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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]

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]

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]

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]

Moxidectin is a frontrunner drug candidate in the treatment of strongyloidiasis. A dose of 8 mg is recommended to treat this indication, which shows a reasonably good efficacy and tolerability profile. Yet, owing to the unique life cycle of Strongyloides stercoralis (S. stercoralis) that entails internal autoinfection, a curative treatment would be desirable. Population‐based pharmacometric modeling that would help to identify an ideal dosing strategy are yet lacking. The aims of this study were to (i) explore the exposure‐efficacy response relationship of moxidectin in treating S. stercoralis and (ii) evaluate whether moxidectin treatment outcomes in terms of cure rates at baseline as compared to post‐treatment could be optimized. Our pharmacodynamic model suggests high predictive power (area under the concentration time curve‐receiver operating characteristic [AUC‐ROC] 0.817) in the probability of being cured by linking an exposure metric (i.e., AUC0‐24 or maximum concentration [Cmax]) to baseline infection intensity. Pharmacometric simulations indicate that with a minimum dose of 4 mg a maximum cure rate of ~ 95% is established in the low infection intensity group (larvae per gram [LPG] ≥0.4–1), whereas in the moderate‐to‐high intensity group (LPG >1) the cure rate plateaus at ~ 87%, following an 8 mg dose. To enhance efficacy further, studies using repeated dosing based on the duration of the autoinfection cycle, for example a two‐dose regimen 3 weeks apart should be considered. Simulations revealed similar Cmax in both treatment courses of a two‐dose regimen; hence safety should not be a concern. Collectively, our results provide evidence‐based guidance for enhanced dosing strategies and should be considered when designing future treatment strategies. [ABSTRACT FROM AUTHOR]

Neural ordinary differential equations (NODEs) are an emerging machine learning (ML) method to model pharmacometric (PMX) data. Combining mechanism‐based components to describe “known parts” and neural networks to learn “unknown parts” is a promising ML‐based PMX approach. In this work, the implementation of low‐dimensional NODEs in two widely applied PMX software packages (Monolix and NONMEM) is explained. Inter‐individual variability is introduced to NODEs and proposals for the practical implementation of NODEs in such software are presented. The potential of such implementations is shown on various demonstrational datasets available in the Monolix model library, including pharmacokinetic (PK), pharmacodynamic (PD), target‐mediated drug disposition (TMDD), and survival analyses. All datasets were fitted with NODEs in Monolix and NONMEM and showed comparable results to classical modeling approaches. Model codes for demonstrated PK, PKPD, TMDD applications are made available, allowing a reproducible and straight‐forward implementation of NODEs in available PMX software packages. [ABSTRACT FROM AUTHOR]

The two main objectives of this analysis were to (i) characterize the relationship between immunoglobulin (Ig) exposure and chronic inflammatory demyelinating polyneuropathy (CIDP) disease severity using data from 171 patients with CIDP who received either subcutaneous Ig (IgPro20; Hizentra®) or placebo (PATH study), and to (ii) simulate and compare exposure coverage with various dosing approaches considering weekly dosing to be the reference dose. IgG pharmacokinetic (PK) parameters, including those from a previous population PK model, were used to predict individual IgG profile and exposure metrics. Treatment‐related changes in Inflammatory Neuropathy Cause and Treatment (INCAT) scores were best described by a maximum effect (Emax) model as a function of ΔIgG (total serum IgG at INCAT score assessment minus baseline IgG levels before intravenous Ig restabilization). Simulations indicate that flexible dosing from daily to biweekly (every other week) provide an exposure coverage equivalent to that of a weekly Ig dose. [ABSTRACT FROM AUTHOR]

