Population pharmacokinetics of temsirolimus and sirolimus in children with recurrent solid tumours: a report from the Children's Oncology Group

Abstract

Aims

Temsirolimus is an inhibitor of the mammalian target of rapamycin (mTOR). Pharmacokinetic (PK) characterization of temsirolimus in children is limited and there is no paediatric temsirolimus population PK model available. The objective of this study was to simultaneously characterize the PK of temsirolimus and its metabolite sirolimus in paediatric patients with recurrent solid or central nervous system tumours and to develop a population PK model.

Methods

The PK data for temsirolimus and sirolimus were collected as a part of a Children's Oncology Group phase I clinical trial in paediatric patients with recurrent solid tumours. Serial blood concentrations obtained from 19 patients participating in the PK portion of the study were used for the analysis. Population PK analysis was performed by nonlinear mixed effect modelling using NONMEM.

Results

A three‐compartment model with zero‐order infusion was found to best describe temsirolimus PK. Allometrically scaled body weight was included in the model to account for body size differences. Temsirolimus dose was identified as a significant covariate on clearance. A sirolimus metabolite formation model was developed and integrated with the temsirolimus model. A two‐compartment structure model adequately described the sirolimus data.

Conclusion

This study is the first to describe a population PK model of temsirolimus combined with sirolimus formation and disposition in paediatric patients. The developed model will facilitate PK model‐based dose individualization of temsirolimus and the design of future clinical studies in children.

What is Already Known About This Subject

• Temsirolimus is a prodrug of sirolimus and both are pharmacologically active inhibitors of mammalian target of rapamycin.

• Clinical studies have demonstrated that temsirolimus is promising for the treatment of solid tumours in paediatric patients; however, large interindividual variability has been observed in the pharmacokinetics of temsirolimus and sirolimus.

• Characterization of the transformation of temsirolimus to sirolimus in children is limited.

What This Study Adds

• A combined population pharmacokinetic model of temsirolimus with its metabolite sirolimus in children was developed with pharmacokinetic data collected in a phase I study in paediatric patients with recurrent solid tumours.

• The developed model will facilitate prediction of both temsirolimus and sirolimus concentrations and exposure in children and can be used to identify age‐appropriate dosing regimens as part of clinical trial design simulations and for Bayesian guided precision dosing.

Tables of Links

TARGETS
CYP3A4
CYP3A5
FKBP
IGFR
mTOR

LIGANDS
Sirolimus
Temsirolimus

These Tables list key protein targets and ligands in this article that are hyperlinked to corresponding entries in http://www.guidetopharmacology.org, the common portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY 1, and are permanently archived in the Concise Guide to PHARMACOLOGY 2015/16 2.

Introduction

The mammalian target of rapamycin (mTOR) is a serine/threonine kinase that is involved in many critical cellular functions including cell growth, proliferation, angiogenesis and cellular survival. Temsirolimus and its primary metabolite sirolimus form complexes with the FK506‐binding protein, an intracellular immunophilin, and these complexes exert antiangiogenesis and antitumour activities by inhibiting mTOR. To date, clinical trials have revealed that intravenous administered temsirolimus prolongs overall survival in patients with advanced renal cell carcinoma (RCC) compared with interferon‐α therapy 3. Temsirolimus was approved for the treatment of advanced RCC in 2007 in the USA and Europe. Since then, many clinical trials have been conducted to explore the effectiveness of temsirolimus for the treatment of other tumours in adult patients 4, 5, 6, 7, 8 and also in children 9, 10. In addition, recently, many combination therapies with other anticancer drugs and/or antibodies have been investigated 11, 12, 13, 14, 15, 16, 17, 18.

Several studies have documented large interpatient variability in temsirolimus pharmacokinetics (PK) 19, 20, 21. In these clinical trials, temsirolimus PK and exposure rather than dose have been associated with adverse drug reactions (ADRs) 21, 22, 23, suggesting that temsirolimus PK is one of the determinants and markers of temsirolimus‐induced toxicity. Indeed, clinically important associations of temsirolimus and sirolimus area under the curve (AUC) with ADR severity were reported for thrombocytopenia, pruritus and hyperlipaemia 22. A correlation between temsirolimus concentration at end of infusion and severity of ADRs has also been observed 22.

