[Adequate medication doses for pregnant women and their unborn children]. / Gepaste medicatiedoseringen voor zwangere en foetus.

Most women use medication during pregnancy. The disposition of drugs may be altered due to changes in pregnant women's bodies. This may call for pregnancy-adjusted doses for certain medications. However, in the face of scarce evidence, such doses are generally lacking, potentially contributing to an increased risk of treatment failure or toxicity in pregnant women and their unborn children. By integrating physiological and/or population data, pharmacokinetic models can be used to determine appropriate medication dosages among pregnant women and their unborn children, as well as other patient groups for which evidence-based doses may be lacking such as children, elderly or obese patients. In order to translate model predictions into clinically usable doses, a number of conditions must be met, including careful model validation, an assessment of evidence from pharmacokinetic modelling alongside available clinical studies by multidisciplinary experts, as well as transparent communication towards end-users on the considerations for determining appropriate medication doses.

Pragmatic physiologically-based pharmacokinetic modeling to support clinical implementation of optimized gentamicin dosing in term neonates and infants: proof-of-concept.

Introduction: Modeling and simulation can support dosing recommendations for clinical practice, but a simple framework is missing. In this proof-of-concept study, we aimed to develop neonatal and infant gentamicin dosing guidelines, supported by a pragmatic physiologically-based pharmacokinetic (PBPK) modeling approach and a decision framework for implementation. Methods: An already existing PBPK model was verified with data of 87 adults, 485 children and 912 neonates, based on visual predictive checks and predicted-to-observed pharmacokinetic (PK) parameter ratios. After acceptance of the model, dosages now recommended by the Dutch Pediatric Formulary (DPF) were simulated, along with several alternative dosing scenarios, aiming for recommended peak (i.e., 8-12 mg/L for neonates and 15-20 mg/L for infants) and trough (i.e., <1 mg/L) levels. We then used a decision framework to weigh benefits and risks for implementation. Results: The PBPK model adequately described gentamicin PK. Simulations of current DPF dosages showed that the dosing interval for term neonates up to 6 weeks of age should be extended to 36-48 h to reach trough levels <1 mg/L. For infants, a 7.5 mg/kg/24 h dose will reach adequate peak levels. The benefits of these dose adaptations outweigh remaining uncertainties which can be minimized by routine drug monitoring. Conclusion: We used a PBPK model to show that current DPF dosages for gentamicin in term neonates and infants needed to be optimized. In the context of potential uncertainties, the risk-benefit analysis proved positive; the model-informed dose is ready for clinical implementation.

Physiologically-Based Pharmacokinetic Modeling for Drug Dosing in Pediatric Patients: A Tutorial for a Pragmatic Approach in Clinical Care.

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.

Lamivudine and Emtricitabine Dosing Proposal for Children with HIV and Chronic Kidney Disease, Supported by Physiologically Based Pharmacokinetic Modelling.

Dose recommendations for lamivudine or emtricitabine in children with HIV and chronic kidney disease (CKD) are absent or not supported by clinical data. Physiologically based pharmacokinetic (PBPK) models have the potential to facilitate dose selection for these drugs in this population. Existing lamivudine and emtricitabine compound models in Simcyp® (v21) were verified in adult populations with and without CKD and in non-CKD paediatric populations. We developed paediatric CKD population models reflecting subjects with a reduced glomerular filtration and tubular secretion, based on extrapolation from adult CKD population models. These models were verified using ganciclovir as a surrogate compound. Then, lamivudine and emtricitabine dosing strategies were simulated in virtual paediatric CKD populations. The compound and paediatric CKD population models were verified successfully (prediction error within 0.5- to 2-fold). The mean AUC ratios in children (GFR-adjusted dose in CKD population/standard dose in population with normal kidney function) were 1.15 and 1.23 for lamivudine, and 1.20 and 1.30 for emtricitabine, with grade-3- and -4-stage CKD, respectively. With the developed paediatric CKD population PBPK models, GFR-adjusted lamivudine and emtricitabine dosages in children with CKD resulted in adequate drug exposure, supporting paediatric GFR-adjusted dosing. Clinical studies are needed to confirm these findings.

Physiologically Based Pharmacokinetic (PBPK) Model-Informed Dosing Guidelines for Pediatric Clinical Care: A Pragmatic Approach for a Special Population.

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.

Feasibility of a Pragmatic PBPK Modeling Approach: Towards Model-Informed Dosing in Pediatric Clinical Care.

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.

Off-Label, but on-Evidence? A Review of the Level of Evidence for Pediatric Pharmacotherapy.

