# Synthesizing Genotoxicity Results in the MultiFlow Assay With Point‐of‐Departure Analysis and ToxPi Visualization Techniques

## TL;DR

This paper introduces a new method combining PROAST and ToxPi to better analyze and visualize genotoxicity data from multiple biomarkers in a DNA damage assay.

## Contribution

The novel integration of PROAST and ToxPi enables quantitative synthesis and visualization of genotoxicity data from multiple endpoints.

## Key findings

- ToxPi effectively visualizes BMD results from multiple biomarkers, providing insights into compound response and genotoxic mechanisms.
- The method supports simultaneous analysis of multiple biomarkers, enhancing chemical potency analysis in complex datasets.
- The approach is demonstrated using the MultiFlow assay with 10 genotoxicants and two biomarkers under different metabolic conditions.

## Abstract

In vitro genotoxicity has historically served a hazard identification role, with simple binary outcomes provided for each of several single endpoint assays. This will need to change, given: (i) efforts to curtail animal testing, (ii) the increased use of multiplexed in vitro assays and the ongoing development of NAMS, and (iii) the desire to holistically consider quantitative results from multiple biomarkers/endpoints that take potency into consideration. To help facilitate more quantitative analyses of multiple biomarkers and/or assay streams, we explored the combined use of PROAST and Toxicological Prioritization Index (ToxPi) software. As a proofofconcept, this investigation employed the MultiFlow DNA damage assay, focusing on γH2AX and p53 biomarkers at two time points, whereby 10 genotoxicants were evaluated in the presence and absence of rat liver S9 metabolic activation. Whereas PROAST was used to calculate BMD point estimates and confidence intervals (CIs), ToxPi synthesized the BMD results into visual, quantitative summaries conveying genotoxicity and metabolic properties. Our analyses suggest that ToxPi's data synthesis and visualization modules provide useful insights into compound response, chemical grouping, and genotoxic mechanisms. By integrating multiple data sources, we find that ToxPi offers a powerful complementary approach to traditional BMD CI graphs, particularly for the simultaneous analysis of multiple biomarkers, enhancing chemical potency analysis of complex datasets.