Applying Neural ODEs to Derive a Mechanism-Based Model for Characterizing Maturation-Related Serum Creatinine Dynamics in Preterm Newborns.

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.

Pharmacometric in silico studies used to facilitate a national dose standardisation process in neonatology - application to amikacin.

BACKGROUND AND AIMS: Pharmacometric in silico approaches are frequently applied to guide decisions concerning dosage regimes during the development of new medicines. We aimed to demonstrate how such pharmacometric modelling and simulation can provide a scientific rationale for optimising drug doses in the context of the Swiss national dose standardisation project in paediatrics using amikacin as a case study. METHODS: Amikacin neonatal dosage is stratified by post-menstrual age (PMA) and post-natal age (PNA) in Switzerland and many other countries. Clinical concerns have been raised for the subpopulation of neonates with a post-menstrual age of 30-35 weeks and a post-natal age of 0-14 days ("subpopulation of clinical concern"), as potentially oto-/nephrotoxic trough concentrations (Ctrough >5 mg/l) were observed with a once-daily dose of 15 mg/kg. We applied a two-compartmental population pharmacokinetic model (amikacin clearance depending on birth weight and post-natal age) to real-world demographic data from 1563 neonates receiving anti-infectives (median birth weight 2.3 kg, median post-natal age six days) and performed pharmacometric dose-exposure simulations to identify extended dosing intervals that would ensure non-toxic Ctrough (Ctrough <5 mg/l) dosages in most neonates. RESULTS: In the subpopulation of clinical concern, Ctrough <5 mg/l was predicted in 59% versus 79-99% of cases in all other subpopulations following the current recommendations. Elevated Ctrough values were associated with a post-natal age of less than seven days. Simulations showed that extending the dosing interval to ≥36 h in the subpopulation of clinical concern increased the frequency of a desirable Ctrough below 5 mg/l to >80%. CONCLUSION: Pharmacometric in silico studies using high-quality real-world demographic data can provide a scientific rationale for national paediatric dose optimisation. This may increase clinical acceptance of fine-tuned standardised dosing recommendations and support their implementation, including in vulnerable subpopulations.

Pharmacokinetics-Based Pediatric Dose Evaluation and Optimization Using Saliva - A Case Study.

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.

Prospective study to characterize adalimumab exposure in pediatric patients with rheumatic diseases.

BACKGROUND: In pediatric rheumatic diseases (PRD), adalimumab is dosed using fixed weight-based bands irrespective of methotrexate co-treatment, disease activity (DA) or other factors that might influence adalimumab pharmacokinetics (PK). In rheumatoid arthritis (RA) adalimumab exposure between 2-8 mg/L is associated with clinical response. PRD data on adalimumab is scarce. Therefore, this study aimed to analyze adalimumab PK and its variability in PRD treated with/without methotrexate. METHODS: A two-center prospective study in PRD patients aged 2-18 years treated with adalimumab and methotrexate (GA-M) or adalimumab alone (GA) for ≥ 12 weeks was performed. Adalimumab concentrations were collected 1-9 (maximum concentration; Cmax), and 10-14 days (minimum concentration; Cmin) during ≥ 12 weeks following adalimumab start. Concentrations were analyzed with enzyme-linked immunosorbent assay (lower limit of quantification: 0.5 mg/L). Log-normalized Cmin were compared between GA-M and GA using a standard t-test. RESULTS: Twenty-eight patients (14 per group), diagnosed with juvenile idiopathic arthritis (71.4%), non-infectious uveitis (25%) or chronic recurrent multifocal osteomyelitis (3.6%) completed the study. GA-M included more females (71.4%; GA 35.7%, p = 0.13). At first study visit, children in GA-M had a slightly longer exposure to adalimumab (17.8 months [IQR 9.6, 21.6]) compared to GA (15.8 months [IQR 8.5, 30.8], p = 0.8). Adalimumab dosing was similar between both groups (median dose 40 mg every 14 days) and observed DA was low. Children in GA-M had a 27% higher median overall exposure compared to GA, although median Cmin adalimumab values were statistically not different (p = 0.3). Cmin values ≥ 8 mg/L (upper limit RA) were more frequently observed in GA-M versus GA (79% versus 64%). Overall, a wide range of Cmin values was observed in PRD (0.5 to 26 mg/L). CONCLUSION: This study revealed a high heterogeneity in adalimumab exposure in PRD. Adalimumab exposure tended to be higher with methotrexate co-treatment compared to adalimumab monotherapy although differences were not statistically significant. Most children showed adalimumab exposure exceeding those reported for RA with clinical response, particularly with methotrexate co-treatment. This highlights the need of further investigations to establish model-based personalized treatment strategies in PRD to avoid under- and overexposure. TRIAL REGISTRATION: NCT04042792 , registered 02.08.2019.

