Population pharmacokinetics and dose rationale for aciclovir in term and pre-term neonates with herpes.

Aciclovir is considered the first-line treatment against Herpes simplex virus (HSV) infections in new-borns and infants. As renal excretion is the major route of elimination, in renally-impaired patients, aciclovir doses are adjusted according to the degree of impairment. However, limited attention has been given to the implications of immature renal function or dysfunction due to the viral disease itself. The aim of this investigation was to characterize the pharmacokinetics of aciclovir taking into account maturation and disease processes in the neonatal population. Pharmacokinetic data obtained from 2 previously published clinical trials (n = 28) were analyzed using a nonlinear mixed effects modeling approach. Post-menstrual age (PMA) and creatinine clearance (CLCR) were assessed as descriptors of maturation and renal function. Simulation scenarios were also implemented to illustrate the use of pharmacokinetic data to extrapolate efficacy from adults. Aciclovir pharmacokinetics was described by a one-compartment model with first-order elimination. Body weight and diagnosis (systemic infection) were statistically significant covariates on the volume of distribution, whereas body weight, CLCR and PMA had a significant effect on clearance. Median clearance varied from 0.2 to 1.0 L/h in subjects with PMA <34 or ≥34 weeks, respectively. Population estimate for volume of distribution was 1.93 L with systemic infection increasing this value by almost 3-fold (2.67 times higher). A suitable model parameterization was identified, which discriminates the effects of developmental growth, maturation, and organ function. Exposure to aciclovir was found to increase with decreasing PMA and renal function (CLCR), suggesting different dosing requirement for pre-term neonates.

Pediatric Extrapolation Approach for U.S. Food and Drug Administration Approval of Brexpiprazole in Patients Aged 13 to 17 Years with Schizophrenia.

A pharmacokinetic (PK) bridging approach was successfully employed to support the dosing regimen and approval of brexpiprazole in pediatric patients aged 13-17 years with schizophrenia. Brexpiprazole was approved in 2015 for the treatment of schizophrenia and the adjunctive treatment of major depressive disorder in adults based on efficacy and safety data from clinical trials. On January 13, 2020, the US Food and Drug Administration issued a general advice letter to sponsors highlighting the acceptance of efficacy extrapolation of certain atypical antipsychotics from adult patients to pediatric patients considering the similarity in disease and exposure-response relationships. Brexpiprazole is the first atypical antipsychotic approved in pediatrics using this approach. The PK data available from pediatric patients aged 13-17 years have shown high variability due to the limited number of PK evaluable subjects, which limits a robust estimation of differences between adult and pediatric patients. The PK model-based approach was thus utilized to evaluate the appropriateness of the dosing regimen by comparing PK exposures in pediatric patients aged 13-17 years with exposures achieved in adults at the approved doses. In addition to exposure matching, safety data from a long-term open-label clinical study in pediatric patients informed the safety profile in pediatric patients. This report illustrates the potential of leveraging previously collected efficacy, safety, and PK data in adult patients to make a regulatory decision in pediatric patients for the indication of schizophrenia.

Innovations in Pediatric Therapeutics Development: Principles for the Use of Bridging Biomarkers in Pediatric Extrapolation.

Even with recent substantive improvements in health care in pediatric populations, considerable need remains for additional safe and effective interventions for the prevention and treatment of diseases in children. The approval of prescription drugs and biological products for use in pediatric settings, as in adults, requires demonstration of substantial evidence of effectiveness and favorable benefit-to-risk. For diseases primarily affecting children, such evidence predominantly would be obtained in the pediatric setting. However, for conditions affecting both adults and children, pediatric extrapolation uses scientific evidence in adults to enable more efficiently obtaining a reliable evaluation of an intervention's effects in pediatric populations. Bridging biomarkers potentially have an integral role in pediatric extrapolation. In a setting where an intervention reliably has been established to be safe and effective in adults, and where there is substantive evidence that disease processes in pediatric and adult settings are biologically similar, a 'bridging biomarker' should satisfy three additional criteria: effects on the bridging biomarker should capture effects on the principal causal pathway through which the disease process meaningfully influences 'feels, functions, survives' measures; secondly, the experimental intervention should not have important unintended effects on 'feels, functions, survives' measures not captured by the bridging biomarker; and thirdly, in statistical analyses in adults, the intervention's net effect on 'feels, functions, survives' measures should be consistent with what would be predicted by its level of effect on the bridging biomarker. A validated bridging biomarker has considerable potential utility, since an intervention's efficacy could be extrapolated from adult to pediatric populations if evidence in children establishes the intervention not only to be safe but also to have substantive effects on that bridging biomarker. Proper use of bridging biomarkers could increase availability of reliably evaluated therapies approved for use in pediatric settings, enabling children and their caregivers to make informed choices about health care.

