but otherwise considered as originating from "median individuals" (body surface area, 1.76 m2; albumin, 41 g/L; 56 years).


Figure 2
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  		Fig. 2. Population predicted time courses of neutrophil concentration based on the final model, observed individual sets of covariates, and population pharmacokinetic after a standard dose of docetaxel (100 mg/m2), paclitaxel (175 mg/m2), a median concentration time profile of etoposide or a standard dose of topotecan (1.5 mg/m2/d i.v. for 5 days); horizontal lines, neutropenia grades 1 to 4 according to WHO (24).

 
From these profiles, the severity and duration of each WHO neutropenia grade (Fig. 2, horizontal lines) can be derived. Whereas only ~4% of the patients' predicted profiles after docetaxel experienced a mild neutropenia (grade <2), 88% suffered from severe grade 4 neutropenia. There were two predicted time courses with a nadir of 0.086 and 0.090·109 cells/L, originating from females with pretreated chemotherapy, unrestricted performance, and very low AAG (0.29 and 0.37 g/L). The observed neutrophil concentrations at the closest time point (168 hours) were 0.24 and 0.44·109 cells/L corresponding well to the predicted ones (0.17·109 cells/L). Grade 4 neutropenia was predicted to start on day 7 continuing for 1 week and grade 3 remained for 9 days, a long period where patients are very immunodeficient.

Neutropenia after paclitaxel, etoposide, and topotecan was less severe with only approximately 9%, 30%, and 25%, being predicted to experience grade 4 neutropenia. The large majority of predicted profiles developed grades 1 to 3 neutropenia. Whereas, on average, patients with docetaxel had a nadir around day 9, it appeared 5, 2, and 4 days later for paclitaxel, etoposide, and topotecan, respectively.

Across-drug comparison
For clinical application, results about the nonsignificant covariate relations (~160 relations) are also important for optimal therapeutic use if the lack of effect can be determined with sufficient precision. For all relations, the fixed-effect parameter {theta}P-COV of the influence and its 95% CIs were estimated. Overall, {theta}P-COV was small and the 95% CI included or was close to zero. Females generally had lower NEUt0 [mean percentage difference from males (95% CI): docetaxel, 12% (–18% to –7%); paclitaxel, –17% (–33% to –3%); etoposide, –21% (–35% to –0.2%); and topotecan, –22% (–31% to -12%)]; whereas the MTT was similar in both sexes [docetaxel, –1.5% (–8% to 5%); paclitaxel, –3.3% (–18% to 12%); etoposide, –5.6% (–14% to 4.5%); and topotecan –20% (–35% to –5%)], all statistically nonsignificant.

Predictors of system-related parameters
Summary results of all relations across drugs irrespective of their statistical significance on NEUt0 and MTT are depicted in Fig. 3 . For dichotomous covariates (e.g., sex), the circle represents the percentage change in MTT or NEUt0 of one sex to the other. The error bars indicate the 95% CI to be regarded as the uncertainty of the overall influence. For continuous covariates, both borders of the box represent the percentage changes of MTT or NEUt0 of individuals with the extremes of that covariate from the "typical" estimate (individual with median covariate). The error bars symbolize the uncertainty around these values as 95% CIs.


Figure 3
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  		Fig. 3. Overall percentage change with 95% CI in MTT (A) and baseline neutrophil concentration NEUt0 (B) by different covariates. Superscripts correspond to docetaxel, paclitaxel, etoposide and topotecan relations in this order: *, significant relation; {dagger}, nonsignificant relation; and -, missing in data set.

