NT-proBNP for Predicting All-Cause Death and Heart Transplant in Children and Adults with Heart Failure.

Plasma N-terminal prohormone B-type natriuretic peptide (NT-proBNP) concentration is a heart failure (HF) biomarker in adults and children. Its prognostic value for HF-related events has been established only in adults. Therefore, we aimed to test the hypothesis that plasma NT-proBNP concentrations predicted the risk of heart transplantation or death in children with HF. We studied the medical records of 109 children with HF enrolled in the IBM Watson Explorys database and from 150 children enrolled in the Pediatric Cardiomyopathy Registry (PCMR). Nonlinear regression was used to assess the relationship between plasma NT-proBNP concentrations and the risk of events in the two cohorts. All children in the PCMR cohort had dilated cardiomyopathy. The Explorys cohort also included children with congenital cardiovascular malformations. Median plasma NT-proBNP concentrations were 1250 pg/mL and 184 pg/mL in the Explorys and PCMR cohorts, respectively. The percentage of deaths/heart transplantations was 7%/22%, over 2 years in the Explorys cohort and 3%/16% over 5 years in the PCMR cohort. Mean estimates of plasma NT-proBNP concentration indicative of half-maximum relative risk for events (EC50 values) at 2 and 5 years were 3730 pg/mL and 4199 pg/mL, respectively, values both close to the mean of 3880 pg/mL established for adults with HF. The plasma NT-proBNP concentration is suitable for estimating relative risk of mortality and heart transplantation in children with HF, independent of etiology and shows similar relations to clinical outcomes as in adults, indicating its likely value as a surrogate marker both for adult and pediatric HF.ClinicalTrials.gov Identifiers: NCT00005391 (May 26, 2000), NCT01873976 (June 10, 2013).

Algorithmic surveillance of ICU patients with acute respiratory distress syndrome (ASIC): protocol for a multicentre stepped-wedge cluster randomised quality improvement strategy.

INTRODUCTION: The acute respiratory distress syndrome (ARDS) is a highly relevant entity in critical care with mortality rates of 40%. Despite extensive scientific efforts, outcome-relevant therapeutic measures are still insufficiently practised at the bedside. Thus, there is a clear need to adhere to early diagnosis and sufficient therapy in ARDS, assuring lower mortality and multiple organ failure. METHODS AND ANALYSIS: In this quality improvement strategy (QIS), a decision support system as a mobile application (ASIC app), which uses available clinical real-time data, is implemented to support physicians in timely diagnosis and improvement of adherence to established guidelines in the treatment of ARDS. ASIC is conducted on 31 intensive care units (ICUs) at 8 German university hospitals. It is designed as a multicentre stepped-wedge cluster randomised QIS. ICUs are combined into 12 clusters which are randomised in 12 steps. After preparation (18 months) and a control phase of 8 months for all clusters, the first cluster enters a roll-in phase (3 months) that is followed by the actual QIS phase. The remaining clusters follow in month wise steps. The coprimary key performance indicators (KPIs) consist of the ARDS diagnostic rate and guideline adherence regarding lung-protective ventilation. Secondary KPIs include the prevalence of organ dysfunction within 28 days after diagnosis or ICU discharge, the treatment duration on ICU and the hospital mortality. Furthermore, the user acceptance and usability of new technologies in medicine are examined. To show improvements in healthcare of patients with ARDS, differences in primary and secondary KPIs between control phase and QIS will be tested. ETHICS AND DISSEMINATION: Ethical approval was obtained from the independent Ethics Committee (EC) at the RWTH Aachen Faculty of Medicine (local EC reference number: EK 102/19) and the respective data protection officer in March 2019. The results of the ASIC QIS will be presented at conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: DRKS00014330.

NT-proBNP Qualifies as a Surrogate for Clinical End Points in Heart Failure.