Pediatric Efficacy Extrapolation in Drug Development Submitted to the US Food and Drug Administration 2015-2020.

Pediatric extrapolation plays a key role in the availability of reliable pediatric use information in approved drug labeling. This review examined the use of pediatric extrapolation in studies submitted to the US Food and Drug Administration and assessed changes in extrapolation approaches over time. Pediatric studies of 125 drugs submitted to the US Food and Drug Administration that led to subsequent pediatric information in drug labeling between 2015 and 2020 were reviewed. The use of pediatric extrapolation for each drug was identified and categorized as "complete," "partial," or "no" extrapolation. Approaches to pediatric extrapolation of efficacy changed over time. Complete extrapolation of efficacy was the predominantly used approach. "Complete," "partial," or "no" extrapolation was used for 51%, 23%, and 26% of the drugs, respectively. This represents a shift in extrapolation approaches when compared to a previous study that evaluated pediatrics drug applications between 2009 and 2014, which found complete, partial, or no extrapolation was used for 34%, 29%, and 37% of the drugs, respectively. Pediatric extrapolation approaches may continue to shift as emerging science fills gap in knowledge of the fundamental assumptions underlying this scientific tool. The international community continues to collaborate on discussions of pediatric extrapolation of efficacy from adults and other pediatric subpopulations to optimize its use for pediatric drug development.

Model-Informed Approach Supporting Drug Development and Regulatory Evaluation for Rare Diseases.

A rare disease is defined as a condition affecting fewer than 200 000 people in the United States by the Orphan Drug Act. For rare diseases, it is challenging to enroll a large number of patients and obtain all critical information to support drug approval through traditional clinical trial approaches. In addition, over half of the population affected by rare diseases are children, which presents additional drug development challenges. Thus, maximizing the use of all available data is in the interest of drug developers and regulators in rare diseases. This brings opportunities for model-informed drug development to use and integrate all available sources and knowledge to quantitatively assess the benefit/risk of a new product under development and to inform dosing. This review article provides an overview of 4 broad categories of use of model-informed drug development in drug development and regulatory decision making in rare diseases: optimizing dose regimen, supporting pediatric extrapolation, informing clinical trial design, and providing confirmatory evidence for effectiveness. The totality of evidence based on population pharmacokinetic simulation as well as exposure-response relationships for efficacy and safety, provides the regulatory ground for the approval of an unstudied dosing regimen in rare diseases without the need for additional clinical data. Given the practical and ethical challenges in drug development in rare diseases, model-informed approaches using all collective information (eg, disease, drug, placebo effect, exposure-response in nonclinical and clinical settings) are powerful and can be applied throughout the drug development stages to facilitate decision making.

Development of a pediatric physiologically-based pharmacokinetic model to support recommended dosing of atezolizumab in children with solid tumors.