Aims: To characterize relationships between apolipoprotein A‐I (apoA‐I) exposure and cholesterol efflux capacity (CEC) and covariate effects following CSL112 (apoA‐I [human]) administration in an integrated population including acute myocardial infarction (AMI) patients. Methods: A pharmacometric analysis utilized data from seven clinical trials, including patients with AMI, subjects with renal impairment and healthy subjects. A population pharmacokinetic (PK) analysis was performed to relate CSL112 doses to changes in apoA‐I plasma concentrations. Covariate analysis was conducted to identify sources of variability in apoA‐I exposure. Exposure‐response modeling was conducted to describe the relationship between apoA‐I exposure and total or ATP binding cassette transporter A1‐(ABCA1)‐dependent CEC and to identify clinical predictors of CEC. Results: A two‐compartment model described apoA‐I PK. ApoA‐I clearance was slightly lower in subjects with AMI, whereas baseline apoA‐I was marginally higher in female and Japanese subjects. Covariate effects on apoA‐I exposure were in the order of 10% and thus not clinically relevant. The relationships between apoA‐I exposure and CECs were described by nonlinear models. Simulations showed CEC elevation resulting from apoA‐I exposure increment was comparable in AMI and non‐AMI subjects; no covariate had clinically meaningful effects on CEC. Simulations also demonstrated that CEC in patients with AMI post 6 g CSL112 dosing was substantially elevated compared to placebo and lower dose levels. Conclusions: The model‐based exposure‐response analysis demonstrated, irrespective of body weight, sex and race, that fixed 6 g CSL112 dosing causes a desired CEC elevation, which may benefit AMI patients by potentially reducing early recurrent cardiovascular event risk. [ABSTRACT FROM AUTHOR]

A model‐based meta‐analysis was performed with reported data from obese subjects and patients with type 2 diabetes (T2DM) to characterize the effects of dipeptidyl peptidase 4 (DPP4) inhibitors, gastric inhibitory polypeptides (GIPs), glucagon‐like peptide‐1 (GLP1), and dual GIP/GLP1 agonists, or a combination of these antidiabetic drugs (ADs) on heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A systematic literature search and review after the Cochrane method identified sources for investigational and approved ADs resulted in a comprehensive database with data from 178 clinical studies in obese subjects and patients with T2DM. Results indicated that there were AD class‐dependent effects on HR and SBP, whereas no clear AD‐related effects on DBP were found. All AD classes, except for DPP4 inhibitors, increased HR. The largest increase of 12 bpm was seen with GLP1 receptor agonists. All AD classes appeared to decrease SBP. DPP4 inhibitors were associated with a marginal decrease of ~ 1 mmHg, whereas GLP1 and GIP/GLP1 dual agonists exhibited the largest decrease of ~ 3 mmHg in SBP. AD‐related effects were similar in obese subjects and patients with T2DM. In conclusion, there are clinically relevant AD‐related effects on both HR and SBP, but not on DBP. DPP4 inhibitors are associated with the smallest (if at all) effects on HR and SBP, whereas GLP1 inhibitors exhibited the largest effects on these two cardiovascular end points. Additional studies are warranted to further investigate how AD‐related SBP decreases combined with HR increases affect long‐term cardiovascular mortality. [ABSTRACT FROM AUTHOR]

Caffeine is widely used in preterm neonates suffering from apnea of prematurity (AOP), and it has become one of the most frequently prescribed medications in neonatal intensive care units. Goal of this study is to investigate how caffeine citrate treatment affects sleep‐wake behavior in preterm neonates. The observational study consists of 64 preterm neonates during their first 5 days of life with gestational age (GA) <32 weeks or very low birthweight of < 1500 g. A total of 52 patients treated with caffeine citrate and 12 patients without caffeine citrate were included. Sleep‐wake behavior was scored in three stages: active sleep, quiet sleep, and wakefulness. Individual caffeine concentration of every neonate was simulated with a pharmacokinetic model. In neonates with GA ≥ 28 weeks, wakefulness increased and active sleep decreased with increasing caffeine concentrations, whereas quiet sleep remained unchanged. In neonates with GA < 28 weeks, no clear caffeine effects on sleep‐wake behavior could be demonstrated. Caffeine increases fraction of wakefulness, alertness, and most probably also arousability at cost of active but not quiet sleep in preterm neonates. As such, caffeine should therefore not affect time for physical and cerebral regeneration during sleep in preterm neonates. [ABSTRACT FROM AUTHOR]

Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well‐recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools. [ABSTRACT FROM AUTHOR]

