My research focuses on understanding why different animal species—and individuals within species—respond differently to drugs, toxins, and nutrients. I use computational tools and physiologically-based pharmacokinetic/pharmacodynamic (PBK/PD) models to integrate data from laboratory studies, clinical cases, and field observations, aiming to uncover the biological and mechanistic basis of these differences. This work supports the development of evidence-based dosing strategies for veterinary medicines such as antimicrobials, antiparasitics, and analgesics, with the goal of improving treatment outcomes, reducing adverse effects and resistance, and minimizing environmental impact. I am also committed to advancing New Approach Methodologies (NAMs) in chemical risk assessment to reduce reliance on animal testing and promote more predictive, humane approaches in veterinary and biomedical science.
This research project investigates the inter- and intraindividual variability in the absorption of antiviral drugs used to treat Feline Infectious Peritonitis (FIP). The project aims to better understand the relationship between drug exposure and therapeutic effect. This knowledge will be used to optimise dosing strategies, ensuring that each cat receives an effective and safe dose, ultimately improving treatment outcomes for FIP.
This research project investigates the risk factors for heart damage (cardiotoxicity) caused by doxorubicin, a common chemotherapy drug used to treat dogs with multicentric B-cell lymphoma. While effective, doxorubicin can harm the heart, particularly after multiple doses. The study aims to validate a method for measuring doxorubicin levels in dogs' blood and to understand how factors like breed, weight, and liver function influence drug concentration and heart effects. By analyzing these factors, the project seeks to improve treatment safety through personalized dosing strategies, ultimately enhancing outcomes for canine cancer patients.
This research project focuses on the application of pharmacokinetic/pharmacodynamic (PK/PD) modeling, including physiologically-based models, to improve pain management in veterinary patients. By simulating how analgesics behave in the body and how they interact with their targets, the project aims to guide the selection of appropriate analgesic drugs and refine their dosage regimens. The ultimate goal is to achieve safe, effective, and individualized pain relief for animals, supporting evidence-based decisions in veterinary analgesia.
To reduce the need for animal studies, innovative modelling tools are being developed both in the Netherlands and internationally to predict the behaviour of (new) substances—such as additives, veterinary medicines, mycotoxins, bioactive compounds, and contaminants. The 'Artificial Cow Model' project builds on existing physiologically based kinetic (PBK) models and advances them into practically applicable computer tools. In parallel, simple and advanced cow-specific in vitro (e.g. organoids) and in silico models are being developed and applied to study the absorption, distribution, metabolism, and excretion (ADME) of substances. These models provide substance-specific parameters that feed directly into the PBK models.
TK Plate 2.0 builds upon the open-source EFSA platform TK-Plate, which integrates physiologically based kinetic (PBK), kinetic-dynamic (PBKD), toxicokinetic-toxicodynamic (TK-TD), and dynamic energy budget (DEB) models for humans and food-producing animals. The platform offers a user-friendly graphical interface to apply these models in the risk assessment of pesticides, food and feed additives, and contaminants.
There is a growing need to expand these models beyond single chemicals to include multiple chemicals and biological stressors in both farm and wild mammalian species, as well as birds. Achieving this requires a deeper understanding of the toxicokinetic and toxicodynamic processes that link chemical exposure to adverse health outcomes across species.
Innovative methods such as allometric scaling, QSAR, machine learning, and deep learning can support the prediction of key parameters like partitioning, metabolism, kinetic rates, bioaccumulation, and substrate-enzyme/transporter interactions. Moreover, the impact of biological stressors and welfare conditions on TK and TD processes must be integrated.
The overarching goal is to advance biologically based models that can address complex risk assessment scenarios involving chemical mixtures, biological stressors, and animal welfare considerations in species relevant to food and feed safety.
This research project aims to optimise antimicrobial therapy in animals by integrating knowledge of the pharmacokinetics and pharmacodynamics of antimicrobial drugs. By aligning treatment strategies with both local and international guidelines on antimicrobial stewardship, the project supports the responsible use of antibiotics in veterinary medicine. The ultimate goal is to improve the selection and dosing of antimicrobials in animal patients, enhancing treatment outcomes while reducing the risk to public health and the environment.
Horizon 2020 research project ALTERNATIVE develops an innovative platform that incorporates new approach methodologies as alternatives for animal testing to assess the cardiotoxicity of chemicals. The novel platform will enable regulators and industry to identify, quantify and prevent cardiotoxic co-exposures to industrial chemicals and pharmaceuticals in a cost-effective way.