Quantitative clinical pharmacology for size and age scaling in pediatric drug development: A systematic review

Abstract

The establishment of drug dosing in children is often hindered by the lack of actual pediatric efficacy and safety data. To overcome this limitation, scaling approaches are frequently employed to leverage adult clinical information for informing pediatric dosing. The objective of this review is to provide a comprehensive overview of the different scaling approaches used in pediatric pharmacotherapy as well as their proper implementation in drug development and clinical use. We will start out with a brief overview of the current regulatory requirements in pediatric drug development, followed by a review of the most commonly employed scaling approaches in increasing order of complexity ranging from simple body weight-based dosing to physiologically-based pharmacokinetic (PBPK) modeling approaches. Each of the presented approaches has advantages and limitations, which will be highlighted throughout the course of the review by the use of clinically-relevant examples. The choice of the approach employed consequently depends on the clinical question at hand and the availability of sufficient clinical data. The main effort while establishing and qualifying these scaling approaches should be directed towards the development of safe and effective dosing regimens in children rather than identifying the best model, ie models should be fit for purpose.

Model Informed Dose Optimization of Dichloroacetate for the Treatment of Congenital Lactic Acidosis in Children

“…In traditional drug development, doses for children are usually determined by extrapolating from adult data, if it is reasonable to assume that the disease progression, response to the intervention, and exposure–response relationship are similar in children and adults . In such a case, a sponsor is usually required to conduct only a PK study to select a dose to achieve similar exposure (full extrapolation) or similar target PD effect (partial extrapolation) as attained in adults . For DCA, studies have shown that steady‐state trough concentrations of 5–25 mg/L are correlated with the clinical efficacy of DCA in adults as well in children .…”

“…23,24 In such a case, a sponsor is usually required to conduct only a PK study to select a dose to achieve similar exposure (full extrapolation) or similar target PD effect (partial extrapolation) as attained in adults. 23,24 For DCA, studies have shown that steady-state trough concentrations of 5-25 mg/L are correlated with the clinical efficacy of DCA in adults 25 as well in children. 21 Consequently, a full extrapolation approach coupled with existing exposure-response information in children was used to inform dosing in children.…”

Characterizing the Developmental Trajectory of Sirolimus Clearance in Neonates and Infants

“…This value of 0.67 was used as the power exponent in the allometric scaling. In general, body size normalization has been widely conducted by linear BW, BSA, and allometric scaling with the power of 0.75 (body weight 0.75 ) . Of note, BSA normalization is nearly equal to allometric scaling with the power exponent of 2/3 (0.67).…”

“…In general, body size normalization has been widely conducted by linear BW, BSA, and allometric scaling with the power of 0.75 (body weight 0.75 ). [22][23][24][25] Of note, BSA normalization is nearly equal to allometric scaling with the power exponent of 2/3 (0.67). Johnson reported that scaling methods with BSA and body weight 0.75 performed better than that with linear BW in the 7-year and 12-year aged groups in predicting maintenance doses of 30 different drugs for children from those used in adults.…”

Challenges and Opportunities for Increasing the Knowledge Base Related to Drug Biotransformation and Pharmacokinetics during Growth and Development

“…Physiologically based models are systems of differential equations including a large number of parameters that can be classified as drug and system related; for pediatrics, the latter includes developmental changes in tissue volumes and composition, organ blood flow, gastric acidity, intestinal transit time, protein binding concentration, among others. Furthermore, developmental trajectories for several drug metabolizing enzymes (DMEs) have been characterized in vitro and in vivo, and ontogeny functions have been incorporated within commercially available PBPK platforms, such as Simcyp, GastroPlus, and PKSim (Johnson and Rostami-Hodjegan, 2011;Abduljalil et al, 2014;Samant et al, 2015). Rioux and Waters (2016) reviewed the experience with PBPK modeling as a quantitative, systems pharmacology-based framework to support drug development in the specialized area of pediatric oncology.…”