Currently used creatinine‐based parameters for monitoring kidney function are not reliable for early detection of kidney injury (KI), particularly tubular damage. Several KI biomarkers allow for early detection of glomerular and tubular damage and may help to prevent drug‐related chronic kidney diseases in pediatrics. This literature review describes the state of current research and investigates reference values for these KI biomarkers in neonates, infants, and children to better understand age‐related changes. A total of 12 of 237 screened studies fulfilled predefined criteria, including 219 preterm neonates, 70 neonates, 596 infants, and 1726 children. KI biomarkers were analyzed in urine (6 studies), in serum/plasma (5 studies) and in serum and urine (1 study). Four studies (n = 555) measured urinary kidney injury molecule‐1, whereas urinary neutrophil gelatinase‐associated lipocalin was assessed in 5 studies (n = 888), and 2 studies (n = 203) investigated serum cystatin C. This review of KI biomarkers in different pediatric age groups indicates that (1) the majority of KI biomarkers are measured in urine; (2) the 3 most commonly analyzed KI biomarkers are urinary neutrophil gelatinase‐associated lipocalin, urinary kidney injury molecule‐1, and serum cystatin C; (3) values of KI biomarkers appear to decrease from prematurity to infancy; and (4) there is an unmet need to further enhance knowledge on age‐dependent changes of KI biomarkers in pediatrics. Studies are needed to better characterize reference values for these key KI biomarkers in healthy pediatric populations and to evaluate the value of these markers in the early detection of drug‐related KI in neonates, infants, and children. [ABSTRACT FROM AUTHOR]

Ivermectin is a commonly used broad‐spectrum antiparasitic drug, yet doses that produce consistent exposure coverage across age have not been characterized, and no data are available in children weighing < 15 kg. First, a population pharmacokinetic model is developed based on data from 200 children and 11 adults, treated with 100–600 μg/kg ivermectin. Second, model‐based simulations are performed to identify a dosing strategy that achieves equivalent exposure coverage in children and adults. Median (90% confidence interval) clearance of 0.346 (0.12–0.73) L/hour/kg in pre–school‐aged (2–5 years) children is similar to 0.352 (0.17–0.69) L/hour/kg in school‐aged (6–12 years) children but higher than in adults (0.199 (0.10–0.31) L/hour/kg), resulting in significantly lower exposure in children following a 200 μg/kg dose. Simulations indicate that a dose increase to 300 and 250 μg/kg in children aged 2–5 and 6–12 years, respectively, will achieve equivalent ivermectin exposure coverage in children and adults. [ABSTRACT FROM AUTHOR]

Aims: A dramatic increase in newborn infants with neonatal abstinence syndrome has been observed and these neonates are frequently treated with complex methadone dosing schemes to control their withdrawal symptoms. Despite its abundant use, hardly any data on the pharmacokinetics (PK) of methadone is available in preterm neonates. Therefore we investigated developmental PK of methadone and evaluated current dosing strategies and possible simplification in this vulnerable population. Methods: A single‐centre open‐label prospective study was performed to collect PK data after a single oral dose of methadone in preterm neonates. A population PK model was built to characterize developmental PK of (R)‐ and (S)‐methadone. Model‐based simulations were performed to identify a simplified dosing strategy to reach and maintain target methadone exposure. Results: A total of 121 methadone concentrations were collected from 31 preterm neonates. A one‐compartment model with first order absorption and elimination kinetics best described PK data for (R)‐ and (S)‐methadone. Clearance increases with advancing gestational age and differs between R‐ and S‐enantiomer, being slightly higher for the former (0.244 vs 0.167 L/h). Preterm neonates reached target exposure after 48 hours with currently used dosing schedules. Output from simulations revealed that target exposures can be achieved with a simplified dosing strategy during the first 4 days of treatment. Conclusion: Methadone clearance in preterm neonates increases with advancing gestational age and its disposition is influenced by its chirality. Simulations that account for developmental PK changes indicate a shorter methadone dosing strategy can maintain target exposure to control withdrawal symptoms. [ABSTRACT FROM AUTHOR]

Ceftazidime is a parenteral cephalosporin frequently used in pregnant women for treatment of urinary tract or intrauterine infections. Despite its regular use in pregnant women, ceftazidime disposition in both mother and fetus is not well understood, and a pharmacokinetic (PK) model that allows characterization and simulation of both maternal and preterm neonatal ceftazidime disposition is not available. In this study, 10 pregnant women with suspected or proven intrauterine infections in the late second and early third trimester were treated with 1 g of ceftazidime intravenously every 6 hours. During ceftazidime treatment, one maternal and umbilical cord blood sample was taken at delivery to quantify ceftazidime concentrations in the mother and preterm neonate. Data showed that ceftazidime concentrations in the mother were comparable to those observed in the neonate. Based on these data, a PK model was developed to describe maternal disposition, ceftazidime distribution over the placenta, and elimination in the neonate. The maternal substructure of the model was parameterized according to a previously reported ceftazidime model with minor adjustments to account for pregnancy‐related effects on renal elimination of ceftazidime. The expanded population PK model with an additional neonatal compartment was fitted to measured drug concentrations in the neonate. The neonatal elimination rate constant at delivery was close to that estimated for the mother. The presented results show that ceftazidime readily crosses the placenta and indicate that perinatal PK behavior of ceftazidime in preterm neonates can be expected to be similar to those observed in their mothers. [ABSTRACT FROM AUTHOR]

