QST and qAOPs for
Toxicological Effect
Assessment
Quantitative Systems Toxicology (QST) provides a mechanistic framework to understand how chemicals influence biological pathways and cause toxic effects. Chemical interactions at the molecular level can worsen the health on the indivdual and population level impact overall health. QST enables the prediction of health impacts under various exposure scenariosder various exposure scenarios for both acute and chronic scenarios.
Mechanism-based models incorporate complex biological processes, such as Adverse Outcome Pathways (AOPs) and Modes of Action (MoAs), to simulate toxic effects based on biological mechanisms. These models can predict how chemical exposures influence health over time, account for sensitivity across populations, and connect exposure levels with toxicity effects and biomarker changes. As part of Next Generation Risk Assessment (NGRA), QST enhances the accuracy of safety evaluations and supports reduced reliance on animal testing, making it an essential tool for ethical, human-relevant risk assessment
What we can offer
ESQlabs provides QST model development to quantitatively link chemical exposure with biological responses, supporting both hazard characterization and risk assessment. Our QST models integrate mechanistic insights from Adverse Outcome Pathways (AOPs) and Modes of Action (MoAs), enabling the simulation of toxicity progression across time and biological scales.
We tailor our models to specific use cases, including acute and chronic exposure scenarios, population variability, and biomarker-based predictions. Our QST workflows often incorporate kinetic-to-dynamic integration with PBK models, enhancing the interpretation of in vitro data and supporting the derivation of mechanistically informed Points of Departure (PoDs).
Our modeling strategy relies on open-source tools (e.g. MoBi, R, and other modular platforms), ensuring transparent and reproducible analyses. Through the integration of QST in Next Generation Risk Assessment (NGRA), we help reduce reliance on animal testing while improving the mechanistic relevance and predictive power of safety assessments.
Related Platforms
QSP Disease and QST Models
Women’s Health
Digital Twins for Micro-physiological Systems: MPSlabs
Related publications and initatives
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.
Marjory Moreau
A leading expert in human systems biology and chemical safety assessment, Marjory brings world-class expertise in physiologically-based pharmacokinetic (PBK) modeling and quantitative in vitro to in vivo extrapolation (QIVIVE). With a PhD in Toxicology from the University of Montreal and a postdoctoral fellowship at Health Canada’s Computational Toxicology Laboratory, she has dedicated her career to advancing 𝐍𝐞𝐱𝐭-𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐑𝐢𝐬𝐤 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 (𝐍𝐆𝐑𝐀) and bridging cutting-edge science with 𝐫𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠. At ESQlabs, she will lead NGRA initiatives in the U.S., positioning us as a trusted regulatory partner while strengthening support for our American clients.
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.
