Close to one million lives could be saved each year in the United States alone by organ transplantation if a sufficient number of organs were available, potentially preventing 35% of all deaths in the nation. In contrast, due to critical shortages of organs, only about 28,000 organ transplants are performed each year, with a waiting list of 120,000 people. A promising potential solution to this shortage is the high quality and production-scale 3D printing of human organs by bio-additive manufacturing (Bio-AM). However, as articulated in the 2016 NSF workshop on Additive Manufacturing for Healthcare, the current use of Bio-AM is impeded by poor organ quality, resulting in part from inadequate process monitoring and lack of integrated process control strategies. As a result, despite enormous strides, it is still not possible to scale Bio-AM to the stringent quality standards mandated for organ transplants. This research will address the compelling need to incorporate advanced process models into sensor-based process control strategies needed to prevent cell damage, decrease cell placement errors, and improve tissue functioning in Bio-AM. If successful methods for reliable, high-volume, high-quality, and safe Bio-AM can be realized, it will have profound socioeconomic benefits in terms of public health, medical safety, and drug discovery. The project will engage grade 6-12 STEM teachers through the Research Experiences for Teachers (RET) Innovation-based Manufacturing Program by providing opportunities for teachers to engage in cutting edge research in Bio-AM.
The goal of the project is to reliably produce viable 3D printed biological constructs (mini-tissues). The central approach is to couple in-situ heterogeneous sensor-based monitoring and real-time closed-loop process control approaches for ensuring the reliable printing of biological constructs. The work involves the following four objectives: (1) using experimentation and modeling to understand the causal effect of process-material interactions on specific Bio-AM defects, (2) employing sensors to detect incipient defects during printing, (3) diagnosing the root causes of detected defects by analyzing sensor data using real-time decision-theoretic models, and (4) preventing propagation of defects through closed-loop process control. The investigation will contribute: (1) fundamental understanding of the causal bio-physical process interactions that govern the quality of printed biological tissue constructs through empirical investigation and sensor-based data analytics, (2) new mathematical models for predicting the layer quality by taking into consideration the complex and dynamic tissue maturation phenomena, (3) real-time and computationally efficient decision-making for accurate classification of defects from sensor data, and (4) a two-stage, real-time, closed-loop quality control approach for preventing propagation of defects by executing smart corrective actions during the printing process.
