Application of population pharmacokinetic modeling in early clinical development of the anticancer agent E7820
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DOI: 10.1007/s10637-008-9164-x
- Cite this article as:
- Keizer, R.J., Zamacona, M.K., Jansen, M. et al. Invest New Drugs (2009) 27: 140. doi:10.1007/s10637-008-9164-x
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Summary
The aim of this study was to assess the population pharmacokinetics (PopPK) of the novel oral anti-cancer agent E7820. Both a non-linear mixed effects modeling analysis and a non-compartmental analysis (NCA) were performed and results were compared. Data were obtained from a phase I dose escalation study in patients with malignant solid tumors or lymphomas. E7820 was administered daily for 28 days, followed by a washout period of 7 days prior to the start of subsequent cycles. A one compartment model with linear elimination from the central compartment was shown to give adequate fit, while absorption was described using a turnover model. Final population parameter estimates of basic PK parameters obtained with the PopPK method were (RSE): clearance, 6.24 L/h (7.1%), volume of distribution, 66.0 L (8.5%), mean transit time to the absorption compartment, 0.638 h (6.5%). The intake of food prior to dose administration slowed absorption (2.8-fold, RSE 13%) and increased relative bioavailability of E7820 by 36% (RSE 14%), although the effect on C_{max} and AUC was not significant. Comparison with the NCA approach showed approximately equal PK parameter estimates and food effect measures, although specific advantages of PopPK included efficiency in use of data and more appropriate assessment of variability.
Keywords
E7820Population pharmacokineticsMixed-effects modelingNon-compartmental analysisOncologyAlpha2-integrinIntroduction
E7820 is a novel sulphonamide derivative with anti-angiogenetic activity based on inhibition of endothelial cell proliferation and tube formation. E7820 has demonstrated excellent anti-angiogenetic activity in both in-vitro and animal models of angiogenesis resulting in antiproliferative effects in breast, colon, and pancreatic cancer models [1, 2]. The drug is currently in phase I clinical development. The inhibitory action of E7820 may in part be due to the inhibition of α_{2}-integrin expression in endothelial cells, or to its inhibition of activity of vascular endothelial growth factors (VEGF) and basic fibroblast growth factor (bFGF), both of which are known to induce tube formation [3–5]. Clinical results of a first-in-man study, have been published previously, and showed that E7820 was well tolerated up to 100 mg (MTD), with toxicities of grade 3 elevated liver enzymes and hemoptysis while at higher doses, haematological toxicities were observed [6, 7]. The objective of this analysis was to characterize the population pharmacokinetics (PopPK) of E7820 through assessment of PK data from this trial.
In pharmacokinetic analyses during clinical development of novel drugs, the use of the PopPK approach is encouraged by the FDA [8]. Already in early clinical stages, this tool can be very useful for gaining insight into mechanisms of drug disposition, to assess linearity in pharmacokinetics over different dosages, to identify covariates that explain between subject variability and to quantify the magnitude of unexplained variability in the population. Despite the advantageous properties of the population approach, published reports on its use in phase I trials in oncology have been only sparse [9–18]. In this article, a PopPK model was built for the novel oral anti-cancer agent E7820 that is currently in phase I development. Subsequently, the results obtained with the population analysis were compared to those of a classical non-compartmental pharmacokinetic analysis (NCA).
