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      string(310) "NCT00113815 - A Randomized, Double-Blind, Placebo-Controlled, Fixed Dose-Ranging Study to Assess the Safety, Tolerability, and Efficacy of Topiramate Oral Liquid and Sprinkle Formulations as an Adjunct to Concurrent Anticonvulsant Therapy for Infants (1-24 Months of Age) With Refractory Partial-Onset Seizures"
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      string(243) "NCT01223352 - An Open-label, Prospective Multicenter Study to Assess the Pharmacokinetics, Tolerability, Safety and Efficacy of the Pediatric Formulation of Bosentan Two Versus Three Times a Day in Children With Pulmonary Arterial Hypertension"
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  ["project_title"]=>
  string(83) "Model-informed drug development and precision dosing in neonates and infants (MONI)"
  ["project_narrative_summary"]=>
  string(610) "Paediatric clinical studies often face limitations. One of the biggest challenges in the younger paediatric group (i.e., neonates, infants and toddlers) is defining a safe and effective dose. Thus, sampling size, growth and maturation are crucial aspects to consider when modeling and simulation tools are used to estimate key parameters that guide dose selection. Diverse strategies are reported; however, no clear approach defines paediatric doses. To achieve this, different methodologies will be investigated and compared in this project concerning their applicability in the context of paediatric trials. "
  ["project_learn_source"]=>
  string(10) "web_search"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(4) "Ana "
    ["last_name"]=>
    string(16) "Rodríguez-Báez"
    ["degree"]=>
    string(50) "Awarded Doctorate (PhD) in Pharmaceutical Sciences"
    ["primary_affiliation"]=>
    string(47) "Clinical Pharmacy Institute; University of Bonn"
    ["email"]=>
    string(20) "arodrigu@uni-bonn.de"
    ["state_or_province"]=>
    string(22) "North Rhine-Westphalia"
    ["country"]=>
    string(8) "Germany "
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      ["p_pers_f_name"]=>
      string(8) "Norbert "
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      string(5) "Benda"
      ["p_pers_degree"]=>
      string(39) "Awarded Doctorate (PhD) in Mathematics "
      ["p_pers_pr_affil"]=>
      string(162) "Federal Institute for Drugs and Medical Devices (BfArM); Biostatistics and Special pharmacokinetics Unit/Research Division. Bonn, North Rhine-Westphalia, Germany."
      ["p_pers_scop_id"]=>
      string(11) " 7004431341"
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      string(6) "Ulrich"
      ["p_pers_l_name"]=>
      string(6) "Jaehde"
      ["p_pers_degree"]=>
      string(39) "Awarded Doctorate (PhD) in Pharmacology"
      ["p_pers_pr_affil"]=>
      string(86) "Clinical Pharmacy Institue; University of Bonn. Bonn, North Rhine-Westphalia, Germany."
      ["p_pers_scop_id"]=>
      string(11) "22953418800"
      ["requires_data_access"]=>
      string(2) "no"
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      ["p_pers_f_name"]=>
      string(10) "Frederike "
      ["p_pers_l_name"]=>
      string(5) "Lentz"
      ["p_pers_degree"]=>
      string(39) "Awarded Doctorate (PhD) in Pharmacology"
      ["p_pers_pr_affil"]=>
      string(162) "Federal Institute for Drugs and Medical Devices (BfArM); Biostatistics and Special pharmacokinetics Unit/Research Division. Bonn, North Rhine-Westphalia, Germany."
      ["p_pers_scop_id"]=>
      string(10) "8567430400"
      ["requires_data_access"]=>
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      ["p_pers_f_name"]=>
      string(5) "Anna "
      ["p_pers_l_name"]=>
      string(8) "Krueger "
      ["p_pers_degree"]=>
      string(18) "Master of Science "
      ["p_pers_pr_affil"]=>
      string(162) "Federal Institute for Drugs and Medical Devices (BfArM); Biostatistics and Special pharmacokinetics Unit/Research Division. Bonn, North Rhine-Westphalia, Germany."
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
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  ["project_ext_grants"]=>
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    ["value"]=>
    string(2) "no"
    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1547) "Background: During first-in-child studies, allometric and maturation functions are applied to scale drug clearance across the paediatric age range; a key aspect to characterize dose-exposure-response and establish dose selection. Factors altering reliability have been explored; however, clear strategies need to be investigated.  

