Development of a web-based platform implementing novel Predictor of Skin Sensitization for Medical Devices (PreSS/MD)

PROJECT SUMMARY Medical devices have been documented to contain toxic chemicals that can leach and cause acute contact dermatitis (ACD) after repeated exposure or prolonged contact of the skin to these toxins. ACD is credited for 10-15% of all occupational illnesses and is also the second highest reported occupational hazard. Given its prevalence, ACD is also a great public health burden with combined yearly costs of up to $1 billion, which spans including medical costs, worker’s compensation and lost working time due to workplace absence. To this end, the U.S. Food and Drug Administration has mandated that all medical devices must be evaluated for possible skin sensitization using in vivo animal assays, which includes the Guinea pig maximization test (GPMT). Although GPMT tests provide valuable data on the skin sensitization effects of potential toxins, these assays are time-consuming and expensive. Moreover, the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) recently published a Strategic Roadmap, calling for the development of alternative approaches to reduce animal testing of chemical and medical agents. Thus, there is a stated need to modernize safety evaluation of medical devices to reduce animal testing and shorten the regulatory review time, which would ultimately bring safer devices to the market faster. To address this unmet need, the key objectives of our FDA Phase I SBIR project are to (i) produce rigorously validated computational models for the GPMT assay integrating data obtained in human, mouse, and in vitro assays; and (ii) integrate these models into a software product termed PreSS/MD (Predictor of Skin Sensitization for Medical Devices). Our specific aims for this study include: 1) collecting, curating, and integrating the largest publicly available dataset for GMPT; 2) creating and validating novel computational models for GMPT data; 3) developing the PreSS/MD web server to allow users to make predictions of skin sensitization potential in medical devices. We will also develop a model for mixtures, including compounds tested jointly in different concentrations, using an approach that we developed previously. Finally, we will implement novel approaches to help users of our PreSS/MD platform interpret the developed models in terms of key chemical features responsible for skin sensitization. In addition, we will employ biomedical knowledge graphs to elucidate Adverse Outcome Pathways (AOPs) for skin sensitizers. Successful execution of this Phase I project will yield in the development of PreSS/MD as a centralized resource to evaluate the skin sensitization potential for medical devices. We expect this software-as-a-service web server platform will be of great value for companies and sponsors seeking regulatory approval of medical devices.PROJECT NARRATIVE Given that medical devices have been documented to contain toxic chemicals that may lead to allergic contact dermatitis, the US Food and Drug Administration requires that all devices be evaluated for possible skin sensitization effects using in vivo assays such as the Guinea pig maximization test. In the effort to modernize skin sensitization safety evaluation methods to reduce in vivo animal testing, herein we propose to develop a software product, PreSS/-MD (Predictor of Skin Sensitization caused by Medical Devices), as an innovative and unique in silico alternative with the potential to better predict human response compared to the existing approaches for skin sensitization assessment. Successful execution of the objectives described in this project will result in a centralized web server platform to evaluate the skin sensitization potential for medical devices, which will be of significant value for companies and sponsors seeking regulatory approval of medical devices.