Methods
Study
Data were obtained from a first-in-man, open-label, dose escalation study in patients with malignant solid tumors or lymphomas aiming to determine the maximum tolerated dose (MTD) and to assess the safety, pharmacokinetics, pharmacodynamics and the food effect of E7820 when administered orally. Cycle 1 consisted of daily dosing for 28 days, followed by a 7-day washout period. Following the washout period, chronic daily dosing was recommenced after an overnight fast. Dosing of E7820 was started at 10 mg and was escalated in consecutive cohorts through doses of 20, 40, 70, 100 to a maximum of 200 mg, using a flat dosing approach. The MTD, defined as the highest dose at which dose limiting toxicities occurred in less than one third of the patients, was determined at 100 mg. No grapefruit or grapefruit-containing foods were allowed while on study and co-administration of drugs known to be modulators of the cytochrome P-450 enzymes, particularly CYP2D6, 3A4, 2C9, and 2C19 were avoided unless necessary. Use of anticoagulants/antiplatelet drugs was prohibited. A food effect study was included at the MTD level in cycle 2 of the study. Full PK curves (t = −1, 0.25, 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, 12, 24) were recorded at the first and 28th day of cycle 1 and the first day of cycle 2, while partial curves (t = −1, 1.5, 3, 6 and 8 h) were recorded at the first day of every following cycle. Patients participating in the food-effect cohort were instructed to take a standard high fat meal on the first day of cycle 2, after which a full PK curve was recorded. Urine collections were spaced at 6-h intervals and gathered on day 1 of the first cycle.
Bioanalysis
Development and validation of the analytical method was based on the good laboratory practice (GLP) principles described in FDA’s GLP regulations and the guidance on bioanalytical method validation [19, 20]. Bioanalysis of plasma and urine samples was performed by MDS Pharma Services (Lincoln, NE, USA) using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The analysis was performed on a reversed phase system using a C18 column (Waters, Milford, MA, USA) and a Perking Elmer Sciex API 3000 mass spectrometer for detection. Precision was <15% while accuracies were within the 85–115% interval. The lower limit of quantitation (LLOQ) was 1 ng/mL, while the upper limit of quantitation was established at 250 ng/mL. The calibration curve was linear over this concentration range.
Noncompartmental analysis
The NCA was performed using WinNonLin version 5.1.1 (Pharsight, Mountain View, CA). Data from full PK profiles on day 1 and 28 of cycle 1 and day 1 of cycle 2 were used, while data below the limit of quantification were discarded. Terminal parts of the curve were used to estimate the elimination half-life (t_{1/2}). Other statistics that were recorded in the analysis were C_{max} and t_{max}. Areas under the plasma concentration-time curves (AUC_{0–24}, \({\text{AUC}}_{{\text{0}} - \infty } {\text{ }}\)), and area under the first moment curve (AUMC) were calculated by using the log-linear trapezoidal rule. Mean residence time was calculated as \({{{\text{AUMC}}} \mathord{\left/ {\vphantom {{{\text{AUMC}}} {{\text{AUC}}_{{\text{0}} - \infty } }}} \right. \kern-\nulldelimiterspace} {{\text{AUC}}_{{\text{0}} - \infty } }}\). Using these pharmacokinetic parameters, estimates of CL/F and steady state distribution volume (V_{ss}/F) were calculated as \({{{\text{Dose}}} \mathord{\left/ {\vphantom {{{\text{Dose}}} {{\text{AUC}}_{{\text{0}} - \infty } }}} \right. \kern-\nulldelimiterspace} {{\text{AUC}}_{{\text{0}} - \infty } }}{\text{ }}\) and CL/F MRT respectively. The fraction excreted renally (f_{ren}) was estimated by dividing the amount of drug recovered in urine by the dose administered.
Summary statistics of these PK parameters for every patient and population mean and standard deviations were calculated for every dose level on every collection day. I.e., a standard two-stage approach in which data from each individual were weighted equally.
The effect of food intake on exposure parameters AUC_{0–24}, \({\text{AUC}}_{{\text{0}} - \infty } \) and C_{max} was evaluated by a linear mixed effect procedure in the statistical software R (http://cran.r-project.org/) using the restricted maximum likelihood estimation method (REML) included in the nlme-library [21]. In the analysis the effect of food-intake on these respective parameters was included as a fixed effect, while individual variability in the effect magnitude and residual variability (RV) were included as random effects. Before the analysis, response data were log-transformed according FDA Guidelines [20]. Mean and standard error of the estimated food effect on response parameters were calculated.