Objective: To validate the a priori guidance of paediatric decision tables to extrapolate clearance across age subgroups and drug characteristics while integrating study design scenarios to improve accuracy and precision of clearance estimation. 

Study Design: Pharmacometric analysis and simulation studies.

Participants: Paediatric population from zero to two years, undergoing intravenous and/or oral administration of drugs subject to phase I metabolic processes, substrate to transporters, hepatically and/or renally excreted.

Main Outcome Measure (s): Accuracy and precision of clearance prediction, defined as a normalized root mean squared error <30% and a relative standard error <20%.

Statistical Analysis: Bias and precision between the reference and predicted clearance will be assessed by calculation of the relative and absolute relative estimation errors. The parameter uncertainty will be evaluated by non-parametric bootstrap, log-likelihood profile-sampling important resampling, and standard error analysis. Finally, a stochastic simulation-estimation approach will be performed to ensure optimality of design factors in the clearance prediction. 

"
  ["project_brief_bg"]=>
  string(3235) "Clinical studies in paediatric drug development often face limitations, such as a small number of patients, restricted sample size, limited blood volumes and ethical implications. These challenges are particularly pronounced in younger paediatric groups (i.e. neonates, infants and toddlers). As a result, to inform treatment regimens, the definition of paediatric doses is typically based on extrapolated knowledge from a reference population (e.g., older children or adults). In this context, clearance is an essential pharmacokinetic parameter in determining dose adjustments required to achieve a similar exposure in adults and children. However, the physiological alterations leading to changes in drug elimination from birth to adulthood complicate the definition of an adequate dose for different paediatric age groups. 

Modeling and simulation approaches are valuable tools to characterize drug pharmacokinetics. Available knowledge on e.g. alterations in body size with age, can be incorporated into the models. However, accounting only for body size is insufficient in children below two years of age; therefore, additional developmental changes need to be considered. To address this, maturation functions are incorporated to characterize the developmental changes in drug-specific elimination pathways (e.g. specific enzymes). 

Furthermore, estimating appropriate sample sizes for paediatric studies (patient numbers as well as samples per patient) investigating the drug characteristics in the paediatric population is crucial. Sample size definition should focus on the precise estimation of key parameters defining paediatric dose selection. 

Different combinations of scaling methods, maturation functions and strategies on sample size selection are reported in literature; however, there are currently no clear strategies to define paediatric doses. To achieve this, diverse strategies available to describe clearance will be investigated with respect to their applicability in the context of first-in-child dosing in paediatric trials. This pharmacometric analysis and simulation study will be conducted at the University of Bonn and the Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany, from July 2025 to September 2026 (Figure 1, file attachment no. 1).

This pharmacometric analysis and simulation study will be based on an established adult model in which scaling functions, such as linear or allometric scaling (using a fixed exponent of 0.75) and maturation functions, will be incorporated in depending on the drug's pharmacokinetic properties and the paediatric age range, as suggested by the decision tables proposed by van Rongen et al. (2022) (Figure 2, file attachment no. 2). Afterwards, precision and bias of the clearance estimation (data-based model) and prediction (theoretical model) will be compared to determine whether using the decision tables leads to an accurate and precise estimation of relevant pharmacokinetic parameters. In a second step, an additional analysis with the data-based model will be performed via stochastic simulation-estimation (SSE), assessing diverse design properties. (Add. information provided in Suppl material)

"
  ["project_specific_aims"]=>
  string(1521) "Based on extensive experience in paediatric studies, Van Rongen et al. (2022) have developed a useful approach for clearance prediction in paediatric patients and provided an a priori guidance in form of decision tables based on specific drug characteristics and patient’s age (pp.17-19). The aim of our analyses is to validate the a priori guidance of their decision tables to extrapolate clearance across age-subgroups and drug characteristics (renal/hepatic elimination, fraction unbound, extraction ratio, and involvement of enzymes or transporters) while integrating study design scenarios to improve accuracy and precision of clearance estimation. 



The specific objectives are: 

1.	Validate the scaling methods and maturation functions obtained from literature (van Rongen et al., 2022) to scale clearance in paediatric patients under two years and compare the predictive performance between the different clearance estimation methods in terms of accuracy and precision. 

2.	Assessment of optimal design scenarios related to number of patients per cohort number of samples and sampling schedule, to ensure a precise clearance estimation.