During recent years there has been a surge in developing and applying physiologically based pharmacokinetic (PBPK) models in pregnant women to better understand and predict changes in drug pharmacokinetics throughout pregnancy. As a consequence, the number of publications focusing on pregnancy PBPK models has increased substantially. However, to date these models, especially across various platforms, have not been systematically evaluated. Hence, this review aims to assess published PBPK models in pregnancy used for therapeutic purposes. [ABSTRACT FROM AUTHOR]

Overdosing occurs frequently because of prescription errors in neonates, infants, children, adolescents, and adults. Currently there is no quantitative approach that can be used by clinicians to adjust dosing so that toxic drug concentrations can be brought back to levels observed with safe and efficacious therapeutic doses. We present a mathematical solution that offers the time between last overdosing and next therapeutic dose to achieve therapeutic drug concentrations as soon as possible. To facilitate applications of this solution in clinical practice, a minimal amount of information has to be provided, and no simulations are necessary to compute the optimal waiting time. For educational purposes, we provide access to an online decision support tool for overdosing situations ( Time to next Dose Calculator) that (1) computes the waiting time after accidental overdosing in patients with normal elimination and (2) computes the waiting time and adjusted reference dosing for patients with abnormal elimination. This user-friendly online tool will help clinicians to quickly adjust a dosing schedule in overdosing situations to mitigate risk for negative clinical consequences. [ABSTRACT FROM AUTHOR]

The article highlights a study on the maturational changes in vancomycin protein binding affect vancomycin dosing in neonates. Topics discussed include information on the concepts for vancomycin dosing optimization in neonates such as total drug target approach and unbound drug target approach; and discussions on the impacts of the dose adjustments in neonates on pharmacokinetic parameters such as clearance and protein binding of drugs.

The presented analysis was performed to characterize the relationship between treatment-related early (week 4) and longer term (3-6 months) weight loss to understand the potential utility of 4-week proof-of-mechanism studies in the early decision-making process during clinical development of new antiobesity compounds. A regression-based meta-analysis was performed leveraging publically available clinical outcomes data to (1) characterize the within-trial relationship between treatment-related early and longer term body weight loss and (2) identify and quantify key covariate effects on this relationship. Data from 89 randomized clinical trials with 209 treatment arms, representing observations from 54 461 patients and 9 treatments, were available for the meta-analysis. Results indicated that (1) there is a correlation between treatment-related early and longer term body weight loss ( r > 0.9), (2) baseline body weight influences the relationship between early and longer term weight loss, whereas comorbidity such as type 2 diabetes mellitus, class of drugs including GLP-1 analogues and the antiobesity compounds lorcaserin or phentermine/topiramate showed no significant effects on this relationship. The model was externally evaluated with data from the investigational compound beloranib, for which longer term weight loss could be successfully predicted based on early response data. Based on these results, the identified strong relationship between treatment-related early and longer term weight loss appears to be independent of mechanism of action. Thus, findings from this analysis can optimize design of clinical studies and facilitate development of new anti-obesity compounds. [ABSTRACT FROM AUTHOR]

Sepsis remains a major cause of mortality and morbidity in neonates, and, as a consequence, antibiotics are the most frequently prescribed drugs in this vulnerable patient population. Growth and dynamic maturation processes during the first weeks of life result in large inter- and intrasubject variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of antibiotics. In this review we (1) summarize the available population PK data and models for primarily renally eliminated antibiotics, (2) discuss quantitative approaches to account for effects of growth and maturation processes on drug exposure and response, (3) evaluate current dose recommendations, and (4) identify opportunities to further optimize and personalize dosing strategies of these antibiotics in preterm and term neonates. Although population PK models have been developed for several of these drugs, exposure-response relationships of primarily renally eliminated antibiotics in these fragile infants are not well understood, monitoring strategies remain inconsistent, and consensus on optimal, personalized dosing of these drugs in these patients is absent. Tailored PK/PD studies and models are useful to better understand relationships between drug exposures and microbiological or clinical outcomes. Pharmacometric modeling and simulation approaches facilitate quantitative evaluation and optimization of treatment strategies. National and international collaborations and platforms are essential to standardize and harmonize not only studies and models but also monitoring and dosing strategies. Simple bedside decision tools assist clinical pharmacologists and neonatologists in their efforts to fine-tune and personalize the use of primarily renally eliminated antibiotics in term and preterm neonates. [ABSTRACT FROM AUTHOR]