PopPK modeling
Data handling
Data were log-transformed prior to analysis. Since PK data from up to 7 cycles was available and incorporation of patients’entire dosage histories into the dataset resulted in very long run-times of our models. As full PK curves were only collected once or twice in every 28-day cycle, we considered alternative modelling approaches that would disregard the period in between PK collections, thereby leading to less computationally intensive runs. Calculation of steady state levels by internal NONMEM routines (SS option) failed since this conflicted with the turnover absorption model that was used. Therefore, we chose to fit the amount of drug in the central compartment to the recorded pre-dose steady state levels (missing dose method) as described by Soy et al. [23]. This entailed estimation of a population steady state level at the pre-dose sampling occasion, assuming a linear relationship between steady state trough levels and dose, while allowing for BSV and WSV in the amount of drug present. To investigate the performance of this approach for this compound and study design, the parameter estimates obtained with the missing dose method were compared with those obtained using full dosing histories. Since exact day-to-day dosing histories were not known in this trial, this analysis was performed retrospectively using data from simulated trials, using the final model and parameter estimates and the same study design as in the original trial.
It was assessed if discarding data points that were below the LLOQ, as opposed to retaining them, significantly influenced estimation of PK parameters [24]. Plasma and urine concentration data below the LLOQ were included in the dataset and modeled by fitting the model parameters to likelihoods of the observed data points, as described by Beal [25], This was done using the F_FLAG option under the LAPLACE estimation method to simultaneously model concentration predictions for predictions above the LLOQ and likelihoods for predictions below the LLOQ. The likelihood of the predicted concentration being under the limit of quantification was estimated using an approximation to the cumulative normal distribution [26]. The parameter estimates thus obtained were compared to parameter estimates obtained when data below the LLOQ data were discarded.
Model evaluation
The model that was developed was subsequently evaluated by estimation-based diagnostics as well as simulation-based diagnostics [27]. These included plots of predicted versus observed concentration, conditional weighted residuals (CWRES) versus time and time after dose, etc. For the calculation of CWRES, the PsN toolkit and the R-package Xpose were used [29, 30]. At the 100 mg level, at which the most patients were included, visual predictive checks were constructed by plotting the 90% confidence interval of the model predictions, obtained by simulating from the model on the same dataset, overlaid by observed plasma concentrations. This was done for the plasma concentrations in the first 24 h of cycle 1 and for plasma concentrations at steady state (after >10 days of consecutive dosing).
Standard errors of parameters were obtained from the NONMEM covariance step but also by a bootstrapping procedure, automated by Perl (ActiveState, Vancouver BC, Canada) scripts and R. Due to the long run time of the model, the number of bootstrap runs was limited to 150. Urine data were not included in the bootstrap analysis since this resulted in large numbers of unsuccessful runs. Therefore, and for the fact that only from a selection of patients urine collections were available, a standard error for the renal fraction was not calculated in the bootstrap analysis. Shrinkage in random effects was calculated as \(1 - \frac{{{\text{SD}}_{{\text{EBE}}} }}{\omega }\) for η-shrinkage and \(1 - {\text{SD}}\left( {\frac{{{\text{OBS}} - {\text{IPRED}}}}{\sigma }} \right)\)for ε-shrinkage, SD_{EBE} describing the standard deviation of the empirical Bayesian estimates, ω the square root of the estimated between- or within-subject variance in parameters, and OBS and IPRED the observed and individual model predictions respectively [27].
Simulation of food effect studies
FDA’s guidance on food effects studies specifies AUC and C_{max} as the relevant statistics for evaluating effects of food intake on drug exposure [20]. When simple models are applied, these statistics can be obtained by reparametrization of the PopPK-model to use AUC and/or C_{max} as model parameters. However, this approach becomes more complicated when more sophisticated models are used. Alternatively, these statistics and their confidence intervals can be obtained from stochastic simulation of food effect trials. Therefore, using the final model, 1,000 simulations of food effect trials in six patients were performed. To account for uncertainty in the parameter estimates of the final model, the 1,000 trial-simulations were performed 1,000 times, each time with a new set of model-parameters sampled from the multivariate parameter distribution obtained from the covariance matrix, output by the NONMEM covariance step of the final model. Food effects on response parameters C_{max}, AUC_{0–24} and \({\text{AUC}}_{{\text{0}} - \infty } \) were assessed by calculating medians of the ratios of these (log transformed) parameters and their respective confidence intervals. To ensure comparability to these statistics obtained in the NCA, C_{max} and AUCs were calculated by the same method as in the NCA, i.e. the C_{max} observed at discrete timepoints and AUCs calculated by the log-linear trapezoidal rule.
