Unraveling the Effects of Acute Inflammation on Pharmacokinetics: A Model-Based Analysis Focusing on Renal Glomerular Filtration Rate and Cytochrome P450 3A4-Mediated Metabolism

Background and Objectives Acute inflammation caused by infections or sepsis can impact pharmacokinetics. We used a model-based analysis to evaluate the effect of acute inflammation as represented by interleukin-6 (IL-6) levels on drug clearance, focusing on renal glomerular filtration rate (GFR) and cytochrome P450 3A4 (CYP3A4)-mediated metabolism. Methods A physiologically based model incorporating renal and hepatic drug clearance was implemented. Functions correlating IL-6 levels with GFR and in vitro CYP3A4 activity were derived and incorporated into the modeling framework. We then simulated treatment scenarios for hypothetical drugs by varying the IL-6 levels, the contribution of renal and hepatic drug clearance, and protein binding. The relative change in observed area under the concentration-time curve (AUC) was computed for these scenarios. Results Inflammation showed opposite effects on drug exposure for drugs eliminated via the liver and kidney, with the effect of inflammation being inversely proportional to the extraction ratio (ER). For renally cleared drugs, the relative decrease in AUC was close to 30% during severe inflammation. For CYP3A4 substrates, the relative increase in AUC could exceed 50% for low-ER drugs. Finally, the impact of inflammation-induced changes in drug clearance is smaller for drugs with a larger unbound fraction. Conclusion This analysis demonstrates differences in the impact of inflammation on drug clearance for different drug types. The effects of inflammation status on pharmacokinetics may explain the inter-individual variability in pharmacokinetics in critically ill patients. The proposed model-based analysis may be used to further evaluate the effect of inflammation, i.e., by incorporating the effect of inflammation on other drug-metabolizing enzymes or physiological processes

Estimation of ontogeny functions for renal transporters using a combined population pharmacokinetic and physiology-based pharmacokinetic approach : application to OAT1,3

To date, information on the ontogeny of renal transporters is limited. Here, we propose to estimate the in vivo functional ontogeny of transporters using a combined population pharmacokinetic (popPK) and physiology-based pharmacokinetic (PBPK) modeling approach called popPBPK. Clavulanic acid and amoxicillin were used as probes for glomerular filtration, combined glomerular filtration, and active secretion through OAT1,3, respectively. The predictive value of the estimated OAT1,3 ontogeny function was assessed by PBPK predictions of renal clearance (CLR) of other OAT1,3 substrates: cefazolin and piperacillin. Individual CL(R)post-hoc values, obtained from a published popPK model on the concomitant use of clavulanic acid and amoxicillin in critically ill children between 1 month and 15 years, were used as dependent variables in the popPBPK analysis. CLR was re-parameterized according to PBPK principles, resulting in the estimation of OAT1,3-mediated intrinsic clearance (CLint,OAT1,3,invivo) and its ontogeny. CLint,OAT1,3,invivo ontogeny was described by a sigmoidal function, reaching half of adult level around 7 months of age, comparable to findings based on renal transporter-specific protein expression data. PBPK-based CLR predictions including this ontogeny function were reasonably accurate for piperacillin in a similar age range (2.5 months-15 years) as well as for cefazolin in neonates as compared to published data (%RMSPE of 21.2 and 22.8%, respectively and %PE within +/- 50%). Using this novel approach, we estimated an in vivo functional ontogeny profile for CLint,OAT1,3,invivo that yields accurate CLR predictions for different OAT1,3 substrates across different ages. This approach deserves further study on functional ontogeny of other transporters

A Bodyweight-Dependent Allometric Exponent for Scaling Clearance Across the Human Life-Span

Purpose: To explore different allometric equations for scaling clearance across the human life-span using propofol as a model drug. Methods: Data from seven previously published propofol studies ((pre)term neonates, infants, toddlers, children, adolescents and adults) were analysed using NONMEM VI. To scale clearance, a bodyweight-based exponential equation with four different structures for the exponent was used: (I) 3/4 allometric scaling model; (II) mixture model; (III) bodyweight-cut-point separated model; (IV) bodyweight-dependent exponent model. Results: Model I adequately described clearance in adults and older children, but overestimated clearance of neonates and underestimated clearance of infants. Use of two different exponents in Model II and Model III showed significantly improved performance, but yielded ambiguities on the boundaries of the two subpopulations. This discontinuity was overcome in Model IV, in which the exponent changed sigmoidally from 1.35 at a hypothetical bodyweight of 0 kg to a value of 0.56 from 10 kg onwards, thereby describing clearance of all individuals best. Conclusions: A model was developed for scaling clearance over the entire human life-span with a single continuous equation, in which the exponent of the bodyweight-based exponential equation varied with bodyweight

Predicting CYP3A-mediated midazolam metabolism in critically ill neonates, infants, children and adults with inflammation and organ failure.

