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Related Experiment Video

Updated: Nov 4, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

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Systems Modeling to Quantify Safety Risks in Early Drug Development: Using Bifurcation Analysis and Agent-Based

Carmen Pin¹, Teresa Collins¹, Megan Gibbs²

¹Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge Science Park, Milton Road, Cambridge, UK.

The AAPS Journal

|May 21, 2021

Summary

Quantitative Systems Toxicology (QST) models predict cancer treatment toxicities like cytopenias. Bifurcation analysis in QST reveals complex hematologic toxicity behaviors, aiding drug development and patient safety.

Keywords:

agent-based modeling bifurcation analysis quantitative systems pharmacology quantitative systems toxicology systems modeling

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Area of Science:

Pharmacology
Toxicology
Computational Biology

Background:

Quantitative Systems Toxicology (QST) models integrate pharmacokinetics and biological responses for drug development.
Predicting toxicities of cancer treatments, such as cytopenias and gastrointestinal effects, is crucial due to narrow therapeutic indexes.

Purpose of the Study:

To highlight the application of QST models in predicting cancer treatment toxicities.
To demonstrate the utility of bifurcation analysis in understanding hematologic toxicity.
To showcase agent-based modeling for simulating gastrointestinal toxicity.

Main Methods:

Utilizing QST models to simulate pharmacokinetics, mechanism of action, and biological responses.
Applying bifurcation analysis to QST models of hematologic toxicity to explore parameter space behaviors.
Employing agent-based modeling to simulate intestinal crypt injury and villus disruption.

Main Results:

QST models can predict the magnitude of injury and recovery dynamics for cancer therapies.
Bifurcation analysis in hematologic QST models identified distinct parameter regions leading to stable, irregular, or oscillating blood cell levels.
Agent-based modeling illustrated how injury location within the intestinal crypt influences villus disruption severity.

Conclusions:

QST modeling approaches offer significant value in supporting drug development decisions.
These modeling strategies align with technological advancements in clinical trial design, including patient and dose selection.
QST modeling enhances patient safety by providing predictive insights into treatment-induced toxicities.