Results
Dataset
Patient characteristics
| Value (range) | |
---|---|---|
Total number | 36 | |
Dose level: | 10 mg | 3 |
20 mg | 4 | |
40 mg | 3 | |
70 mg | 3 | |
100 mg | 17 | |
200 mg | 6 | |
Sex: | Male | 20 |
Female | 16 | |
Race | Caucasian | 31 |
Black | 1 | |
Hispanic | 4 | |
Weight (kg) | 70.9 (43.2–113.6) | |
Height (cm) | 167.3 (150.5–182.9) | |
BSA (m2) | 1.805 (1.375–2.402) | |
Age (years) | 64.8 (40.0–82.0) |
NCA
Variables obtained in NCA analysis
Dose level | Recorded statistics | Calculated parameters | |||||
---|---|---|---|---|---|---|---|
n | AUC_{0–24}, ng h/mL (CV%) | C_{max}, ng h/mL (CV%) | n | t_{1/2}, h (CV%) | CL/F, L h^{−1} (CV%) | V_{ss}/F, L (CV%) | |
Cycle 1, day 1 | |||||||
10 mg | 3 | 1,186 (46%) | 190 (21%) | 2 | 5.61 (61%) | 10.8 (11%) | 84.6 (51%) |
20 mg | 4 | 2,809 (45%) | 447 (26%) | 4 | 6.37 (21%) | 7.76 (51%) | 67.0 (31%) |
40 mg | 3 | 5,261 (49%) | 740 (69%) | 2 | 7.05 (17%) | 5.37 (3%) | 54.5 (14%) |
70 mg | 4 | 17,628 (40%) | 1,770 (33%) | 2 | 6.30 (2%) | 3.20 (41%) | 29.3 (43%) |
100 mg | 17 | 14,760 (52%) | 1,487 (46%) | 9 | 6.58 (26%) | 8.44 (62%) | 76.7 (61%) |
200 mg | 7 | 21,650 (48%) | 1,863 (39%) | 1 | 8.61 (–) | 6.05 (–) | 75.2 (–) |
Mean | 37 | – | – | 20 | 6.45 (25%) | 7.67 (46%) | 68.2 (47%) |
Variability in observed AUC_{0–24} and C_{max} were moderate to high at most dose levels (range 8–77%). In calculated parameters, BSV for CL/F and V_{ss}/F was ∼50%, while BSV in t_{1/2} was 25%.
Assessment of food effect on bioavailability parameters using NCA and compartmental analysis
Response parameter | NCA | PopPK simulation | ||
---|---|---|---|---|
Ratio fed/fasted | 90% CI | Median ratio | 90% CI | |
ln (C_{max}) | 1.057 | 0.913–1.087 | 1.027 | 0.966–1.067 |
ln (AUC_{0–24}) | 1.031 | 0.971–1.028 | 1.031 | 1.005–1.054 |
ln (AUC_{0–inf}) | – | – | 1.033 | 1.010–1.055 |
PopPK modeling
Objective function values (OFV) of pivotal models in development of the structural model
Absorption | Distribution | Elimination | OFV | Implication |
---|---|---|---|---|
1st order | 1 comp | 1st order | \(\left. {\matrix {52.3} \\ { - 174.1} \\ {205.0} \\ \begin{aligned} & - 167.7 \\ & - 154.6 \\ & - 341.1 \\ & {\text{ - }} \\ & - 629.6 \\ \end{aligned} \ } \right\}{\text{Turnover absorption model gave best fit}}\) | |
1st order with lag-time | 1 comp | 1st order | ||
Zero order | 1 comp | 1st order | ||
Combined 1st-zero order | 1 comp | 1st order | ||
Two 1st order processes | 1 comp | 1st order | ||
1 Weibull function | 1 comp | 1st order | ||
2 Weibull functions | 1 comp | 1st order | ||
Turnover model, k_{a}=k_{tr} | 1 comp | 1st order | ||
Turnover model, estimate k_{a} | 1 comp | 1st order | −632.8 | Separate k_{a} not significantly better |
Turnover model | 2 comp, linear distribution | 1st order | −629.6 | No peripheral compartment |
Turnover model | 1 comp | E_{max} | −634.0 | No non-linear elimination |
In the distribution and elimination phase, the observed data were adequately described using a one-compartment PK model, with linear elimination from this compartment. Including one or more peripheral compartments in the model did not result in an improved fit. Also no improvement was observed by non-linear elimination (E_{max}-model) from the plasma compartment.
