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Background Hemodialysis (HD) dose targets and ultrafiltration rate (UFR) limits for pediatric patients on chronic HD are not known and are derived from adults (spKt/V>1.4 and Methods Retrospective analysis on a cohort of patients Results A total of 1780 patients were included (age at the start of HD: 0-12y: n=321, >12-18y: n=1459; median spKt/V=1.55, eKt/V=1.31, Kt/BSA=31.2 L/m.sup.2, UFR=10.6 mL/kg/h). (I) spKt/V1.4-1.6 (P1.6 (P Conclusions Our results suggest usefulness of targeting Kt/BSA>30 L/m.sup.2 for best long-term outcomes, corresponding to spKt/V>1.4 (>12 years) and >1.6 (, Author(s): Verena Gotta [sup.1] , Olivera Marsenic [sup.2] , Andrew Atkinson [sup.1] , Marc Pfister [sup.1] [sup.3] Author Affiliations: (1) grid.6612.3, 0000 0004 1937 0642, Pediatric Pharmacology and Pharmacometrics, University [...]

Background: The pharmacodynamics of propofol in children have previously been described with the proprietary bispectral index (BIS) as an effect‐site marker, and it has been suggested that the rate of onset of propofol might be age dependent, that is, a shorter time to peak effect in younger children. However, these analyses were potentially confounded by co‐administered drugs, in particular opioids and benzodiazepines. Thus, the goal of this prospective study was to characterize the influence of age and weight on the onset of hypnotic effects from propofol, reflected by the time to peak of propofol effect‐site concentration in the absence of additional drugs. Methods: A total of 46 healthy children aged 2–12 years presenting for elective surgery were included in our observational cohort study. Solely propofol was administered via a target‐controlled infusion pump programmed with the Paedfusor pharmacokinetic model. The BIS and infusion pump data were recorded. The effect of an induction "bolus" was recorded having stopped the pump once a propofol plasma target concentration of 7 μg.mL−1 was achieved. A direct‐response and an indirect‐response model in the context of nonlinear mixed‐effects modeling was used to characterize and compare BIS data in children aged 2–6 years and older children aged 8–12 years. Results: Time to peak of propofol effect‐site concentration had a difference (p‐value <.01) for age and weight, that is 84 [74, 96] (median [IQR] secs for children aged 2–6 years vs. 99 [91, 113] secs for children aged 8–12 years and 82 [71, 95] secs for weight 11–25 kg vs. 99 [91, 114] secs for weight 30–63 kg). The plasma effect‐site equilibration rate constant for propofol had a heterogeneous distribution with a median of 2.36 (IQR: 2.05–2.93; range: 0.83–7.31) per minute but showed a weight‐dependent effect in patients with weight below 45 kg. Conclusions: In children, the age and weight have an influence on time to peak effect of propofol. In the absence of opioids and benzodiazepines, time to peak effect was approximately 20% longer in children aged 8–12 years as compared to younger children. Such clinically relevant age and weight effects are an important consideration in the individualized titration of propofol dosing. [ABSTRACT FROM AUTHOR]

Ivermectin has been used since the 1980s as an anthelmintic and antiectoparasite agent worldwide. Currently, the only available oral formulation is tablets designed for adult patients. A patient‐friendly orodispersible tablet formulation designed for pediatric use (CHILD‐IVITAB) has been developed and is entering early phase clinical trials. To inform the pediatric program of CHILD‐IVITAB, 16 healthy adults were enrolled in a phase I, single‐center, open‐label, randomized, 2‐period, crossover, single‐dose trial which aimed to compare palatability, tolerability, and bioavailability and pharmacokinetics of CHILD‐IVITAB and their variability against the marketed ivermectin tablets (STROMECTOL) at a single dose of 12 mg in a fasting state. Palatability with CHILD‐IVITAB was considerably enhanced as compared to STROMECTOL. Both ivermectin formulations were well tolerated and safe. Relative bioavailability of CHILD‐IVITAB compared to STROMECTOL was estimated as the ratios of geometric means for Cmax, AUC 0‐∞, and AUC0‐last, which were 1.52 [90% CI: 1.13‐2.04], 1.27 [0.99‐1.62], and 1.29 [1.00‐1.66], respectively. Maximum drug concentrations occurred earlier with the CHILD‐IVITAB formulation, with a median Tmax at 3.0 h [range 2.0‐4.0 h] versus 4.0 h [range 2.0‐5.0 h] with STROMECTOL (P =.004). With CHILD‐IVITAB, variability in exposure was cut in half (coefficient of variation: 37% vs 70%) compared to STROMECTOL. Consistent with a more controlled absorption process, CHILD‐IVITAB was associated with reduced variability in drug exposure as compared to STROMECTOL. Together with a favorable palatability and tolerability profile, these findings motivate for further clinical studies to evaluate benefits of such a patient‐friendly ODT formulation in pediatric patients with a parasitic disease, including infants and young children <15 kg. [ABSTRACT FROM AUTHOR]

