Physiologically-Based Pharmacokinetic (PBPK) Modeling of Buprenorphine in Adults, Children and Preterm Neonates

Abstract

Buprenorphine plays a crucial role in the therapeutic management of pain in adults, adolescents and pediatric subpopulations. However, only few pharmacokinetic studies of buprenorphine in children, particularly neonates, are available as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling allows the prediction of drug exposure in pediatrics based on age-related physiological differences. The aim of this study was to predict the pharmacokinetics of buprenorphine in pediatrics with PBPK modeling. Moreover, the drug-drug interaction (DDI) potential of buprenorphine with CYP3A4 and P-glycoprotein perpetrator drugs should be elucidated. A PBPK model of buprenorphine and norbuprenorphine in adults has been developed and scaled to children and preterm neonates, accounting for age-related changes. One-hundred-percent of the predicted AUClast values in adults (geometric mean fold error (GMFE): 1.22), 90% of individual AUClast predictions in children (GMFE: 1.54) and 75% in preterm neonates (GMFE: 1.57) met the 2-fold acceptance criterion. Moreover, the adult model was used to simulate DDI scenarios with clarithromycin, itraconazole and rifampicin. We demonstrate the applicability of scaling adult PBPK models to pediatrics for the prediction of individual plasma profiles. The novel PBPK models could be helpful to further investigate buprenorphine pharmacokinetics in various populations, particularly pediatric subgroups.

Preparation of solid dispersion systems for enhanced dissolution of poorly water soluble diacerein: In-vitro evaluation, optimization and physiologically based pharmacokinetic modeling

“…Moreover, in another study by Ellison et al [ 26 ], PBPK modeling approach, using in - vitro and in - silico inputs, was applied to develop human oral PBPK models for caffeine and diphenhydramine. In addition, Kovar et al [ 27 ] utilized the PBPK modeling in successfully predicting the pharmacokinetics of buprenorphine in children, specifically in neonates, where conducting clinical trials within this population is really challenging. It was also reported by Rioux et al [ 28 ] that application of PBPK modeling in drug development for pediatric cancers is relatively nascent where, the regulatory authorities with the United States Food and Drug administration (FDA) have recommended the application of PBPK modeling in the FDA Strategic Plan for accelerating the development of therapies for pediatric diseases.…”

Physiologically-Based Pharmacokinetic (PBPK) Modeling Providing Insights into Fentanyl Pharmacokinetics in Adults and Pediatric Patients

“…PBPK modeling permits rational scaling between adult and pediatric patients by defining the PK of a drug as a function of anatomy, physiology and biochemistry and successful applications have recently been shown in different modeling efforts [ 21 , 22 , 23 ]. This study demonstrates the applicability of PBPK modeling to predict both clearance values as well as plasma concentration–time profiles and the corresponding AUCs for the analgesic drug fentanyl for preterm neonates to up to 3-year-old children within a whole-body PBPK framework.…”

“…In pediatrics, PBPK approaches have also proven its usefulness in designing and optimizing clinical trials and are supported by both the FDA and the EMA [ 12 , 16 , 17 , 18 , 19 , 20 ]. For a priori PBPK predictions in pediatrics, the PBPK model first needs to be informed and evaluated with published PK data in adults and subsequently extrapolated to pediatric populations—a workflow which has recently been implemented and successfully executed for several drugs [ 10 , 21 , 22 , 23 , 24 , 25 ].…”

Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions

“…The model includes rifampicin transport via OATP1B1 and P-glycoprotein (Pgp), metabolism via the arylacetamide deacetylase (AADAC), as well as auto-induction of OATP1B1, Pgp and AADAC (26). The good DDI performance of the model was demonstrated in many different applications (26,(28)(29)(30)(31). Mathematical implementation of the DDI processes is specified in Section 1 of the ESM.…”