In humans, temsirolimus is rapidly hydrolyzed by carboxyesterases to its major metabolite sirolimus 24. Sirolimus exhibits comparable mTOR inhibitory activity and is predominantly metabolized by CYP3A4 and CYP3A5 to multiple metabolites including hydroxyl‐ and demethyl‐forms 25, 26, 27. To date, two temsirolimus population PK analyses have been reported in adult patients, one in patients with advanced RCC 22 and one in patients with breast cancer 5. PK characterization of temsirolimus in children is limited and there is no paediatric population PK model of temsirolimus available 10, 28, 29.

In this study, we aimed to characterize the PK of temsirolimus in children and develop a population PK model of both temsirolimus and sirolimus. The population PK model could serve as a basis for clinical trial design simulations and for PK‐guided dosing using Bayesian adaptive control.

Methods

Patients and sampling

The population PK analysis was performed using PK data collected in a subset of patients who participated in a Children's Oncology Group phase I dose finding and safety trial of the anti‐ insulin‐like growth factor I receptor antibody cixutumumab (IMC‐A12), combined with temsirolimus in children with recurrent solid tumours (ADVL0813, ClinicalTrials.gov Identifier: NCT00880282) 28. Demographic characteristics of participants are described in Table 1. In brief, 6 mg kg⁻¹ of IMC‐A12 in combination with 8, 10 or 15 mg m⁻² of temsirolimus was administered once weekly in 28 day cycles. As described previously 28, blood samples were collected at predose, at 15 and 30 min, and 1, 3, 6 and 24 h after intravenous infusion of temsirolimus on day 1 of cycle 1, and were used for the PK analysis. In addition to those data, the later blood samples collected at 48, 72 and 168 h postdose were also used in this population PK analysis. The infusion duration was documented and ranged from 15 to 63 min. Whole blood concentrations of temsirolimus and sirolimus were determined by validated liquid chromatography tandem‐mass spectrometry assay 30. The range of reliable quantification, the interday imprecision and interday accuracy for the temsirolimus assay are 0.5–100 ng ml⁻¹, 3.8–7.0% and 99.9–108.2%, respectively. For the sirolimus assay, ranges are 0.1–100 ng ml⁻¹, 6.0–12.4% and 98.5–106%, respectively. The Institutional Review Boards of Cincinnati Children's Hospital Medical Center and other participating institutions approved the protocol 28. Informed consent and assent, as appropriate, were obtained according to local institutional guidelines 28.

Table 1.

Demographic characteristics of patients at enrolment in this study

Characteristic	Total (n = 19)
Age, years
Mean ± SD	11.9 ± 5.7
Median	10.9
Range	1–19
Body weight, kg
Mean ± SD	45.7 ± 28
Median	35.7
Range	7.3–114.7
Sex, n (%)
Female	8 (42.1)
Male	11 (57.9)
Race, n (%)
Caucasian	17 (89.5)
African–American	1 (5.3)
Asian	1 (5.3)
Temsirolimus dose level, n (%)
8 mg m −2	11 (57.9)
10 mg m −2	3 (15.8)
15 mg m −2	5 (26.3)

SD, standard deviation

Population PK modelling

Population PK analysis was performed by nonlinear mixed effect modelling using NONMEM (version 7.2, ICON, Ellicott City, MD, USA) with Perl speaks NONMEM (PsN) version 3.6.2 31 and Pirana version 2.7.1 (Pirana Software & Consulting BV, http://pirana.sourceforge.net) as the interface. The first‐order conditional estimation with interaction method (FOCE‐I) was applied for all runs. Different compartment models were explored to describe the temsirolimus and sirolimus blood concentration‐time profiles. Model selection was based on goodness‐of‐fit diagnostic plots, comparisons based on the minimum objective function value (OFV) and evaluation of the estimates of population fixed and random effect parameters. Interpatient variability was assessed using an exponential variability model (Equation (1)):

$$P_i=P_{\mathit{pop}}\times exp\left({\eta }_{\mathrm{i}}\right)$$ (1)

where P_i is the estimated parameter value for individual i, P_pop is the typical population value (geometric mean) of the PK parameters such as clearance and volume of distribution, η_i is an interindividual random effect for individual i with the mean of zero and variance of ω². A proportional error model and a combined proportional and additive error model were examined to describe the residual error. All PK models were parameterized in terms of values of clearance (CL), volume of distribution (V) and intercompartmental clearances (Q).

