1 Introduction
Invasive fungal disease (IFD) remains an important cause of morbidity and mortality in immunocompromised children [
1,
2]. Despite the development of new diagnostic methods and the availability of new antifungal agents, the incidence and mortality from IFD remains unacceptably high. Posaconazole is a second-generation, broad-spectrum, fluorinate triazole that inhibits ergosterol synthesis in the fungal cell wall. It is active against most pathogenic yeasts and moulds, including
Aspergillus spp.,
Candida spp.,
Cryptococcus spp., filamentous fungi, dimorphic fungi and endemic mycoses [
3‐
6].
Despite currently being unlicensed for use in the paediatric population, posaconazole has successfully been used for the prevention and treatment of IFD in this group [
7], and is recommended for prophylaxis against invasive Aspergillus and Candida infections after allogeneic haematopoietic stem cell transplantation in adolescents [
7]. Additionally, posaconazole has been used as a salvage treatment for IFD with favourable outcomes [
8,
9].
Two oral formulations of posaconazole are currently available, a gastro-resistant tablet and an oral suspension. Posaconazole pharmacokinetics are variable, particularly during absorption and with the suspension formulation, and very limited paediatric data have been published to date [
10]. Pharmacokinetic models to inform optimal dosing in infants and young children, in particular, are therefore lacking.
Therapeutic drug monitoring (TDM) for most triazoles is recommended owing to high inter-individual variability and the potential for drug–drug interactions. According to the British Society for Medical Mycology, a posaconazole target trough concentration of greater than 0.7 and 1 mg/L should be used for the prophylaxis and treatment of IFD, respectively, and as yet no upper limit for toxicity has been defined [
11].
Our study aimed to develop a population-pharmacokinetic model of posaconazole in a large cohort of paediatric patients. Focussing on children aged 12 years and under, the resulting model was then used to identify patient groups at risk of sub-optimal posaconazole exposure, and to suggest initial dosing.
2 Patients and Methods
2.1 Patients and Data Collection
In- and out-patients at a tertiary paediatric hospital receiving posaconazole between January 2010 and December 2016 were studied. Patients receiving posaconazole for prophylaxis or the treatment of IFD and who had at least one TDM sample taken, and had full dosing and sample timing history available were included. The time and date of the posaconazole TDM sample, along with the reported concentration, were extracted from electronic TDM records. For inpatients, dosing history was taken from electronic nursing administration history, whereas for outpatients the time of the last dose was taken from the TDM request. In addition, demographics, concomitant medications, presence of diarrhoea on the day of sampling and purpose (prophylaxis or treatment) were collected from electronic records. Medical notes including clinic letters and inpatient treatment records coinciding with each sampling occasion were read to extract information on the indication and the presence of diarrhoea. Because the data were collected by clinical staff retrospectively and were anonymised prior to analysis, ethical review and the need for informed consent were waived by the institute’s research and development office.
For dosing data from the electronic prescribing and administration system, all doses from the first dose to the first TDM sample were included. Thereafter, only the doses in the preceding 48 h prior to a TDM sample were used, with the first of these assumed to be at steady state. For outpatient samples, the preceding dose was assumed to be at steady state based on the reported dose and frequency.
During the recruitment period, posaconazole assays were sent to the following accredited laboratories for analysis: Department of Microbiology, Wythenshawe Hospital, Manchester, UK; the Mycology Reference Laboratory, Leeds, UK; and Mycology Reference Laboratory, Bristol, UK. The lower limits of quantification ranged between 0.07 and 0.2 mg/L.
2.2 Population-Pharmacokinetic Modelling
Because most samples were pre-dose troughs and posaconazole is known to have a long elimination half-life, a one-compartment model with first-order absorption was used. Allometric scaling with exponents of 0.75, 1 and − 0.25 on clearance (CL), central volume and absorption rate constant (Ka) were added a priori, and a sigmoidal maturation function based on postmenstrual age was tested [
12].
Because posaconazole tablets have been reported to have higher bioavailability than the suspension [
13], and tablet pharmacokinetics are linear in the therapeutic range [
14], whereas suspension has been shown to have nonlinear absorption [
15], the following expression was used to describe relative bioavailability between a tablet and a suspension, and the nonlinear suspension bioavailability:
$$F = F_{\text{tab}} - \frac{D}{{D + \beta_{\text{dose}} }},$$
where
\(F\) is the bioavailability of the suspension relative to the tablet,
\(F_{\text{tab}}\) is the apparent tablet bioavailability that was fixed to 1,
\(D\) is the dose in mg/m
2, and
\(\beta_{\text{dose}}\) is the estimated dose in mg/m
2 to yield a 50% decrease in bioavailability of the suspension relative to tablets.