Low-dimensional neural ODEs and their application in pharmacokinetics.

Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics.

Pharmacokinetics of oral spironolactone in infants up to 2 years of age.

PURPOSE: Spironolactone is a potassium sparing diuretic used for decades. Until now, pharmacokinetic (PK) studies of spironolactone have not been conducted in infants and therefore pediatric dosing is based on expert opinion. We aimed to describe the PK profiles of spironolactone and its main metabolites (7alpha-thiomethylspironolactone (TMS) and canrenone (CAN)) in infants up to two years of age. METHODS: The PK of spironolactone and its main metabolites were evaluated following an oral administration of spironolactone (1 mg/kg/dose) to pediatric patients with chronic heart failure, ascites, and/or oedema. The plasma concentration of spironolactone and metabolites (TMS and CAN) was determined using an ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). Based on rich sampling PK data, the estimation of population PK parameters was performed using nonlinear mixed-effects modelling software Monolix 2018R2. RESULTS: A total of 150 spironolactone, 158 TMS, and 158 CAN concentrations from 23 patients (ages: 3 days-21 months; median weight 4.3 kg (2.2-12.6)) were available for PK analysis. A one-compartment model for spironolactone, TMS, and CAN best fitted the data. The median (range) of individual estimated apparent clearance values were 47.7 (11.9-138.1) L/h for spironolactone, 9.7 (1.5-66.9) L/h for TMS, and 1.0 (0.2-5.9) L/h for CAN. The disposition of spironolactone and metabolites was mainly affected by size of the patient: body weight explained 22% of inter-individual variability of spironolactone clearance. None of the undesirable effects of spironolactone was documented during the study period. CONCLUSION: The pharmacokinetics of spironolactone and its metabolites was highly variable between patients below 2 years of age. Body weight explained a significant part of this variability; this highlights the need to take it into account for dosing prescription in this population. (Clinical trial Registration Number 2013-001189-40).

Blood glucose forecasting from temporal and static information in children with T1D.

Background: The overarching goal of blood glucose forecasting is to assist individuals with type 1 diabetes (T1D) in avoiding hyper- or hypoglycemic conditions. While deep learning approaches have shown promising results for blood glucose forecasting in adults with T1D, it is not known if these results generalize to children. Possible reasons are physical activity (PA), which is often unplanned in children, as well as age and development of a child, which both have an effect on the blood glucose level. Materials and Methods: In this study, we collected time series measurements of glucose levels, carbohydrate intake, insulin-dosing and physical activity from children with T1D for one week in an ethics approved prospective observational study, which included daily physical activities. We investigate the performance of state-of-the-art deep learning methods for adult data-(dilated) recurrent neural networks and a transformer-on our dataset for short-term (30 min) and long-term (2 h) prediction. We propose to integrate static patient characteristics, such as age, gender, BMI, and percentage of basal insulin, to account for the heterogeneity of our study group. Results: Integrating static patient characteristics (SPC) proves beneficial, especially for short-term prediction. LSTMs and GRUs with SPC perform best for a prediction horizon of 30 min (RMSE of 1.66 mmol/l), a vanilla RNN with SPC performs best across different prediction horizons, while the performance significantly decays for long-term prediction. For prediction during the night, the best method improves to an RMSE of 1.50 mmol/l. Overall, the results for our baselines and RNN models indicate that blood glucose forecasting for children conducting regular physical activity is more challenging than for previously studied adult data. Conclusion: We find that integrating static data improves the performance of deep-learning architectures for blood glucose forecasting of children with T1D and achieves promising results for short-term prediction. Despite these improvements, additional clinical studies are warranted to extend forecasting to longer-term prediction horizons.