Drug transporters play a key role in mediating the uptake of endo- and exogenous substances into cells as well as their efflux. Therefore, variability in drug transporter activity can influence pharmaco- and toxicokinetics and be a determinant of drug safety and efficacy. In children, particularly in neonates and young infants, the contribution of tissue-specific drug transporters to drug absorption, distribution, and excretion may differ from that in adults. In this review 5 major factors and their interdependence that may influence drug transporter activity in children are discussed: developmental differences, genetic polymorphisms, pediatric comorbidities, interacting comedication, and environmental factors. Even if data are sparse, altered drug transporter activity due to those factors have been associated with clinically relevant differences in drug disposition, efficacy, and safety in pediatric patients. Single nucleotide polymorphisms in drug transporter-encoding genes were the most studied source of drug transporter variability in children. However, in the age group where drug transporter activity has been reported to differ from that in adults, namely neonates and young infants, hardly any studies have been performed. Longitudinal studies in this young population are required to investigate the age- and disease-dependent genotype-phenotype relationships and relevance of drug transporter drug-drug interactions. Physiologically based pharmacokinetic modeling approaches can integrate drug- and patient-specific parameters, including drug transporter ontogeny, and may further improve in silico predictions of pediatric-specific pharmacokinetics. [ABSTRACT FROM AUTHOR]

The article discusses the hemodialysis in children with risk of cardiovascular related death.

Aim(s) This study assessed the effect of differences in renal function on the pharmacokinetics and pharmacodynamics of dapagliflozin, a renal sodium glucose co-transporter-2 (SGLT2) inhibitor for the treatment of type 2 diabetes mellitus ( T2DM). Methods A single 50 mg dose of dapagliflozin was used to assess pharmacokinetics and pharmacodynamics in five groups: healthy non-diabetic subjects; patients with T2DM and normal kidney function and patients with T2DM and mild, moderate or severe renal impairment based on estimated creatinine clearance. Subsequently, 20 mg once daily multiple doses of dapagliflozin were evaluated in the patients with T2DM. Formation rates of dapagliflozin 3- O-glucuronide ( D3OG), an inactive metabolite, were evaluated using human isolated kidney and liver microsomes. Results Plasma concentrations of dapagliflozin and D3OG were incrementally increased with declining kidney function. Steady-state Cmax for dapagliflozin were 4%, 6% and 9% higher and for D3OG were 20%, 37% and 52% higher in patients with mild, moderate and severe renal impairment, respectively, compared with normal function. AUC(0,τ) was likewise higher. D3OG formation in kidney microsomes was three-fold higher than in liver microsomes and 109-fold higher than in intestine microsomes. Compared with patients with normal renal function, pharmacodynamic effects were attenuated with renal impairment. Steady-state renal glucose clearance was reduced by 42%, 83% and 84% in patients with mild, moderate or severe renal impairment, respectively. Conclusions These results indicate that both kidney and liver significantly contribute to dapagliflozin metabolism, resulting in higher systemic exposure with declining kidney function. Dapagliflozin pharmacodynamics in diabetic subjects with moderate to severe renal impairment are consistent with the observation of reduced efficacy in this patient population. [ABSTRACT FROM AUTHOR]

The draft United States Food and Drug Administration guidance recommends a dedicated pharmacokinetic (PK) study for most drugs intended for use in patients with chronic kidney disease (CKD), including stage V patients requiring hemodialysis (HD) treatment. However, clinical studies conducted in CKD patients are challenged by difficulties in recruiting stage IV and V patients not yet on dialysis, and in balancing patient characteristics among each studied CKD group. Furthermore, study design for dialysis patients often involves a single dose administered before an HD session. The design may not necessarily represent usual patient care in current clinical practice, in which multiple doses are more likely to be administered at varying times between HD sessions. Using exposure modeling of saxagliptin and its active metabolite, 5-hydroxy saxagliptin, as an example, this work illustrates how these challenges can be alleviated by modeling the impact of HD on drug exposure in clinical studies and simulating the expected exposure level in patient care. The modeled PK profiles in dialysis patients were used to understand PK profiles in patients with various CKD stages. The applied pharmacometric approach increased the efficiency of knowledge generation from existing data. [ABSTRACT FROM AUTHOR]