A step-wise covariate model (SCM) building exercise with a forward inclusion limit set to a p value of 0.05 and backwards elimination limit set to a p value of 0.01 was then undertaken to identify whether any of the following dichotomous covariates were associated with suspension apparent bioavailability: diarrhoea, treatment/prophylaxis, macrolides, echinocandins, terbinafine, ciclosporin, tacrolimus, mycophenolate, rifamycins, carbamazepine, phenytoin, histamine H2-receptor antagonists, proton pump inhibitors (PPIs) or valaciclovir. The following concomitant medications were also tested on CL: macrolides, echinocandins, ciclosporin, tacrolimus, mycophenolate, rifampicin, carbamazepine, phenytoin or valaciclovir.
Model diagnostics included plots of observations vs. population predictions and conditional weighted residuals vs. time and prediction. Simulation properties were tested with a visual predictive check. Parameter stability was investigated using a non-parametric bootstrap. Modelling was undertaken using NONMEM Version 7.3 (ICON PLC, Dublin, Ireland) [
16] with the first-order conditional estimation algorithm with interaction. A combined additive plus proportional error model was used throughout model building, and then removal of the additive or proportional element considered at the final model step.
A decrease in − 2 log likelihood [the objective function value (OFV) in NONMEM] between two nested models asymptotically follows a
\(\chi^{2}\) distribution with degrees of freedom equal to the number of additional parameters. This was used to guide covariate inclusion with a
p value threshold set to 0.01. Perl-speaks NONMEM (University of Uppsala, Sweden) was used for the SCM (forward inclusion
p < 0.05, backward elimination
p < 0.01), visual predictive check and bootstrap preparation [
17], and data manipulations and plotting were performed using R Version 3.2 (R Foundation, Vienna, Austria) [
18].
A dataset of 1000 hypothetical patients for each significant covariate in the final model and for each formulation was created by re-sampling from the demographics (weight, age) of the original dataset. Using this dataset and the final model, simulations of steady-state trough concentration were produced to assess probability of target attainment for prophylaxis (0.7 mg/L) and treatment (1 mg/L) targets [
11].
4 Discussion
To the best of our knowledge, this is the first population-pharmacokinetic analysis of posaconazole tablets and suspension in immunocompromised children. We studied the pharmacokinetics in 117 patients, including 105 aged under 13 years. This is a larger cohort than the largest published adult clinical cohort to date by Dolton et al. [
20], who studied 102 patients. Our major finding is that as soon as children are able to swallow whole tablets, they should be given the tablet formulation because a poor and saturable suspension bioavailability, particularly in patients with diarrhoea or those taking concurrent PPI therapy, means a therapeutic target attainment with suspension may be as low as 30% even on the highest feasible dose (Fig.
3).
A one-compartment model with inter-individual variability best described the pharmacokinetics of posaconazole in this study (Fig.
2). This was consistent with previous adult models [
15,
19,
20]. Our estimated CL/F and apparent volume related to the tablet formulation and standardised to a 70-kg individual were 14.95 L/h and 201.68 L, respectively. In a recent study of adults, CL/F and apparent volume were estimated to be 7.3 L/h and 420 L, respectively [
19], which fall within the 95% confidence intervals of our estimates (Table
2). Fixing the absorption rate to that previously reported in adults had a negligible effect on model fit, and the flat profiles, the fact our data did not include any patients sampled after their first dose, and the limited number of samples in the absorption phase all account for this. The residual variability was rather high in our study (Table
2), reflecting the fact that these were observational TDM data on a drug with highly variable pharmacokinetics. However, model diagnostics show a reasonable fit (Fig.
2). We did not find a significant relationship between age and posaconazole CL/F, which could be owing to the fact that our youngest patient was 6 months old, whereas rapid pharmacokinetic maturation tends to occur in the neonatal to early infant age group [
12].
The gastro-resistant tablet formulation has been widely reported to have improved bioavailability over the suspension [
21‐
24]. In addition, suspension bioavailability has previously been reported to be saturable in adults [
25‐
28], although we are not aware of this relationship having previously been modelled using the population approach. In children, we found a dose–proportional relationship with our estimated dose to reach a 50% relative bioavailability reduction in tablets relative to the suspension of 99 mg/m
2. In this expression, we scaled dose by body surface area under the assumption that gastrointestinal surface area and body surface area would be correlated, and that gastrointestinal surface area is important for absorption. Validating this assumption is not straight-forward because accurate measurement of gastrointestinal surface area is difficult [
29], and no extensive studies appear to have been conducted on how it might scale with age [
30]. However, our model did provide an adequate fit to our data and our estimate ought to be robust because we studied a large dose range (Table
1).
