Population pharmacokinetics of naloxegol in paediatric subjects receiving opioids

doi:10.21203/rs.3.rs-2612997/v1

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Population pharmacokinetics of naloxegol in paediatric subjects receiving opioids

https://doi.org/10.21203/rs.3.rs-2612997/v1

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Purpose To characterize the pharmacokinetics (PK) of naloxegol in paediatric subjects (≥ 6 months to <18 years of age) who are either with or at risk of developing opioid-induced constipation (OIC) following single dose administration.

Methods Subjects in three age groups (≥12 to <18 years [adolescents], ≥6 to <12 years, and ≥6 months to <6 years) received at least one low or high dose of naloxegol estimated to achieve plasma exposures comparable to adult 12.5 mg or 25 mg doses, respectively. Plasma naloxegol concentrations were used to estimate PK parameters using non-compartmental (NCA; ≥6 to <18 years) and Population PK (PPK; ≥6 months to < 18 years of age) analyses. The PPK model was developed using previously collected adult data and paediatric data from the current study.

Results Naloxegol exhibits comparable PK characteristics in paediatric and adult subjects. Neither age nor body weight was identified as a significant covariate in the prior (adult only data), or current model. Naloxegol NCA- and PPK-derived AUC_0–∞ values normalized to the adult 12.5 mg or 25 mg dose in the ≥6 to <12 years and ≥12 to <18 years age groups were comparable to adults. PPK model-predicted naloxegol AUC_0–∞ values for the 25 mg adult equivalent dose for all paediatric age groups were comparable to adults.

Conclusion

The PK of naloxegol was well characterized in paediatric subjects ≥ 6 months of age utilizing both NCA and PPK analysis and was shown to be comparable to adult subjects.

Naloxegol

population pharmacokinetics

paediatrics

opioid

constipation

Opioids have multiple effects on the gastrointestinal system via µ-opioid receptor agonism of the enteric nervous system [Pappagallo 2001; Panchal et al. 2007; Camilleri 2011] which can result in opioid-induced constipation (OIC) in ~50% of non-cancer patients. However, the proportion can be highly variable (15–90%) depending on many factors, e.g., type of opioid, duration of treatment, disease status [Allan et al. 2001; Kalso et al. 2004; Moore et al. 2005; Bell et al. 2009; Cook et al. 2018]. Non-pharmacological treatment with laxatives, bulking agents, and stool softeners are suboptimal in the treatment of OIC [Pappagallo 2001; Leonard & Baker 2015; Andresen & Layer 2018]. Treatment of recalcitrant OIC necessitates pharmacological therapy [Andresen & Layer 2018; Farmer et al. 2018]. Naloxegol, a peripherally acting µ-opioid receptor antagonist (PAMORA), is a PEGylated derivative of naloxone specifically designed to undergo restricted uptake across the blood-brain barrier and, therefore, a reduced capacity to counteract the central analgesic effects of opioids [Garnock-Jones 2015; Leonard & Baker 2015]. It is approved in Europe (Moventig™) for the treatment of OIC in adults who have had an inadequate response to laxatives [Moventig 2014], and in the US (Movantik™) for the treatment of OIC in adult patients with chronic non-cancer pain, and includes cancer patients with chronic pain who do not require frequent (e.g., weekly) opioid dosage escalation [Movantik 2022].

Paediatric subjects taking a course of opioids for longer than 2 to 3 days are likely to develop OIC [Yaster & Deshpande 1988] and it is recognized that longer-term treatment requires active bowel management to counteract constipation [Santucci & Mack 2007; Zernikow et al. 2009]. The characteristics of OIC in children are expected to be qualitatively the same as those in adults, as the underlying physiological mechanisms for opioid response, and adaptation and dependence in the gastrointestinal system have developed in children by as young as 6 months [Salazar-Lindo et al. 2000; Tofil et al. 2006]. There are currently no approved medicines for treatment of OIC in children.

The PK properties of naloxegol are well characterized in adult subjects and were shown to be similar in healthy adults and adult subjects who have OIC (AL-Huniti et al., 2015; Bui et al., 2017). Single dose administration of naloxegol was shown to be linear over the range of 5 mg to 1000 mg in healthy subjects and from 5 mg to 50 mg in adult subjects who have OIC. Drug-drug interaction studies identified naloxegol as a substrate for CYP3A enzymes as well as the P-glycoprotein (P-gp) efflux transporter (naloxegol package insert).

A Population PK (PPK) model containing data from Phase 1 studies conducted in healthy adult subjects and the Phase 2 studies conducted in adult subjects who have OIC was developed to characterize the PK properties of naloxegol in adults (Al-Huniti et al, 2015). The PPK analysis indicated that naloxegol was best described by a two-compartment model with dual absorptions, comprising first order absorption and a second more complex absorption with a transit compartment. Numerous covariates (i.e., study, gender, race, concomitant moderate CYP3A4 inhibitors, P-gp inhibitors or inducers, naloxegol formulation, baseline creatinine clearance and baseline opioid dose) were shown to alter the PK of naloxegol. However, only strong CYP3A4 inhibitors and inducers demonstrated marked effects on the exposure of the compound in adults.