Background: Atezolizumab has been studied in multiple indications for both pediatric and adult patient populations. Generally, clinical studies enrolling pediatric patients may not collect sufficient pharmacokinetic data to characterize the drug exposure and disposition because of operational, ethical, and logistical challenges including burden to children and blood sample volume limitations. Therefore, mechanistic modeling and simulation may serve as a tool to predict and understand the drug exposure in pediatric patients. Objective: To use mechanistic physiologically-based pharmacokinetic (PBPK) modeling to predict atezolizumab exposure at a dose of 15 mg/kg (max 1,200 mg) in pediatric patients to support dose rationalization and label recommendations. Methods: A minimal mechanistic PBPK model was used which incorporated age-dependent changes in physiology and biochemistry that are related to atezolizumab disposition such as endogenous IgG concentration and lymph flow. The PBPK model was developed using both in vitro data and clinically observed data in adults and was verified across dose levels obtained from a phase I and multiple phase III studies in both pediatric patients and adults. The verified model was then used to generate PK predictions for pediatric and adult subjects ranging from 2- to 29-year-old. Results: Individualized verification in children and in adults showed that the simulated concentrations of atezolizumab were comparable (76% within two-fold and 90% within three-fold, respectively) to the observed data with no bias for either over- or under-prediction. Applying the verified model, the predicted exposure metrics including Cmin, Cmax, and AUCtau were consistent between pediatric and adult patients with a geometric mean of pediatric exposure metrics between 0.8- to 1.25-fold of the values in adults. Conclusion: The results show that a 15 mg/kg (max 1,200 mg) atezolizumab dose administered intravenously in pediatric patients provides comparable atezolizumab exposure to a dose of 1,200 mg in adults. This suggests that a dose of 15 mg/kg will provide adequate and effective atezolizumab exposure in pediatric patients from 2- to 18-year-old.

Knowledge Gaps in the Pharmacokinetics of Therapeutic Proteins in Pediatric Patients.

Therapeutic proteins such as monoclonal antibodies and their derivatives, fusions proteins, hormone analogs and enzymes for replacement therapy are an ever-growing mainstay in our pharmacopoeia. While a growing number of these medications are developed for and used in younger and younger pediatric patients, knowledge gaps in the basic understanding of the molecular and physiologic processes governing the disposition of these compounds in the human body and their modulation by age and childhood development are a hindrance to the effective and timely development and clinical use of these compounds, especially in very young pediatric patient populations. This is particularly the case for the widespread lack of information on the ontogeny and age-associated expression and function of receptor systems that are involved in the molecular processes driving the pharmacokinetics of these compounds. This article briefly highlights three receptor systems as examples, the neonatal Fc receptor, the asialoglycoprotein receptor, and the mannose receptor. It furthermore provides suggestions on how these gaps should be addressed and prioritized to provide the field of pediatric clinical pharmacology the urgently needed tools for a more effective development and clinical utilization of this important class of drugs with rapidly evolving importance as cornerstone in pediatric pharmacotherapy.

Evaluation of non-linear-mixed-effect modeling to reduce the sample sizes of pediatric trials in type 2 diabetes mellitus.

Recruitment for pediatric trials in Type II Diabetes Mellitus (T2DM) is very challenging, necessitating the exploration of new approaches for reducing the sample sizes of pediatric trials. This work aimed at assessing if a longitudinal Non-Linear-Mixed-Effect (NLME) analysis of T2DM trial could be more powerful and thus require fewer patients than two standard statistical analyses commonly used as primary or sensitivity efficacy analysis: Last-Observation-Carried-Forward (LOCF) followed by (co)variance (AN(C)OVA) analysis at the evaluation time-point, and Mixed-effects Model Repeated Measures (MMRM) analysis. Standard T2DM efficacy studies were simulated, with glycated hemoglobin (HbA1c) as the main endpoint, 24 weeks' study duration, 2 arms, assuming a placebo and a treatment effect, exploring three different scenarios for the evolution of HbA1c, and accounting for a dropout phenomenon. 1000 trials were simulated, then analyzed using the 3 analyses, whose powers were compared. As expected, the longitudinal modeling MMRM analysis was found to be more powerful than the LOCF + ANOVA analysis at week 24. The NLME analysis gave slightly more accurate drug-effect estimations than the two other methods, however it tended to slightly overestimate the magnitude of the drug effect, and it was more powerful than the MMRM analysis only in some scenarios of slow HbA1c decrease. The gain in power afforded by NLME was more apparent when two additional assessments enriched the design; however, the gain was not systematic for all scenarios. Finally, this work showed that NLME analyses may help to reduce significantly the required sample sizes in T2DM pediatric studies, but only for enriched designs and slow HbA1c decrease.