Aims: Inflammation and organ failure have been reported to have an impact on cytochrome P450 (CYP) 3A-mediated clearance of midazolam in critically ill children. Our aim was to evaluate a previously developed population pharmacokinetic model both in critically ill children and other populations, in order to allow the model to be used to guide dosing in clinical practice. Methods: The model was evaluated externally in 136 individuals, including (pre)term neonates, infants, children and adults (body weight 0.77–90 kg, C-reactive protein level 0.1–341 mg l–1 and 0–4 failing organs) using graphical and numerical diagnostics. Results: The pharmacokinetic model predicted midazolam clearance and plasma concentrations without bias in postoperative or critically ill paediatric patients and term neonates [median prediction error (MPE) 180%). Conclusion: The recently published pharmacokinetic model for midazolam, quantifying the influence of maturation, inflammation and organ failure in children, yields unbiased clearance predictions and can therefore be used for dosing instructions in term neonates, children and adults with varying levels of critical illness, including healthy adults, but not for extrapolation to preterm neonates

Systematic Evaluation of the Descriptive and Predictive Performance of Paediatric Morphine Population Models

Purpose: A framework for the evaluation of paediatric population models is proposed and applied to two different paediatric population pharmacokinetic models for morphine. One covariate model was based on a systematic covariate analysis, the other on fixed allometric scaling principles. Methods: The six evaluation criteria in the framework were 1) number of parameters and condition number, 2) numerical diagnostics, 3) prediction-based diagnostics, 4) η-shrinkage, 5) simulation-based diagnostics, 6) diagnostics of individual and population parameter estimates versus covariates, including measurements of bias and precision of the population values compared to the observed individual values. The framework entails both an internal and external model evaluation procedure. Results: The application of the framework to the two models resulted in the detection of overparameterization and misleading diagnostics based on individual predictions caused by high shrinkage. The diagnostic of individual and population parameter estimates versus covariates proved to be highly informative in assessing obtained covariate relationships. Based on the framework, the systematic covariate model proved to be superior over the fixed allometric model in terms of predictive performance. Conclusions: The proposed framework is suitable for the evaluation of paediatric (covariate) models and should be applied to corroborate the descriptive and predictive properties of these models

The role of population PK-PD modelling in paediatric clinical research

Children differ from adults in their response to drugs. While this may be the result of changes in dose exposure (pharmacokinetics [PK]) and/or exposure response (pharmacodynamics [PD]) relationships, the magnitude of these changes may not be solely reflected by differences in body weight. As a consequence, dosing recommendations empirically derived from adults dosing regimens using linear extrapolations based on body weight, can result in therapeutic failure, occurrence of adverse effect or even fatalities. In order to define rational, patient-tailored dosing schemes, population PK-PD studies in children are needed. For the analysis of the data, population modelling using non-linear mixed effect modelling is the preferred tool since this approach allows for the analysis of sparse and unbalanced datasets. Additionally, it permits the exploration of the influence of different covariates such as body weight and age to explain the variability in drug response. Finally, using this approach, these PK-PD studies can be designed in the most efficient manner in order to obtain the maximum information on the PK-PD parameters with the highest precision. Once a population PK-PD model is developed, internal and external validations should be performed. If the model performs well in these validation procedures, model simulations can be used to define a dosing regimen, which in turn needs to be tested and challenged in a prospective clinical trial. This methodology will improve the efficacy/safety balance of dosing guidelines, which will be of benefit to the individual child

Prediction of glomerular filtration rate maturation across preterm and term neonates and young infants using inulin as marker

Describing glomerular filtration rate (GFR) maturation across the heterogeneous population of preterm and term neonates and infants is important to predict the clearance of renally cleared drugs. This study aims to describe the GFR maturation in (pre)term neonates and young infants (PNA < 90 days) using individual inulin clearance data (CL inulin). To this end, published GFR maturation models were evaluated by comparing their predicted GFR with CL inulin retrieved from literature. The best model was subsequently optimized in NONMEM V7.4.3 to better fit the CL inulin values. Our study evaluated seven models and collected 381 individual CL inulin values from 333 subjects with median (range) birthweight (BWb) 1880 g (580–4950), gestational age (GA) 34 weeks (25–43), current weight (CW) 1890 g (480–6200), postnatal age (PNA) 3 days (0–75), and CL inulin 2.20 ml/min (0.43–17.90). The De Cock 2014 model (covariates: BWb and PNA) performed the best in predicting CL inulin, followed by the Rhodin 2009 model (covariates: CW and postmenstrual age). The final optimized model shows that GFR at birth is determined by BWb, thereafter the maturation rate of GFR is dependent on PNA and GA, with a higher GA showing an overall faster maturation. To conclude, using individual CL inulin data, we found that a model for neonatal GFR requires a distinction between prenatal maturation quantified by BWb and postnatal maturation. To capture postnatal GFR maturation in (pre)term neonates and young infants, we developed an optimized model in which PNA-related maturation was dependent on GA. Graphical abstract: [Figure not available: see fulltext.]

Beyond the randomized clinical trial: innovative data science to close the pediatric evidence gap.

Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real‐world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo‐responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.ISSN:0009-9236ISSN:1532-653