Prioritization scheme for quantitative structure-permeability relationship (QSPR) models to predict dermal absorption of chemicals

Journal of Environmental Exposure Assessment (2026) DOI:10.20517/jeea.2025.42

 

Authors:

Ashish C. Jachak, PhD, MBA     Benjamin J. Heckman, MPH, CIH       Frank Pagone, PhD, CIH     

 

Why Dermal Absorption Matters for Risk Assessment

Dermal contact is a major route of human exposure for many industrial, environmental, and consumer-use chemicals. Accurate estimates of how chemicals penetrate the skin are essential for occupational safety evaluations, product stewardship, and regulatory decision-making.

In practice, dermal absorption parameters – particularly the skin permeability coefficient (kp) – are often estimated using models rather than direct testing. However, model performance can vary widely across chemical classes, and inappropriate model selection can lead to systematic underestimation or overestimation of exposure and risk.

This research addresses a critical need: how to select and apply dermal absorption models more reliably and defensibly when experimental data are limited or unavailable.

What Does RHP Risk Management’s Research Mean for Professionals Who Rely on Dermal Absorption Estimates?

The study provides a comprehensive evaluation of ten widely used quantitative structure–permeability relationship (QSPR) models, all of which estimate dermal permeability using two commonly available molecular descriptors:

• Molecular weight (MW)
• Octanol-water partition coefficient (log Kₒₓ)

Using a curated dataset of 445 experimental skin permeation measurements, the authors systematically assessed model performance based on:

• Prediction error and directional bias (over- vs. under-prediction)
• Goodness-of-fit (R²)
• Root mean square error (RMSE)

Building on this evaluation, the study introduces a hierarchical prioritization framework to guide model selection for dermal exposure assessments.

 

 

Key Scientific Insights

Model choice matters

Although many QSPR models rely on the same molecular inputs, their predictions differ substantially. Some models consistently underpredict dermal permeability, which can result in non-conservative exposure estimates.

A prioritization framework improves confidence

Rather than applying models uniformly, the proposed framework helps practitioners identify QSPR models that provide more reliable and health-protective estimates for a given chemical.

Functional group similarity improves predictions

The study demonstrates that grouping chemicals by structural and functional similarity – illustrated using alcohols – significantly improves model performance:

• R² values increased from ~0.46 to ~0.8
• RMSE values decreased by up to 60–70%

This finding confirms that targeted, chemistry-informed model application outperforms one-size-fits-all approaches.

Practical Value for Industry and Regulators

This work provides a scientifically grounded, practical tool for professionals who rely on dermal absorption estimates, including:

• Risk assessors and toxicologists
• Industrial hygienists and product safety teams
• Regulatory and compliance professionals

Key benefits include:

• Reduced reliance on costly and time-intensive laboratory testing
• More defensible and transparent dermal exposure estimates
• Improved consistency and conservatism in risk characterization
• Applicability across occupational, consumer product, and environmental assessments

Business-Relevant Takeaway

QSPR models remain essential tools in modern exposure science – but how they are selected and applied directly affects risk conclusions. By combining systematic model evaluation with functional group–based refinement, this research bridges the gap between academic modeling and real-world decision-making.

The result is a practical, science-driven framework that helps organizations make better-informed, health-protective choices when managing chemical risks.

Contact RHP Risk Management to Learn More About Our Peer-Reviewed Research, “Prioritizing Dermal Absorption Models to Improve Chemical Exposure Predictions”

To discuss how this framework can be applied to specific chemicals, products or regulatory programs, contact RHP Risk Management by calling (866) 481-8188.