The dynamics of pharmacometrics to personalize the treatment of inborn bleeding disorders

The thesis explores how pharmacometric modelling can optimize and personalize therapies for bleeding disorders, such as hemophilia and von Willebrand disease (VWD). By combining pharmacokinetics, pharmacodynamics, and emerging tools like artificial intelligence (AI), this research aims to improve treatment precision and outcomes for patients.
A key focus is on understanding individual variability in drug responses, driven by factors like genetic variants and physiological differences. The thesis demonstrates how pharmacometric models can predict drug behavior, optimize dosing regimens, and identify effective treatment strategies. For example, modeling the response to desmopressin and factor concentrates highlights ways to reduce variability in achieving therapeutic targets, minimizing risks such as bleeding or thrombosis.
Innovative applications of AI and machine learning further streamline data analysis and model development, offering faster and more complex insights. The work also incorporates physiologically based pharmacokinetic modeling to explore drug distribution in previously unmeasured compartments, such as the extravascular space, broadening the understanding of drug effects.
Overall, the thesis describes the potential of pharmacometrics to bridge the gap between modelling and clinical practice. It sets the stage for a future where treatments are not only guided by population but are tailored to the unique characteristics of each patient.