Our latest paper, published in the Computational and Structural Biotechnology Journal, presents DoseRider, a new tool for pathway-level dose-response modeling. Understanding dose-response relationships is foundational in chemical risk assessment. While traditional analyses often focus on single genes or apical outcomes, mechanistic toxicology increasingly calls for evaluating how entire biological pathways respond across exposure gradients.

That’s where DoseRider comes in.

DoseRider is an R package and web-based tool we developed for dose-response analysis at the pathway level, using generalized mixed models and flexible spline fitting. By operating on multi-omics data—including transcriptomics, metabolomics, or proteomics—DoseRider helps identify pathway-level effects that reflect coordinated molecular responses to chemical exposure.

🔗 Try the tool: https://doserider.i-med.ac.at

Beyond traditional metrics like benchmark doses (BMDs), DoseRider introduces a novel concept: Trend Change Doses (TCDs). These are inflection points in non-linear dose-response curves that often signal biologically meaningful shifts or transitions in mechanism. Together, BMDs and TCDs can provide a more nuanced view of adverse outcome pathways and molecular initiating events.

This approach is especially valuable for interpreting sub-lethal and non-monotonic responses—common features in endocrine disruption and low-dose exposures.

The development of DoseRider is a core part of my PhD project at the Medical University of Innsbruck. I’m excited to share that our paper describing the methodology and case applications is now published in the Computational and Structural Biotechnology Journal!

📄 Read the publication: https://doi.org/10.1016/j.csbj.2025.04.004

Huge thanks to my supervisors, Hubert Hackl and Johanna Gostner, for their support throughout this journey.