Model-informed drug development (MIDD) was applied to determine the appropriate dose of naloxegol for adult subjects who have OIC. A PPK pharmacodynamic (PK/PD) model comprised of data from the Phase 2 clinical studies was used to determine the naloxegol dose regimens (12.5 mg and 25 mg) that were evaluated in the two Phase 3 studies (Al-Huniti et al, 2016). Subsequent PK/PD modeling of the Phase 3 clinical studies indicated that the 12.5 mg dose of naloxegol provided comparable clinical benefit over placebo to the 25-mg dose (Al-Huniti et al, 2017). These predicted exposure/response findings were accepted by the Food and Drug Administration and included in the naloxegol prescribing information (naloxegol package insert). Additionally, physiologically-based PK (PBPK) modeling predictions were used to determine comprehensive dosage recommendations when naloxegol is co-administered with CYP3A modulators (Zhou et al. 2016).

The SAFARI study was a Phase 1, open-label, multicenter study to assess the PK and safety of naloxegol in paediatric subjects aged ≥6 months to <18 years receiving treatment with opioids. The study also applied an MIDD approach to estimate the appropriate dosing regimens for paediatric subjects with OIC. A PPK model was used to characterize the PK properties of naloxegol across the age range evaluated in the study. The PPK model comprised the PK data from adults contained in the prior PPK model (Al-Huniti et al, 2015) and from paediatric subjects enrolled in the SAFARI study. The PPK model was used to characterize the naloxegol PK properties in paediatric subjects after administration of oral tablets or oral solution of either a 12.5 mg or 25 mg adult equivalent dose, based on a PBPK model (Zhou et al., 2016) with allometric scaling.

Paediatric study

SAFARI was a multicenter, Phase 1, open-label study to assess the PK and safety of naloxegol in paediatric subjects with newly diagnosed OIC or a history of constipation treated with laxatives and exacerbated by or initiated by starting opioids. The study consisted of three paediatric groups stratified by age (≥ 12 years to < 18 years, ≥ 6 years to < 12 years, and ≥ 6 months to < 6 years). Subjects in each age group received at least one dose of naloxegol estimated to achieve plasma exposures comparable to an adult-equivalent 12.5 mg dose (low dose; Cohorts 1, 3 and 5), based on a previously developed PBPK model (Zhou et al, 2016) and allometric scaling (Table 1). The two highest age groups contained cohorts that received an adult-equivalent 25 mg dose (high dose; Cohorts 2 and 4), based on NCA and PPK analysis of the lower dose cohort. The study proceeded sequentially from the oldest to the youngest age group (Fig. 1 and Table 1). A minimum of eight paediatric subjects were targeted for Cohorts 1 to 3 and a minimum of four paediatric subjects were targeted for Cohorts 4 and 5. Key exclusion criteria were the occurrence of vomiting within 4 hours after first administration of the naloxegol dose, or administration of either strong and moderate inhibitors/inducers of CYP3A4 enzymes or Pgp during the PK phase of the study. Intensive PK sampling was conducted for subjects enrolled in Cohorts 1 to 4 (≥ 12 to < 18 and ≥ 6 to < 12 years of age who received the 12.5 mg or 25 mg adult equivalent doses of naloxegol, respectively). Sparse PK sampling was collected in subjects enrolled in Cohort 5 (≥ 6 months to < 6 years of age) that received the 12.5 mg equivalent adult dose of naloxegol.

Table 1

12.5 mg adult-equivalent naloxegol dose stratified by age groups and body weight
Age group	Age	Weight range (kg)	Planned Starting Dose (mg)	IP oral formulation
		> 60	12.5	tablet 12.5 mg
				or liquid formulation 2.5 mg/mL
12 y to < 18 y		40–60	10	tablet 2 x 5 mg or liquid formulation 2.5 mg/mL
		< 40	6.25	liquid formulation 2.5 mg/mL
		> 35	6.25	liquid formulation 2.5 mg/mL
6 y to < 12 y		25–35	3.75	liquid formulation 2.5 mg/mL
		< 25	2.5	liquid formulation 2.5 mg/mL
		> 18	2.5	liquid formulation 2.5 mg/mL
	2 y to < 6 y	14–18	1.6	liquid formulation 0.8 mg/mL
≥ 6 mo to < 6 y		< 14	1.2	liquid formulation 0.8 mg/mL
	6 mo to < 2 y	> 11	1.2	liquid formulation 0.8 mg/mL
		9–11	0.8	liquid formulation 0.8 mg/mL
		< 9	0.4	liquid formulation 0.8 mg/mL
IP = investigational product; mL = milliliter; mg = milligram; mo = months; y = years

Naloxegol was administered orally either as tablets or a solution. All subjects in Cohorts 3 to 5 received the oral solution. Seven out of 9 subjects in Cohort 1 and 12 out of 13 subjects in Cohort 2 were administered tablets.