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.

Best Evidence-Based Dosing Recommendations for Dexmedetomidine for Premedication and Procedural Sedation in Pediatrics: Outcome of a Risk-Benefit Analysis By the Dutch Pediatric Formulary.

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.

Assessment of Maternal and Fetal Dolutegravir Exposure by Integrating Ex Vivo Placental Perfusion Data and Physiologically-Based Pharmacokinetic Modeling.

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 (Ctrough ) 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.

Review article: direct-acting antivirals for the treatment of HCV during pregnancy and lactation - implications for maternal dosing, foetal exposure, and safety for mother and child.

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 lactation METHODS: 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.

Prediction of Fetal Darunavir Exposure by Integrating Human Ex-Vivo Placental Transfer and Physiologically Based Pharmacokinetic Modeling.

BACKGROUND: Fetal antiretroviral exposure is usually derived from the cord-to-maternal concentration ratio. This static parameter does not provide information on the pharmacokinetics in utero, limiting the assessment of a fetal exposure-effect relationship. OBJECTIVE: The aim of this study was to incorporate placental transfer into a pregnancy physiologically based pharmacokinetic model to simulate and evaluate fetal darunavir exposure at term. METHODS: An existing and validated pregnancy physiologically based pharmacokinetic model of maternal darunavir/ritonavir exposure was extended with a feto-placental unit. To parameterize the model, we determined maternal-to-fetal and fetal-to-maternal darunavir/ritonavir placental clearance with an ex-vivo human cotyledon perfusion model. Simulated maternal and fetal pharmacokinetic profiles were compared with observed clinical data to qualify the model for simulation. Next, population fetal pharmacokinetic profiles were simulated for different maternal darunavir/ritonavir dosing regimens. RESULTS: An average (±standard deviation) maternal-to-fetal cotyledon clearance of 0.91 ± 0.11 mL/min and fetal-to-maternal clearance of 1.6 ± 0.3 mL/min was determined (n = 6 perfusions). Scaled placental transfer was integrated into the pregnancy physiologically based pharmacokinetic model. For darunavir 600/100 mg twice a day, the predicted fetal maximum plasma concentration, trough concentration, time to maximum plasma concentration, and half-life were 1.1, 0.57 mg/L, 3, and 21 h, respectively. This indicates that the fetal population trough concentration is higher or around the half-maximal effective darunavir concentration for a resistant virus (0.55 mg/L). CONCLUSIONS: The results indicate that the population fetal exposure after oral maternal darunavir dosing is therapeutic and this may provide benefits to the prevention of mother-to-child transmission of human immunodeficiency virus. Moreover, this integrated approach provides a tool to prevent fetal toxicity or enhance the development of more selectively targeted fetal drug treatments.

Genetic polymorphism 609C>T in NAD(P)H:quinone oxidoreductase 1 enhances the risk of proximal colon cancer.

Gastrointestinal (GI) cancer is responsible for the majority of deaths among all types of cancer. Lifestyle factors may not only be the main risk factor for GI cancer but reactive oxygen species (ROS) may also be involved. The single-nucleotide polymorphisms (SNPs) 609C>T (rs1800566) and 465C>T (rs1131341) in the NAD(P)H: quinone oxidoreductase 1 (NQO1) gene lead to a decline in NQO1 enzyme activity. NQO1 catalyzes the two-electron reduction of quinones to hydroquinones, thereby preventing the formation of ROS. Such polymorphisms in NQO1 may increase the risk of GI cancer. The aim of this study was to evaluate the influence of the SNPs rs1800566 and rs1131341 in the NQO1 gene on the risk of GI cancer in the Netherlands. Real-time polymerase chain reaction techniques were conducted to determine the NQO1 genotypes of 1457 patients with GI cancer and 1457 age- and gender-matched controls in a case-control study. Binary logistic regression analyses showed no statistically significant difference in genotype distributions between patients and controls: odds ratios (ORs) with 95% confidence interval (CI) for rs1800566 were 1.09 (0.93-1.28) and 1.17 (0.77-1.77) for the CT and TT genotypes, respectively. ORs for rs1131341 CT and TT genotypes were 1.21 (0.90-1.63) and 0.54 (0.05-5.94), respectively. For rs1800566, a significant association between the CT genotype and proximal colon cancer was detected (OR=1.60; 95% CI=1.09-2.35). The NQO1*2 T allele of SNP rs1800566 was found associated with an increased risk for proximal colorectal cancer, whereas SNP rs1131341 was rare in our Dutch population and was not associated with GI cancer.