N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a well-established biomarker in heart failure (HF) but controversially discussed as a potential surrogate marker in HF trials. We analyzed the NT-proBNP/mortality relationship in real-world data (RWD) of 108,330 HF patients from the IBM Watson Health Explorys database and compared it with the NT-proBNP / clinical event end-point relationship in 20 clinical HF studies. With a hierarchical statistical model, we quantified the functional relationship and interstudy variability. To independently qualify the model, we predicted outcome hazard ratios in five phase III HF studies solely based on NT-proBNP measured early in the respective study. In RWD and clinical studies, the relationship between NT-proBNP and clinical outcome is well described by an Emax model. The NT-proBNP independent baseline risk (R0 , RWD/studies median (interstudy interquartile range): 5.5%/3.0% (1.7-4.9%)) is very low compared with the potential NT-proBNP-associated maximum risk (Rmax : 55.2%/79.4% (61.5-89.0%)). The NT-proBNP concentration associated with the half-maximal risk is comparable in RWD and across clinical studies (EC50 : 3,880/2,414 pg/mL (1,460-4,355 pg/mL)). Model-based predictions of phase III outcomes, relying on short-term NT-proBNP data only, match final trial results with comparable confidence intervals. Our analysis qualifies NT-proBNP as a surrogate for clinical outcome in HF trials. NT-proBNP levels after short treatment durations of less than 10 weeks quantitatively predict hazard ratios with confidence levels comparable to final trial readout. Early NT-proBNP measurement can therefore enable shorter and smaller but still reliable HF trials.

The Developing Regorafenib Eye drops for neovascular Age-related Macular degeneration (DREAM) study: an open-label phase II trial.

AIMS: This programme investigated topical regorafenib, a multikinase inhibitor, in patients with neovascular age-related macular degeneration (nAMD). METHODS: Topical regorafenib was investigated in an open-label, phase IIa/b study in which patients with choroidal neovascularization (CNV) secondary to nAMD received regorafenib (25 µl, 30 mg ml-1 ) three times a day for 12 weeks. The primary endpoint of the phase II/a/b study was mean change in best-corrected visual acuity (BCVA) from baseline to weeks 4 and 12. RESULTS: In nAMD patients (N = 51), mean changes in BCVA were +1.2 [90% confidence interval (CI) -0.61, 2.97] and -2.4 (90% CI -4.18, -0.54) letters at weeks 4 and 12, respectively. Ocular treatment-emergent adverse events (TEAEs) (study eye) were reported in 21 patients by week 12. There was one serious ocular TEAE (visual acuity reduced) that was not drug related. Twenty patients required rescue (intravitreal ranibizumab). CONCLUSIONS: The programme was terminated after phase IIa ended because efficacy was lower than with current nAMD treatments. According to elaborate post hoc analyses, the most likely reason was insufficient exposure in the target compartment (back of the eye).

Inhaled sGC Modulator Can Lower PH in Patients With COPD Without Deteriorating Oxygenation.

This study uses a highly fidelity computational simulator of pulmonary physiology to evaluate the impact of a soluble guanylate cyclase (sGC) modulator on gas exchange in patients with chronic obstructive pulmonary disease (COPD) and pulmonary hypertension (PH) as a complication. Three virtual patients with COPD were configured in the simulator based on clinical data. In agreement with previous clinical studies, modeling systemic application of an sGC modulator results in reduced partial pressure of oxygen (PaO2 ) and increased partial pressure of carbon dioxide (PaCO2 ) in arterial blood, if a drug-induced reduction of pulmonary vascular resistance (PVR) equal to that observed experimentally is assumed. In contrast, for administration via dry powder inhalation (DPI), our simulations suggest that the treatment results in no deterioration in oxygenation. For patients under exercise, DPI administration lowers PH, whereas oxygenation is improved with respect to baseline values.

Estimation of Intra-vitreal Half-Lifes in the Rabbit Eye with Semi-mechanistic Equations.