Bioanalytical methodology

Plasma naloxegol concentrations were analyzed using a validated high-performance liquid chromatography method with tandem mass spectrometric detection (LCMS/MS). The lower limit of quantification (LLOQ) for naloxegol was 0.1 ng/mL. Quality control samples were prepared at concentrations from 0.1 to 250 ng/mL and were measured together with each sample run in duplicate. The bias of the method (percent inaccuracy) was 2.6–12.8% and the imprecision (percent standard deviation) was 1.9–5.9% [Bergstrand et al. 2011].

Data assembly

The SAFARI paediatric study and eight PK studies in adults used in the PPK model are described in Table 2. Naloxegol PK samples below the LLOQ were set to zero values and identified by a flag, BQLF = 1. These samples were excluded from the analysis [Beal 2001; Ahn et al. 2008]. Only subjects under fasted conditions were included in the PPK analysis.

Table 2

Studies included in the population pharmacokinetic analysis
Study	Study population (no.)	Naloxegol dose regimen and PK sampling
D3820C00016	Paediatric patients receiving opioid ≥ 6 months to < 18 years (n = 43)	12.5 and 25 mg adult-equivalent dose tablet or solution od rich sampling after first dose and multiple sampling after multiple doses
D3820C00009	Subjects with and without renal impairment (n = 32))	25 mg tablet single dose fasting rich sampling
D3820C00010	Subjects with and without hepatic impairment (n = 24)	25 mg tablet single dose fasting rich sampling
D3820C00014	Healthy subjects: effect on QTcF interval (n = 51)	25 and 150 mg tablet single dose fasting rich sampling
D3820C00020	Healthy subjects: ascending dose and effect of food (n = 40)	12.5, 25, 50, or 100 mg od tablet single dose fasting rich sampling
05-IN-OX001	Healthy subjects: ascending dose (n = 48)	8, 15, 30, 60, 125, 250, 500, and 1000 mg solution single dose rich sampling
07-IN-NX002	Healthy subjects: ascending dose (n = 24)	25, 60, 125, or 250 mg solution bid fasting rich sampling
08-PNL-04	Healthy subjects: tablet vs solution (n = 20)	100 mg tablet or solution single dose rich sampling
07-IN-NX003	Adult OIC patients: ascending dose (n = 21)	5, 25, and 50 mg solution single dose fasting rich sampling
bid = twice daily; od = once daily; OIC = opioid-induced constipation.

The identification of potential outliers was performed by visual inspection of the raw dataset and the diagnostic plots (i.e., direct value vs individual predicted concentration [IPRED]) generated from the base model. Descriptive statistics for baseline covariates are detailed as Supplementary Information (Table S1).

Non-compartmental analysis

Non-compartmental analysis was performed following the first administration of naloxegol for subjects enrolled in Cohorts 1 to 4. The individual elapsed sampling times were used for the determination of plasma PK parameters of naloxegol. After the maximum plasma concentration (C_max), any measurable concentrations observed after ≥ 2 consecutive concentrations below the LLOQ were excluded from the PK analyses. One of the subjects in Cohort 1 was excluded from the analysis as their AUC value was at least 10-fold lower compared to other subjects in the same cohort. In addition, one naloxegol outlier data record of the paediatric study (ID = 40, TIME = 1.0 hr) was excluded from the analysis because it was less than 10% of expected value. Approximately 1.3% (43/3427) of naloxegol PK samples were below the LLOQ, leaving a total of 3384 samples from 296 subjects in the analysis dataset.

Estimated PK parameters included peak concentration (C_max), time to C_max (t_max), area under the plasma concentration-time curve (AUC) from 0 to the last quantifiable point calculated by linear-log trapezoidal method (AUC_0–t), AUC from 0–24 hr (AUC_0–24), AUC from 0 extrapolated to infinity (AUC_0–∞), apparent terminal rate constant (λ_z), terminal phase elimination half-life (t_1/2z), apparent clearance (CL/F), and apparent volume of distribution (V_z/F). Special consideration was given to the estimation of λ_z and corresponding t_1/2z values. The first and last data points used for determination of λ_z and corresponding t_1/2z values were reported in a tabular format. A minimum of three data points in the terminal phase (excluding C_max) and an R² of ≥ 70% were required to obtain a reliable estimate of t_1/2z. Where an AUC value was extrapolated to infinity, the percentage of the extrapolated area to the total AUC was assessed and, if > 30% of the total AUC value, would be flagged as unreliable.