Hypothesis evaluated: The a priori incorporation of specific scaling methods and maturation functions adapted to paediatric age ranges and supported by optimal design scenarios, can improve the precision of clearance estimation in model-based approaches for children under two years when integrated into paediatric trials. 

"
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(8) "meth_res"
    ["label"]=>
    string(23) "Methodological research"
  }
  ["project_purposes"]=>
  array(2) {
    [0]=>
    array(2) {
      ["value"]=>
      string(34) "research_on_clinical_trial_methods"
      ["label"]=>
      string(34) "Research on clinical trial methods"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(5) "other"
      ["label"]=>
      string(5) "Other"
    }
  }
  ["project_purposes_exp"]=>
  string(76) "Modeling and simulation, statistical methods, support clinical trial design "
  ["project_research_methods"]=>
  string(1433) "The proposed study will rely on data requested from the YODA Project, external providers in the Vivli Platform: The National Institute of Child Health and Human Development (NICHD), and BioMarin Pharmaceutical  

For each requested clinical study, the same inclusion/exclusion criteria are applied: 



Inclusion criteria: Paediatric population from zero to two years, receiving intravenous and/ or oral administration of drugs with the following scenarios: 

a) Metabolized by phase I metabolism via CYP450 (bosentan, clindamycin, fluticasone propionate, methadone, sildenafil).

b) Renally excreted via tubular secretion and or glomerular filtration (ampicillin. cefepime, fluconazole, levofloxacin, meropenem, topiramate, vosoritide).

c) Substrate to transporters (ampicillin, cefepime, fluticasone, lamotrigine, methadone, meropenem, rifampin, sildenafil)



Exclusion criteria: Patients under treatment with biological drugs (e.g., adalimumab, abatacept) and renal replacement patients. 



It is planned to evaluate diverse scenarios with drugs that may differ significantly from each other. Thus, it is not intended to combine any of the requested datasets. Instead, we plan to analyze each study/platform separately, as indicated in this SAP. Table 1 (file attachment no. 3) provides additional information on the characteristics of the pre-selected drugs.

"
  ["project_main_outcome_measure"]=>
  string(966) "This project focuses on methodological approaches, i.e. pharmacometric and simulation analysis of concentration data.  The project is divided into two parts, a pharmacokinetic part (PK) and a study design part (SD). The primary and secondary outcomes were defined accordingly. 



1.	Pharmacokinetic part: 

Primary outcome measure: Accuracy and precision of estimating individual clearance. 

Secondary outcome measures: Accuracy and precision of estimating additional individual pharmacokinetic parameters (e.g., volume of distribution).



2.	Study design  part:

Primary outcome measure: Minimum number of patients and samples per patient required to obtain sufficiently precise estimates of clearance. 

Secondary outcome measures: Minimum number of patients and samples per patient required to obtain sufficiently precise estimates of additional pharmacokinetic parameters (e.g., volume of distribution).

"
  ["project_main_predictor_indep"]=>
  string(511) "The independent variables relevant to our study are sample collection time (expressed in time units), and patient-specific factors: weight, creatinine clearance, PMA, and age categorized according to the International Council for Harmonisation for Technical Requirements (ICH) into preterm newborn infants, term newborn infants (0-27 days), and infants and toddlers (28 days to 23 months). In addition, the dependent variable for this study is the drug plasma concentration (expressed in mass per volume units)."
  ["project_other_variables_interest"]=>
  string(351) "Drug data: dose, dose interval, administration rate, infusion rate, sampling schedule.

Modeling file types: Control stream of the final model .mod and .csv files (if available).

Clinical and anthropometric data: height

Pathological data (if applicable): mechanical ventilation, concomitant treatment, sepsis, etc. 



"
  ["project_stat_analysis_plan"]=>
  string(3838) "The paediatric clinical data was selected based on the characteristics of the included population (from zero to two years) and the types of drugs studied (pharmacokinetic properties, i.e., renally or hepatically cleared, extraction ratios, fraction unbound of the drug, substrate to transporters and availability of ontogeny data for the enzymes participating in the drug metabolism).   

The proposed study will rely on data requested from Vivli and external providers from the YODA Project and BioMarin Pharmaceutical. As mentioned before, it is planned to evaluate diverse scenarios with drugs that may differ significantly from each other. Thus, it is not intended to combine any of the requested datasets. Instead, we plan to analyze each study/platform separately, as indicated in this SAP.  