Characterizing Associations of QTc Interval with Nocturnal Glycemic Control in Children with Type 1 Diabetes.

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.

Modeling time-delayed concentration-QT effects with ACT-1014-6470, a novel oral complement factor 5a receptor 1 (C5a1 receptor) antagonist.

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.

Clinically practical pharmacometrics computer model to evaluate and personalize pharmacotherapy in pediatric rare diseases: application to Graves' disease.

Objectives: Graves' disease (GD) with onset in childhood or adolescence is a rare disease (ORPHA:525731). Current pharmacotherapeutic approaches use antithyroid drugs, such as carbimazole, as monotherapy or in combination with thyroxine hormone substitutes, such as levothyroxine, as block-and-replace therapy to normalize thyroid function and improve patients' quality of life. However, in the context of fluctuating disease activity, especially during puberty, a considerable proportion of pediatric patients with GD is suffering from thyroid hormone concentrations outside the therapeutic reference ranges. Our main goal was to develop a clinically practical pharmacometrics computer model that characterizes and predicts individual disease activity in children with various severity of GD under pharmacotherapy. Methods: Retrospectively collected clinical data from children and adolescents with GD under up to two years of treatment at four different pediatric hospitals in Switzerland were analyzed. Development of the pharmacometrics computer model is based on the non-linear mixed effects approach accounting for inter-individual variability and incorporating individual patient characteristics. Disease severity groups were defined based on free thyroxine (FT4) measurements at diagnosis. Results: Data from 44 children with GD (75% female, median age 11 years, 62% receiving monotherapy) were analyzed. FT4 measurements were collected in 13, 15, and 16 pediatric patients with mild, moderate, or severe GD, with a median FT4 at diagnosis of 59.9 pmol/l (IQR 48.4, 76.8), and a total of 494 FT4 measurements during a median follow-up of 1.89 years (IQR 1.69, 1.97). We observed no notable difference between severity groups in terms of patient characteristics, daily carbimazole starting doses, and patient years. The final pharmacometrics computer model was developed based on FT4 measurements and on carbimazole or on carbimazole and levothyroxine doses involving two clinically relevant covariate effects: age at diagnosis and disease severity. Discussion: We present a tailored pharmacometrics computer model that is able to describe individual FT4 dynamics under both, carbimazole monotherapy and carbimazole/levothyroxine block-and-replace therapy accounting for inter-individual disease progression and treatment response in children and adolescents with GD. Such clinically practical and predictive computer model has the potential to facilitate and enhance personalized pharmacotherapy in pediatric GD, reducing over- and underdosing and avoiding negative short- and long-term consequences. Prospective randomized validation trials are warranted to further validate and fine-tune computer-supported personalized dosing in pediatric GD and other rare pediatric diseases.

Meeting Challenges of Pediatric Drug Delivery: The Potential of Orally Fast Disintegrating Tablets for Infants and Children.

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.

New model of glucose-insulin regulation characterizes effects of physical activity and facilitates personalized treatment evaluation in children and adults with type 1 diabetes.

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.

Computing optimal drug dosing with OptiDose: implementation in NONMEM.

