QSP Disease and
QST Models
Quantitative Systems Pharmacology (QSP) disease models use mechanistic, multiscale representations of human pathophysiology to capture how diseases emerge, progress, and respond to therapeutic intervention. By integrating biological pathways, cellular interactions, and organ-level dynamics, these models provide a coherent framework to test hypotheses for drug discovery and drug development questions, like target validation, interspecies translation, and probability of success in diverse populations.
What we can offer
Our ESQlabs provides modular and flexible Quantitative Systems Pharmacology (QSP) (disease) models seamlessly integrated with Physiologically Based Pharmacokinetic (PBPK) model frameworks to deliver end-to-end modeling support. We specialize in building mechanistic disease and drug-response models, rigorously validating them against experimental and clinical data, and applying them to real-world research and development questions. This integrated QSP–PBPK approach enables a consistent translation from molecular mechanisms to whole-body pharmacokinetics and clinical effect, allowing us to support dose optimization, biomarker identification, virtual trials, and informed decision-making across the drug development pipeline.
ESQlabs has a couple of (disease) QSP models available and validated for the following therapeutic areas:
Meet the Team
Alexander Kulesza
Alexander is a Chemist by training with a PhD focusing on theoretical and computational methods for structural and optical property predictions.
After spending several years in academia (U. of Lyon) working on molecular dynamics simulation and free energy methods, Alex has most recently been working with CROs in applying large-scale disease and quantitative systems pharmacology models integrated into clinical trial simulations, across a number of disease areas.
Alex leads QSP/T and qAOP / Systems Pharmacology with the aim to promote widespread application of physiologically based and mechanistic modeling and to create robust and qualified, yet versatile models and applications for high impact decision making.
Carmine Schiavine
Carmine is a PhD candidate in a joint program between the Houston Methodist Research Institute and the University of Naples Federico II. With a solid background in Chemical Engineering, he now focuses on computational immunology — developing mechanistic models to characterize inter-individual variability in human vaccine responses and optimize dosing regimens for special populations.
At ESQlabs, he’s using his modeling knowledge to predict and enhance the activity of bispecific antibody engagers.
Jorin Diemer
Jorin studied Biophysics at Humboldt University of Berlin and completed a PhD in Theoretical Biophysics, jointly hosted by Humboldt University and the Australian National University. His research focused on Systems Biology, particularly the ion regulation of the malaria parasite, driven by a long-standing passion for applying mathematical modelling to biomedical questions.
Nicoletta Ceres
Nicoletta is a biomedical researcher with experience across academia and industry in computational modeling, spanning fundamental and applied work. Before joining esqLABS, she worked at Novadiscovery, connecting PBPK pharmacokinetics to QSP models for virtual clinical trials in oncology and metabolic diseases. There, she combined consultancy with method development and workflow optimization (model construction, calibration/validation, documentation) and gained hands-on exposure to regulatory pathways for in silico evidence and model credibility.
She holds a PharmD in pharmaceutical sciences and medicinal chemistry from the University of Naples (Italy). In academia, she moved from numerical methods for small-molecule design to a PhD in computational structural biology, building coarse-grained 3D models for mechanics and protein–protein interactions. More recently, she evaluated AI-based structure predictors for conformational changes in transmembrane drug targets.
Sophie Fischer-Holzhausen
Sophie is a biophysicist dedicated to unraveling the complex interactions underlying physiological processes through mathematical modeling and simulation. She joined ESQlabs in early 2024 as a scientist systems pharmacology.
She earned her Master’s degree in Biophysics from Humboldt University of Berlin, Germany. For her PhD, Sophie joined Prof. Susanna Röblitz’s Computational Systems Biology group at the University of Bergen, Norway, where she helped develop a mechanistic model of menstrual cycle’s endocrine regulation. Prior to joining ESQlabs, she worked as a Pharmacometrician at AstraZeneca in Gothenburg, Sweden.
Sophie is especially passionate about women’s health and leads related initiatives at ESQlabs.
Vanessa Baier
Vanessa Baier is bioinformatician by training, with a focus on computational modeling in the field of systems biology/systems pharmacology. She has experience with lab data management tools, Bayesian population PBPK techniques, and the contextualization of in vitro data and mechanistic PBPK models. Her main area at ESQlabs lies in vitro/in vivo extrapolation and toxicity modeling within PBPK QSP
Vanessa Baier studied computer science at TU Braunschweig and Bioinformatics at Goethe University Frankfurt. After an internship at Sanofi, she completed her master thesis at Bayer in the group of Complex Systems Modeling / Applied Mathematics. She then joined the group of Lars Kuepfer at RWTH Aachen University to complete her PhD on PBPK modeling of drug-induced liver injury (DILI) .
- Evaluation of BCRP‑Related DDIs Between Methotrexateand Cyclosporin A Using Physiologically Based PharmacokineticModelling
- A generic avian physiologically-based kinetic (PBK) model and itsapplication in three bird species
- Reproductive toxicity in birds predicted by physiologically-based kinetics and bioenergetics modelling
- A generic avian physiologically-based kinetic (PBK) model and its application in three bird species
Venetia Karamitsou
Venetia Karamitsou is a mathematician with expertise in mechanistic and predictive modeling and an interest in utilizing state-of-the-art machine learning methods to aid the drug development process.
Before joining ESQlabs, she worked as a postdoc at Sanofi as part of the Translational Disease Modeling team. There, she contributed to the development of a quantitative systems pharmacology model for inflammatory bowel disease and to the generation and optimization of a virtual patient population.
Venetia obtained her PhD from the University of Cambridge under the supervision of Prof. Julia Gog in the Disease Dynamics group. Her thesis focused on developing a cross-scale model for the evolution of influenza and the effects of vaccination.