The population data includes all paediatric patients under two years of age for whom pharmacokinetic information is available. Missing data are anticipated in the following scenarios: (a) missing dependent variable (drug plasma concentration); (b) missing dependent variable due to concentrations below the limit of quantification; (c) missing covariates (e.g., age, weight, PMA, creatinine clearance) or (d) missing time points. Multiple imputation methods based on the missing-at-random (MAR) assumption will be applied if needed to address these problems. 

The descriptive analysis will consider the demographic and clinical characteristics of the study population. For continuous variables (e.g., age, weight, PMA, clearance), measures of central tendency and range, such as mean, median, and standard deviation, will be used. For categorical covariates (e.g., sex, comorbidities, race, etc.), frequency and proportion will be analysed. 

After the exploratory analysis, a nonlinear mixed-effects model (NLME) will be developed to extrapolate an adult to a paediatric model using the scaling methods and maturation functions, resulting in a theoretical-model. Moreover, to evaluate the accuracy of clearance predictions, comparing the predicted clearance (theoretical model) with the reference clearance (data-based model) values (assuming log-normal distribution), using two key metrics to evaluate the bias and precision of the predictive performance: (i) RER and (ii) rBias, respectively. Moreover, parameter uncertainty is going to be evaluated by non-parametric bootstrap (n=1000 simulations) and commonly used methods (i.e., log-likelihood profiling – sampling importance resampling (LLP-SIR), and standard error).  

To assess the impact of different design options on clearance prediction, we will develop a parametric bootstrap analysis, also known as SSE. Datasets are going to be simulated based on the data-based model, using the final parameter estimates and the study design. First, we will perform a pilot evaluation to examine the impact of the number of SSE replications on parameter estimation (number of simulations K=100, 500, 1000), including an evaluation of the SSE scenarios convergence. Then, parameters will be re-estimated (e.g., K=1000) by exploring diverse design scenarios, modifying the N patients per cohort and sampling schedules in clinical trial scenarios (minimal N per cohort =3 and sampling points = 2; with a desired precision to target the 95% confidence intervals within an initial interval of 70-141% of the geometric mean estimate of clearance for each designed group with an 80% power. Finally, a comparison of the clearance values initially used for each simulation from the data-based model will be performed by RER, rBias, and to determine the most adequate design factor, the NRMSE is going to be calculated considering acceptable a cut-off value of <30% and a precise clearance estimate (CLRSE <20%)  in the different clinical scenarios. 

"
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  string(1027) "•	It was estimated by Vivli Center for global clinical research data, an average of 5.4 months considering the approval process, signing the DUA, and uploading the data; therefore, the target analysis was modified accordingly:

•	Anticipated Project Start Date: November 2025 (or when data received)

•	Analysis Completion Date: June  2027 (this estimate reflects general results from all the databases requested from additional providers; while data will be analyzed independently, coordination of timelines across providers approvals is considered).

•	Manuscript draft and first submitted for publications: Three months after completion of analysis, the initial manuscript will be completed and submitted to a peer-reviewed journal for publication consideration (considering the data use agreement from the YODA Project)

Results report to the YODA Project: Within 30 days of acceptance for publication, in accordance with the YODA Project data agreement and reporting requirements.  

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  string(594) "The results of these analyses will be submitted as abstracts and presented at international meetings, e.g. of the Population Approach Group in Europe (PAGE), the American Society for Clinical Pharmacology and Therapeutics (ASCPT), the Iberoamerican Pharmacometric Network (RedIF), and the American Association of Pharmaceutical Scientists (AAPS). In addition, it is planned to submit the research findings to peer-reviewed journals targeted at the scientific community, such as Clinical Pharmacokinetics, CPT:  Pharmacometrics & Systems Pharmacology, and European Journal of Paediatrics.   "
  ["project_bibliography"]=>
  string(469) "
van Rongen, A., Krekels, E. H., Calvier, E. A et al., (2022). An update on the use of allometric and other scaling methods to scale drug clearance in children: towards decision tables. Expert Opinion on Drug Metabolism & Toxicology, 18(2), 99-113.
Owen, J. S., & Fiedler-Kelly, J. (2014). Introduction to population pharmacokinetic/ pharmacodynamic analysis with nonlinear mixed effects models. John Wiley & Sons.
 
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pi affil
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products
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res
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