Serum creatinine in neonates follows complex dynamics due to maturation processes, most pronounced in the first few weeks of life. The development of a mechanism‐based model describing complex dynamics requires high expertise in pharmacometric (PMX) modeling and substantial model development time. A recently published machine learning (ML) approach of low‐dimensional neural ordinary differential equations (NODEs) is capable of modeling such data from newborns automatically. However, this efficient data‐driven approach in itself does not result in a clinically interpretable model. In this work, an approach to deriving an interpretable model with reasonable PMX‐type functions is presented. This "translation" was applied to derive a PMX model for serum creatinine in neonates considering maturation processes and covariates. The developed model was compared to a previously published mechanism‐based PMX model whereas both models had similar mechanistic structures. The developed model was then utilized to simulate serum creatinine concentrations in the first few weeks of life considering different covariate values for gestational age and birth weight. The reference serum creatinine values derived from these simulations are consistent with observed serum creatinine values and previously published reference values. Thus, the presented NODE‐based ML approach to model complex serum creatinine dynamics in newborns and derive interpretable, mathematical‐statistical components similar to those in a conventional PMX model demonstrates a novel, viable approach to facilitate the modeling of complex dynamics in clinical settings and pediatric drug development. [ABSTRACT FROM AUTHOR]

The antiparasitic drug ivermectin is approved for persons > 15 kg in the US and EU. A pharmacometric (PMX) population model with clinical PK data was developed (i) to characterize the effect of the patient-friendly ivermectin formulation CHILD-IVITAB on the absorption process and (ii) to evaluate dosing for studies in children < 15 kg. Simulations were performed to identify dosing with CHILD-IVITAB associated with similar exposure coverage in children ≥ 15 kg and < 15 kg as observed in adults receiving the reference formulation STROMECTOL®. A total of 448 ivermectin concentrations were available from 16 healthy adults. The absorption rate constant was 2.41 h−1 (CV 19%) for CHILD-IVITAB vs. 1.56 h−1 (CV 43%) for STROMECTOL®. Simulations indicated that 250 µg/kg of CHILD-IVITAB is associated with exposure coverage in children < 15 kg consistent with that observed in children ≥ 15 kg and adults receiving 200 µg/kg of STROMECTOL®. Performed analysis confirmed that CHILD-IVITAB is associated with faster and more controlled absorption than STROMECTOL®. Simulations indicate that 250 µg/kg of CHILD-IVITAB achieves equivalent ivermectin exposure coverage in children < 15 kg as seen in children ≥ 15 kg and adults. [ABSTRACT FROM AUTHOR]

Understanding pharmacokinetics (PK) in children is a prerequisite to determine optimal pediatric dosing. As plasma sampling in children is challenging, alternative PK sampling strategies are needed. In this case study we evaluated the suitability of saliva as alternative PK matrix to simplify studies in infants, investigating metamizole, an analgesic used off‐label in infants. Six plasma and 6 saliva PK sample collections were scheduled after a single intravenous dose of 10 mg/kg metamizole. Plasma/saliva pharmacometric (PMX) modeling of the active metabolites 4‐methylaminoantipyrine (4‐MAA) and 4‐aminoantipyrine (4‐AA) was performed. Various reduced plasma sampling scenarios were evaluated by PMX simulations. Saliva and plasma samples from 25 children were included (age range, 5–70 months; weight range, 8.7–24.8 kg). Distribution of metamizole metabolites between plasma and saliva was without delay. Estimated mean (individual range) saliva/plasma fractions of 4‐MAA and 4‐AA were 0.32 (0.05–0.57) and 0.57 (0.25–0.70), respectively. Residual variability of 4‐MAA (4‐AA) in saliva was 47% (28%) versus 17% (11%) in plasma. A simplified sampling scenario with up to 6 saliva samples combined with 1 plasma sample was associated with similar PK parameter estimates as the full plasma sampling scenario. This case study with metamizole shows increased PK variability in saliva compared to plasma, compromising its suitability as single matrix for PK studies in infants. Nonetheless, rich saliva sampling can reduce the number of plasma samples required for PK characterization, thereby facilitating the conduct of PK studies to optimize dosing in pediatric patients. [ABSTRACT FROM AUTHOR]