Delivered dose of hemodialysis has long been an important predictor of mortality. The limitations of conventional hemodialysis treatments have led to a renewed interest in more frequent and longer hemodialysis treatments. As alternative hemodialysis schedules have become more prevalent, a need for modified metrics to measure adequacy has emerged. In addition, there is an interest in finding measures of hemodialysis adequacy that are more reliable in certain subgroups of patients, such as women, ethnic minority groups, or people with small body size. Finally, extended hemodialysis schedules suggest a need for metrics that can measure the clearance of solutes other than urea, such as middle-size molecules, and solutes for which clearance depends on intercompartmental transport across membranes. New metrics to quantify clearance in extended and alternate hemodialysis schedules are needed. As new metrics are developed, it is anticipated that they will also contribute to more accurate assessments of associations between clinical outcomes and delivered dose of dialysis in more intensive, nontraditional hemodialysis schedules. This review provides a historical prospective of dialysis dose and adequacy and describes the need for new metrics from both solute type and dialysis dose prospective as alternative hemodialysis schedules have emerged and become more prevalent. [ABSTRACT FROM AUTHOR]

Chronic kidney disease has been shown to alter the pharmacokinetics of drugs that are eliminated not only via the renal pathway but also by metabolism or nonrenal transport. Guidance documents from regulatory agencies on the pharmacokinetics of drugs in patients with impaired kidney function provide a framework for facilitating study design, conduct, data analysis, and the generation of dosing recommendations. Design considerations include establishment of appropriate enrollment criteria, selection of appropriate matched control group(s), and staging of impaired kidney function by estimated glomerular filtration rate or creatinine clearance. When studies in hemodialysis patients are conducted, optimizing the timing of characterization of the pharmacokinetics profile based on the schedule of hemodialysis sessions will allow for a robust assessment in these patients. In addition to traditional noncompartmental approaches, the use of pharmacometric approaches can integrate data from multiple clinical studies and provide a quantitative rationale for dose selection in patients with impaired kidney function. This article addresses the challenges and opportunities associated with the design, conduct, analysis, and interpretation of clinical studies to allow for their future facilitation and for the establishment of safe and efficacious dosing in patients with impaired kidney function. [ABSTRACT FROM AUTHOR]

Characterizing the key determinants of variability in the exposure of orally administered drugs may be important in understanding the implications of exposure variability on clinical responses. In particular, partitioning overall variability into interoccasion variability (IOV) and interindividual variability (IIV) allows a better assessment of the clinical importance of exposure variability. The IOV characterizes the dose-to-dose variability in exposure within a subject and is likely to be less clinically relevant than IIV for chronically administered drugs as the effect of IOV averages out over repeated dosing. The main aims of this model-based analysis were (1) to characterize the IOV and IIV of dasatinib, a novel, orally administered, multitargeted kinase inhibitor of BCR-ABL and SRC family kinases that is indicated for the treatment of chronic myeloid leukemia and Philadelphia-positive acute lymphoblastic leukemia and (2) to demonstrate using simulated data that it is possible to estimate IIV and IOV in relative bioavailability (FR) of an orally administered drug, given an adequate sampling scheme. Variability in dasatinib exposure was estimated to be mainly due to IOV in FR (44% coefficient of variation [CV]) and, to a lesser extent, due to IIV in FR and IIV in clearance (32% and 25% CV, respectively). The IIV is expected to be more clinically relevant than IOV for chronically administered oral drugs such as dasatinib, as the overall variability in cumulative exposure will be mainly due to IIV. The analysis of simulated data demonstrated that models ignoring either IIV or IOV in FR resulted in upwardly biased estimates of interindividual or residual variability. Thus, it may be important to account for both IIV and IOV in FR, particularly for orally administered agents that exhibit absorption-related variability in exposure. [ABSTRACT FROM AUTHOR]

