Integration of preclinical and clinical knowledge to predict intravenous PK in human: Bilastine case study

Abstract

Modern pharmacometrics can integrate and leverage all prior proprietary and public knowledge. Such methods can be used to scale across species or comparators, perform clinical trial simulation across alternative designs, confirm hypothesis and potentially reduce development burden, time and costs. Crucial yet typically lacking in integration is the pre-clinical stage. Prediction of PK in man, using in vitro and in vivo studies in different animal species, is increasingly well theorized but could still find wider application in drug development. The aim of the present work was to explore methods for bridging pharmacokinetic knowledge from animal species (i.v. and p.o.) and man (p.o.) into i.v. in man using the antihistamine drug bilastine as example. A model, predictive of i.v. PK in man, was developed on data from two pre-clinical species (rat and dog) and p.o. in man bilastine trials performed earlier. In the knowledge application stage, two different approaches were used to predict human plasma concentration after i.v. of bilastine: allometry (several scaling methods) and a semi-physiological method. Both approaches led to successful predictions of key i.v. PK parameters of bilastine in man. The predictive i.v. PK model was validated using later data from a clinical study of i.v. bilastine. Introduction of such knowledge in development permits proper leveraging of all emergent knowledge as well as quantification-based exploration of PK scenario, e.g. in special populations (pediatrics, renal insufficiency, comedication). In addition, the methods permit reduction or elimination and certainly optimization of learning trials, particularly those concerning alternative off-label administration routes.

Application of a dual mechanistic approach to support bilastine dose selection for older adults

“…doses in the range between 5 mg and 220 mg per day (Table S4 ). 10 , 18 Moreover, the mean area under the curve (AUC) and maximum plasma concentration (C max ) values from observations and predictions were also compared (Figure S4 and S5). The model was considered appropriate when the ratio of the PK parameters from mean of observations and mean of predictions was less than twofold.…”

“…scenario was simulated, and it compared with observations for additional verification of the PBPK model (Figure S6 and Table S5 ). 10 , 18 Available mass‐balance data were also used for verification (Figures S8 and S9 ). 19 , 20 The PBPK model was then extrapolated to healthy geriatrics.…”

“…The values for Vmax and Km were not available from in vitro studies and were thus incorporated in the model using a sensitivity analysis to fit the PK data (Figure S7). After the inclusion of transporters, the PBPK model was further qualified using external data from 12 clinical trials after single and/or multiple PO doses in the range between 5 mg to 220 mg per day (Table S4) 10,18 . Moreover, the mean area under the curve (AUC) and maximum plasma concentration (Cmax) values from observations and predictions were also compared (Figures S4 and S5).…”

“…The model was considered appropriate when the ratio of the PK parameters from mean of observations and mean of predictions was less than 2-fold. Furthermore, multiple dose of 20mg PO QD scenario was simulated, and it compared with observations for additional verification of the PBPK model (Figure S6 and Table S5) 10,18 .…”

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

“…There are many examples for the successful application of between species scaling of PK using allometric scaling approaches . However, there are also examples, particularly for within species scaling, where allometric scaling approaches have met variable success.…”