Tissue Chip Modeling of Acute and Chronic Pain in Osteoarthritis
Abstract
Knowledge Gap to Be Studied: We aim to develop a novel pain-enabled model for studying the transition from acute to chronic osteoarthritis pain following mechanical injury. With this robust system, we may also identify new biomarkers that can differentiate between patients experiencing acute and chronic osteoarthritis pain, as well as predict treatment outcomes in humans. Rationale: Osteoarthritis is a very painful and debilitating disease that is also highly prevalent. For example, 27 million US citizens suffer from osteoarthritis, including ~25% of those >50 years of age. Pain is one of the primary reasons that osteoarthritis patients seek medical attention, yet there are no consistently effective treatments of osteoarthritis pain that are not associated with serious, potentially fatal side effects. This situation leaves surgery as the only long-term viable treatment option. However, given the risks and costs of surgery, there remains a tremendous need for more effective alternatives. The underlying strategy in this proposal is based on two clinical observations. First, over 80% of patients are pain free after knee replacement surgery. This suggests that the pain of osteoarthritis is coming from the knee joint or the surrounding tissue. Second, drugs that help with osteoarthritis pain initially lose their effect with time. This suggests that the exact reasons for osteoarthritis pain change with time. Unfortunately, it is not known what causes the pain after drugs like aspirin stop working. Nevertheless, these observations lead to the central hypothesis of this proposal, which is that the biological factors underlying chronic osteoarthritis pain are distinct from those underlying acute osteoarthritis pain. If we can identify the new factors that lead to the generation of chronic pain, we may be able to more effectively treat chronic osteoarthritis pain by targeting them. Objective: To test this hypothesis, we propose to engineer a miniaturized artificial knee joint (iMecChip) made up of the same cells that make up a normal human knee joint. This miniaturized knee joint that can be stimulated in a controlled manner via compressive loading for extended periods of time, thereby replicating the excessive and prolonged loading so often associated with the emergence of osteoarthritis in humans. More importantly, the sensory neurons, which are the cells in the body that can sense the stimulations and generate pain signal, will be included in the iMecChip. We predict that the excess mechanical stimulation to the joint tissues will result in the sensitization and/or activation of sensory afferents that can be suppressed by the aspirin-like drugs in the short term. However, prolonged mechanical stimulation will drive significant changes in both tissues and nerve that are responsible for chronic pain, which may not be controlled by aspirin-like drugs. Expected Outcomes: The proposed work will be done in laboratories without the use of live animals or humans. Therefore, it may not immediately achieve a person-related outcome. However, based on this novel osteoarthritis pain-enabled model, we will be able to identify mediator(s) responsible for chronic osteoarthritis pain as well as the mechanisms underlying the transition from acute and chronic pain. With the ability to use patient cell-derived tissues in the iMecChip, it will be possible to not only identify factors responsible for pain in different subpopulations of osteoarthritis patients, but also develop patient-specific therapeutic approaches, thus enabling the development of truly personalized osteoarthritis pain medications. After completing this study, we will seek further funding to conduct research in humans, which aims to validate the clinical relevance of outcomes generated from the iMecChip. In addition, based on the results from this study, we will work with companies to develop new drugs for the treatment of chronic osteoarthritis pain
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 29, 2021
- Source ID
- W81XWH2010902
Entities
People
- Hang Lin
Organizations
- United States Army
- University of Pittsburgh