Population pharmacokinetic analysis

A PPK model characterizing naloxegol plasma PK profiles of oral administration of either naloxegol solution or tablets was developed. Additional details of PPK model and its development are detailed as Supplementary Information. The concentration-time data from seven Phase 1 studies conducted in healthy adults, one Phase 2 study conducted in adult subjects with OIC, and one Phase 1 study in paediatric patients receiving opioid treatment were used for model development. PK data of naloxegol following oral administration of naloxegol solution or tablets were model-fitted simultaneously. The dataset includes a total of 3,384 measurable naloxegol samples from 296 subjects. A two-compartment PK model with one first-order absorption, and one absorption with a transit compartment (transit compartment model), and a two-compartment PK model with Weibull-type absorption (Weibull absorption model) were initially evaluated as the oral dose structural PK model of naloxegol. Rate of absorption of naloxegol after oral tablet and oral solution were expected to be different, so naloxegol PK data after oral tablet and oral solution administration were tested separately in these two structural PK models. The Akaike’s information criterion and residual error of Weibull absorption models were consistently lower than those of the transit compartment models. Thus naloxegol PK data following administration of oral tablet and solution were combined to characterize absorption. Covariate effects were tested for significance in a stepwise manner using the likelihood ratio test (LRT). The relationship between random effects of apparent clearance (CL/F,ETA1), apparent volume of distribution of the central compartment (V₁/F) (ETA2), apparent volume of distribution of the peripheral compartment (V₂/F) (ETA4), alpha (ETA5), and beta (ETA6) of the base PPK model and the various discrete and continuous covariates were evaluated. The final PPK model was validated by visual predictive check. Six of 12 covariates were found to be statistically significant in model forward addition steps: creatinine clearance (CR_CL), and race on CL/F, the effects of body weight, and CR_CL on V₁/F, and the effect of sex, and disease status on alpha were found to be statistically significant and were added to the full model.

Software

Non-linear mixed effects modelling (NONMEM) was performed on plasma concentration-time data of naloxegol using NONMEM version 7.50 (ICON Development Solutions, Ellicott City, MD) [NONMEM Users Guide 2013]. The NONMEM first-order conditional estimation (FOCE) method was used. Perl-speaks-NONMEM Version 4.9.0 was used for NONMEM parallel retries. The package Xpose 4.7 was used for model evaluation [Jonsson & Karlsson 1999]. In addition, Wings for NONMEM 7 (version 740) was used to organize and document NONMEM runs [Holford 2007]. The software R (version 4.0 or higher) and SAS (version 9.4 or higher, SAS Institute Inc., Cary, NC) were used for data management, post-processing of NONMEM runs, and all other analyses.

Non-compartmental analysis of paediatric subjects ≥ 6 to < 18 years of age

The results of non-compartmental analysis for paediatric subjects ≥ 6 to < 12 and ≥ 12 to < 18 years of age receiving opioid treatment are shown in Table 3. The mean naloxegol PBPK and allometric scale estimated 12.5 and 25 adult-equivalent doses administered for the ≥ 6 to < 12 years of age subjects were 4.9 mg (39.2%) and 10 mg (40%), respectively The mean naloxegol PBPK and allometric scale estimated 12.5 and 25 adult-equivalent doses administered for the ≥ 12 to < 18 years of age subjects were 10.2 mg (81.6%) and 24.5 mg (98%), respectively. Observed geometric mean naloxegol AUC_0–∞ values for the ≥ 12 to < 18 year paediatric age group at the 12.5 and 25 mg adult equivalent dose of naloxegol were comparable to those in healthy adults (118% and 108%, respectively). It should be noted that subjects within this age group received > 80% of the 12.5 or 25 mg doses. In contrast, naloxegol geometric mean AUC_0–∞ values for the ≥ 6 to < 12 year subjects that received either the 12.5 and 25 mg adult equivalent dose of naloxegol were 56% and 39% of the AUC_0–∞ values in adults, respectively. The lower naloxegol exposures for the paediatric subjects ≥ 6 to < 12 years of age are associated with the lower doses administered to subjects within this age range, compared to the 12.5 mg or 25 mg dose. However, observed dose-normalized naloxegol AUC _0−∞ values for both age groups were comparable to the healthy adult values (Table 4).

Table 3

Observed (based on NCA analysis) naloxegol AUC_0−∞ and C_max values in paediatric OIC patients and healthy adults
PK parameter	12.5 mg equivalent dose			25 mg equivalent dose
	Healthy adults (n = 6)	Paediatric patients ≥ 12–<18 yr (n = 7)	Paediatric patients ≥ 6–<12 yr (n = 8)	Healthy adults (n = 282)	Paediatric patients ≥ 12–<18 yr (n = 8)	Paediatric patients ≥ 6–<12 yr (n = 4)
AUC_0−∞ (ng/mL*hr)
Geo Mean (%CV) [range]	81.9 (38.3) [60.7–151.7]	96.6 (48.3) [43.9–157]	46.1 (90.9) [18.0–146]	168.8 (52.0) [48.6–749]	182 (53.5) [99.6–451]	65.4 (67.8) [34.7–142]
Ratio to adult	1.00	1.18	0.563	1.00	1.08	0.387
C_max (ng/mL)
Geo Mean (%CV) [range]	18.3 (27.0) [10.2–25.7]	11.6 (40.6) [6.67–21.9]	9.26 (95.0) [3.48–30.2]	41.3 (51.3) [7.4–180]	31.7 (81.4) [10.7–110]	21.5 (60.9) [11.5–37.9]
Ratio to adult	1.00	0.635	0.506	1.00	0.768	0.521
Geo Mean = geometric mean; %CV = coefficient of variation; AUC_0−∞ = area under the plasma concentration-time curve from 0 to infinity; C_max = maximum plasma concentration; OIC = opioid-induced constipation; yr = year.

