Creating a web platform to predict toxicity of medical device chemicals
Official title: Development of a web-based platform implementing novel Predictor of Toxicity for Medical Devices (PredTox/MD)
Funded at Predictive, LLC · NIH-11015809 · Data updated 2026-11-12
This study is creating an online tool that uses smart technology to predict how safe chemicals in medical devices are for your skin, helping to make sure that the devices you use are safer without needing to test on animals.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Predictive, LLC NIH-funded |
| Lab location | 1 site (Raleigh, United States) |
| Project start | September 9, 2020 |
| Project end | December 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $287,526 |
| NIH project number | 5R44ES032371-03 |
| Project ID | NIH-11015809 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients who use or are considering the use of medical devices that may contain potentially harmful chemicals.
✕Less likely to help
Patients who do not use medical devices or are not affected by chemical sensitivities may not receive direct benefits from this research.
What this research studies
This research focuses on developing a web-based platform called PredTox/MD that utilizes advanced machine learning models to predict the toxicity of chemicals found in medical devices. By analyzing a large dataset from the Guinea Pig Maximization Test, the platform aims to provide reliable assessments of skin sensitization, irritation, and cytotoxicity, which are critical for ensuring the safety of medical devices. The project seeks to eliminate the need for animal testing by offering computational tools that comply with international safety standards. Patients can benefit from safer medical devices through improved toxicity predictions.
Why it matters
Potential benefit: If successful, this research could lead to the development of safer medical devices with reduced risk of adverse effects for patients.
How similar studies have gone: Other research has shown success in using machine learning models for toxicity predictions, indicating a promising approach in this area.
Where this research is happening
-
Predictive, LLC
Raleigh, United States Active
Researchers
- Principal investigator: Causey, Kevin — Predictive, LLC
- Study coordinator: Causey, Kevin
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.