Allometrically scaled body weight was used to account for differences in body size as follows (Equation (2)):

$$P_i=P_{\mathit{pop}}\times {\left(\frac{{\mathit{BW}}_i}{{\mathit{BW}}_{\mathit{\text{standard}}}}\right)}^{\mathit{\text{power}}}$$ (2)

where BW_i is body weight for individual i, BW_standard is 70 kg, and power is the coefficient set at 0.75 for CL and 1 for V 32. The temsirolimus dose was examined as a covariate to account for the nonlinearity between dose and AUC, which was observed in the prior noncompartmental analysis 28 (Equation (3)).

$$P_i=P_{\mathit{pop}}\times {\left(\frac{{\mathit{\text{Dose}}}_i}{{\mathit{\text{Dose}}}_{\mathit{\text{median}}}}\right)}^{{\mathrm{\theta }}_{\mathit{\text{dose}}}}$$ (3)

where Dose_i is the temsirolimus dose normalized by body weight. Age and sex of the patient were examined in the covariate analysis with following equations (Equations (4) and (5)):

$$P_i=P_{\mathit{pop}}\times {\left(\frac{{\mathit{Age}}_i}{{\mathit{Age}}_{\mathit{\text{median}}}}\right)}^{{\mathrm{\theta }}_{\mathit{age}}}$$ (4)

$$P_i=P_{\mathit{pop}}\times {\left({\mathrm{\theta }}_{\mathit{\text{gender}}}\right)}^{\mathit{\text{power}}}$$ (5)

where power is 0 or 1 when the patient is male or female, respectively.

Model evaluation

The following diagnostic plots were used to evaluate the models: observed value (DV) vs. population predicted value (PRED), DV vs. individual predicted value (IPRED), conditional weighted residuals vs. PRED and conditional weighted residuals vs. time after dose to identify a bias corresponding to model mis‐specification. The final model was evaluated using nonparametric bootstrap analysis 33. The original dataset was resampled with 500 replicate data sets, and the estimated medians and 95% confidence intervals (CIs) of parameter estimates from the bootstrap analysis were compared to the final model estimates. The prediction‐corrected visual predictive check (pcVPC) was used for the validation 34. One thousand replicates of simulated datasets were generated using the final model and the distribution of simulated observations was compared with the actual observations.

Results

Population PK modelling for temsirolimus

A total of 135 temsirolimus blood concentration results from 19 participants were available for the population PK analysis. Five to nine blood samples were collected from each patient. Two samples were below the lower limit of quantification 30. A three‐compartment model with zero‐order infusion was found to adequately describe the temsirolimus PK profiles after intravenous infusion (Figure 1). A combined proportional and additive residual error model provided better model fit than a proportional error model. Including allometrically scaled body weight resulted in significant improvement in model fit (ΔOFV = 39.6, P < 0.001). This model was defined as the base model and the population PK parameter estimates are summarized in Table 2. The temsirolimus dose was tested as a covariate in order to account for the observed nonlinearity between the temsirolimus dose and AUC. Dose was retained in the final model for CL, Q2 and Q3 because of significant improvement in model fit (ΔOFV = 6.20, P < 0.05). No further significant improvement in model fit was observed by adding any of the other covariates. PK parameter estimates for the final model are also summarized in Table 2. The IIV for V3 could not be estimated reasonably and then was not included in the model. The η‐shrinkage values are summarized in Table 2. Goodness‐of‐fit plots indicated that the developed final model predicts the temsirolimus blood concentration profiles adequately (Figure 2). The bootstrap analysis indicated that the final model was stable (Table 2). The simulated blood concentrations by pcVPC were in reasonable agreement with the observed concentrations (Figure 3).

Figure 1.

Pharmacokinetic structural model for temsirolimus and sirolimus. IV, intravenous; CL, clearance; V, volume of distribution; Q, intercompartmental clearance; F_m, fraction of metabolism of temsirolimus to sirolimus

Table 2.

