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Everolimus is currently approved in Europe as an adjunctive therapy for patients aged ≥ 2 years with tuberous sclerosis complex (TSC)-associated treatment-refractory partial-onset seizures, based on the EXIST-3 study (NCT01713946) results. As TSC-associated seizures can also affect children aged between 6 months and 2 years, a modeling and simulation (M&S) approach was undertaken to extrapolate exposure (trough plasma concentration (C min )) after a dose of 6 mg/m 2 and reduction in seizure frequency (RSF). A physiologically based pharmacokinetic model using Simcyp was developed to predict C min in adult and pediatric patients, which was then used by a population pharmacodynamic model and a linear mixed effect model to predict short-term and long-term efficacy in adults (for validation) and in children, respectively. Based on the results of the M&S study, everolimus at the dose of 6 mg/m 2 is anticipated to be an efficacious treatment in children 6 months to 2 years of age (up to 77.8% RSF) with concentrations within the recommended target range., (© 2020 Novartis Pharmaceutical Company. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

In adult patients, nilotinib is indicated for chronic myeloid leukemia at an approved oral dose of 300 or 400 mg BID. Physiologically based pharmacokinetic (PBPK) model was developed to describe and supplement limited PK data in the pediatric population ranging from 2 to less than 6 years of age and ultimately inform dosing regimen. An adult Simcyp PBPK model was established and verified with clinical pharmacokinetic data after a single or multiple oral doses of 400 mg nilotinib (230 mg/m 2 ). The model was then applied to a pediatric PBPK model, taking account of ontogeny profiles of metabolizing enzymes and pediatric physiological parameters. The model was further verified using observed pediatric PK data in 12- to <18-year-old and from 6- to <12-year-old patients. The PBPK models were able to recover, describe, and supplement the limited nilotinib concentration-time data profile in 2- to <6-year-old patients after a single dose and C min,ss after BID dosing. The exposure (C max,ss , C min,ss , and AUC tau,ss ) was predicted to be similar across age groups. PBPK model simulations confirmed that body surface area-normalized dosing regimen of 230 mg/m 2 is considered appropriate for pediatric patients >2 to <18 years of age., (Copyright © 2019 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave ® trials were included in this analysis. An indirect-response model developed by van Wart et al was used as a starting point. Inclusion of drug effect was dose driven using a population dose- and time-dependent pharmacodynamic (DTPD) model. Additionally, a population-pharmacokinetic parameter- and data (PPPD)-driven model was developed using the final DTPD model structure and final parameter estimates from a previously developed population pharmacokinetic model based on available Contrave ® pharmacokinetic concentrations. Last, MM was developed to predict transition rate probabilities among responder, nonresponder, and dropout states driven by the pharmacodynamic effect resulting from the DTPD or PPPD model. Covariates included in the models and parameters were diabetes mellitus and race. The linked DTPD-MM and PPPD-MM was able to predict transition rates among responder, nonresponder, and dropout states well. The analysis concluded that body-weight change is an important factor influencing dropout rates, and the MM depicted that overall a DTPD model-driven approach provides a reasonable prediction of clinical trial outcome probabilities similar to a pharmacokinetic-driven approach., (© 2017, The Authors. The Journal of Clinical Pharmacology published by Wiley Periodicals, Inc. on behalf of American College of Clinical Pharmacology.)