Urinary data could be modeled simultaneously with plasma data and the fraction eliminated in urine (f_{ren}) could be estimated with appropriate precision at 0.011% (RSE 18%).
In patients that were allowed to take a standard high fat meal, the MTT was estimated a factor 2.78 (RSE 13%) higher, indicating a decreased absorption rate in presence of food. Furthermore, relative bioavailability was estimated 36% (RSE 14%) higher in the fed state.
BSV could be estimated on PK parameters of distribution (V/F) and elimination (CL/F), while also covariance between these parameters was found to be significant. BSV could not be estimated on the absorption parameters (MTT or N), since this resulted in unstable models and/or very wide confidence intervals for the estimates. Estimation of BSV in f_{ren} decreased the stability of the model, probably due to the low number of patients with urinary data available, and was therefore abandoned. Incorporation of WSV improved model-fit when included on all PK parameters when tested univariately. The improvement was however most pronounced when included on MTT and N. Since WSV could not be estimated on all parameters simultaneously, it was chosen to include WSV only on the absorption parameters MTT and N. WSV was also estimated for the estimated steady state trough levels. No covariates were found that could describe part of the observed BSV in any of the basic PK parameters, although the number of patients in the current dataset was probably too low to draw conclusions for the entire patient population.
The error model, relating predictions to observed concentrations, was a combined proportional and additive model. Separate proportional and additive residual errors were estimated for the urinary data. Furthermore, despite efforts mentioned above, the absorption phase could only be modeled with moderate precision. Therefore a factor was included on the proportional residual error for plasma concentrations during the absorption phase (arbitrarily set at the first 3 h post-dose). This way, fitting of data in the absorption phase was allowed to be less strict and estimation of parameters for distribution and elimination relied more heavily on data from the elimination phase. The relative error was estimated 23% higher in the absorption phase than in the elimination phase.
Final parameter estimates PopPK
PK parameter | Estimate (RSE%) | Bootstrap geometric mean (CV%) | Shrinkage |
---|---|---|---|
Structural model | |||
CL/F | 6.24 L h^{−1} (7.1%) | 6.46 L h^{−1} (16%) | |
V/F | 66.0 L (8.5%) | 64.0 L (12%) | |
MTT | 0.638 h (6.5%) | 0.695 h (19%) | |
N^{a} | 3 | 3 | |
f_{food∼MTT} | 2.78 (13%) | 2.91 (23%) | |
f_{food∼F_rel} | 1.36 (14%) | 1.39 (19%) | |
f_{ren}^{b} | 0.011% (18%) | – | – |
BSV | |||
ω_{CL} | 51.1% (12%) | 53.2% (17%) | 2.3% |
ω_{V} | 48.1% (17%) | 47.2% (13%) | 1.3% |
ρ_{CL∼V} | 62.1% (12%) | 69.0% (21%) | |
WSV | |||
κ_{MTT} | 62.8% (8%) | 67.2% (7%) | 28% |
κ_{N} | 137% (17%) | 121% (12%) | 55% |
κ_{SS} | 120% (15%) | 143% (14%) | 61% |
Residual error plasma | 9.1% | ||
Proportional error h | 30.2% (9.1%) | 27.4% (11%) | |
Prop. error factor t < 3 h | 1.23 (9.0%) | 1.41 (19%) | |
Additive error | 4.2 ng/mL (7.0%) | 5.0 ng/mL (47%) | |
Residual error urine | <1% | ||
Proportional error | 90% (9.1%) | ||
Additive error | 180 ng (31%) |
Model evaluation
The fixed effect parameters could be estimated with adequate precision (RSE 7–18%, estimated by covariance step) while random effects also could be estimated precisely (RSE 12–31%). BSV was moderate to high for all pharmacokinetic parameters with BSV in CL/F and V/F around 50%, while WSV on absorption parameters MTT (63%) and N (137%), and estimated steady state levels (120%) were high. Means of all inter-individual variability estimates were not significantly different from 0 (p > 0.05) and the condition number of the final model was low (<100). Low shrinkage was seen in ε’s (<10%), while shrinkage in η’s was small for IIV’s (1–3%) and fairly high for IOV (28–61%). Magnitudes of standard errors for both fixed and random effects calculated in the bootstrap analysis corresponded well with those obtained from the covariance step.
