Sensor technologies for quality control in engineered tissue manufacturing

Abstract

The use of engineered cells, tissues, and organs has the opportunity to change the way injuries and diseases are treated. Commercialization of these groundbreaking technologies has been limited in part by the complex and costly nature of their manufacture. Process-related variability and even small changes in the manufacturing process of a living product will impact its quality. Without real-time integrated detection, the magnitude and mechanism of that impact are largely unknown. Real-time and non-destructive sensor technologies are key for in-process insight and ensuring a consistent product throughout commercial scale-up and/or scale-out. The application of a measurement technology into a manufacturing process requires cell and tissue developers to understand the best way to apply a sensor to their process, and for sensor manufacturers to understand the design requirements and end-user needs. Furthermore, sensors to monitor component cells' health and phenotype need to be compatible with novel integrated and automated manufacturing equipment. This review summarizes commercially relevant sensor technologies that can detect meaningful quality attributes during the manufacturing of regenerative medicine products, the gaps within each technology, and sensor considerations for manufacturing.

Keywords: biosensor; measurement; organoid; process analytic technology (PAT); regenerative medicine; tissue engineered medical product; tissue engineering.

Figure 1.

Steps of the cell and tissue manufacturing process. Many unique bioreactor form factors exist to expand cells (e.g. hollow fiber systems, stir tanks, wave bags, vertical wheels) and grow organs and tissues (e.g. bone-ligament bone (CGEM^™ by STEL Technologies), heart valves (Aptus Bioreactors), muscle (TEMR by George Christ at Univ. of Virginia, bioreactor by DEKA Integrated Solutions Corp), or organoids (System by Tommy Angelini)) (graphic created with BioRender.com).

Figure 2.

Example CMAs, CPPs, and CQAs that need to be measured in a manufacturing process.

Figure 3.

Measurement approaches to assess a given attribute. Highlighted areas identify measurement approaches that are not easily integrated into a manufacturing process because they are considered an off-line measurement or prohibitively destructive to the product. Non-highlighted areas have a product either in development or commercially available that is will meet the criteria of being at-, on-, or in-line sensor. This chart is meant as an overview; there may be other approaches or measureable attributes not included. Abbreviations: mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), Fourier transform infrared spectroscopy (FTIR), optical coherence tomography (OCT), magnetic resonance imaging (MRI), immunohistochemistry (IHC), immunocytochemistry (ICC).

Figure 4.

Diagram illustrating in-line, on-line, at-line, and off-line measurements in a TEMP bioprocess (graphic created with BioRender.com).

Figure 5.

Sensing of an attribute is a data snapshot within a complex dynamic multi-scale system. Sensing is required as both an input and output of the system and can be representative of an attribute across different structural scales. The difficulty of conducting a measurement increases as the environment that is being sensed gets smaller and the measurement more specialized. Any attribute measurement must be contextualized within the full spectrum of the tissue culture system.

Figure 6.

Approximate ranges of spatial resolution and imaging depth achievable by imaging modalities for tissues. US = ultrasound, PAM = photoacoustic microscopy, MRI = magnetic resonance imaging, MFM = multiphoton fluorescence microscopy, OCT = optical coherence tomography. Reprinted from [46], Copyright (2013), with permission from Elsevier.