An association between QT prolongation (Bazett's corrected QT interval, QTcB) of 7 milliseconds and nocturnal hypoglycemia, compared with euglycemia, has been observed in children with type 1 diabetes (T1D). The objective of this pharmacometric analysis was to understand this association and other sources of QTc variability quantitatively. Data originate from a prospective observational study (25 cardiac healthy children with T1D, aged 8.1‐17.6 years) with continuous subcutaneous glucose and electrocardiogram measurements for 5 consecutive nights. Mixed‐effect modeling was used to compare QTcB with individual heart‐rate correction (QTcI). Covariate models accounting for circadian variation, age, and sex were evaluated, followed by an investigation of glucose–QTc relationships (with univariable and combined adjusted analysis). Factors potentially modifying sensitivity to QTc lengthening were explored. Random inter‐individual variability was reduced in the QTcI versus QTcB model (±12.6 vs 14.1 milliseconds), and was further reduced in the adjusted covariate model (±9.7 milliseconds), accounting for the significantly (P <.01) shortened QTc in adolescent boys (–14.6 milliseconds), circadian variation (amplitude, 19.2 milliseconds; shift, 2.9 hours), and linear glucose–QTc relationship (delay rate, 0.56−h; slope, 0.76 milliseconds [95%CI 0.67‐ 0.85 milliseconds] per 1 mmol/L decrease in glucose). Differing sensitivity was suggested to depend upon hemoglobin A1c (HbA1c), T1D duration, and time spent in nocturnal hypoglycemia. In conclusion, a clinically mild association of QTc prolongation with nocturnal hypoglycemia was confirmed and quantified in this pharmacometric analysis, and the longest QTc interval was around 03:00 a.m. The characterized delayed association with glucose highlights the relevance of both the extent and the duration of hypoglycemia. Further clinical studies are warranted to investigate whether these factors contribute to increased risk of hypoglycemia‐associated cardiac arrhythmia in children with T1D. [ABSTRACT FROM AUTHOR]

The novel oral complement factor 5a receptor 1 antagonist ACT‐1014‐6470 was well tolerated in single‐ and multiple‐ascending dose studies, including 24 h Holter electrocardiogram (ECG) recordings evaluating its cardiodynamics based on data from single doses of 30–200 mg and twice‐daily (b.i.d.) dosing of 30–120 mg for 4.5 days. By‐time point, categorical, and morphological analyses as well as concentration‐QT modeling and simulations were performed. No relevant effect of ACT‐1014‐6470 on ECG parameters was observed in the categorical and morphological analyses. After single‐dose administration, the by‐time point analysis indicated a delayed dose‐dependent increase in placebo‐corrected change from baseline in QT interval corrected with Fridericia's formula (ΔΔQTcF) at >6 h postdose. After b.i.d. dosing, ΔΔQTcF remained elevated during the 24‐h recording period, suggesting that the effect was not directly related to ACT‐1014‐6470 plasma concentration. The concentration‐QT model described change from baseline in QTcF (ΔQTcF)‐time profiles best with a 1‐oscillator model of 24 h for circadian rhythm, an effect compartment, and a sigmoidal maximum effect model. Model‐predicted ΔΔQTcF was derived for lower doses and less‐frequent dosing than assessed clinically. Median and 90% prediction intervals of ΔΔQTcF for once‐daily doses of 30 mg and b.i.d. doses of 10 mg did not exceed the regulatory threshold of 10 ms but would achieve ACT‐1014‐6470 plasma concentrations enabling adequate target engagement. Results from cardiodynamic assessments identified dose levels and dosing regimens that could be considered for future clinical trials, attempting to reduce QT liability. [ABSTRACT FROM AUTHOR]