Retrospective assessment of the missing dose method showed that parameter estimates thus obtained were comparable (<5% different) to those obtained with full dosing histories, indicating that the use of the missing dose method in the PK analysis of the real phase I data was justifiable.
Simulation of food effect studies
Estimates for the parameters F_{rel} and MTT indicated that food intake prior to administration of E7820 both decreased the absorption rate and increased bioavailability. Results of the simulation study showed increases in (log transformed) exposure variables C_{max}, AUC_{0–24} and \({\text{AUC}}_{{\text{0}} - \infty } \) (Table 3) in the fed state. However, as in the NCA, these results showed no evidence of significant food effect measured by these statistics, according to FDA definitions: confidence intervals for food effects on AUC an C_{max} were well within the 80–125% interval.
Comparison of NCA and PopPK results
As can be seen in Tables 2 and 3, the results for elimination (CL/F) and distribution (V/F) parameters obtained in both methods show close agreement. Elimination half-life in the PopPK analysis, estimated by reparametrization, was 7.10 h (RSE 8%), while the mean t_{1/2} in the NCA calculated over all days was 6.42 (CV 26%). In the NCA, the t_{1/2}, CL/F and V/F could not be calculated in profiles where not sufficient samples were collected, which was the case in 26 out of 66 profiles. The estimate for f_{ren} was also approximately the same in both methods. In both approaches no significant food effect on exposure parameters AUC and C_{max} was found. BSV for CL/F and V/F in the PopPK analysis as well as in the NCA were both estimated at about 50%. WSV in absorption parameters was high in the PopPK analysis and was not estimated in the NCA due to the low number of occasions (three or less for every patient).
Discussion
In the present study we described the assessment of the PK characteristics of the new oral anti-cancer agent E7820. Both population based non-linear mixed effect modelling, and a traditional NCA were performed.
PopPK modeling
A model that could describe all available data simultaneously, was developed. A rapid absorption was observed, with approximately half of the absorption process being completed after 1 h in the typical patient. The effect of food-intake prior to dose administration showed an effect on absorption kinetics: in the presence of food, the rate of absorption was decreased (increased MTT) and relative bioavailability was increased modestly. Both effects could be explained by delayed passage through the intestinal system due to the food intake. These effects did not result in significant changes in exposure measures AUC and C_{max}. Elimination half-life of E7820 was estimated at 7 h, thereby supporting qd or bid dosing. While high within patient variability in absorption rate and onset was observed, between-patient variability in clearance and distribution was moderate to high.
The PopPK model was evaluated by bootstrapping, assessment of shrinkage [27], and evaluation of goodness-of-fit plots and visual predictive checks. The low degree of η-shrinkage in BSV on CL/F and V/F indicated that the data at hand werre rich enough to accurately estimate the degree of variability in these parameters. The higher degree of η-shrinkage in WSV-measures indicated that the dataset was not informative enough to determine this type of variability with a high degree of accuracy.
