SBIR Phase I: Development of a system for automated pain behavior testing across preclinical disease models in rodents

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project will include fewer resources being needed for pain behavior studies, and fewer mice being needed. The system that results from this project may reduce the time necessary for experiment completion by 50% and training of researchers from 5-6 months to 1 hour, and reduce waste by producing cleaner data faster, saving money, and assisting in research animal use reduction efforts. The complete automation of the system’s operation could further benefit the ~$3.3B global preclinical pain research market while improving the translational viability by generating cleaner data, measuring pain, and not just stimulus sensitivity. By year 3 of production post-award, $1.85 million in revenue is expected from hardware and software products. In addition, this system lowers the impediments and reduces injury potential for researchers who traditionally could not perform pain behavior assays due to their high training requirements or physically demanding nature. This is expected to benefit trainees, spur innovation by allowing new labs to easily access these assays. The proposed project aims to assess the commercial feasibility of a new behavior testing and analysis system for use in preclinical pain research. Preclinical rodent pain research is a key part of the development of new pain-relieving therapies for the 20% of US adults currently living with undertreated chronic pain. Unfortunately, the current gold standard of von Frey testing has significant confounds due to manual aiming and stimulus delivery and suffers from limited behavioral readouts, hurting translatability. The new system eliminates these confounds by automating delivery and using machine learning to measure both high-speed reflexive and affective pain behaviors. This system has only been validated in inflammatory models, but new tools must demonstrate robust validation across various established models to be viable, as validation often makes or breaks new research tools. This project will address this challenge by testing it across three diverse rodent pain models for neuropathy, chemotherapy, and osteoarthritis, comparing it to von Frey. This project will then use the resulting data to determine if a new version of this automated analysis strategy, combining 3D tracking with machine learning behavior identification, can overcome previous accuracy limitations to create the groundwork for a commercially viable analysis software product. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.