Parameter estimates for the temsirolimus population pharmacokinetic model

Parameters	Base model			Final model
	Estimates	RSE (%)	Shrinkage (%)	Estimates	RSE (%)	Shrinkage (%)	Bootstrap analysis
							Median	95% CI
								Lower	Upper
CL (l h −1 70 kg −1 )	3.40	27	—	3.61	18	—	3.71	2.22	5.80
V1 (l 70 kg −1 )	9.49	30	—	14.2	8.9	—	12.8	8.51	18.6
Q2 (l h −1 70 kg −1 )	40.8	17	—	30.7	17	—	29.9	20.9	42.8
V2 (l 70 kg −1 )	24.7	8	—	22.9	16	—	23.0	16.4	29.2
Q3 (l h −1 70 kg −1 )	7.76	11	—	7.39	14	—	7.38	5.45	9.46
V3 (l 70 kg −1 )	244	18	—	220	14	—	217	108	296
θ dose	—	—	—	0.855	19	—	0.878	0.428	1.65
Interindividual variability (CV%)
IIV for CL	85.6	24	6	70.9	21	6	63.5	36.2	104
IIV for V1	37.4	26	55	56.0	25	33	53.6	22.8	76.9
IIV for Q2	68.0	29	20	50.7	33	27	53.0	12.8	77.8
IIV for V2	64.1	23	7	69.8	24	9	59.1	32.4	99.5
IIV for Q3	54.5	18	12	55.2	17	15	55.1	38.2	80.1
IIV for V3	—	—	—	—	—	—	—	—	—
Residual variability
Proportional error (CV%)	16.1	17	25	16.6	16	25	16.2	11.0	20.3
Additive error (ng ml −1 )	6.84	31	25	6.60	34	25	6.03	2.74	10.3

CI, confidential interval; CL, clearance; CV, coefficient of variation; IIV, interindividual variability; Q, intercompartmental clearance; RSE, relative standard error; V, volume of distribution.

Figure 2.

Goodness‐of‐fit plots for the final pharmacokinetic model of temsirolimus. Observed vs. (A) population‐predicted and (B) individual‐predicted temsirolimus concentrations (line of identity shown for clarity). The conditional weighted residuals (CWRES) vs. (C) time after dose and (D) population‐predicted temsirolimus concentration

Figure 3.

Prediction‐corrected visual predictive check (pcVPC) for the final model of temsirolimus. (A) All observations and (B) enlarged picture from 0 to 25 h. Open circle, observed blood concentrations; lines represent the median, 5th and 95th percentiles of the simulated data (n = 1000)

Population PK modelling of temsirolimus combined with its metabolite sirolimus

The population PK model for sirolimus was developed by integrating metabolite formation with the temsirolimus PK model. A total of 137 sirolimus concentration measurements were obtained in 19 patients and used for the analysis. To develop the combined model, mass balance and concentrations were adjusted based on the respective molecular weights. A two‐compartment structural sirolimus model linked with the three‐compartmental temsirolimus model best described the PK data (Figure 1). PK parameter estimates for the final combined model are summarized in Table 3. The exponent for dose was fixed to 0.855 in the analysis. A combined proportional and additive error model best described residual error. Inclusion of allometrically scaled body weight on clearance improved the model fit (ΔOFV = 20.1, P < 0.001). The η‐shrinkage values for CL and V1 are summarized in Table 3. IIV for Q2, Q4, V2, V3 and V5 could not be estimated reasonably and then were not included. Goodness‐of‐fit plots showed that the developed final model adequately predicted the concentration profiles of both temsirolimus and sirolimus (Figure 4). Bootstrap analysis confirmed stability of the final model (Table 3). The simulated concentrations by pcVPC were in reasonable agreement with the observed values (Figure 5).

Table 3.

Parameter estimates for the combined population pharmacokinetic model of temsirolimus with its metabolite sirolimus

Parameters	Final model
	Estimates	RSE (%)	Shrinkage (%)	Bootstrap analysis
				Median	95% CI
					Lower	Upper
CL TEM (l h −1 70 kg −1 )	4.31	21	—	4.42	2.40	6.16
F m	0.459	29	—	0.476	0.389	0.707
V1 (l 70 kg −1 )	18.9	23	—	18.1	10.7	26.3
Q2 (l h −1 70 kg −1 )	10.4	23	—	10.4	7.00	22.8
V2 (l 70 kg −1 )	12.9	23	—	12.9	7.46	19.4
Q3 (l h −1 70 kg −1 )	9.15	29	—	9.01	5.87	12.3
V3 (l 70 kg −1 )	140	24	—	129	92.5	254
θ dose	0.855	FIXED	—	0.855	—	—
CL SIR (l h −1 70 kg −1 )	6.08	31	—	6.44	3.15	10.9
V4 (l 70 kg −1 )	48	30	—	51.9	22.9	95.0
Q5 (l h −1 70 kg −1 )	11.6	36	—	13.3	9.51	22.0
V5 (l 70 kg −1 )	72.8	41	—	86.4	55.8	140
Interindividual variability (CV%)
IIV for CL TEM	49.5	17	8	51.7	31.7	79.4
IIV for V1	67	23	14	68.0	38.6	102
IIV for Q2	—	—	—	—	—	—
IIV for V2	—	—	—	—	—	—
IIV for Q3	79.1	29	5	73.5	43.4	120
IIV for V3	—	—	—	—	—	—
IIV for CL SIR	103	14	7	103	67.7	147
IIV for V4	121	34	3	118	41.9	169
IIV for Q5	—	—	—	—	—	—
IIV for V5	—	—		—	—	—
Residual variability
Temsirolimus
Proportional error (CV%)	23.9	20	14	21.7	14.7	29.3
Additive error (ng ml −1 )	7.25	92	14	8.06	1.01	14.2
Sirolimus
Proportional error (CV%)	25.5	20	14	24.2	8.92	32.9
Additive error (ng ml −1 )	1.69	47	14	1.52	0.833	3.89