A majority of therapeutics are not available as suitable dosage forms for administration to pediatric patients. The first part of this review provides an overview of clinical and technological challenges and opportunities in the development of child-friendly dosage forms such as taste masking, tablet size, flexibility of dose administration, excipient safety and acceptability. In this context, developmental pharmacology, rapid onset of action in pediatric emergency situations, regulatory and socioeconomic aspects are also reviewed and illustrated with clinical case studies. The second part of this work discusses the example of Orally Dispersible Tablets (ODTs) as a child-friendly drug delivery strategy. Inorganic particulate drug carriers can thereby be used as multifunctional excipients offering a potential solution to address unique medical needs in infants and children while maintaining a favorable excipient safety and acceptability profile in these vulnerable patient populations. [ABSTRACT FROM AUTHOR]

Introduction: Oral ibuprofen is more effective than intravenous (IV) ibuprofen for closure of a patent ductus arteriosus (PDA). This study explored whether higher concentrations of the biologically active S-enantiomer or increased R- to S-conversion following oral dosing could explain this finding. Methods: Two datasets containing 370 S- and R-ibuprofen concentrations from 95 neonates with PDA treated with oral (n = 27, 28%) or IV ibuprofen were analyzed using nonlinear mixed effects modeling. Concentration-time profiles in typical neonates were explored and compared in different dosing or R- to S-conversion scenarios. Results: Postnatal age (PNA), gestational age (GA), and being small for GA impacted S- and R-ibuprofen clearance. Upon oral dosing, S-ibuprofen concentrations were lower compared to IV ibuprofen for a large part of the dosing interval. We could show that R- to S-conversion will not exceed 45%. Exploration of a 30% presystemic R- to S-conversion resulted in a 25–32% increase in S-ibuprofen exposure following oral administration with AUC72h values varying between 700–2,213 mg*h/L (oral) and 531–1,762 (IV) for the standard or 1,704–2,893 (oral) and 1,295–2,271 mg*h/L (IV) for PNA-based dosing. Discussion: The absence of higher S-ibuprofen concentrations does not support a beneficial concentration-time profile after oral dosing. While a fraction of up to 45% presystemic R- to S-conversion could not be ruled out, the impact of such a low conversion might be only relevant for the standard but not high dosing regimens, considering reported exposure-response targets. Perhaps, the lack of high peak concentrations observed following IV dosing may play a role in the observed effects upon oral dosing. [ABSTRACT FROM AUTHOR]

Accurate treatment adjustment to physical activity (PA) remains a challenging problem in type 1 diabetes (T1D) management. Exercise-driven effects on glucose metabolism depend strongly on duration and intensity of the activity, and are highly variable between patients. In-silico evaluation can support the development of improved treatment strategies, and can facilitate personalized treatment optimization. This requires models of the glucose-insulin system that capture relevant exercise-related processes. We developed a model of glucose-insulin regulation that describes changes in glucose metabolism for aerobic moderate- to high-intensity PA of short and prolonged duration. In particular, we incorporated the insulin-independent increase in glucose uptake and production, including glycogen depletion, and the prolonged rise in insulin sensitivity. The model further includes meal absorption and insulin kinetics, allowing simulation of everyday scenarios. The model accurately predicts glucose dynamics for varying PA scenarios in a range of independent validation data sets, and full-day simulations with PA of different timing, duration and intensity agree with clinical observations. We personalized the model on data from a multi-day free-living study of children with T1D by adjusting a small number of model parameters to each child. To assess the use of the personalized models for individual treatment evaluation, we compared subject-specific treatment options for PA management in replay simulations of the recorded data with altered meal, insulin and PA inputs. Author summary: Exercise represents a cornerstone of diabetes management. Yet, many people with type 1 diabetes refrain from exercising, since it increases the risk for hypoglycemia and requires adjusted insulin treatment. The effect of exercise on blood glucose levels depends on exercise duration and intensity, but also varies strongly between individuals, making accurate adjustment a challenge. Mathematical models can help to better understand exercise physiology and to devise new treatment strategies. Here, we propose a model of glucose-insulin regulation that captures the effects of exercise on glucose metabolism and personalize it to individual children with type 1 diabetes, allowing subject-specific treatment assessment. [ABSTRACT FROM AUTHOR]