PURPOSE: To develop an alternative method for estimating vitreal half-lifes in the rabbit eye based on simple equations for the physical processes of dissipation and the physiochemical properties of therapeutic substances applied by intravitreal drug administration. METHODS: Equations were derived to describe diffusion in the vitreous humor and permeation through the back-of-the-eye tissue, and the volume of distribution. The model was validated using reported half-life values from 83 compounds collected from literature. RESULTS: The rate limiting step for dissipation from the vitreous depends mainly on the molecular weight. Dissipation of very low molecular weight (MW) substances (<350 Da) is limited by diffusional transport to the back of the eye, for substances with a MW >350 Da uptake into the back of the eye tissue becomes limiting, and large molecules >500 Da predominantly take an alternative path being cleared through the front of the eye for which diffusion towards the posterior chamber turns out to be limiting. Taking the three rate determining processes into account, the derived model can estimate dissipation rates and respectively vitreal half-life values of small compounds and macromolecules from their molecular weight with very few exceptions. CONCLUSIONS: The equations derived in this analysis provide a simple method to predict vitreal half-lifes for a diverse group of molecules and can be easily implemented in early drug development.

How TK-TD and population models for aquatic macrophytes could support the risk assessment for plant protection products.

This case study of the Society of Environmental Toxicology and Chemistry (SETAC) workshop MODELINK demonstrates the potential use of mechanistic effects models for macrophytes to extrapolate from effects of a plant protection product observed in laboratory tests to effects resulting from dynamic exposure on macrophyte populations in edge-of-field water bodies. A standard European Union (EU) risk assessment for an example herbicide based on macrophyte laboratory tests indicated risks for several exposure scenarios. Three of these scenarios are further analyzed using effect models for 2 aquatic macrophytes, the free-floating standard test species Lemna sp., and the sediment-rooted submerged additional standard test species Myriophyllum spicatum. Both models include a toxicokinetic (TK) part, describing uptake and elimination of the toxicant, a toxicodynamic (TD) part, describing the internal concentration-response function for growth inhibition, and a description of biomass growth as a function of environmental factors to allow simulating seasonal dynamics. The TK-TD models are calibrated and tested using laboratory tests, whereas the growth models were assumed to be fit for purpose based on comparisons of predictions with typical growth patterns observed in the field. For the risk assessment, biomass dynamics are predicted for the control situation and for several exposure levels. Based on specific protection goals for macrophytes, preliminary example decision criteria are suggested for evaluating the model outputs. The models refined the risk indicated by lower tier testing for 2 exposure scenarios, while confirming the risk associated for the third. Uncertainties related to the experimental and the modeling approaches and their application in the risk assessment are discussed. Based on this case study and the assumption that the models prove suitable for risk assessment once fully evaluated, we recommend that 1) ecological scenarios be developed that are also linked to the exposure scenarios, and 2) quantitative protection goals be set to facilitate the interpretation of model results for risk assessment.

Modeling and Simulation of In Vivo Drug Effects.

The concept of a pharmacokinetics-pharmacodynamics (PK/PD) assessment of drug development candidates is well established in pharmaceutical research and development, and PK/PD modeling is common practice in all pharmaceutical companies. A recent analysis (Morgan et al., Drug Discov Today 17(9-10):419-424, 2012) revealed however that insufficient certainty in the integrity of the causal chain of fundamental pharmacological steps from drug dosing through systemic exposure, target tissue exposure, and engagement of molecular target to pharmacological response is still the major driver of failure in phase II of clinical drug development. Despite the rise of molecular biomarkers, ethical, scientific, and practical constraints very often still prevent a direct assessment of each necessary step ultimately leading to an intended drug effect or an unintended adverse reaction. Yet, incomplete investigation of the causality of drug responses is a major risk for translational assessments and the prediction of drug responses in different species or other populations. Mechanism-based modeling and simulation (M&S) offers a means to investigate complex physiological and pharmacological processes and to complement experimental data for non-accessible steps in the pharmacological causal chain. With the help of two examples, it is illustrated, what level of physiological detail, state-of-the-art models can represent, how predictive these models are and how mechanism-based approaches can be combined with empirical correlation-based concepts.

