Webinar Series on the Use of In Silico Tools in Toxicology 

In silico models are computational tools that use chemical structure, biological data, and mechanistic knowledge to predict potential toxic effects. They can be used to rapidly assess large numbers of chemicals, integrate diverse data streams, and provide mechanistic insight that strengthens hazard assessments, thereby providing human-relevant information. By helping scientists anticipate potential risks earlier and more efficiently, in silico models play an important role in modernising chemical safety evaluations and supporting cost-effective decision-making.

This webinar series is co-organised by PETA Science Consortium International and the Institute for In Vitro Sciences.

Please see here for a glossary of terms that will be updated for each webinar.

Supplemental Materials:
In Silico NAMs Quick Reference Sheet
Additional Resources

Module 1. In Silico Toxicology 101 

This interactive module comprises three webinars and will provide participants with foundational knowledge needed to interpret and apply computational workflows in modern toxicology. Webinars will focus on seven in silico approaches that are commonly used for chemical hazard characterisation and risk assessment: 1) quantitative structure-activity relationship (QSAR); 2) read-across; 3) physiologically-based pharmacokinetic (PBPK) modeling; 4) in vitro to in vivo extrapolation (IVIVE); 5) quantitative adverse outcome pathway (qAOP); 6) molecular modeling; and 7) machine/deep learning and artificial intelligence (AI).

The first webinar will provide a brief history of the use of in silico models for chemical hazard characterisation, as well as current applications and status in regulatory frameworks. The second and third webinars will introduce the fundamentals of the seven in silico approaches, including their purpose, inputs, outputs, and basic information flow. Brief case studies will be presented to illustrate how each in silico approach can complement or replace in vitro and in vivo data to strengthen weight-of-evidence evaluations.

Webinar 1: Computational tools for chemical hazard characterisation 
10 December 2025
Brad Reisfeld, Inotiv; Victoria Hull, Inotiv (slides)

  • Learning Objectives

    Focus: Overview of tools, applications, and regulatory status

    After attending this webinar, participants will be able to:

    • Describe some current uses of in silico tools and the state of their acceptance for regulatory applications.
    • Explain how in silico models can complement other data sources for chemical hazard characterisation.
    • Define important terms used to describe various aspects of in silico models.
    • List the purpose, inputs and outputs, and basic flow of information for common in silico tools.

Webinar 2: Applications and Case Studies: Part I 
17 December 2025
Aswani Unnikrishnan, Inotiv; Xiaoqing Chang, Inotiv (slides)

  • Learning Objectives

    Focus: QSAR, read-across, PBPK modeling, and IVIVE

    After attending this webinar, participants will be able to:

    • Explain what is involved in developing, implementing, applying, and verifying in silico approaches.
    • Describe several ways in which an in silico model can be evaluated and validated.
    • Summarise the benefits and limitations of using in silico models.
    • Give examples of the types of data needed to support the development and testing of various types of in silico tools.
    • Identify the strengths and limitations (e.g., applicability or chemical space) of in silico approaches relative to other methods for the case studies presented.
    • Recognise some software used in the development and evaluation of in silico tools.

Webinar 3: Applications and Case Studies: Part II
18 December 2025
Bridgett Hill, Inotiv; Brad Reisfeld, Inotiv; Bryant Chambers, Inotiv (slides)

  • Learning Objectives

    Focus: qAOP, molecular modeling, and machine/deep learning and AI

    After attending this webinar, participants will be able to:

    • Explain what is involved in developing, implementing, applying, and verifying in silico approaches.
    • Describe several ways in which an in silico model can be evaluated and validated.
    • Summarise the benefits and limitations of using in silico models.
    • Give examples of the types of data needed to support the development and testing of various types of in silico tools.
    • Identify the strengths and limitations (e.g., applicability or chemical space) of in silico approaches relative to other methods for the case studies presented.
    • Recognise some software used in the development and evaluation of in silico tools.