Improving pharmacometrics analysis efficiency using DataCheQC: An interactive, Shiny-based app for quality control of pharmacometrics datasets.

DataCheQC is an interactive application based on the R Shiny framework developed for the purposes of performing quality control (QC) checks on pharmacometric datasets, and thereby supporting the implementation of model-informed drug development. Features include visual inspection of variables and data entries for errors and/or anomalies, and ensuring structural integrity through comparison with a dataset specification file. The app, which requires no programming knowledge to operate, allows the user to collect all findings into a summary report downloadable directly from the app itself. The source code for the app is freely available on GitHub under an open-source license (https://github.com/DotanOr/DataCheQC) and can also be accessed online (https://dotanor.shinyapps.io/DataCheQC/).

Scaling Approaches for Pediatric Dose Selection: The Fremanezumab (AJOVY®) Journey to Select a Phase 3 Dose Using Pharmacokinetic Data from a Phase 1 Study.

Fremanezumab, a fully humanized IgG2Δa/kappa monoclonal antibody, selectively targets the calcitonin-gene-related peptide (CGRP) and prevents it from binding to the CGRP receptor. The safety, tolerability, pharmacokinetics (PK), and efficacy of fremanezumab for treating migraines administered as a once monthly 225 mg dose or a once quarterly 675 mg dose have been well characterized in adults. The fremanezumab exposure and body weight relationship supported the use of the approved 225 mg monthly adult dose for pediatric patients weighing ≥45 kg. In the pediatric Phase 3 program, a 120 mg dose for patients weighing <45 kg was determined using the results of an open-label study and a population PK modeling and simulation strategy. A thorough evaluation was conducted to further characterize the population PK of fremanezumab and assess the predictive performance of the adult population PK model when applied to the Phase 1 pediatric data, the predictive performance of alternative pediatric population PK models, and the predictive performance of the selected pediatric population PK model via a noncompartmental-based approach. This latter comparison to noncompartmental results provided additional evidence that the pediatric population PK model predicts the observed data well and supports the 120 mg monthly dose in patients weighing <45 kg.

Dose selection for fremanezumab (AJOVY) phase 3 pediatric migraine studies using pharmacokinetic data from a pediatric phase 1 study and a population pharmacokinetic modeling and simulation approach.

BACKGROUND: Potential fremanezumab doses for pediatric patients were evaluated using pharmacokinetic modeling and simulation. An open-label phase 1 pharmacokinetic and safety study was conducted in pediatric patients with migraine. This study's results together with refinement of the adult population pharmacokinetic model were used to determine fremanezumab dose recommendations for phase 3 pediatric studies. METHODS: Initial application of the adult model suggested that a 75 mg dose in pediatric patients would match exposures determined safe and efficacious in adults; thus, in the phase 1 study, 15 patients, aged 6-11 years and weighing 17-45 kg received a single subcutaneous 75 mg fremanezumab dose. The sparse pharmacokinetic data collected were used to refine the adult model and simulate concentration-time profiles for monthly subcutaneous doses (60 to 225 mg) in a virtual pediatric population. RESULTS: In the phase 1 pediatric study, the safety profile was similar to that of adults. A two-compartment model with first-order absorption and elimination and body weight effects on clearance and central volume was found to adequately describe the pediatric pharmacokinetic data. CONCLUSIONS: Using exposure matching to the effective adult fremanezumab dose (225 mg subcutaneous monthly), modeling and simulations predict recommended dose of 120 mg in pediatric patients weighing < 45 kg.Registration: The phase 1 study of this report is registered at EudraCT with the identifier 2018-000734-35.

Enhancing population pharmacokinetic modeling efficiency and quality using an integrated workflow.

Population pharmacokinetic (popPK) analyses are at the core of Pharmacometrics and need to be performed regularly. Although these analyses are relatively standard, a large variability can be observed in both the time (efficiency) and the way they are performed (quality). Main reasons for this variability include the level of experience of a modeler, personal preferences and tools. This paper aims to examine how the process of popPK model building can be supported in order to increase its efficiency and quality. The presented approach to the conduct of popPK analyses is centered around three key components: (1) identification of most common and important popPK model features, (2) required information content and formatting of the data for modeling, and (3) methodology, workflow and workflow supporting tools. This approach has been used in several popPK modeling projects and a documented example is provided in the supplementary material. Efficiency of model building is improved by avoiding repetitive coding and other labor-intensive tasks and by putting the emphasis on a fit-for-purpose model. Quality is improved by ensuring that the workflow and tools are in alignment with a popPK modeling guidance which is established within an organization. The main conclusion of this paper is that workflow based approaches to popPK modeling are feasible and have significant potential to ameliorate its various aspects. However, the implementation of such an approach in a pharmacometric organization requires openness towards innovation and change-the key ingredient for evolution of integrative and quantitative drug development in the pharmaceutical industry.