Model-informed precision dosing of adalimumab in patients with inflammatory bowel disease
- Autores: Silvia Márquez Megías
- Directores de la Tesis: José Ricardo Nalda Molina (dir. tes.), Amelia Ramón López (dir. tes.)
- Lectura: En la Universidad Miguel Hernández de Elche ( España ) en 2024
- Idioma: español
- Tribunal Calificador de la Tesis: José Vicente Segura Heras (presid.), Juan José Pérez Ruixo (secret.), Dolors Soy Muner (voc.)
- Programa de doctorado: Programa de Doctorado en Estadística, Optimización y Matemática Aplicada por la Universidad Miguel Hernández de Elche
- Materias:
- Enlaces
- Tesis en acceso abierto en: RediUMH
- Resumen
Inflammatory Bowel Diseases (IBD), including Crohns Disease (CD) and Ulcerative Colitis (UC), are chronic autoimmune diseases characterized by intermittent destructive inflammation in different areas of the digestive tract because of the release of inflammatory mediators, such as cytokines, interleukins, and tumor necrosis factor (TNF) by the immune system.
Biological therapies, particularly anti-TNF drugs like adalimumab, have emerged as revolutionary options for the treatment of IBD. Adalimumab is a humanized IgG1 monoclonal antibody that specifically binds to the TNF and neutralizes its biological function. Despite its effectiveness, a significant proportion of patients annually face a loss of response (LOR), requiring dose adjustments or treatment changes. Prolonged subtherapeutic drug levels can lead to LOR or the development of antibodies against adalimumab (AAA) that reduce treatment response rates. Optimizing treatment through Model-Informed Precision Dosing (MIPD) is crucial to prevent immunogenicity and lead to reduced surgery rates and lower AAA risk, along with economic advantages.
In clinical settings, rich profiles of plasma drug concentration measurements are often unavailable, which limits the development of population pharmacokinetic (PopPK) models. Consequently, dosage individualization relies on existing PopPK models from the literature. However, the selected PopPK model must be developed from a population similar to the studied population and must be validated before using them in the clinical setting. The re-estimation of the PopPK parameters would lead to an improvement in the precision and accuracy of the model's plasma concentration predictions.
The first objective of this Thesis was to conduct a systematic review to evaluate studies on the cost-effectiveness analysis of Therapeutic Drug Monitoring (TDM) of antiTNF in IBD. A cross-sectional descriptive study of studies found in the literature was conducted following the structure of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of the included studies was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. Thirteen studies from 2013 to 2021 were reviewed, with eight achieving very good to excellent rankings on the CHEERS checklist. This systematic review demonstrated the cost-effectiveness and potential cost-saving benefits of implementing TDM for anti-TNF drugs in IBD management.
The second objective of this Thesis was to evaluate the predictive performance of PopPK models of adalimumab in IBD patients, identified in the literature, to determine the PopPK model that best suited the target population of the Dr. Balmis General University Hospital of Alicante to integrate it into clinical routines. A retrospective observational study involving 134 patients was conducted between 2014 and 2019. Model adequacy was assessed through individual PK parameter distribution and Normalized Prediction Distribution Errors (NPDE) plots, while predictive performance was assessed by calculating bias and precision. Additionally, stochastic simulations were performed to optimize maintenance doses in clinical protocols, to achieve a trough target of 8 mg/L in at least 75% of the population. Among the PopPK models for adalimumab in IBD found in the literature, two were superior in terms of model adequacy and predictive performance. Nevertheless, it was observed that the Empirical Bayesian Estimates (EBEs) were biased from the population mean values, suggesting the need for model refinement based on available data. Furthermore, stochastic simulations with these models suggested potential benefits in increasing the maintenance dose in the protocol to reach the 8 mg/L target.
The last objective of this Thesis was to optimize the selected PopPK model of adalimumab for IBD, looking for the improvement in predictive performance and clinical impact. In this study, the selected model from the previous objective was considered as a reference model. A retrospective observational study involving 54 IBD patients was conducted to refine the reference model. The refinement of the PK parameters was performed using two different methods: estimating the PK parameters without priors (estimated model) and incorporating informative priors in some parameters (prior model). The criterion for model selection were the evaluation of predictive performance and the clinical impact. This final model effectively characterized adalimumab PK in the studied population and improved by up to 50%, compared to the reference model in terms of bias and precision. The main structural difference between both models was the inclusion of the albumin as a meaningful covariate on CL/F. Moreover, the final model significantly improved the clinical impact on the target population, suggesting more accurate dose optimization and increased efficacy in adalimumab treatment.
