Many drugs are still prescribed off‐label to the pediatric population. Although off‐label drug use not supported by high level of evidence is potentially harmful, a comprehensive overview of the quality of the evidence pertaining off‐label drug use in children is lacking. The Dutch Pediatric Formulary (DPF) provides best evidence‐based dosing guidelines for drugs used in children. For each drug‐indication‐age group combination—together compiling one record—we scored the highest available level of evidence: labeled use, systematic review or meta‐analysis, randomized controlled trial (RCT), comparative research, noncomparative research, or consensus‐based expert opinions. For records based on selected guidelines, the original sources were not reviewed. These records were scored as guideline. A total of 774 drugs were analyzed comprising a total of 6,426 records. Of all off‐label records (n = 2,718), 14% were supported by high quality evidence (4% meta‐analysis or systematic reviews, 10% RCTs of high quality), 20% by comparative research, 14% by noncomparative research, 37% by consensus‐based expert opinions, and 15% by selected guidelines. Fifty‐eight percent of all records were authorized, increasing with age from 30% in preterm neonates (n = 110) up to 64% in adolescents (n = 1,630). Many have advocated that off‐label use is only justified when supported by a high level of evidence. We show that this prerequisite would seriously limit available drug treatment for children as the underlying evidence is low across ages and drug classes. Our data identify the drugs and therapeutic areas for which evidence is clearly missing and could drive the global research agenda.

Summary: Background: With the global efforts to eradicate hepatitis C virus (HCV), treatment during pregnancy is becoming a priority for research as this, and maternal cure should reduce vertical transmission. However, as information on the efficacy and safety of direct‐acting antivirals (DAAs) in pregnancy is generally lacking, treatment of HCV infection during pregnancy is not currently recommended.Aim: To provide an overview of current knowledge regarding maternal exposure, placental handling and safety of DAAs during pregnancy and lactationMethods: A literature search was performed focusing on the effect of pregnancy on maternal exposure to DAAs, the placental handling of DAAs, the safety of DAAs for mother and child during pregnancy and the safety of DAAs during lactation.Results: Exposure to all DAAs studied is likely to be altered during pregnancy, mostly related to pregnancy‐induced effects on drug absorption and metabolism. Although animal studies show that most DAAs are reported to cross the placenta and transfer into breast milk, most DAA combinations show a favourable safety profile. Because of the rapid viral decline after treatment initiation, and to avoid the critical period of organogenesis, treatment may be started at the end of the second trimester or early third trimester.Conclusions: Treatment of HCV infection during pregnancy is realistic, as DAAs are highly effective and treatment duration is relatively short. There is an urgent need to study DAAs during pregnancy and lactation to contribute to the goal of HCV elimination.

Antiretroviral therapy during pregnancy reduces the risk of vertical HIV‐1 transmission. However, drug dosing is challenging as pharmacokinetics (PK) may be altered during pregnancy. We combined a pregnancy physiologically‐based pharmacokinetic (PBPK) modeling approach with data on placental drug transfer to simulate maternal and fetal exposure to dolutegravir (DTG). First, a PBPK model for DTG exposure in healthy volunteers was established based on physiological and DTG PK data. Next, the model was extended with a fetoplacental unit using transplacental kinetics obtained by performing ex vivo dual‐side human cotyledon perfusion experiments. Simulations of fetal exposure after maternal dosing in the third trimester were in accordance with clinically observed DTG cord blood data. Furthermore, the predicted fetal trough plasma concentration (C_trough) following 50 mg q.d. dosing remained above the concentration that results in 90% of viral inhibition. Our integrated approach enables simulation of maternal and fetal DTG exposure, illustrating this to be a promising way to assess DTG PK during pregnancy.

Physiologically based pharmacokinetic (PBPK) modeling can be an attractive tool to increase the evidence base of pediatric drug dosing recommendations by making optimal use of existing pharmacokinetic (PK) data. A pragmatic approach of combining available compound models with a virtual pediatric physiology model can be a rational solution to predict PK and hence support dosing guidelines for children in real-life clinical care, when it can also be employed by individuals with little experience in PBPK modeling. This comes within reach as user-friendly PBPK modeling platforms exist and, for many drugs and populations, models are ready for use. We have identified a list of drugs that can serve as a starting point for pragmatic PBPK modeling to address current clinical dosing needs.

Background Dexmedetomidine is currently off-label for use in pediatric clinical care worldwide. Nevertheless, it is frequently prescribed to pediatric patients as premedication prior to induction of anesthesia or for procedural sedation. There is ample literature on the pharmacokinetics, efficacy and safety of dexmedetomidine in this vulnerable patient population, but there is a general lack of consensus on dosing. In this project, we aimed to use the standardized workflow of the Dutch Pediatric Formulary to establish best evidence-based pediatric dosing guidelines for dexmedetomidine as premedication and for procedural sedation. Method The available literature on dexmedetomidine in pediatrics was reviewed in order to address the following three questions: (1) What is the right dose? (2) What is known about efficacy? (3) What is known about safety? Relevant literature was compiled into a risk–benefit analysis document. A team of clinical experts critically appraised the analysis and the proposed dosing recommendations. Results Dexmedetomidine is most commonly administered via the intravenous or intranasal route. Clearance is age dependent, warranting higher doses in infants to reach similar exposure as in adults. Dexmedetomidine use results in satisfactory sedation at parent separation, adequate sedation and a favorable recovery profile. The safety profile is good and comparable to adults, with dose-related hemodynamic effects. Conclusion Following the structured approach of the Dutch Pediatric Formulary, best evidence-based dosing recommendations were proposed for dexmedetomidine, used as premedication prior to induction of anesthesia (intranasal dose) and for procedural sedation (intranasal and intravenous dose) in pediatric patients.

Background and Objective More than half of all drugs are still prescribed off-label to children. Pharmacokinetic (PK) data are needed to support off-label dosing, however for many drugs such data are either sparse or not representative. Physiologically-based pharmacokinetic (PBPK) models are increasingly used to study PK and guide dosing decisions. Building compound models to study PK requires expertise and is time-consuming. Therefore, in this paper, we studied the feasibility of predicting pediatric exposure by pragmatically combining existing compound models, developed e.g. for studies in adults, with a pediatric and preterm physiology model. Methods Seven drugs, with various PK characteristics, were selected (meropenem, ceftazidime, azithromycin, propofol, midazolam, lorazepam, and caffeine) as a proof of concept. Simcyp^® v20 was used to predict exposure in adults, children, and (pre)term neonates, by combining an existing compound model with relevant virtual physiology models. Predictive performance was evaluated by calculating the ratios of predicted-to-observed PK parameter values (0.5- to 2-fold acceptance range) and by visual predictive checks with prediction error values. Results Overall, model predicted PK in infants, children and adolescents capture clinical data. Confidence in PBPK model performance was therefore considered high. Predictive performance tends to decrease when predicting PK in the (pre)term neonatal population. Conclusion Pragmatic PBPK modeling in pediatrics, based on compound models verified with adult data, is feasible. A thorough understanding of the model assumptions and limitations is required, before model-informed doses can be recommended for clinical use.