An example of population-level risk assessments for small mammals using individual-based population models.

This article presents a case study demonstrating the application of 3 individual-based, spatially explicit population models (IBMs, also known as agent-based models) in ecological risk assessments to predict long-term effects of a pesticide to populations of small mammals. The 3 IBMs each used a hypothetical fungicide (FungicideX) in different scenarios: spraying in cereals (common vole, Microtus arvalis), spraying in orchards (field vole, Microtus agrestis), and cereal seed treatment (wood mouse, Apodemus sylvaticus). Each scenario used existing model landscapes, which differed greatly in size and structural complexity. The toxicological profile of FungicideX was defined so that the deterministic long-term first tier risk assessment would result in high risk to small mammals, thus providing the opportunity to use the IBMs for risk assessment refinement (i.e., higher tier risk assessment). Despite differing internal model design and scenarios, results indicated in all 3 cases low population sensitivity unless FungicideX was applied at very high (×10) rates. Recovery from local population impacts was generally fast. Only when patch extinctions occured in simulations of intentionally high acute toxic effects, recovery periods, then determined by recolonization, were of any concern. Conclusions include recommendations for the most important input considerations, including the selection of exposure levels, duration of simulations, statistically robust number of replicates, and endpoints to report. However, further investigation and agreement are needed to develop recommendations for landscape attributes such as size, structure, and crop rotation to define appropriate regulatory risk assessment scenarios. Overall, the application of IBMs provides multiple advantages to higher tier ecological risk assessments for small mammals, including consistent and transparent direct links to specific protection goals, and the consideration of more realistic scenarios.

A toxicokinetic and toxicodynamic modeling approach using Myriophyllum spicatum to predict effects caused by short-term exposure to a sulfonylurea.

Toxicokinetic and toxicodynamic models are a promising tool to address the effects of time-variable chemical exposure. Standard toxicity tests usually rely on static concentrations, but these chemical exposure patterns are unlikely to appear in the field, where time-variable exposure of chemicals is typical. In the present study, toxicodynamic processes were integrated into an existing model that includes the toxicokinetics and growth of the aquatic plant Myriophyllum spicatum, to predict the impact on plant growth of 2 iofensulfuron short-term exposure patterns. To establish a method that can be used with standard data from risk assessments, the toxicodynamics of iofensulfuron were based on effect data from a 14-d standard toxicity test using static concentrations. Modeling showed that the toxicokinetic and toxicodynamic growth model of M. spicatum can be successfully used to predict effects of short-term iofensulfuron exposure by using effect data from a standard toxicity test. A general approach is presented, in which time-variable chemical exposures can be evaluated more realistically without conducting additional toxicity studies.

Toxicokinetic-toxicodynamic modeling of quantal and graded sublethal endpoints: a brief discussion of concepts.

We report on the advantages and problems of using toxicokinetic-toxicodynamic (TKTD) models for the analysis, understanding, and simulation of sublethal effects. Only a few toxicodynamic approaches for sublethal effects are available. These differ in their effect mechanism and emphasis on linkages between endpoints. We discuss how the distinction between quantal and graded endpoints and the type of linkage between endpoints can guide model design and selection. Strengths and limitations of two main approaches and possible ways forward are outlined.

Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares.

