Small Molecules
and Chemicals

Small molecules, typically low molecular weight compounds under 900 Daltons, have a central role across sectors including pharmaceuticals, agrochemicals, consumer products, and environmental safety. Due to their size and physicochemical properties, many of these compounds can be systemically absorbed and can interact with multiple biological targets, underscoring the need for detailed, mechanism-based modeling to predict internal exposures and potential health effects.

Physiologically based (pharma)cokinetic  or PB(P)K modeling is a well-established approach for simulating the absorption, distribution, metabolism, and excretion (ADME) of small molecules. By combining compound-specific characteristics with species- and population-level physiology, PB(P)K models can predict internal concentrations under diverse exposure conditions. These insights are crucial for interpreting in vitro data, guiding dose selection, and informing risk assessment.

In parallel, Quantitative Systems Pharmacology (QSP) and Quantitative Systems Toxicology (QST) provide a mechanistic framework to link PBPK-derived concentrations to biological pathways and toxicological outcomes. While QSP focuses on pharmacological mechanisms and disease models, QST enables prediction of adverse outcomes by integrating kinetics with systems-level responses in target organs such as the liver, thyroid, or bone marrow. These approaches enhance the interpretation of in vitro data, support read-across strategies and improve the definition of Points of Departure (PoDs).

Here at ESQlabs, we developed this platform to support the integration of PBPK, QSP, and QST for the predictive assessment of small molecules and chemicals. Built on open-source, modular technologies, our platform enables transparent and scalable solutions for clients across regulatory and innovation-driven environments.

Physiologically based biopharmaceutics modeling (PBBM) is a specific field of PBPK model applications that aims to establish the link between the formulation’s properties and in vivo performance.

This field of application of PBPK modeling is evolving at a fast-pace and offers the link between in vivo and in vitro to support pharmaceutical development in the selection of the best drug substance and product, as well as later in development in the establishment of manufacturing quality and controls.
Dissolution testing is often a key input in PBBM. Results from in vitro experiments characterizing drug substances and the formulation behavior (e.g., solubility, particle size, dissolution) can be linked to key ADME parameters and integrated into full PBPK models to predict PK exposure in plasma and/or specific tissues or organs.

These models can also be linked to Pharmacodynamic (PD) relationships to derive the impact of physicochemical, drug and formulation properties on safety and efficacy. The role of Physiologically Based Biopharmaceutics Modeling (PBBM) in drug development spans multiple stages, including supporting patient-centric design, guiding life cycle management, informing regulatory submissions, streamlining development processes, optimizing dosing strategies, enhancing study design, and aiding in formulation development and developability assessment.

What we can offer

Our platform brings together PBPK, QSP, and QST to deliver predictive, human-relevant insights into small molecule behavior and safety. We provide flexible solutions tailored to research, regulatory, and commercial applications.

We offer:

  • PBPK modeling across species, age groups, and exposure scenarios
  • Quantitative Systems Pharmacology (QSP) models to simulate pharmacological effects and biological networks
  • Quantitative Systems Toxicology (QST) models to link exposure with adverse outcome pathways and toxicological responses
  • High-throughput PBPK (HT-PBPK) for screening chemical libraries and prioritizing testing
  • Reverse dosimetry using in vitro or biomonitoring data
  • Organ- and system-level modeling to link kinetics with toxicodynamic responses
  • Mechanistic PoD estimation for risk assessment and chemical ranking
  • Integration with omics data, AOPs, and in vitro bioactivity assays
  • Support for regulatory submissions including FDA, EMA, MHRA, REACH, TSCA, biocides, and cosmetics
  • Custom workflows interoperable with client databases and modeling environments
  • Open-source platforms such as PK-Sim®, MoBi®, and R-based QSP/QST frameworks

Related publications and initatives

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Meet the Team

Diane Lefaudeux

Scientist
Consultant
Google Scholar

Diane Lefaudeux is an interdisciplinary scientist with a strong drive to understand complex mechanisms and in particular those arising from biological systems.

Before joining ESQlabs, she worked at Novadiscovery where she developed PBPK-QSP models on various therapeutic areas to predict outcomes using virtual populations.

Diane obtained her Master’s degree in General Engineering from École Centrale Paris, France, as well as in Control Systems Engineering and Systems Biology from University of Stuttgart, Germany.

 

Laura Villain

Scientist
Consultant
Google Scholar

Laura Villain is a biomodeler with interests in various mechanistic modeling approaches and statistical methods. She has experience with popPK, QSP, and PBPK models, complemented by her biostatistics skills.

Before joining ESQlabs, she worked at Novadiscovery, where she developed and improved several PBPK and PBPK-QSP models to reproduce in silico trials. She gained in-depth knowledge of all the steps required to develop a model that can reproduce clinical outputs in compliance with regulatory guidelines. She is also interested in R programming and has provided internal training on R within the company. Before that, she worked as a postdoctoral researcher in biostatistics in the oncology field at INRIA (Bordeaux University). There, she explored high-dimensional methods to link omics data with time-to-event outcomes.

Laura received her Master’s degree in biomodeling sciences from INSA Lyon (France). For her Ph.D. thesis, she studied the impact of interleukin 7 injections on HIV-infected patients. She used a mechanistic model and proposed an adaptive protocol of injections. Her PhD research was supervised by Dr. Daniel Commenges and Prof. Rodolphe Thiébaut at the Department of Public Health at Bordeaux University.

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Marco Siccardi

Principal Scientist
Lead Toxicology & PBPK
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Marco is a Clinical Biologist by training with a PhD in molecular pharmacology and PK/PD modelling. He spent over 15 years at the University of Liverpool working on the topic of pharmacogenetics and in developing PBPK approaches for the optimisation of drug delivery, including HIV therapy optimisation.

Marco has most recently been working with CROs in taking this approaches for modelling and simulation approaches and PKTK (Systems Toxicology) models across a number of disease areas.

Marco leads the Systems Toxicology team with the aim to promote collaborative innovation and to develop novel modeling approaches to streamline the toxicological assessment.

Stephan Schaller, PhD

Principal Scientist
Lead Scientist, Founder & CEO
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Stephan Schaller is a Systems Scientist passionate about Model Based Drug Discovery, Development and Dosing (MI4D) with over eight years of industry experience. His experience ranges from target validation in the early phases of drug discovery to development of automated decision support systems for drug dosing at the point of care.

Stephan Schaller founded ESQlabs GmbH to advance the integration of computational methods in healthcare to derive effective computational platforms for drug-, device- and treatment-development and -optimization (i.e. personalization).

During his time in industry, he has leveraged physiologically-based concepts for decision-making support to drug discovery and development teams in various therapeutic areas, including oncology, immunology, hematology, cardiovascular and metabolic disease.

Stephan Schaller studied Control Systems Engineering and Systems Biology at the University of Stuttgart, Germany and received his PhD from the RWTH Aachen University, Germany in collaboration with Bayer in Computational Engineering for the development of an automated decision support system for insulin dosing in type 1 diabetes patients.