CI, confidential interval; CL, clearance; CV, coefficient of variation; F_m, fraction of metabolism to sirolimus; IIV, interindividual variability; Q, intercompartmental clearance; RSE, relative standard error; V, volume of distribution.

Figure 4.

Goodness‐of‐fit plots for the final pharmacokinetic model of temsirolimus with sirolimus. The observed versus population predicted (A) and individual predicted (B) temsirolimus (open circle) and sirolimus (blue circle) concentrations (line of identity shown for clarity). The conditional weighted residuals (CWRES) vs. time after dose (C) and population predict temsirolimus (open circles) and sirolimus (blue circles) concentrations (D)

Figure 5.

Prediction‐corrected visual predictive check (pcVPC) for the final model of temsirolimus with sirolimus. (A, C) All observations and (B, D) enlarged picture from 0 to 25 h. Open circles, observed temsirolimus concentrations (A, B) and sirolimus concentrations (C, D); lines represent the median, 5th and 95th percentiles of the simulated data (n = 1000)

Individual estimates

The PK parameters for each individual patient were generated using posthoc Bayesian estimation with NONMEM. When CL was standardized to allometrically scaled body weight, no age effects were observed over the age range of patients in this study (Age range 1–19 years, with only one patient younger than 2 years; Figure S1).

Discussion

This study generated a combined population PK model of temsirolimus with its metabolite sirolimus in paediatric patients with recurrent solid tumours. To the best of our knowledge, this is the first population PK modelling analysis of temsirolimus in children. The analysis confirms that temsirolimus PK is nonlinear with dose consistent with that reported in adult patients 5, 22.

Nonlinearity in the relationship between temsirolimus dose and systemic exposure has been well documented 10, 19, 20, 21, 23, 35. In a previous population PK analysis in 50 adult patients, Boni et al. 22 reported a less‐than‐proportional AUC increase with increased dose that they were able to model by including an exponential function of dose 24. Similarly, we incorporated dose as a covariate for CL, Q2 and Q3 in this PK modelling, resulting in a significant improvement in model fit despite the relatively narrow dose range (8–15 mg m⁻²) and the small number of patients. Although the mechanism underlying this phenomenon has not been well characterized, the saturable specific binding to tissue and binding proteins in blood cells are thought to contribute the nonlinear PK behaviour 22 Indeed, a decreased blood‐to‐plasma ratio with increasing dose has been reported 36, indicating that distribution of temsirolimus to blood cells is saturable. It has been suggested that unbound temsirolimus distributes to some tissues that could serve as ‘sink’ compartments and this phenomenon could be an explanation of the nonlinear relationship between dose and drug exposure. A study including a mechanism‐based approach will be required to further elucidate the precise mechanism of this phenomenon.

We did not observe an age‐effect on temsirolimus CL after adjusting for body weight. This could be explained by the age distribution of the patients in the present study, with all subjects being older than 4 years except for one patient who was age 1 year. The observation of no age‐effect on temsirolimus CL is in line with the expectation that age‐dependent changes in clearance due to maturation of organ function and ontogeny of drug metabolic enzymes typically are observed between ages of 0 and 2–3 years 37. For sirolimus we showed that maturation of the CYP3A4/5 pathways in neonates and infants is responsible for lower drug clearance only in the younger patients 38, 39. We also compared our results with temsirolimus CL estimates reported in one paediatric and three adult studies in patients who were treated at a similar dose level. Temsirolimus CL estimates in the current study are somewhat lower than values reported in the adult studies 19, 20, 35 while clearance was comparable to the previous paediatric study results 10. To document developmental changes in temsirolimus PK and to update the population model with a descriptor of maturation, as described for sirolimus 38, further studies in neonates and infants will be needed.