Kinetic parameters of environmental fate processes are usually inferred by fitting appropriate kinetic models to the data using standard nonlinear least squares (NLS) approaches. Although NLS is appropriate to estimate the optimum parameter values, it implies restrictive assumptions on data variances when the confidence limits of the parameters must also be determined. Particularly in the case of degradation and metabolite formation, the assumption of equal error variance is often not realistic because the parent data usually show higher variances than those of the metabolites. Conventionally, such problems would be tackled by weighted NLS regression, which requires prior knowledge about the data errors. Instead of implicitly assuming equal error variances or giving arbitrary weights decided by the researcher, we use an iteratively reweighted least squares (IRLS) algorithm to obtain the maximum likelihood estimates of the model parameters and the error variances specific for the different species in a model. A study with simulated data shows that IRLS gives reliable results in the case of both unequal and equal error variances. We also compared results obtained by NLS and IRLS, with probability distributions of the parameters inferred with a Markov-Chain Monte-Carlo (MCMC) approach for data from aerobic transformation of different chemicals in soil. Confidence intervals obtained by IRLS and MCMC are consistent, whereas NLS leads to very different results when the error variances are distinctly different between different species. Because the MCMC results can be assumed to reflect the real parameter distribution imposed by the observed data, we conclude that IRLS generally yields more realistic estimates of confidence intervals for model parameters than NLS.

Statistical analysis of chemical transformation kinetics using Markov-Chain Monte Carlo methods.

For the risk assessment of chemicals intentionally released into the environment, as, e.g., pesticides, it is indispensable to investigate their environmental fate. Main characteristics in this context are transformation rates and partitioning behavior. In most cases the relevant parameters are not directly measurable but are determined indirectly from experimentally determined concentrations in various environmental compartments. Usually this is done by fitting mathematical models, which are usually nonlinear, to the observed data and such deriving estimates of the parameter values. Statistical analysis is then used to judge the uncertainty of the estimates. Of particular interest in this context is the question whether degradation rates are significantly different from zero. Standard procedure is to use nonlinear least-squares methods to fit the models and to estimate the standard errors of the estimated parameters from Fisher's Information matrix and estimated level of measurement noise. This, however, frequently leads to counterintuitive results as the estimated probability distributions of the parameters based on local linearization of the optimized models are often too wide or at least differ significantly in shape from the real distribution. In this paper we identify the shortcoming of this procedure and propose a statistically valid approach based on Markov-Chain Monte Carlo sampling that is appropriate to determine the real probability distribution of model parameters. The effectiveness of this method is demonstrated on three data sets. Although it is generally applicable to different problems where model parameters are to be inferred, in the present case for simplicity we restrict the discussion to the evaluation of metabolic degradation of chemicals in soil. It is shown that the method is successfully applicable to problems of different complexity. We applied it to kinetic data from compounds with one and five metabolites. Additionally, using simulated data, it is shown that the MCMC method estimates the real probability distributions of parameters well and much better than the standard optimization approach.

Common errors and clinical guidelines for manual muscle testing: "the arm test" and other inaccurate procedures.

BACKGROUND: The manual muscle test (MMT) has been offered as a chiropractic assessment tool that may help diagnose neuromusculoskeletal dysfunction. We contend that due to the number of manipulative practitioners using this test as part of the assessment of patients, clinical guidelines for the MMT are required to heighten the accuracy in the use of this tool. OBJECTIVE: To present essential operational definitions of the MMT for chiropractors and other clinicians that should improve the reliability of the MMT as a diagnostic test. Controversy about the usefulness and reliability of the MMT for chiropractic diagnosis is ongoing, and clinical guidelines about the MMT are needed to resolve confusion regarding the MMT as used in clinical practice as well as the evaluation of experimental evidence concerning its use. DISCUSSION: We expect that the resistance to accept the MMT as a reliable and valid diagnostic tool will continue within some portions of the manipulative professions if clinical guidelines for the use of MMT methods are not established and accepted. Unreliable assessments of this method of diagnosis will continue when non-standard MMT research papers are considered representative of the methods used by properly trained clinicians. CONCLUSION: Practitioners who employ the MMT should use these clinical guidelines for improving their use of the MMT in their assessments of muscle dysfunction in patients with musculoskeletal pain.

Whole body physiologically-based pharmacokinetic models: their use in clinical drug development.