Determining a drug dosing recommendation with a PKPD model can be a laborious and complex task. Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal doses for any pharmacometrics/PKPD model for a given dosing scenario. In the present work, we reformulate the underlying optimal control problem and elaborate how to solve it with standard commands in the software NONMEM. To demonstrate the potential of the OptiDose implementation in NONMEM, four relevant but substantially different optimal dosing tasks are solved. In addition, the impact of different dosing scenarios as well as the choice of the therapeutic goal on the computed optimal doses are discussed.

Semimechanistic modeling of copeptin and aldosterone kinetics and dynamics in response to rehydration treatment for diabetic ketoacidosis in children.

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.

Oral Ibuprofen Is More Effective than Intravenous Ibuprofen for Closure of a Patent Ductus Arteriosus: Can Pharmacokinetic Modeling Help Us to Understand Why?

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.

Pharmacokinetics and tolerability of intranasal or intravenous administration of nalbuphine in infants.

OBJECTIVES: Intranasal nalbuphine could be a safe, efficacious and non-invasive alternative to parenteral pain medication in infants. We aimed to assess pharmacokinetics (PK) and tolerability of intranasal and intravenous nalbuphine administration in infants. METHODS: Prospective open-label study including infants 1-3 months of age admitted to the emergency department, receiving nalbuphine for procedural pain management. Patients were alternately allocated to a single nalbuphine dose of 0.05 mg/kg intravenously or 0.1 mg/kg intranasally. Nalbuphine PK samples were collected 15, 30 and 120-180 min after dosing. Area under the concentration time curve (AUC0-Tlast) was calculated by non-compartmental analysis (NCA) and compared by Wilcoxon test. Neonatal Infant Pain Score was assessed during nalbuphine administration and the following interventions: venous access, urinary catheterisation, lumbar puncture. RESULTS: Out of 52 study subjects receiving nalbuphine, 31 were eligible for NCA (11 intravenous, 20 intranasal). Median AUC0-Tlast after 0.05 mg/kg intravenously was 8.7 (IQR: 8.0-18.6) µg×L/hour vs 7.6 (5.4-10.4) µg×L/hour after intranasal administration of 0.1 mg/kg (p=0.091). Maximum serum concentration (Cmax) was observed 30 min after intranasal administration (3.5-5.6 µg/L). During intravenous and intranasal nalbuphine administration, mild to no pain was recorded in 71% and 67% of study subjects, respectively. CONCLUSION: This is the first study investigating intranasal administration of nalbuphine in infants suggesting an intranasal bioavailability close to 50%. Non-invasive intranasal application was well tolerated. Additional studies are warranted to optimise dosing and timing of interventions as Cmax is delayed by half an hour after intranasal administration. TRIAL REGISTRATION NUMBER: NCT03059511.

National survey in Switzerland calls for improved diagnosis and treatment in children with scabies.

