Coming full circle: The potential utility of real-world evidence to discern predictions from a physiologically based pharmacokinetic model

Today real word data (RWD) are playing a greater role in informing health care decisions. A physiologically based pharmacokinetic model (PBPK) and observed exposure-risk relationship predicted an increased bleeding risk induced by rivaroxaban (RXB) in patients with mild to moderate chronic kidney disease (CKD) taking concomitant medications that are combined Pgp-CYP3A inhibitors. In this commentary, we explore the potential use of RWD to assess the clinical consequence of this complex drug-drug interaction predicted from PBPK. This is a retrospective, case control, pilot study using a RWD dataset of 896,728 patients with mild to moderate chronic kidney disease and rivaroxaban use that was refined based upon combined Pgp-CYP3A inhibitor exposure and report of drug-induced bleeding (DIB). The odds ratio of patients with mild to moderate chronic kidney disease taking rivaroxaban with or without concurrent Pgp-CYP3A inhibitor use having a DIB was calculated. The odds ratio for DIB was 2.04 (CI95 1.82, 2.3; p < 0.001) suggesting an approximate doubling of bleeding risk which is consistent with the rivaroxaban exposure changes predicted by the published PBPK model and observed exposure-risk relationship. This exploratory analysis demonstrated the potential utility of RWD to assess model-based predictions as part of a drugs life cycle management.

Parametric and Nonparametric Methods in Population Pharmacokinetics: Experts' Discussion on Use, Strengths, and Limitations

Population pharmacokinetics consists of analyzing pharmacokinetic (PK) data collected in groups of individuals. Population PK is widely used to guide drug development and to inform dose adjustment via therapeutic drug monitoring and model-informed precision dosing. There are 2 main types of population PK methods: parametric (P) and nonparametric (NP). The characteristics of P and NP population methods have been previously reviewed. The aim of this article is to answer some frequently asked questions that are often raised by scholars, clinicians, and researchers about P and NP population PK methods. The strengths and limitations of both approaches are explained, and the characteristics of the main software programs are presented. We also review the results of studies that compared the results of both approaches in the analysis of real data. This opinion article may be informative for potential users of population methods in PK and guide them in the selection and use of those tools. It also provides insights on future research in this area.

Application of PBPK Modeling and Simulation for Regulatory Decision Making and Its Impact on US Prescribing Information: An Update on the 2018-2019 Submissions to the US FDA's Office of Clinical Pharmacology

Since 2016, results from physiologically based pharmacokinetic (PBPK) analyses have been routinely found in the clinical pharmacology section of regulatory applications submitted to the US Food and Drug Administration (FDA). In 2018, the Food and Drug Administration's Office of Clinical Pharmacology published a commentary summarizing the application of PBPK modeling in the submissions it received between 2008 and 2017 and its impact on prescribing information. In this commentary, we provide an update on the application of PBPK modeling in submissions received between 2018 and 2019 and highlight a few notable examples.

Model-Informed Pediatric Drug Development: Application of Pharmacometrics to Define the Right Dose for Children

One of the biggest challenges in pediatric drug development is defining a safe and effective dose in pediatric populations, which span across a wide age and development range from neonates to adolescents. Model-informed drug development approaches are particularly suited to address knowledge gaps including data leveraging to increase the success of pediatric studies. Considering the often limited number of patients available for study and logistic difficulties to collect the necessary data in pediatric populations, the application of pharmacometrics and modeling and simulation techniques can improve clinical trial efficiency, increase the probability of regulatory success, and optimize therapeutic individualization in support of dedicated trials. This review describes the state of pediatric model-informed drug development to define the right dose for children and provides suggestions for future development.

The European Medicines Agency Experience With Pediatric Dose Selection

Getting the right dose regimen for children and adolescents is important but poses great scientific, practical, and ethical challenges. At the same time, the availability of data in adults is a huge advantage and needs to be used optimally when designing studies in children and analyzing pediatric data. Furthermore, the processes of maturation and growth are always key when selecting doses for children. All the above make study adaptations and model-informed approaches imperative for dose exposure-response characterization and dose selection in children. This article summarizes the experience gained in the European Medicines Agency on this topic and proposes some general guiding principles for defining objectives, study designs, and methodology tools for pediatric dose selection.

Dosing Recommendations for Pediatric Patients With Renal Impairment

A treatment gap exists for pediatric patients with renal impairment. Alterations in renal clearance and metabolism of drugs render standard dosage regimens inappropriate and may lead to drug toxicity, but these studies are not routinely conducted during drug development. The objective of this study was to examine the clinical evidence behind current renal impairment dosage recommendations for pediatric patients in a standard pediatric dosing handbook. The sources of recommendations and comparisons included the pediatric dosing handbook (Lexicomp), the U.S. Food and Drug Administration-approved manufacturer's labels, and published studies in the literature. One hundred twenty-six drugs in Lexicomp had pediatric renal dosing recommendations. Only 14% (18 of 126) of Lexicomp pediatric renal dosing recommendations referenced a pediatric clinical study, and 15% of manufacturer's labels (19 of 126) described specific dosing regimens for renally impaired pediatric patients. Forty-two products had published information on pediatric renal dosing, but 19 (45%) were case studies. When pediatric clinical studies were not referenced in Lexicomp, the renal dosing recommendations followed the adult and pediatric dosing recommendations on the manufacturer's label. Clinical evidence in pediatric patients does not exist for most renal dosing recommendations in a widely used pediatric dosing handbook, and the adult renal dosing recommendations from the manufacturer's label are currently the primary source of pediatric renal dosing information.