Physics-informed AI could accelerate development of controlled-release drug patches, bandages

A breakthrough in artificial intelligence has emerged from Brown University, offering a novel approach to the development of controlled-release drug delivery systems such as patches and bandages. Researchers have devised a method utilizing physics-informed neural networks (PINNs), which promises to significantly reduce the time traditionally required for designing these medical devices. This innovation could lead to faster production timelines and potentially lower costs for pharmaceuticals that rely on precise drug release mechanisms. The traditional process of creating controlled-release materials involves extensive experimentation. Scientists design a material, conduct tests, adjust the design, and repeat the cycle until they achieve the desired results. This iterative process is both time-consuming and resource-intensive. However, the new method leverages advanced AI techniques grounded in physical principles to streamline this development phase. Vikas Srivastava, an associate professor of engineering at Brown, led the research alongside Daanish Qureshi and Khemraj Shukla. Their work builds upon the foundational contributions of George Karniadakis, a Brown professor known for pioneering PINNs. These networks incorporate fundamental physical laws directly into their architecture, allowing them to generate accurate predictions with minimal training data compared to conventional neural networks. The study, published in the Journal of Drug Delivery Science and Technology, evaluated how effectively PINNs could predict the behavior of various controlled-release materials. By integrating Fick’s Law of Diffusion—a principle describing molecular movement from regions of higher concentration to lower concentration—the researchers were able to forecast long-term drug release patterns based on limited initial data. For simpler, flat materials, the model achieved accuracy with just 6% of the available experimental data. More intricate materials featuring folds or textures required 33% of the dataset but still yielded reliable predictions. This efficiency translates into substantial time savings. According to Srivastava, the approach could cut experimental time by up to 94% for straightforward materials and by 67% for more complex ones. In the fast-paced world of pharmaceutical development, where time equates to financial investment, such reductions hold significant promise for accelerating product availability to patients. To enhance the reliability of their predictions, the team also integrated Bayesian statistical methods into the PINN framework. This addition allows the model to account for uncertainties inherent in experimental settings, producing outputs that align more closely with actual empirical results. Such refinements ensure that the AI-generated insights remain robust even when faced with variability or noise within the data. Although the current focus of the research centers around external applications like patches and bandages, the underlying methodologies possess broader applicability. Similar principles could be extended to internal drug delivery systems such as oral medications or implants. Srivastava emphasized that the demonstrated approach highlights a transformative potential for AI in healthcare, capable of improving patient care through more efficient product development processes. As the field continues to evolve, the integration of physics-based AI models represents a pivotal shift towards more intelligent and adaptive approaches in biomedical engineering. With ongoing research and refinement, these technologies may soon become integral components of modern drug delivery strategies, paving the way for innovative treatments tailored to individual patient needs.