 
For all relations on MTT, it can be derived that there was most probably no clinically relevant influence because changes from the "typical" estimate were not larger than +10% to 20% with narrow CIs, except for three. Extremely tall patients would show clinically significantly higher MTT values. But, the significant height effect (only topotecan) may be artificial because this database was composed of two different populations: the smaller French (median, 157 cm; range, 136-177 cm) were all treated with i.v. and the taller Dutch (median, 171 cm; range, 152-194 cm) were all treated with oral topotecan. For bilirubin, statistically significant for paclitaxel, the model predicted a 30% to 40% shorter MTT for the highest bilirubin of the population. Due to the small number of individuals with extremely high creatinine values, the 95% CI for the influence in this range was large (high uncertainty) and included zero. This relation should be investigated in more patients with high creatinine values.

Several covariates (age, bilirubin, creatinine, aspartate aminotransferase, alanine aminotransferase, height, and weight) influenced NEUt0 by <+10% and had 95% CIs for the extremes, including or being very close to zero. Females and chemotherapy pretreated patients statistically significantly decreased, whereas patients with restricted performance increased NEUt0. However, these changes were <20% from typical NEUt0 and may be regarded as clinically irrelevant. High NEUt0 also have a low clinical effect on neutropenia and risk of infection. Additionally, in clinical practice, a measured pretreatment neutrophil concentration will (most often) be available. Although the clinical effect on NEUt0 might ultimately be low, this conclusion is a result of our analyses revealing quantitative relations with only minor influences on NEUt0. Overall, it can be concluded that there was a high consistency across the drugs in influencing system-related parameters.


   Discussion
Top
 Abstract
 Materials and Methods
 Results
 Discussion
References
 
In this study, covariate analyses of a physiology-based pharmacokinetic-pharmacodynamic model were done for the cytotoxic agents docetaxel, paclitaxel, etoposide, and topotecan administered to 944 patients. First, basic models using first order conditional estimation, with interaction when appropriate, were developed because this method has been shown to produce P levels close to the nominal in the covariate model building (30).

With regard to cytotoxic effects on progenitor cells, our analyses provided quantitative data on the neutropenia degree and time course of different drugs. They also allowed direct comparison among the taxanes on a molar basis (i.e., the 5.5-fold less toxic effect of paclitaxel with much lower WHO neutropenia grades and shorter duration). The different occurrence of neutropenia has also been reported previously in a case-control comparison of the frequency of neutropenia grades between docetaxel and paclitaxel in ovarian cancer (31), although these studies are always dependent on the dose regimens administered. Our quantitative measure of drug sensitivity on a molar basis is independent from the dose sizes.

For docetaxel, AAG was identified as a predictor of interindividual variability in the drug-specific variable SLOPE as reported previously by Bruno et al. (16, 32, 33). By covariate analysis, we could additionally quantify the relation as a strong influence (i.e., the drug appearing less toxic at elevated AAG). Because AAG binding of docetaxel determined in human serum in vitro is high (34), increased and decreased AAG (reference range, 0.55-1.05 g/L) may affect unbound concentrations at the effect site. Because AAG is more variable in cancer patients (0.29-3.63 g/L here), our results suggest that AAG has to be considered in clinical practice for docetaxel. The influence of plasma binding on etoposide drug effect confirmed previous results (4, 35–37). Due to the physiology-based model, these relations could directly be related on the drug-specific parameter SLOPE and be explained by alteration in plasma binding (38), thereby affecting the unbound fraction. For paclitaxel, Minami et al. (6) investigated total concentrations and found a negative correlation of age with the paclitaxel exposure producing 50% inhibition of leukocyte production. The current covariate analysis did not reveal any statistically significant covariate of the 15 ones investigated on SLOPE. Here, unbound drug concentrations were used (i.e., no covariates related to binding would be expected to be influential). Covariate analysis for topotecan, but solely on SLOPE, has been done previously using the same database by Leger et al. (20). They found the same significant relations: the effect of route (oral versus i.v.) was very similar [57% (ref. 20) and –58%] but the magnitude of increase by concomitant cisplatin medication differed [2.46 (ref. 20) and 4.34]. Although a 2.46-fold increase was also included in the 95% CI of our estimate, the difference might be due to different minimization methods and log-untransformed data. Due to the GLS method used here, our quantitative estimate should be less biased and additionally corresponded to preclinical data of a highly synergistic pharmacodynamic interaction between both drugs (39). A pharmacokinetic interaction has been excluded by others (20, 40). Overall, both significant and nonsignificant relations on SLOPE of all drugs were in good agreement with published data and can be regarded as a quality control of our analyses.

