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.

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.

Antenatal betamethasone and dexamethasone are prescribed to women who are at high risk of premature birth to prevent neonatal respiratory distress syndrome (RDS). The current treatment regimens, effective to prevent neonatal RDS, may be suboptimal. Recently, concerns have been raised regarding possible adverse long-term neurological outcomes due to high fetal drug exposures. Data from nonhuman primates and sheep suggest maintaining a fetal plasma concentration above 1 ng/mL for 48 hours to retain efficacy, while avoiding undesirable high fetal plasma levels. We aimed to re-evaluate the current betamethasone and dexamethasone dosing strategies to assess estimated fetal exposure and provide new dosing proposals that meet the efficacy target but avoid excessive peak exposures. A pregnancy physiologically based pharmacokinetic (PBPK) model was used to predict fetal drug exposures. To allow prediction of the extent of betamethasone and dexamethasone exposure in the fetus, placenta perfusion experiments were conducted to determine placental transfer. Placental transfer rates were integrated in the PBPK model to predict fetal exposure and model performance was verified using published maternal and fetal pharmacokinetic data. The verified pregnancy PBPK models were then used to simulate alternative dosing regimens to establish a model-informed dose. Ex vivo data showed that both drugs extensively cross the placenta. For betamethasone 15.7±1.7% and for dexamethasone 14.4±1.5%, the initial maternal perfusate concentration reached the fetal circulations at the end of the 3-hour perfusion period. Pregnancy PBPK models that include these ex vivo-derived placental transfer rates accurately predicted maternal and fetal exposures resulting from current dosing regimens. The dose simulations suggest that for betamethasone intramuscular, a dose reduction from 2 dosages 11.4 mg, 24 hours apart, to 4 dosages 1.425 mg, 12 hours apart would avoid excessive peak exposures and still meet the fetal response threshold. For dexamethasone, the dose may be reduced from 4 times 6 mg every 12 hours to 8 times 1.5 mg every 6 hours. A combined placenta perfusion and pregnancy PBPK modeling approach adequately predicted both maternal and fetal drug exposures of 2 antenatal corticosteroids (ACSs). Strikingly, our PBPK simulations suggest that drug doses might be reduced drastically to still meet earlier proposed efficacy targets and minimize peak exposures. We propose the provided model-informed dosing regimens are used to support further discussion on an updated ACS scheme and design of clinical trials to confirm the effectiveness and safety of lower doses.

It is well‐accepted that off‐label drug dosing recommendations for pediatric patients should be based on the best available evidence. However, the available traditional evidence is often low. To bridge this gap, physiologically‐based pharmacokinetic (PBPK) modeling is a scientifically well‐founded tool that can be used to enable model‐informed dosing (MID) recommendations in children in clinical practice. In this tutorial, we provide a pragmatic, PBPK‐based pediatric modeling workflow. For this approach to be successfully implemented in pediatric clinical practice, a thorough understanding of the model assumptions and limitations is required. More importantly, careful evaluation of an MID approach within the context of overall benefits and the potential risks is crucial. The tutorial is aimed to help modelers, researchers, and clinicians, to effectively use PBPK simulations to support pediatric drug dosing.

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.

Introduction: The annual number of patients > 65 years old about whom the Dutch Poisons Information Center (DPIC) was consulted has more than doubled in the last decade. We aimed to gain insight in the type and circumstances of exposures reported to the DPIC involving older patients, in order to help prevent future poisonings. Methods: Enquiries to the DPIC involving patients > 65 years old were prospectively included from January 2019 to June 2019. Data were collected on patient characteristics (e.g., age, gender, and living situation) and exposure characteristics (e.g., type and exposure scenario). Results: In the first half of 2019, the DPIC was consulted about 1051 patients > 65 years old. The median age of the patients was 77 years old (range: 66–104 years) and women were over-represented (61%). A total of 1650 different substances were reported, 1213 pharmaceutical exposures (74%) and 437 non-pharmaceutical exposures (26%), mostly household products (n = 162). Most pharmaceutical exposures involved cardiovascular agents (n = 367, 30%), central and peripheral nervous system agents (n = 354, 29%), and analgesics (n = 152, 13%). In 71% of the patients exposed to pharmaceuticals, the drugs were taken unintentionally (n = 471), frequently caused by medication errors made by the patients themselves (n = 357, 76%). Most common scenarios included inadvertently taken/given a double (n = 140, 30%) or more than double (n = 94, 20%) dose or the wrong medication (n = 124, 26%). The most common scenario for unintentional exposure to non-pharmaceuticals was “mistook product for food/drink” (n = 122, 37%). Conclusions: The majority of intoxications in older adults are accidental and often involve medication errors. Unintentional poisoning is often preventable. If patients are cognitively impaired, potentially harmful substances should be kept out of their reach and medication should only be administered under direct supervision. Clear labelling, simplified drug regimens and the use of automatic medication dispensers could reduce the risk of medication errors in older patients.