Dissertation

Model-informed Design of Antibiotic Therapy against Antimicrobial Resistance

Antibiotic resistance is a growing global health threat, while the development of new antibiotics has slowed.

Author: S.T. Tandar
Date: 27 May 2026
Links: Thesis in Leiden Repository

This thesis addresses how existing antibiotics can be used more effectively by designing treatment strategies that clear infections while reducing the emergence of resistance. It combines laboratory experiments, clinical data, and pharmacometric modelling to study the interaction between the patient, the drug, and the pathogen.The first part investigates how antibiotics affect bacteria and how resistance develops. Studies on teicoplanin, piperacillin-tazobactam, and mixed bacterial communities show that antibiotic response depends on pathogen species, resistance mechanisms, drug exposure patterns, and biological context. Resistance is therefore not a simple yes-or-no outcome, but a dynamic process shaped by multiple factors.The second part focuses on antibiotic exposure in patients and tissues. Work on teicoplanin dosing in hemodialysis patients shows the need for individualized dosing, while lung distribution models help predict whether antibiotics reach infection sites at effective concentrations. The thesis also proposes a framework for optimizing dosing regimens across competing goals such as efficacy, safety, and resistance prevention.The final part explores collateral sensitivity, where resistance to one antibiotic increases sensitivity to another. Using clinical data, experiments, and models, the thesis shows that collateral sensitivity-based combinations may help suppress resistance and improve treatment outcomes. Overall, the work supports model-informed antibiotic therapy as a strategy to preserve existing antibiotics.