Table 4

Observed paediatric naloxegol dose-normalized AUC_0-∞ and C_max values compared to adults
	Descriptive Statistics	Normalized to 12.5 mg dose			Normalized to 25 mg dose
	Descriptive Statistics	Healthy Adults	12 y to < 18 y	6 y to < 12 y	Healthy Adults	12 y to < 18 y	6 y to < 12 y
AUC_0−∞ (hr*ng/mL)	N	6	7	8	282	8	4
	Geo Mean	81.9	121	140	168.8	209	169
	%CV	38.3	54.5	113	52	52.7	93.1
	Min	60.7	54.8	36.1	48.6	124	69.4
	Max	151.7	198	728	749	564	475
	Ratio to Adult	1.00	1.48	1.71	1.00	1.24	1.00
C_max (ng/mL)	n	6	7	8	282	8	4
	Geo Mean	18.3	14.6	28.2	41.3	36.5	55.6
	%CV	27	57.4	79.7	51.3	79.8	103
	Min	10.2	6.67	11.6	7.4	13.4	23.0
	Max	25.7	27.4	76.5	180	138	126
	Ratio to Adult	1.00	0.797	1.54	1.00	0.883	1.35
Geo Mean = geometric mean; Max = maximum; Min = minimum; y = years; %CV = coefficient of variation

To evaluate the dose proportionality of naloxegol exposure, individual AUC_0–∞ and C_max were plotted against dose (Fig. 2). The slope of the regression lines for AUC_0–∞ and C_max indicated a linear increase of exposure with increased dose of naloxegol for subjects from ≥ 6 to < 18 years of age.

Population pharmacokinetic model analysis

PPK parameter estimates of the final PPK model are provided as Supplementary Information (Table S2). The PPK model comprising plasma concentration data from adult and paediatric subjects indicates that the PK properties of naloxegol in both adult and paediatric subjects were best described using a 2-compartment model with Weibull-type absorption. Comparison of typical PK profiles of naloxegol in healthy volunteer subjects and OIC patients following a single dose of 12.5 mg naloxegol oral solution and tablets are shown in Fig. 3. The alpha parameter estimate of OIC patients is 2.88 times the alpha estimate of volunteer subjects, indicating slower absorption of naloxegol in OIC patients.

Covariate assessment of key demographic parameters were conducted on the final PPK model (Fig. 4). The reference subject is a Caucasian volunteer adult with CR_CL of 110 mL/min/1.73 m² who received naloxegol oral solution. Estimated CL/F of naloxegol was 109 L/hr and apparent volume of distribution at steady state Vd_ss/F (V₁/F + V₂/F) was 668 L. The between-subject variability (BSV) of CL/F and V₁/F was 50.7% and 63.7%, respectively, compared to 54.5% and 66.3%, respectively, for the base model. The median half-life of naloxegol in paediatric subjects receiving opioids (11.0 hr) was comparable to that in adults (10.6 hr). No bias in the conditional weighted residuals (CWRES) was observed with respect to time or to population predicted concentrations of naloxegol. Between-subject random effects of CL/F, V₁/F, V₂/F, alpha, and beta were normally distributed approximately around zero, indicating that statistical assumptions were not violated. Neither the age or body weight were identified as significant covariates in the PPK model. Assessment of baseline body weight and age on CL/F and V1/F indicated a trend towards lower values in paediatric patients from 6 month to > 6 years of age. However, these values fall within the distribution of the rest of the paediatric and adult population (Fig. 5).

A prediction-corrected visual prediction check (pcVPC) stratified by volunteer subject and OIC patients was used to evaluate model performance with the purpose of determining whether the final covariate PPK model is capable of reproducing the variability in the observed naloxegol data following naloxegol oral dose. The observed naloxegol data following naloxegol oral tablet or solution were generally contained within the 95% CIs of the model-predicted values, indicating that the model adequately described the observed data in adult volunteer subjects, adult subjects who have OIC and paediatric subjects receiving opioids. In addition, pcVPC plots of naloxegol from paediatric subjects alone stratified by oral tablet and oral solution indicated that the model adequately describes the observed naloxegol data in paediatric subjects (Figure S1).

