PBPK versus PK modeling of Tacrolimus drug in patients with renal transplant as knowledge engines for personalized posology software: PhysPK® development and preliminary results
Manuel Prado-Velasco(1), Almudena Rueda(1), Alberto Borobia(2), Antonio Carcas(2), Diego García-Álvarez(1), Jenifer Serna(1)
(1) PhysPK Unit, Empresarios Agrupados Internacional, Spain; (2) University Hospital La Paz, Madrid, Spain
Objectives: To develop of a customized posology software for defining doses and administration times of Tacrolimus drug in kidney transplanted children and adolescents. A PK model for Tacrolimus is compared with a PBPK model. Both models are built and optimized through a clinical data study, and used subsequently as knowledge engines of a posology software for clinical environment.
Methods: PK and PBPK models were developed starting from previous published Tacrolimus models [1, 2] and optimized by standard methodologies with data from a clinical study. An external validation based on subsequent measurements of the same population was also performed. The models were built using the PhysPK® M&S software system [3], which is an object-oriented cutting edge platform for PK/PD/PBPK advanced modelling. It provides built-in modules for population estimation, optimization and validation of models, and a three-layer architecture that implements the mechanisms (first layer), the pharmacokinetic and physiological elements (second layer), and non-mechanistic computing components for metrics and signal processing (third layer).
The posology software was generated through a pre-built template of the optimization posology module of PhysPK®.
Results: A two compartment model with elimination, and absorption process based on transit compartment kinetics structure was selected as PK model. The PBPK model was made by 13 flow limited tissues for portal vein zone, lung, liver, fat tissue, kidney, brain, heart, skin, muscle, tendon and others, with several relationships among clearance, and plasmatic unbound fraction of Tacrolimus, with genotype and DDI. As expected, the PBPK model delivered a better predictive capacity and behaviour than the PK model, although the parameters’ setting was more complex.
Two customized posology software systems were generated, using PK and PBPK, respectively. The software was executed from MS Excel, and includes the initial register of the patient associated with an automatic fitting of their customized parameters, subsequent adjustments, and support for posology definition. Clinicians can execute what-if scenarios before take a posology decision.
Conclusions: The study has shown the reliability of PK and PBPK models to be used as knowledge engines in a customized posology software, built automatically through PhysPK® modelling and simulation software. The software will be tested in a university hospital to validate the accuracy and reliability.
References:
[1] Andreu, F., Colom, H., Grinyó, J. M., Torras, J., Cruzado, J. M., & Lloberas, N. (2015). Development of a Population PK Model of Tacrolimus for Adaptive Dosage Control in Stable Kidney Transplant Patients. Therapeutic Drug Monitoring, 37(2).
[2] Gérard, C., Stocco, J., Hulin, A., Blanchet, B., Verstuyft, C., Durand, F., Tod, M. (2014). Determination of the most influential sources of variability in tacrolimus trough blood concentrations in adult liver transplant recipients: a bottom-up approach. The AAPS Journal, 16(3), 379–91
[3] M. Prado-Velasco, “PhysPK/EcosimPro: Sistema de modelado y simulación de sistemas fisiológicos. Metodología, arquitectura y aplicación a problemas PBPK/PK/PD,” in VI Jornadas de Modelización y Simulación en Biomedicina, 2015, p. 15