Our covariate analysis explored the influence of a large number of demographic and clinical chemistry factors on the system-related parameters NEUt0 and MTT. For the vast majority, including weight, age, creatinine, and several liver enzymes, we did not find a statistically significant influence. As patient characteristics in the populations used for the modeling of the four drugs naturally differed, this might introduce a potential bias in the between-drug comparison of the system-related parameters but the joint analysis and the magnitude of difference and SEs of the estimates for the four drugs support our approach. The across-drug comparison revealed that the 95% CIs did not include relevant changes for these covariates (except extreme values of bilirubin, creatinine, and height on MTT and albumin and creatinine on NEUt0) if defined as ±20% overall change where we generally investigated a large distribution of each covariate. That nearly all patient characteristics can be regarded as not clinically relevant on these system-specific parameters can be of value in clinical practice and development of new agents.

Further studies might investigate whether an individual dose increase might be favorable in patients with highly elevated binding partners as we generally observed the trend of higher NEUt0 and shorter MTT. The statistically significant relation between bilirubin and MTT (paclitaxel) caused a shortening in MTT of the same range that has been reported previously for granulocyte colony-stimulating factor as concomitant treatment in cancer patient (41). This and the other significant relations [i.e., female and previously chemotherapy (docetaxel)–treated or radiotherapy (paclitaxel)–treated patients or patients with unrestricted performance (docetaxel) having lower NEUt0 (higher risk of severe neutropenia)] should in future be confirmed across more cytotoxic drugs. Several studies have identified certain patient groups being at higher risk of febrile neutropenia (e.g., older breast cancer patients with prior myelosuppressive therapy and concomitant/prior radiotherapy or patients with non-Hodgkin's lymphoma with albumin of <35 g/L and lactate dehydrogenase of >reference range; ref. 42).

A simultaneous analysis of all pharmacokinetic-pharmacodynamic data of all drugs for estimation of the variables was precluded due to run-time and software issues. However, the comparison revealed that there was high consistency among the different data sets with respect to the sign of all four relations between one system-related parameter and one covariate that was available for all drugs (sex, age, and performance status; e.g., females always had lower NEUt0 and older patients had lower MTT). The influence of sex on baseline neutrophil concentrations is not supported by hematologic reference ranges in healthy populations (43, 44), where in general, females do not show lower neutrophil concentrations. Although literature is sparse, previous reports, however, investigating risk factors for neutropenia indicate a higher risk in female patients (45, 46), which could be related to lower baseline neutrophil concentrations. In addition, previous chemotherapy might be a confounder for this relation as we found a correlation between both covariates in our data set and pretreatment is a well-known influencing factor on baseline neutrophil concentrations.

The knowledge of patient characteristics significantly influencing system- and drug-related parameters might help to identify patients/subgroups of patients with higher risk of neutropenia before treatment and thus individualize the dosing regimen. During clinical trial simulations, these relations might help to improve predictive performance. The awareness of patient factors that are not influencing system-related parameters might be of value when analyzing new data sets or even with new compounds in clinical trials. Covariate analyses in future may thus be more efficient.


   Acknowledgments
 
We thank René Bruno (docetaxel), Peter Nygren (paclitaxel), Mark Ratain (etoposide), and Jaap Verweij and Alex Sparreboom (topotecan) for providing data.


   Footnotes
 
Grant support: Swedish Cancer Society (M.O. Karlsson).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received 4/ 5/06; revised 6/28/06; accepted 7/11/06.


   References
Top
 Abstract
 Materials and Methods
 Results
 Discussion
 References
 

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