The process of drug development is long and burdensome as it requires a deep understanding of the underlying mechanisms of drug action and effect. This is particularly challenging when biological drugs are involved, as they display unique pharmacokinetic (PK) features, with respect to more well-defined chemical drugs. Monoclonal antibodies (mAbs) can be considered the most difficult biological drugs to characterize because of their large molecules and complex structures. They often display nonlinear PK, that is usually due, at least partly, to target mediated drug disposition (TMDD): the binding of the mAb to its pharmacological target influences its own disposition. Mathematical models are recognized as valuable tools to gain a deeper understanding of mAb PK, and in particular of the role played by the target on drug disposition. In this thesis, different models focusing on TMDD are explored theoretically and also applied on a real dataset in oncology, ranging from more complex and mechanistic models, to simpler empirical ones. More in detail, a minimal physiologically based PK (mPBPK) model is integrated with different mechanistic TMDD models, giving rise to four mPBPK-TMDD models. These are inspected with a particular focus on identifiability issues, to evaluate their use for simulation and identification on real PK data. A TMDD model is then explored via a tool built with the R package Shiny, to demonstrate how to gain more confidence with the model at hand through simulations. Finally, an empirical model describing nonlinear PK built for an anticancer mAb is refined via covariate inclusion exploiting data coming from a real clinical trial, to confirm that the observed interaction with a co-administered small-molecule drug is partly due to TMDD. Overall, this thesis presents methods for exploring, building and refining mathematical models for mAb PK of diverse complexity. The application of these methods leads to a greater confidence with the model in use, allows the exploration of possible alternative study designs, and finally bears a deepening in the understanding of mAbs PK processes.
|Titolo:||Methodological and practical issues in TMDD modeling with application to oncology|
|Data di pubblicazione:||26-gen-2018|
|Appare nelle tipologie:||8.01 Tesi di dottorato|