Abatacept is a recombinant soluble fusion protein that inhibits the CD80/CD86:CD28 costimulatory signal required for T cell activation and has demonstrated efficacy in the treatment of rheumatoid arthritis. The objectives of this analysis were to provide support for a body weight—tiered dosing regimen approximating 10 mg/kg by (1) quantifying the effect of body weight on exposure and (2) characterizing the relationship between exposure and serum interleukin (IL)—6 concentration. The abatacept exposure and exposure-response models were developed with 2148 abatacept serum concentrations (from 388 subjects) and 1894 IL-6 serum concentrations (from 799 subjects), respectively, followed by simulation with these models to address the above objectives. Abatacept exposure was characterized by a linear 2-compartmental model, in which clearance was linearly related to body weight. The IL-6 response was characterized by an indirect-response model, in which the IL-6 production rate increased with baseline C-reactive protein levels. Model-based simulations demonstrated that body weight—tiered dosing was desirable to ensure consistent steady-state abatacept trough concentrations across a range of body weights; doses approximating 10 mg/kg (500, 750, 1000 mg for subjects weighing <60, 60-100, and >100 kg, respectively) provided consistent exposure across the body weight groups. In addition, doses >10 mg/kg did not result in further increases in IL-6 suppression. These modeling and simulation results indicate that the body weight—tiered abatacept therapeutic doses approximating 10 mg/kg will ensure consistent abatacept exposure and optimal IL-6 suppression. [ABSTRACT FROM PUBLISHER]

The efficacy of the combination therapy of albendazole and ivermectin against Trichuris trichiura infection is higher in Tanzania than in Côte d'Ivoire. This study therefore aimed to investigate the difference between the population pharmacokinetics (PK) at these study sites and to determine if an exposure‐response analysis could explain the low efficacy of the combination therapy in Côte d'Ivoire. Twenty‐four participants (aged 12‐19 years) receiving single doses of ivermectin (200 µg/kg) and albendazole (400 mg) were included in the population PK modeling. A regression analysis was performed to investigate the relationship between the reduction of fecal whipworm eggs and different exposure metrics (peak concentration, area under the plasma drug concentration‐time curve [AUC], and time above a certain threshold). The PK profile of ivermectin was best described by a one‐compartment model, first‐order absorption, and no delay in absorption, with the absorption rate constant estimated as 0.26 per h, an apparent volume of distribution of 162.43 L, and an apparent clearance of 7.82 L/h. In Tanzania, all patients showed a very high reduction in egg count independent of exposure. In Côte d'Ivoire, a relationship was found between higher ivermectin exposure and egg reduction, although not statistically significant. There was no significant difference between the PK profiles at both study sites, despite a difference in clinical outcome. Model‐based simulations indicate that higher ivermectin doses such as 400 and 600 µg/kg may be associated with reduced egg count. Larger clinical studies are warranted to explore further the exposure‐efficacy response relationship at 200 µg/kg and higher ivermectin doses in adults and children. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, which focuses on factors that affect drug dosing, systemic exposure, efficacy, and safety in patients with chronic kidney disease.

In this article the authors discuss the importance of optimizing drug development and use in patients with chronic kidney disease. They are critical of a lack of current and clinically relevant data for guiding drug selection and dosing in patients with kidney disease which exists. They are supportive of research which is being conducted on the treatment of kidney disease and of efforts to develop effective drugs which the patients can use with kidney disease.

An introduction is presented for this issue which focuses on the impact of methodological and technological innovation as well as multidisciplinary integration of research in the field of pharmacometrics.

Systematic implementation of model-based drug development (MBDD) to drug discovery and development has the potential to significantly increase the rate of medical breakthroughs and make available new and better treatments to patients. An analysis of the strengths, weaknesses, opportunities, and threats (ie, SWOT) was conducted through focus group discussions that included 24 members representing 8 pharmaceutical companies to systematically assess the challenges to implementing MBDD into the drug development decision-making process. The application of the SWOT analysis to the successful implementation of MBDD yielded 19 strengths, 27 weaknesses, 34 opportunities, and 22 threats, which support the following conclusions. The shift from empirical drug development to MBDD requires a question-based mentality; early, proactive planning; dynamic access to multisource data; quantitative knowledge integration; multidisciplinary collaboration; effective communication and leadership skills; and innovative, impactful application of pharmacometrics focused on enhancing quantitative decision making. The ultimate goal of MBDD is to streamline discovery and development of innovative medicines to benefit patients. [ABSTRACT FROM PUBLISHER]

An introduction is presented for this issue which focuses on the field of pharmacometrics, the American Society of Pharmacometrics, and innovation.

