Individualized Tacrolimus dosing in pediatric kidney transplantation: identification of new genomic, proteomic and metabolomic biomarkers and PhysPK® development of PBPK-based predictive models, simulations and applications in different phamacogenomic subpopulations.
Andrea Guzmán-de Antonio (1), Lucía Díaz-García (1), Sergio Sánchez-Herrero (2), Marina Cuquerella-Gilabert (2); Jenifer Serna (2); Almudena Rueda-Ferreiro (2), Alejandro Zarauza-Santoveña (3), Diego Morante Martínez (3), Antonio Carcas-Sansuán (1).
(1) Clinical Pharmacology Department, IdiPAZ, La Paz University Hospital, School of Medicine, Autonomous University of Madrid, Madrid, Spain. (2) Simulation Department, Empresarios Agrupados Internacional S.A., Madrid, Spain. (3) Pediatric Nephrology Department, La Paz University Hospital, Madrid, Spain
Objectives: Tacrolimus (TCR) is the primary immunosuppressive (IS) drug used in pediatric kidney transplantation. As other IS, TCR presents a high variability in its pharmacokinetics (PK) and response. Although with limitations, the monitoring serum concentrations of TCR is one of the basic tools for achieve an adequate balance in the degree of immunosuppression and avoid complications due to over- or under-immunosuppression. In order to improve the individualization of treatment with TCR, it has been identified genomic variants in CYP3A5 metabolizing enzyme but without clear clinical evidence. Therefore, it is necessary to identify biomarkers of tacrolimus disposition and for this purpose a multicenter cross-sectional study in pediatric patients with stable renal transplantation has been performed. Genomic, proteomic and metabolomic data were collected. Massive analysis of the data was performed to identify associations between the omic variables and to develop models and new algorithms that can explain this extensive PK variability. The present study aims to improve PBPK modeling based on Prado Prado-Velasco et al., 2020 [1] with CYP3A5 polymorphisms implementation as well as the incorporation of a multi-omic approach for defining the relationship between the dosing regimen and the body’s exposure to drug as indicated by the concentration-time curve. Moreover, develop user-friendly standlone application for individualization of TCR dosing in routine clinical practice.
Methods: Phase IV, multicenter, open-label, prospective, uncontrolled, exploratory clinical trial. The pharmacokinetic parameter area under the curve (AUC) was determined through the Limited Sampling Strategy. Blood samples were extracted in three different time points: before the administration of the drug (C0); 1 hour after the administration of the drug (C1) and 3 hours after the administration of TCR (C3). With the plasma resulting from centrifuging the whole blood aliquot extracted from the patient, the proteomic and metabolomic profiles were obtained. PBPK model was developed from previous published Tacrolimus model [1] and optimized by standard methodologies with data from a clinical study adapted with pharmacogenomics data. An external validation based on subsequent measurements of the same population was also performed. The model was built using the PhysPK® (version 2.4) M&S software system [2,3], which is an object-oriented innovative platform for PK/PD/PBPK advanced modelling. It provides built-in modules for population estimation, optimization and validation of models. PhysPK models could be exported to create a posology user-friendly standalone application in Excel, Webservice, Python, etc.
Results: Concentration-time (0, 1 and 3 hours) and AUC (0-24h) data were used to validate the new PBPK model. As expected, the PBPK model adapted to CYP3A5 polymorphisms in TCR treatment delivered a better predictive capacity and behaviour than previous PBPK models without pharmacogenomics data. Population PBPK model parameters’ mean and IIV estimates (RSE) for TCR fitting were re-calculated for being applied in validation process. A better PBPK model fit and multi-dose projection was obtained for TCR with a p-value<0.05 compared to the previous PBPK model.
PBPK customized posology standalone application was generated, using PhysPK® features. 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.
Conclusions: The study has shown the reliability of PBPK models based on pharmacogenomics 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] Prado-Velasco, M., Borobia, A., & Carcas-Sansuan, A. (2020). Predictive engines based on pharmacokinetics modelling for tacrolimus personalized dosage in paediatric renal transplant patients. Scientific Reports, 10(1), 7542.
[2]Reig-Lopez, Javier, et al. "A multilevel object-oriented modelling methodology for physiologically-based pharmacokinetics (pbpk): Evaluation with a semi-mechanistic pharmacokinetic model." Computer Methods and Programs in Biomedicine 189 (2020): 105322.
[3] Prado-Velasco, Manuel. "III-58: Manuel Prado-Velasco Bridging the gap between open and specialized modelling tools in PBPK/PK/PD with PhysPK/EcosimPro modelling system: PBPK model of methotrexate and 6-mercaptopurine in humans with focus in reusability and multilevel modelling features."