Sirolimus is the primary metabolite of temsirolimus in humans; however, information on the fraction of parent compound that is converted into sirolimus and contribution by other metabolic pathways is still limited. In this analysis, the fraction of temsirolimus metabolized into sirolimus was estimated to be 45.9% (95% CI: 38.9–70.7%). It should be noted that the observed sirolimus to temsirolimus AUC ratios were highly variable indicating large variability in clearance and the fraction metabolized. To date, lower sirolimus exposures (AUC) have been observed in paediatric studies including this study as compared to what has been reported for adults. For instance, the AUC_0‐∞ ratio of sirolimus to temsirolimus was 1.21 ± 1.18 (mean ± SD) in patients receiving a dose of 15 mg m⁻². The mean AUC ratio in our study was approximately two‐fold lower that observed in adult patients (2.28 ± 0.99) receiving the same temsirolimus dose of 15 mg m⁻² 19. In addition to the present study, other paediatric clinical studies have reported lower sirolimus to temsirolimus AUC ratios compared to the ratios in adult patients 9, 10, 19. These observations suggest that the temsirolimus fraction converted to sirolimus in adults may be higher than in children.

Temsirolimus‐induced severe adverse events often lead to discontinuation of treatment. The most common adverse reactions are asthenia, skin/mucosal toxicity and laboratory abnormalities such as hypertriglyceridemia, hypophosphatemia and anaemia. To date, several clinical trials/studies demonstrated the possible relationship between the temsirolimus exposure and toxicity. For instance, a clinical study of temsirolimus mono‐therapy in adult patients with advanced RCC revealed significant associations between temsirolimus AUC and severity of adverse events such as thrombocytopenia, pruritus and hyperlipaemia 22. Furthermore, in a phase I study of temsirolimus monotherapy in paediatric patients, one patient who developed grade 4 thrombocytopenia, showed an unusually high temsirolimus C_max value 10. These observations indicate that high temsirolimus (and sirolimus) concentrations are likely to increase the risk of adverse events, although no clear target exposure range has been defined. In terms of therapeutic outcomes, no definitive relationship between temsirolimus dose, PK (C_max or AUC achieved) and PD such as complete tumour response, progression free survival or relative phosphorylation of the mTOR‐targeted proteins such as S70S6 and 4 EB1 has been documented 5, 10. Further well‐controlled studies are warranted to establish these relationships. As part of such studies, a PK‐guided dosing strategy would be attractive as it would allow temsirolimus (and sirolimus) concentration control using sparse sampling and a model based prediction of the drug exposure–response relationship. A similar approach has been successfully used for sirolimus PK guided dosing in infants and children with neurofibromatosis and vascular anomalies 40, 41, 42.

A limitation of this study is the relatively small number of patients (n = 19) available to describe the population PK of temsirolimus in children. In the covariate analysis inclusion of age and sex did not significantly improve the temsirolimus model. However, given the limited sample size, these covariates should still be evaluated in an adequately powered future study. Finally, this analysis was based on the data obtained in patients receiving a combination therapy of temsirolimus and cixutumumab. The CL observed in the previous Phase I study of combination therapy was comparable with that observed at the same dose in the study of temsirolimus monotherapy 10, 28. However, sample size may not have been large enough to conclude no effect of coadministration of cixutumumab on temsirolimus pharmacokinetics. Further study is warranted.

Conclusion

We successfully developed a population PK model of temsirolimus and its metabolite sirolimus in paediatric patients with recurrent solid tumours. The findings of this study and the developed model will facilitate model‐based dose individualization of temsirolimus and further optimize clinical trial design in children.

Competing Interests

There are no competing interests to declare.

This work was supported in part by the Children's Oncology Group Phase I and Pilot Consortium (study ADVL0813) and the research was supported by the National Cancer Institute (NCI) of the National Institutes of Health (NIH) under award number CA097452 (U01 & UM1). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NCI. T.M. was supported by the Japan Research Foundation for Clinical Pharmacology and the Uehara Memorial Foundation.

Supporting information

Figure S1 Individual posthoc Bayesian temsirolimus clearance (CL) estimates. (A) CL (l h^–1) vs. age (years) and (B) allometrically scaled CL (l h^–1 70 kg^–1) vs. age. Solid line represents the line of fit by the E_max model

Supporting info item

Mizuno, T. , Fukuda, T. , Christians, U. , Perentesis, J. P. , Fouladi, M. , and Vinks, A. A. (2017) Population pharmacokinetics of temsirolimus and sirolimus in children with recurrent solid tumours: a report from the Children's Oncology Group. Br J Clin Pharmacol, 83: 1097–1107. doi: 10.1111/bcp.13181.