Gastrointestinal complications are common in cancer patients and haematopoietic stem cell transplantation recipients. In this analysis, 20% of patients had diarrhoea during treatment, and the majority were receiving concomitant acid suppression therapy (Table
1). Diarrhoea results in increased gastric emptying with reduced gastrointestinal residence time. This disruption in gastrointestinal function was associated with a significant reduction in bioavailability and therefore target attainment (Fig.
3). The association of diarrhoea with decreased posaconazole exposure has previously been noted in adults [
20,
31], with Dolton et al. [
20] finding a 45% reduction in apparent bioavailability. Our estimate of 33% shows a similar relationship in children.
Concomitant use of PPIs was associated with a 42% reduction in relative bioavailability. Concomitant PPI therapy has been shown to be associated with decreased bioavailability in adults [
32‐
35], and our estimate is similar to that obtained by Dolton et al. [
20], who found a 45% decrease. In contrast with PPIs, fewer studies have shown the potential effect of histamine H
2-receptor antagonists on posaconazole exposure [
20,
31], and we also did not find this effect, suggesting the more potent acid suppression of PPIs [
36,
37] limits posaconazole absorption. It is unlikely that this interaction is cytochrome P450 mediated because posaconazole undergoes limited metabolism primarily by UDP-glucuronosyltransferase UGT1A4 [
38]. Information on whether the dose was taken with food and whether mucositis was present was unavailable in our study but these may also have been significant covariates based on adult experience [
20]. Partly because of the low number of children in our data taking tablets, and also the fact that PPIs and histamine H
2-receptor antagonists have been shown not to affect posaconazole tablet bioavailability in adults [
39], we did not perform covariate analysis on the tablet formulation.
Patients undergoing haematopoietic stem cell transplantation usually require immunosuppressive agents for the prevention and treatment of graft vs. host disease in combination with antifungal prophylaxis for IFD. Concurrent use of posaconazole potentially results in increased drug exposure of several immunosuppressive drugs including ciclosporin, tacrolimus, sirolimus and everolimus [
40‐
42]. We did not find these agents to affect posaconazole pharmacokinetics, but future work on our data to investigate and quantify the effect of posaconazole on immunosuppressant levels is planned. We did not find phase II glucuronide enzyme inducers such as rifampicin or phenytoin to be significantly associated with either posaconazole CL/F or bioavailability during the SCM. The likely explanation for this is that our study contained a small proportion of samples taken concurrently with these drugs (5 and 1%, respectively), but it is also possible that immaturity of drug-metabolising enzyme expression in younger children means such interactions are less pronounced. We also tested prophylaxis vs. treatment as a covariate with the concern that the differences seen may be owing to data inaccuracies because prophylaxis patients were more likely to be outpatients with less reliable dosing history than inpatients for whom we had electronic administration data. The fact that this did not emerge as a covariate on CL/F or bioavailability indicates no such bias was present.
In Fig.
1, we show the dosing by age split between initial dose and post-TDM dosing. The key features of this plot are that a flat 200-mg dose was often used, regardless of age, and the following TDM doses were generally increased, particularly in younger patients. Clinical practice has evolved in our centre from weight-scaled dosing to fixed 200-mg dosing regardless of age, based on repeated failures to achieve therapeutic target trough concentrations. The added insight provided by simulations from our model (Fig.
3) indicates that absolute dose increases above 200 mg are rather futile owing to the saturable bioavailability. For example, a 1-year-old individual with a body surface area of 0.5 m
2 receiving 100, 200 or 400 mg of suspension will have a relative bioavailability of 0.33, 0.2 or 0.11, respectively. Increasing from 100 to 200 mg decreases the bioavailability by 40%, whereas increasing from 200 to 400 mg decreases the bioavailability by almost half, explaining the marginal increases in trough concentration with increasing doses. In common with findings for itraconazole [
43], increasing the frequency is more successful, but dose administrations of greater than four times per day are simply impractical.
Whilst we have modelled the largest paediatric posaconazole pharmacokinetics dataset to date, our study does have limitations that should be considered when interpreting the results. First, as mentioned above, these were retrospective TDM data collected over 7 years in a single centre, and owing to inconsistent reporting of the sample time and dose time in the outpatient data, we had to exclude 242 samples. Furthermore, we are likely to have collected more data on patients with poor target attainment because those patients would be sampled more frequently following dose escalation. Ideally, we would have run a prospective study with optimally designed pharmacokinetic sampling [
44], but this would have resulted in a smaller dataset and then potentially missing covariates of interest. Having said this, maximum likelihood methods should not be biased by this type of data and our prediction-corrected visual predictive check showed good agreement with observations. Further data pooling experience from multiple centres would however be useful to confirm our findings. We have also performed simulations aiming for trough concentration targets based on adult data [
11], whereas either a different target or use of a metric such as the area under the curve may be more appropriate in children. Whilst we did not collect outcome data during this particular study, there is a clear need for such data in this population.