The article comments on the planning for the American Conference on Pharmacometrics (ACoP) meetings in Tucson, Arizona in 2008 and Mystic, Connecticut in 2009, and calls on the pharmacometric community to volunteer their time and skills to the organization via the ACOP Web site which is at www.to-acop.org.

Advances in experimental medicine and technological innovation during the past century have brought tremendous progress in modern medicine and generated an ever-increasing amount of data from bench and bedside. The desire to extend scientific knowledge motivates effective data integration. Technological innovation makes this possible, which in turn accelerates the advancement in science. This mutually beneficial interaction is illustrated by the development of an expanded mechanism-based model for understanding a novel mechanism, sodium— glucose cotransporter-2 SGLT2 inhibition for potential treatment of type 2 diabetes mellitus. [ABSTRACT FROM PUBLISHER]

During the past decade, the pharmaceutical industry has seen the increasing application of pharmacometrics approaches in drug development. However, the full potential of incorporating model-based approaches in drug development and its impact on decision making has not been fully realized to date. In 2009, a survey on model-based drug development (MBDD) was conducted (1) to further understand the current state of MBDD in the pharmaceutical industry and (2) to identify opportunities to realize the full potential of MBDD. Ten large and mid-sized pharmaceutical companies provided responses to this survey. The results indicate that MBDD is achieving broad application in early and late development and is positively affecting both internal and regulatory decisions. Senior leadership (vice president and higher) within the companies indicated widely accepted utility for dose selection and gaining acceptance for study design and regulatory interactions but limited acceptance in discovery and commercial/pipeline decisions. Mounting appreciation for the impact of MBDD on internal and regulatory decision-making bodes well for the future of the pharmacometric discipline and the growth of opportunities to realize the full potential of MBDD. [ABSTRACT FROM PUBLISHER]

Model-based drug development (MBDD) is a promising approach to improve decision making in drug development. The pharmaceutical industry has made substantial progress from engaging in empirical decision making to increasingly using pharmacometrics (ie, modeling and simulation [M&S]) as a quantitative decision-making tool. Focusing on culture and an organizational structure perspective, this commentary summarizes experiences and vision from industry M&S leaders on implementing MBDD. A culture for MBDD needs to have wide acceptance of MBDD, enhanced decision making with probability-based evidence and transparent rationale, quantitative impact metrics, and a brand that emphasizes cross-disciplinary collaboration and ownership. An organizational structure for MBDD needs to have a dedicated pharmacometrics function, fine balance between quick wins and impact on long-term R&D goals, and collaborative MBDD teams among clinical pharmacologists, statisticians, pharmacometricians, and clinicians. Pharmaceutical companies with these characteristics are prepared to fully embrace and implement MBDD. [ABSTRACT FROM PUBLISHER]

Aims The pharmacokinetic profile of candesartan cilexetil might be altered in patients with end-stage renal disease (ESRD). No data are available about the pharmacokinetics and haemodynamics of the angiotensin II receptor antagonist candesartan cilexetil in ESRD patients on regular haemodialysis (HD). Methods We performed a repeated dose study (8 mg candesartan cilexetil once daily) in eight male HD patients over a treatment period of 5 days with an additional observation period of 3 days. Results Pharmacokinetic analysis with nonlinear mixed effects modeling (NONMEM) over the whole treatment period revealed a dependency of the volume of distribution on body weight and of the metabolic clearance on age and body weight in the studied population. No significant drug elimination by HD was observed. The estimated metabolic and intercompartmental clearances were 83 ml min[sup -1] (CV 39%) and 9.9 ml min[sup -1], respectively. The unexplained random variability of the final two compartment model was 30%. In one patient with adult polycystic kidney disease oral clearance decreased during the observation period, attributable to a significant increase in bioavailability. Maximum observed changes in blood pressure were -50/-27±14/8 mmHg on day 5 with haemodialysis therapy as compared with changes in blood pressure of -14/-12±14/8 mmHg on day 1 without haemodialysis treatment. The observed maximum decrease in systolic blood pressure correlated with the amount of ultrafiltration during the HD session on day 5 (r=0.70, P<0.05). In two patients, one of whom was binephrectomized, severe hypotensive episodes were observed during this HD session. Conclusions HD does not influence the elimination kinetics of candesartan. The observed inter- and intraindividual variability of oral clearance and the pronounced influence of HD-induced volume contraction on the haemodynamic effects of candesartan makes it mandatory to carefully monitor HD patients treated with candesa... [ABSTRACT FROM AUTHOR]