BACKGROUND: Whole-body physiologically-based pharmacokinetic (WB-PBPK) models mathematically describe an organism as a closed circulatory system consisting of compartments that represent the organs important for compound absorption, distribution, metabolism and elimination. OBJECTIVES: To review the current state of WB-PBPK model use in the clinical phases of drug development. METHODS: A qualitative description of the WB-PBPK model structure is included along with a review of the varying methods available for input parameterisation. Current and potential WB-PBPK model application in clinical development is discussed. CONCLUSIONS: This modelling tool is at present used for small and large molecule drug development primarily as a means to scale pharmacokinetics from animals to humans based on physiology. The pharmaceutical industry is active in employing these models to clinical drug development although the applications in use now are narrow in comparison to the potential. Expanded integration of WB-PBPK models into the drug development process will only be achieved with staff training, managerial will, success stories and regulatory agency openness.

Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment: the first steps.

The increasing use of tissue dosimetry estimated using pharmacokinetic models in chemical risk assessments in various jurisdictions necessitates the development of internationally recognized good modelling practice (GMP). These practices would facilitate sharing of models and model evaluations and consistent applications in risk assessments. Clear descriptions of good practices for (1) model development i.e., research and analysis activities, (2) model characterization i.e., methods to describe how consistent the model is with biology and the strengths and limitations of available models and data, such as sensitivity analyses, (3) model documentation, and (4) model evaluation i.e., independent review that will assist risk assessors in their decisions of whether and how to use the models, and also model developers to understand expectations for various purposes e.g., research versus application in risk assessment. Next steps in the development of guidance for GMP and research to improve the scientific basis of the models are described based on a review of the current status of the application of physiologically based pharmacokinetic (PBPK) models in risk assessments in Europe, Canada, and the United States at the International Workshop on the Development of GMP for PBPK Models in Greece on April 27-29, 2007.

General approach for the calculation of tissue to plasma partition coefficients.

A new mechanistic, universal model for the calculation of steady state tissue:plasma partition coefficients (Kt:p) of organic chemicals in mammalian species was developed. The approach allows the estimation of Kt:p-values based on the composition of the tissues in terms of water, neutral lipids, neutral and acidic phospholipids and proteins using the lipophilicity, the binding to phospholipid membranes, the pKa and the unbound fraction in blood plasma as compound specific parameters. Taking explicitly into account the sign and fraction of the charge of the compounds at the physiological pH the method is universally applicable to neutral, acidic, basic or multiply charged substances and has thus a significantly extended applicability compared to previously published approaches. The model was applied to 59 chemically diverse drug compounds for which tissue:plasma partition coefficients are reported in the literature. In total 474 experimentally observed Kt:p values for 12 tissues and the red blood cells were available and could be compared to model results. For 73% of the calculated values a deviation less than 3-fold from the respective observed value was found, proving the validity of the approach.

Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs.

In clinical development stages, an a priori assessment of the sensitivity of the pharmacokinetic behavior with respect to physiological and anthropometric properties of human (sub-) populations is desirable. A physiology-based pharmacokinetic (PBPK) population model was developed that makes use of known distributions of physiological and anthropometric properties obtained from the literature for realistic populations. As input parameters, the simulation model requires race, gender, age, and two parameters out of body weight, height and body mass index. From this data, the parameters relevant for PBPK modeling such as organ volumes and blood flows are determined for each virtual individual. The resulting parameters were compared to those derived using a previously published model (P(3)M). Mean organ weights and blood flows were highly correlated between the two models, despite the different methods used to generate these parameters. The inter-individual variability differed greatly especially for organs with a log-normal weight distribution (such as fat and spleen). Two exemplary population pharmacokinetic simulations using ciprofloxacin and paclitaxel as model drugs showed good correlation to observed variability. A sensitivity analysis demonstrated that the physiological differences in the virtual individuals and intrinsic clearance variability were equally influential to the pharmacokinetic variability but were not additive. In conclusion, the new population model is well suited to assess the influence of individual physiological variability on the pharmacokinetics of drugs. It is expected that this new tool can be beneficially applied in the planning of clinical studies.

Development and evaluation of a generic physiologically based pharmacokinetic model for children.

