Optimizing pharmacotherapy in the elderly using modelling and simulation methods. Application to Bilastine

Resumen

This research project was designed to acquire a deeper knowledge on how physiological changes during aging mayaffect the pharmacokinetics (PK) and pharmacodynamics (PD). Elderly adults are a vulnerable population due to thephysiological changes associated with age and the additional problems of polypharmacy and comorbidity diseases.Therefore, a better understanding of the clinical pharmacology of old age is important in the development ofpharmacometric strategies that could support dose selection in this special population. In order to establish if the useof predictive PK/PD models that include changes in the ADME processes as a function of age, already applied topediatrics, will also be also predictive in the geriatric population, the antihistamine drug bilastine was employed as aprobe drug. On the one hand due to the wide knowledge of the research team on this drug, and on the other hand as aPK/PD data package was available in geriatric patients to inform the model building and to validate the predictions.As a first step in this research project, a database was created using data from the public domain used to develop theequations that relate ADME processes with physiological metrics as a function of aging. These equations were thenused to develop the Senescence model. The mechanistic approach proposed in this research project integrated theavailable drug PK knowledge in adults as well as the impact of the physiological changes in elderly affecting the PKand/or PD of bilastine followed by the application of modelling and simulation techniques. The underlyingmechanisms affecting the PK of bilastine were elucidated by combining a compartmental model structure togetherwith principles of physiology related to distribution and elimination processes in order to determine the impact of keyphysiological parameters that were predictive of PK behavior of bilastine in adults and also in the geriatricpopulation. Moreover, considering that the proposed predictive model was built on a physiological basis, it was usedto predict PK in elderly patients with renal impairment with the aim of exploring the impact of thispathophysiological condition on bilastine PK. Overall, in both scenarios, the model¿s predictive performance wassatisfactory evaluated using available geriatric PK data indicating that the main assumptions concerning the agedependentprocesses determining the pharmacokinetics of bilastine, are overall well described.