Using the final PPK model of naloxegol oral dose, Monte Carlo simulation of concentration versus time curves of naloxegol at steady state were developed in paediatric and adult subjects with OIC. Simulated steady-state concentration-time profiles of naloxegol following daily oral doses of 12.5 mg naloxegol solution and tablet for 8 days were similar between adults and the three paediatric age groups (Fig. 6).

Monte Carlo model-predicted median naloxegol AUC_0–∞ estimates following a single 25 mg dose administration of naloxegol for ≥ 12 to < 18 year, ≥ 6 to < 12 year, and ≥ 6 month to < 6 year age groups were 106%, 112%, and 119%, % of the AUC_0−∞ relative to healthy adults, respectively (refer to Table 5).

Table 5

Monte Carlo simulations to predicted population naloxegol C_max and AUC_0–∞ values following a 25 mg dose of naloxegol
	Descriptive statistics	OIC adults (n = 6218)	Healthy adults (n = 6214)	12 y to < 18 y (n = 4072)	6 y to < 12 y (n = 2813)	6 m to < 6 y (n = 1343)
AUC_0–∞ (ng/mL•hr)	Median (5th–95th pctl)	208 (91.0–485)	204 (89.3–467)	220 (96.2–515)	234 (105–554)	248 (107–551)
	Ratio to healthy adults	1.02	1.00	1.08	1.15	1.21
	Ratio to OIC adults	1.00	0.981	1.06	1.12	1.19
C_max (ng/mL)	Median (5th–95th pctl)	35.8 (13.2–99.3)	39.8 (14.7–107)	37.6 (14.4–104)	40.7 (15.2–111)	42.4 (14.9–115)
	Ratio to healthy adults	0.900	1.00	0.947	1.02	1.07
	Ratio to OIC adults	1.00	1.11	1.05	1.14	1.18
AUC_0–∞= area under the plasma concentration-time curve from time 0 to infinity; C_max= maximum plasma concentration; m = months; OIC = opioid-induced constipation; pctl = percentile; y = years;

The SAFARI study implemented an MIDD-based approach to estimating the naloxegol dosing regimens for paediatric subjects with OIC. The prior PPK model used to determine the naloxegol dose regimen for adult subjects who have OIC contained naloxegol concentration data from adult healthy subjects, adult non-OIC subjects, and adult subjects who have OIC. This prior PPK model incorporated data from Phase 1 and 2 studies with dense sampling to Phase 3 studies with sparse sampling (Al-Huniti, 2017). In the current model, the sparsely sampled Phase 3 data were not included in the analysis in order to focus on describing the paediatric data and simplify the model, similar to the base structural development of the prior model. The current PPK model comprised of both adult and paediatric data demonstrates consistent characteristics to the prior model. Both the previous and current PPK models indicate that naloxegol is best described by a two-compartment model with dual absorptions. The prior model captured the absorption process with one first order absorption and a second more complex absorption with a transit compartment whereas the current model used a Weibull function that provided a better fit. This modification is not likely to have clinical relevance. However due to a lower RSE, it also allowed for covariate assessment on the absorption parameters. Specifically, naloxegol rate of absorption following oral administration in adult subjects who have OIC and paediatric subjects receiving opioid treatment is slower than that in adult healthy subjects. The slower absorption is likely due to decreased GI motility in patients on opioid treatment (Leppert, 2012). An additional similarity is that both the prior and current PPK models identified covariates of renal function, CR_CL and race on CL/F, and CR_CL on V₁/F as significant. However, for both models, these predictors were not interpreted to have a marked effect on naloxegol exposure (Huniti et al., 2017 and current report).

Assessment of the final PPK model indicates that naloxegol exhibits dose linearity for both CL/F and V1/F across a dose range of 5 to 25 mg. Dose linearity above and below the 12.5 mg and 25 mg dose supports the prediction of the 25 mg dose in the paediatric patients from 6 months to > 6 years of age. The linear kinetics are also supported by the NCA of the exposures in the two paediatric age groups (6 to < 12 years, and 12 to < 18 years).

Paediatric subjects enrolled in the SAFARI study were administered adult-equivalent 12.5 mg or 25 mg doses of naloxegol based on PBPK modelling and allometric scaling. However, in both the prior and current model, neither age nor body weight were identified as a covariate on either CL/F or V1/F. Body weight of subjects in the current model ranged from 8.0 to 133.9 kg, yet CL/F and V₁/F of naloxegol were independent of body weight. These predictions are supported by the NCA analysis indicating lower naloxegol exposures in paediatric subjects administered OIC < 12 years of age that were administered approximately 40% of the adult equivalent 12.5 mg or 25 mg dose. Additionally, the 12.5 mg and 25 mg dose-normalized AUC_0∞ values for paediatric subjects ≥ 6 to < 12 years of age were comparable to healthy adults. Thus, both observed and PPK analysis indicate that allometric scaling of CL/F and V₁/F is not needed to extrapolate the exposure of naloxegol paediatric subjects ≥ 6 to < 18 years of age. Monte Carlo simulations from the PPK model predict that following administration of approximately 10% of the naloxegol 25 mg dose in paediatric subjects ≥ 6 months to < 6 years would result in approximately 25% of the adult exposure. This is in alignment with dose-normalized values for this age range predicted to result in comparable exposures to those following administration of naloxegol 25 mg to adults.

