Pharmacology & Toxicology

Skin wound healing is a dynamic physiological process involving inflammation, tissue regeneration, matrix remodelling, and vascular restoration. When the skin barrier is damaged, the absorption, local retention, and systemic clearance of therapeutic drugs change dramatically compared to intact tissue.

Physiologically based pharmacokinetic (PBPK) modelling offers a mechanistic framework to simulate how drugs and signalling factors permeate damaged skin, interact with local tissue layers, and enter the systemic circulation [1, 2]. By combining skin anatomy, biophysical barrier disruption, and formulation science, this project aims to develop a predictive dermal PBPK model that captures the changing microenvironment of healing wounds to guide safer and more effective topical therapies [3].

Objectives

1. Build a multi-compartment dermal PBPK framework that captures physical changes in wounded skin, such as stratum corneum loss, altered blood perfusion, exudate flow, and progressive barrier recovery over time.
2. Integrate physicochemical properties with diffusion and partition equations across damaged epidermal and dermal layers to predict local drug exposure and systemic absorption.
3. Calibrate and validate the model against published in vitro permeation testing (IVPT) and clinical wound-healing datasets, quantifying how wound depth and healing stages affect local therapeutic bioavailability.

References

1. Duong, J., et al. "Characterizing local and systemic exposure to clobetasol propionate in healthy subjects and patients with atopic dermatitis." British Journal of Clinical Pharmacology (2025).
2. Patel, N., et al. "Multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) model to predict local and systemic exposure of drug products applied on skin." CPT: Pharmacometrics & Systems Pharmacology (2022).
3. Polak, S., et al. "Prediction of concentration–time profile and its inter-individual variability following dermal drug absorption." Journal of Pharmaceutical Sciences (2012).

Work environment

The student will work within an interdisciplinary environment alongside computational modellers, pharmacologists, and biomedical scientists at UvA and AmsterdamUMC. You will gain practical expertise in mechanistic modelling, quantitative pharmacology, and model-informed drug development (MIDD), contributing directly to computational tools that reduce reliance on animal testing and optimize wound care.

Physiologically based kinetic models of skin accurately describe drug and chemical permeation through intact skin barriers [1, 2]. However, a wound fundamentally changes the story: the disrupted skin barrier alters permeation kinetics, while the wound itself triggers a local and, in some cases, systemic immune response. To date, no PBK model integrates both aspects — existing models address skin permeation alone, without representing how a wound disturbs the immune system.

This project develops a PBK model of wounded human skin that mechanistically couples altered skin permeation kinetics with local and systemic inflammatory dynamics. By capturing the interplay between tissue permeation and immune response, the model will enable exploration of how topically administered compounds interact with, and are modulated by, the evolving wound-healing state.

Objectives

1. Review existing PBK skin-permeation and immune/inflammation models to identify a suitable coupling structure.
2. Develop the wounded-skin PBK model and integrate local and systemic inflammation.
3. Model calibration & validation.
4. In silico exploration.

References

1. Liu et al. (2025). https://doi.org/10.1002/jat.4944
2. Krumpholz et al. (2024). https://doi.org/10.1002/jat.4687

Expectations

Good programming skills (Python).

Work environment

The student will join a diverse team of researchers from the Informatics Institute (IvI), including experts in AI development, modelling, experimental design, and validation. You will contribute to a computational model mechanistically bridging skin pharmacokinetics and systemic wound-healing dynamics.

Sex-specific drug response and toxicity remain a critical knowledge gap in pharmacology. While computational models of the reproductive (HPG) axis exist for model organisms like zebrafish [1], female human hormonal dynamics lack integrated mechanistic tools.

This project bridges that gap by translating and scaling an existing zebrafish PBK-HPG kinetic model (currently implemented in MCSim) into an extensible Python framework. Clinical kinetics data will be collected from the literature to parameterize the hormonal system controlling reproductive function. Using Bayesian Markov Chain Monte Carlo methods, the model will be calibrated against human physiological data, and toxicodynamic simulations will evaluate how systemically administered therapeutics and chemical perturbations disrupt hormone homeostasis in women.

Objectives

1. Translate the zebrafish PBK-HPG model into Python.
2. Mine clinical / toxicokinetic time-series data from the literature.
3. Calibrate model parameters via MCMC.
4. Explore in silico perturbation experiments to assess drug effects and endocrine-disruption risk in female physiology.

References

1. Ly et al. (2025). https://doi.org/10.1016/j.aquatox.2025.107337

Expectations

Good programming skills (Python).

Work environment

The student will join a diverse team of researchers from the Informatics Institute (IvI), including experts in AI development, modelling, experimental design, and validation. You will contribute to building scalable, open-source Python tools for predictive toxicology in women's health.

The Adverse Outcome Pathway (AOP) framework links a Molecular Initiating Event to an Adverse Outcome through intermediate Key Events (KEs) and is increasingly used in chemical risk assessment. Since KEs are frequently shared across AOPs, they gather into AOP networks that reveal hub events and convergent pathways to disease. Reconstructing such networks from AOP-Wiki has recently been made accessible through the open-source AOP-networkFinder tool [1], but this tool is purely structural — it provides no weight of evidence, edge strength, or probabilistic/causal quantification attached to its nodes or relationships.

This project develops a statistical framework, generalizable across the AOP-Wiki network, for quantifying weight of evidence and key event relationship strength from dose–response and time-course data using Bayesian and dynamic Bayesian approaches [2]. The framework will be built and validated on an endorsed AOP — the pulmonary fibrosis AOP — and then generalized to the whole network.

Objectives

1. Literature search to identify dose–response and time-course datasets suitable for quantifying the pulmonary fibrosis AOP network.
2. Quantify key event relationships via Bayesian / dynamic Bayesian network inference.
3. Use LASSO to identify the most predictive causal edges.
4. Validate the quantified pulmonary fibrosis qAOP.

References

1. Yarar et al. (2025). 10.1093/bioadv/vbaf007
2. Ito et al. (2024). https://doi.org/10.1038/s41598-024-55220-4

Expectations

Good programming skills (Python).

Work environment

The student will join a diverse team of researchers from the Informatics Institute (IvI), including experts in AI development, modelling, experimental design, and validation. You will contribute to turning static AOP networks into predictive, data-driven chemical risk assessment tools.