BACKGROUND: Clinical trials in children are being encouraged by regulatory authorities in light of the immense off-label and unlicensed use of drugs in the paediatric population. The use of in silico techniques for pharmacokinetic prediction will aid in the development of paediatric clinical trials by guiding dosing regimens, ensuring efficient blood sampling times, maximising therapeutic effect and potentially reducing the number of children required for the study. The goal of this study was to extend an existing physiologically based pharmacokinetic (PBPK) model for adults to reflect the age-related physiological changes in children from birth to 18 years of age and, in conjunction with a previously developed age-specific clearance model, to evaluate the accuracy of the paediatric PBPK model to predict paediatric plasma profiles. METHODS: The age-dependence of bodyweight, height, organ weights, blood flows, interstitial space and vascular space were taken from the literature. Physiological parameters that were used in the PBPK model were checked against literature values to ensure consistency. These included cardiac output, portal vein flow, extracellular water, total body water, lipid and protein. Five model compounds (paracetamol [acetaminophen], alfentanil, morphine, theophylline and levofloxacin) were then examined by gathering the plasma concentration-time profiles, volumes of distribution and elimination half-lives from different ages of children and adults. First, the adult data were used to ensure accurate prediction of pharmacokinetic profiles. The model was then scaled to the specific age of children in the study, including the scaling of clearance, and the generated plasma concentration profiles, volumes of distribution and elimination half-lives were compared with literature values. RESULTS: Physiological scaling produced highly age-dependent cardiac output, portal vein flow, extracellular water, total body water, lipid and protein values that well represented literature data. The pharmacokinetic profiles in children for the five compounds were well predicted and the trends associated with age were evident. Thus, young neonates had plasma concentrations greater than the adults and older children had concentrations less than the adults. Eighty-three percent, 97% and 87% of the predicted plasma concentrations, volumes of distribution and elimination half-lives, respectively, were within 50% of the study reported values. There was no age-dependent bias for term neonates to 18 years of age when examining volumes of distribution and elimination half-lives. CONCLUSION: This study suggests that the developed paediatric PBPK model can be used to scale pharmacokinetics from adults. The accurate prediction of pharmacokinetic parameters in children will aid in the development of dosing regimens and sampling times, thus increasing the efficiency of paediatric clinical trials.

A mechanistic approach for the scaling of clearance in children.

BACKGROUND AND OBJECTIVE: Clearance is an important pharmacokinetic concept for scaling dosage, understanding the risks of drug-drug interactions and environmental risk assessment in children. Accurate clearance scaling to children requires prior knowledge of adult clearance mechanisms and the age-dependence of physiological and enzymatic development. The objective of this research was to develop and evaluate ontogeny models that would provide an assessment of the age-dependence of clearance. METHODS: Using in vitro data and/or in vivo clearance values for children for eight compounds that are eliminated primarily by one process, models for the ontogeny of renal clearance, cytochrome P450 (CYP) 3A4, CYP2E1, CYP1A2, uridine diphosphate glucuronosyltransferase (UGT) 2B7, UGT1A6, sulfonation and biliary clearance were developed. Resulting ontogeny models were evaluated using six compounds that demonstrated elimination via multiple pathways. The proportion of total clearance attributed to each clearance pathway in adults was delineated. Each pathway was individually scaled to the desired age, inclusive of protein-binding prediction, and summed to generate a total plasma clearance for the child under investigation. The paediatric age range included in the study was premature neonates to sub-adults. RESULTS: There was excellent correlation between observed and predicted clearances for the model development (R2 = 0.979) and test sets (Q2 = 0.927). Clearance in premature neonates could also be well predicted (development R2 = 0.951; test Q2 = 0.899). CONCLUSION: Paediatric clinical trial development could greatly benefit from clearance scaling, particularly in guiding dosing regimens. Furthermore, since the proportion of clearance via different elimination pathways is age-dependent, information could be gained on the developmental extent of drug-drug interactions.