Plots of population predictions versus observed plasma concentrations showed some bias, which was not present in the plots of individual predictions versus observations (Fig. 4a,b). This might indicate some degree of misspecification by the final structural model. However, as described, extensions or modification to the structural model did not result in improved fit. Plots of CWRES versus time after dose and CWRES versus time (Fig. 4c,d) showed no apparent bias in residual errors, indicating that the model is unbiased in its predictions. Visual predictive checks at the MTD, shown in Fig. 5a,b, demonstrated that model predicted confidence intervals corresponded adequately with the observed data.
Comparison of PopPK approach with NCA
Since both of the approaches taken here entail specific advantages and limitations, it is important to recognize the difference in aim and applicability in drug development. While PopPK modeling aims to develop models describing drug disposition and to gain insight into mechanisms by which disposition occurs, the aim of a NCA is generally the estimation of elimination rates and the gathering of information about exposure statistics (AUC and C_{max}) over several dose levels. Below, two key differences between the two methods that we encountered are discussed.
The first important difference involves the efficiency in use of data. Attaining high efficiency is especially relevant in phase I of clinical development as at this stage, PK data of only a small number of patients are available. In the present study, at every dose level only few patients were available, except at the MTD level. In contrast with the NCA, data from several occasions could be used to obtain overall parameter estimates.Because the NCA requires at least several data points in the elimination curve and C_{max} levels, only full PK curves could be used to estimate elimination parameters and statistics. Therefore, PK data recorded in cycles 2 and up, had to be discarded in the NCA, while also PK curves that were incompletely recorded could not be used to estimate t_{1/2} (26 out of 66 curves). In addition, data points in an NCA are imperatively discarded, or handled in a less correct manner. When significant portions of the elimination part of the curve are under the LLOQ, which is often the case at lower dose levels, estimates of PK parameters might be biased. In our PopPK analysis, it could be confirmed that the influence of discarding these points was marginal. Furthermore, in the PopPK analysis a model was jointly fitted to plasma and urine data thereby using the extra information obtained from the urine samples in fitting of the model while, simultaneously, information from the plasma compartment is used in estimation of a renal elimination fraction.
The second important issue is the approach by which variability was addressed. In the NCA, parameters were calculated separately for each recorded PK curve for each individual, and pooled afterwards to obtain mean and variatibility measures for every dose level on every occasion, weighing data from each individual equally. This resulted in low numbers of patients for each doselevel/occasion, and therefore high variability and low precision in parameter estimates. In contrast, in the PopPK approach, data from all patients on all occasions were used simultaneously to obtain parameter distributions for the fixed and random effects, accordingly leading to more accurate estimates. Furthermore, the PopPK provided uncertainty measures for all parameter estimates, whereas the NCA could only provide point estimates and variation measures.
It must be stated however, that in the present analysis, population estimates for basic PK parameters showed fairly good agreement between both methods. Also, the fact that performing a NCA requires less effort and can be performed much faster, does offer an advantage over performing a PopPK analysis. However, it is our opinion that the advantages discussed above demonstrate the benefits of implementation of PopPK modeling, already in phase I of drug development. It is anticipated that when data from subsequent clinical trials are included in the analysis, applying the PopPK approach will become even more advantageous, e.g. in the assessment of covariate and genotype effects.
Conclusion
In this study, we presented the first population pharmacokinetic analysis of the novel anticancer agent E7820. This analysis shows that population pharmacokinetic modeling can be used in phase I oncology trials to gather early information about pharmacokinetics. The developed PK model can be used, e.g. to simulate different dosage regimes and in the design optimalization of sampling schemes for phase II trials. Data collected from subsequent phase I/II trials should be used to update and refine the current PK model. Combining the PK model with models for that describe the pharmacodynamics (PD) of the drug, may aid in limiting toxicity.
We showed in this article that the PopPK method has marked advantages over classical non-compartmental PK analyses, such as the incorporation of sparse data and data below the LLOQ, as well as the possibility to simulate from the model. However, the NCA has the advantage of being more easily applicable, and can be applied to gain information about disposition prior to a PopPK analysis.