AIM OF THE STUDY: The global prevalence of scabies is estimated to be up to 200 million cases annually, with young children particularly affected. In Europe, most cases are thought to originate in migrant populations. Scabies management is challenging in children. To identify knowledge gaps and research needs, we aimed to descriptively evaluate the management of children with scabies by different Swiss healthcare providers. METHODS: An invitation for an anonymous online survey (36 questions) was sent to members of Swiss societies of dermatologists, general practitioners, paediatricians, paediatric dermatologists, paediatric infectious diseases specialists, and tropical medicine specialists, inviting clinicians to participate from 25th May to 8th August 2020. One reminder invitation was sent. Hospital pharmacies and the distributor of permethrin were contacted to report consumption trends of scabicides in 2018 and 2019. RESULTS: The survey was completed by 248 clinicians: 146 (59%) paediatricians, 47 (19%) dermatologists, 28 (11%) general practitioners, 6 (2%) paediatric dermatologists, 13 (5%) paediatric infectious diseases specialists, and 8 (3%) tropical medicine specialists. Most consulted up to 10 scabies cases within a 16-month period, with similar numbers in migrant and Swiss children. Dermoscopy was used by 24% of non-dermatologists. Non-dermatologists did not consider co-treatment of close contacts in up to 59% of cases. While permethrin was the first-line treatment, treatment failures were frequently reported in children aged <5 years. Up to 67% of paediatric dermatologists regularly used oral ivermectin off-label in children weighing <15 kg. None of the paediatric dermatologists, 15% of the dermatologists, and 9% of the non-dermatologists used only one treatment cycle.Scabicide consumption increased. Treatment studies on ivermectin use in children weighing <15 kg had the highest research priority. CONCLUSION: In Switzerland, scabies is a frequent dermatosis in migrant and Swiss children. While accessible, optimal diagnostics are underutilised, and treatment is suboptimal. Permethrin resistance appears to be an increasing problem. Dermatologists regularly use ivermectin off-label in children weighing <15 kg. Treatment studies on ivermectin use in children weighing <15 kg, user-friendly diagnostic tools, new treatment protocols, and child-friendly dosage forms are needed to improve the diagnosis and treatment of children with scabies.

Evaluation of machine learning methods for covariate data imputation in pharmacometrics.

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.

Understanding efficacy-safety balance of biologics in moderate-to-severe pediatric psoriasis.

Background: Psoriasis is a chronic immune-mediated inflammatory skin disease affecting both adults and children. To better understand the efficacy-safety profile of biologics in children with moderate-to-severe psoriasis, this study aimed to analyze efficacy and safety data of randomized controlled trials (RCTs) performed in pediatric psoriasis and to compare efficacy outcomes in children with those in adults. Methods: RCTs investigating biologics in children with moderate-to-severe psoriasis were identified in a systematic literature review. PASI75/90 treatment responses at weeks 11/12 were analyzed comparing biologics with control arms. Serious adverse events (SAEs) were analyzed at the end of each study. Efficacy data from RCTs in adults with psoriasis were selected for the same biologics. Risk ratios (RR) of selected RCTs were pooled together in a statistical random effects model using the inverse variance method. Results: For children, there were 1 etanercept, 2 secukinumab, 1 ixekizumab and 1 ustekinumab placebo-controlled RCTs and 1 adalimumab RCT using methotrexate as reference arm at weeks 11/12. For adults, out of 263 RCTs, 7 adalimumab and 15 etanercept (TNF inhibitors) and 4 ixekizumab and 12 ustekinumab (IL-17 and IL-12/23 inhibitors) RCTs reported PASI75/90 efficacy responses at weeks 11/12. Regarding efficacy, all biologics showed improved PASI responses over control arms. RRs ranges were 2.02-7.45 in PASI75 and 4.10-14.50 in PASI90. The highest PASI75 responses were seen for ustekinumab 0.375 mg/kg (RR = 7.25, 95% CI 2.83-18.58) and ustekinumab 0.75 mg/kg (RR = 7.45, 95% CI 2.91-19.06) in the CADMUS study. The highest PASI90 response was seen for ixekizumab (RR = 14.50, 95% CI 4.82-43.58) in the IXORA-PEDS study. SAE incidences in pediatric and adult arms with biologics were 0 to 3% except for a pediatric arm with adalimumab 0.40 mg/kg (8%). For adults, pooled RR also showed improved PASI responses over placebo for all biologics, with highest PASI75 response observed for ixekizumab (pooled RR = 16.18, 95% CI 11.83-22.14). Conclusion: Both adults and children with psoriasis show superior efficacy with biologics compared to control arms after 3 months of treatment with SAE incidences in the low percentages. Additional longer-term clinical studies are warranted to fully understand the overall efficacy-safety profile of biologics in children with moderate-to-severe psoriasis.

Machine learning-based algorithm as an innovative approach for the differentiation between diabetes insipidus and primary polydipsia in clinical practice.

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.