In summary, both NCA and PPK analysis demonstrate comparable PK characteristics of naloxegol in adults and paediatric subjects ≥ 6 months of age.

Acknowledgments

The authors thank Peter Todd, PhD (Tajut Ltd, Kaiapoi, New Zealand) for medical editorial assistance with this article, for which he received financial compensation from Kyowa Kirin, Inc. (Princeton, New Jersey).

Ethical Approval

The study was performed in accordance with Good Clinical Practice and the Declaration of Helsinki. The study was conducted at multiple clinical sites in Europe and Israel. The protocol was approved by the Ethic’s Review and Investigator Review boards at each clinical site.

Consent to Participate

Informed consent was obtained from all individual participants included in the study

Consent to Publish

Not applicable

Authors Contributions

Authors PD and JK designed the study; authors MM, DM, JK, SL, MH and LL analysed the data.

Funding

This study was supported and funded by Kyowa Kirin, Inc. (Princeton, New Jersey).

Competing Interests

MM, MH, and DM are employees of Kyowa Kirin, Inc. SL and LL are paid consultants for Kyowa Kirin, Inc.

Data sharing statement

Requests for access to data should be addressed to the corresponding author.

Ahn JE, Karlsson MO, Dunne A, Ludden TM. (2008) Likelihood based approaches to handling data below the quantification limit using NONMEM VI. J Pharmacokinet Pharmacodyn 35:401–421. https://doi.org/10.1007/s10928-008-9094-4.
Al-Huniti N, Chapel S, Xu H, Bui KH, Sostek M. (2015) Population pharmacokinetics of naloxegol in a population of 1247 healthy subjects and patients. Br J Clin Pharmacol 81:89–100. https://doi.org/10.1111/bcp.12756
Al-Huniti, N, NielsenJC, Hutmacher MM, Lappalainen J, Cantagallo K, Sostek M. (2016). Population exposure/response modeling of naloxegol in patients with noncancer-related pain and opioidinduced constipation. CPT Pharmacometrics Syst. Pharmacol. 5:359–366.
Al- Huniti N, Zhou, D, Xu H, Aksenov S, Bui KH, Fox R, Helmlinger G, Stansk D. (2017). Naloxegol Efficacy and Safety:Impact on Dose and Label Clin Pharm Ther 102(5):741-744
Allan L, Hays H, Jensen NH, de Waroux BL, Bolt M, Donald R, Kalso E. (2001) Randomised crossover trial of transdermal fentanyl and sustained release oral morphine for treating chronic non-cancer pain. BMJ 322:1154–1158. https://doi.org/10.1136/bmj.322.7295.1154.
Andresen V, Layer P. (2018) Medical therapy of constipation: current standards and beyond. Visc Med 34:123–127. https://doi.org/10.1159/000488695
Beal SL. (2001) Ways to fit a PK model with some data below the quantification limit. J Pharmacokinet Pharmacodyn 28:481–504. https://doi.org/10.1023/a:1012299115260.
Bell TJ, Panchal SJ, Miaskowski C, Bolge SC, Milanova T, Williamson R. (2009) The prevalence, severity, and impact of opioid-induced bowel dysfunction: results of a US and European Patient Survey (PROBE 1). Pain Med 10:35–42. https://doi.org/10.1111/j.1526-4637.2008.00495.x
Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. (2011 ) Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J 13:143–151. https://doi.org/10.1208/s12248-011-9255-z
Bui, K.H., Zhou, D., Xu, H., Floettmann, E. & Al-Huniti, N. Clinical pharmacokinetics and pharmacodynamics of naloxegol, a peripherally acting μ-opioid receptor antagonist. Clin. Pharmacokinet. (2017). Doi:10.1007/s40262-016-0479-z
Camilleri M. (2011) Opioid-induced constipation: challenges and therapeutic opportunities. Am J Gastroenterol 106:835–842. https://doi.org/10.1038/ajg.2011.30
Cook SF, Lanza L, Zhou X, Sweeney CT, Goss D, Hollis K, Mangel AW, Fehnel SE. (2008)Gastrointestinal side effects in chronic opioid users: results from a population-based survey. Aliment Pharmacol Ther 27:1224–1232. https://doi.org/10.1111/j.1365-2036.2008.03689.x
Farmer AD, Holt CB, Downes TJ, Ruggeri E, Del Vecchio S, De Giorgio R. (2018) Pathophysiology, diagnosis, and management of opioid-induced constipation. Lancet Gastroenterol Hepatol 3:203–212. https://doi.org/10.1016/S2468-1253(18)30008-6.
Garnock-Jones KP. (2015) Naloxegol: a review of its use in patients with opioid-induced constipation. Drugs 75:419–425. https://doi.org/10.1007/s40265-015-0357-2. PMID: 25666542.
