As the European Union moves away from animal testing for cosmetics, validation of alternative methods to assess the safety and hazards of compounds in such products is vital. Individual in vitro tests can provide key information on specific parts of the mechanism of disease, yet they may not be able to represent the multiple, sequential steps (what toxicologists refer to as the “adverse outcome pathway”) that result in the ultimate disease endpoint. An additional useful tool for chemical hazard identification is in silico modeling, which uses data on the structure or properties of compounds to predict their interactions with biological systems. There are many challenges with this approach, though, and previous in silico models have demonstrated limited accuracy.
However, researchers at George Washington University have developed a new modeling platform specifically for skin sensitization, CADRE-SS, that seeks to use different information in the prediction process. By incorporating data on molecular properties, rather than only on structure, to model the behavior of compounds in a biological environment, they have made significant improvements in the predictive capacity of such in silico tools.
To develop CADRE-SS, the researchers examined each part of the skin sensitization adverse outcome pathway — skin penetration, enzymatic activation, and protein binding — and then determined the specific physical-chemical properties or energy states that would lead to progression along the pathway. Linking these key chemical parameters for each part of the pathway allowed them to develop the final CADRE-SS model representing the whole skin sensitization process.
Initial tests demonstrated that this new model is highly accurate. Furthermore, not only is the model able to predict chemicals likely to cause skin sensitization, but it is also able to categorize chemicals based on their degree of sensitization potential (extreme, moderate, or weak) according on international classification systems.
If we ever hope to obtain health and safety information on the growing number of chemicals in commerce, then we must utilize methods other than traditional rodent testing (which is costly and time-intensive). Key data will likely come from a combination of in vitro, in silico, and high-throughput alternative animal assays. Thus, by improving the methods by which chemical activity in biological systems can be predicted, these researchers have moved us one step closer towards closing the existing data gap. In the future, these tools could also be used early in the chemical design process to screen out potentially problematic chemicals at the outset and direct companies towards the development of safer products.