Diabetic ketoacidosis (DKA), a frequent complication of type 1 diabetes (T1D), is characterized by hyperosmolar hypovolemia. The response of water‐regulating hormones arginine vasopressin (AVP; antidiuretic hormone) and aldosterone to DKA treatment in children is not well understood, although they may have potential as future diagnostic, prognostic, and/or treatment monitoring markers in diabetic patients. We aimed to characterize the dynamics of the response in copeptin (marker for AVP) and aldosterone secretion to rehydration treatment in pediatric patients with DKA. Data originated from a prospective, observational, multicenter study including 28 pediatric T1D patients treated for DKA (median age, 11.5 years; weight, 35 kg). Serial measurements of hormone levels were obtained during 72 h following rehydration start. Semimechanistic pharmacometric modeling was used to analyze the kinetic/dynamic relationship of copeptin and aldosterone secretion in response to the correction of hyperosmolality and hypovolemia, respectively. Modeling revealed different sensitivities for osmolality‐dependent copeptin secretion during the first 72 h of rehydration, possibly explained by an osmotic shift introduced by hypovolemia. Response in aldosterone secretion to the correction of hypovolemia seemed to be delayed, which was well described by an extra upstream turnover compartment, possibly representing chronic upregulation of aldosterone synthase (cytochrome P450 11B2). In conclusion, semimechanistic modeling provided novel physiological insights in hormonal water regulation in pediatric patients during DKA treatment, providing rationale to further evaluate the potential of monitoring copeptin, but not aldosterone due to its delayed response, for future optimization of rehydration treatment to reduce the risk of acute complications such as cerebral edema. [ABSTRACT FROM AUTHOR]

Objective: Differentiation between central diabetes insipidus (cDI) and primary polydipsia (PP) remains challenging in clinical practice. Although the hypertonic saline infusion test led to high diagnostic accuracy, it is a laborious test requiring close monitoring of plasma sodium levels. As such, we leverage machine learning (ML) to facilitate differential diagnosis of cDI. Design: We analyzed data of 59 patients with cDI and 81 patients with PP from a prospective multicenter study evaluating the hypertonic saline test as new test approach to diagnose cDI. Our primary outcome was the diagnostic accuracy of the ML-based algorithm in differentiating cDI from PP patients. Methods: The data set used included 56 clinical, biochemical, and radiological covariates. We identified a set of five covariates which were crucial for differentiating cDI from PP patients utilizing standard ML methods. We developed ML-based algorithms on the data and validated them with an unseen test data set. Results: Urine osmolality, plasma sodium and glucose, known transsphenoidal surgery, or anterior pituitary deficiencies were selected as input parameters for the basic ML-based algorithm. Testing it on an unseen test data set resulted in a high area under the curve (AUC) score of 0.87. A further improvement of the ML-based algorithm was reached with the addition of MRI characteristics and the results of the hypertonic saline infusion test (AUC: 0.93 and 0.98, respectively). Conclusion: The developed ML-based algorithm facilitated differentiation between cDI and PP patients with high accuracy even if only clinical information and laboratory data were available, thereby possibly avoiding cumbersome clinical tests in the future. [ABSTRACT FROM AUTHOR]

Missing data create challenges in clinical research because they lead to loss of statistical power and potentially to biased results. Missing covariate data must be handled with suitable approaches to prepare datasets for pharmacometric analyses, such as population pharmacokinetic and pharmacodynamic analyses. To this end, various statistical methods have been widely adopted. Here, we introduce two machine‐learning (ML) methods capable of imputing missing covariate data in a pharmacometric setting. Based on a previously published pharmacometric analysis, we simulated multiple missing data scenarios. We compared the performance of four established statistical methods, listwise deletion, mean imputation, standard multiple imputation (hereafter "Norm"), and predictive mean matching (PMM) and two ML based methods, random forest (RF) and artificial neural networks (ANNs), to handle missing covariate data in a statistically plausible manner. The investigated ML‐based methods can be used to impute missing covariate data in a pharmacometric setting. Both traditional imputation approaches and ML‐based methods perform well in the scenarios studied, with some restrictions for individual methods. The three methods exhibiting the best performance in terms of least bias for the investigated scenarios are the statistical method PMM and the two ML‐based methods RF and ANN. ML‐based approaches had comparable good results to the best performing established method PMM. Furthermore, ML methods provide added flexibility when encountering more complex nonlinear relationships, especially when associated parameters are suitably tuned to enhance predictive performance. [ABSTRACT FROM AUTHOR]