Holford NH. (2007) Wings for NONMEM.
Jonsson EN, Karlsson MO. (1999) Xpose: an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput Methods Programs Biomed 58:51–64. https://doi.org/10.1016/s0169-2607(98)00067-4
Kalso E, Edwards JE, Moore AR, McQuay HJ. (2004) Opioids in chronic non-cancer pain: systematic review of efficacy and safety. Pain 112:372–380. https://doi.org/10.1016/j.pain.2004.09.019
Leonard J, Baker DE. (2015) Naloxegol: treatment for opioid-induced constipation in chronic non-cancer pain. Ann Pharmacother 49:360–365. https://doi.org/10.1177/1060028014560191
Leppert W (2012) The impact of opioid analgesics on the gastrointestinal tract function and the current management possibilities. Contemporary Oncology 16 (2): 125–131 https://doi.org/10.5114/wo.2012.28792
Moore RA, McQuay HJ. (2005) Prevalence of opioid adverse events in chronic non-malignant pain: systematic review of oventigd trials of oral opioids. Arthritis Res Ther 7:R1046–1051. https://doi.org/10.1186/ar1782
Movantik^TM (naloxegol) tablets. US prescribing information. Apr 2020. https://movantik.com/pdf/MovantikPrescribingInformation.pdf. Accessed 7 Mar 2022.
Moventig. EU summary of product characteristics. 2014. https://www.ema.europa.eu/en/documents/product-information/moventig-epar-product-information_en.pdf. Accessed 7 Mar 2022.
NONMEM Users Guide, Introduction to NONMEM 7.3.0. (2013) Bauer RJ. Icon Development Solutions, Hanover, MD.
Panchal SJ, Müller-Schwefe P, Wurzelmann JI. (2007) Opioid-induced bowel dysfunction:prevalence, pathophysiology and burden. Int J Clin Pract 61:1181–1187. https://doi.org/10.1111/j.1742-1241.2007.01415.x
Pappagallo M. (2001) Incidence, prevalence and management of opioid bowel dysfunction. Am J Surg 182(5A Suppl):11S–18S. https://doi.org/10.1016/s0002-9610(01)00782-6
Salazar-Lindo E, Santisteban-Ponce J, Chea-Woo E, Gutierrez M. (2000) Racecadotril in the treatment of acute watery diarrhea in children. N Engl J Med 343:463–467. https://doi.org/10.1056/NEJM200008173430703
Santucci G, Mack JW. (2007) Common gastrointestinal symptoms in paediatric palliative care: nausea, vomiting, constipation, anorexia, cachexia. Pediatr Clin North Am 54:673–689. https://doi.org/10.1016/j.pcl.2007.06.001
Tofil NM, Benner KW, Faro SJ, Winkler MK. (2006) The use of enteral naloxone to treat opioid-induced constipation in a paediatric intensive care unit. Pediatr Crit Care Med 7:252–254. https://doi.org/10.1097/01.PCC.0000216421.72002.09
Yaster M, Deshpande JK. (1988) Management of paediatric pain with opioid analgesics. J Pediatr 113:421–429. https//doi.org/10.1016/s0022-3476(88)80622-x.
Zernikow B, Michel E, Craig F, Anderson BJ. (2009) Paediatric palliative care: use of opioids for the management of pain. Paediatr Drugs 11:129–151. https://doi.org/10.2165/00148581-200911020-00004
Zhou, D. Bui K, Sostek M, Al-Huniti N. (2016) Simulation and prediction of the drug-drug interaction potential of naloxegol by physiologically based pharmacokinetic modeling. CPT Pharmacometrics Syst. Pharmacol. 5:250–257.

Competing interest reported. Peter Todd, PhD (Tajut Ltd, Kaiapoi, New Zealand) provided medical editorial assistance with this article, for which he received financial compensation from Kyowa Kirin, Inc. (Princeton, New Jersey).

The SAFARI study was supported and funded by Kyowa Kirin, Inc. (Princeton, New Jersey).

Declaration of conflicting interests Mary Ann Mascelli, Matt Hrushka, and Doug Marstellar are employees of Kyowa Kirin, Inc. Sam Liao and Lori Liao are paid consultants for Kyowa Kirin, Inc.

PopulationPKofnaloxegolinpediatricpatientsreceivingopioids21FEB23SM.docx

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Population pharmacokinetics of naloxegol in paediatric subjects receiving opioids

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Abstract

Figures

Introduction

Materials And Methods

Paediatric study

Bioanalytical methodology

Data assembly

Non-compartmental analysis

Population pharmacokinetic analysis

Software

Results

Non-compartmental analysis of paediatric subjects ≥ 6 to < 18 years of age

Population pharmacokinetic model analysis

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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