Process Analytical Technologies, Imaging, and Data Intelligence for Regenerative Medicine Manufacturing

Translating regenerative medicine from lab research to large-scale clinical and commercial production requires robust, scalable, and tightly controlled bioprocesses. A key challenge is maintaining consistent product quality amid regulatory demands, given the complexity and variability of cell- and tissue-based therapies. Traditional end-of-line testing struggles with real-time insights, leading to high batch failure rates, prolonged development timelines, and regulatory hurdles that impede clinical translation. Here, Process Analytical Technologies (PAT), advanced imaging, and data analytics have emerged as vital tools for real-time monitoring, control, and optimization in regenerative medicine manufacturing.This Research Topic addresses the challenges of translating regenerative medicine from benchtop research to scalable clinical and commercial manufacturing by advancing Process Analytical Technologies (PAT), sophisticated imaging (optical, fluorescence, label-free), and AI-driven data analytics for real-time process monitoring, control, and optimization. By integrating in-line sensors, multi-modal imaging, and machine learning for predictive modeling, anomaly detection, and adaptive workflows, we aim to enable Quality-by-Design (QbD) principles. Ultimately, this will foster reproducible, automated bioprocesses that bridge lab innovation to industrial-scale production, accelerating safe, effective regenerative therapies to patients. This Research Topic will include articles that highlight recent progress in analytical sensors, imaging, and data-driven methods that enhance process understanding and Quality-by-Design (QbD) for producing cell therapies, tissue-engineered products, and regenerative biomaterials. Articles focusing on in-line, at-line, and real-time PAT, combined with imaging techniques like optical, fluorescence, and label-free methods, delivering key insights into cell status, morphology, viability, and differentiation for biomanufacturing will also be considered. Additionally, as there is a surge in high-dimensional process and imaging data and the use of machine learning and AI models for predictive modeling, adaptive control, anomaly detection, and decision-making in biomanufacturing, articles employing integrated multi-modal data with intelligent analytics for supporting scalable, automated, and reproducible workflows in regenerative medicine production are also encouraged.This Research Topic encompasses innovations in analytical tools, imaging, and data intelligence to advance scalable, quality-controlled manufacturing of regenerative medicines, including cell therapies, tissue-engineered constructs, and biomaterials. In view of this, we welcome articles addressing, but not limited to, the following themes:• In-line/at-line PAT for real-time monitoring of cell viability, metabolism, and differentiation• Advanced imaging modalities (optical, fluorescence, label-free) for process visualization and quality assessment• Machine learning and AI for predictive modeling, anomaly detection, and adaptive control• Multi-modal data integration for enhanced process understanding and QbD implementation• Scalable bioprocessing platforms bridging lab-to-industrial translation• Regulatory strategies and validation of PAT/AI tools for GMP compliance• Case studies on clinical translation and commercialization challengesWe welcome original research articles, comprehensive reviews, and short communications demonstrating fundamental-to-applied advancements. Topic editors: Kunal Pal, DEBLINA BISWAS, Anupam Das Source: https://www.frontiersin.org/research-topics/78153/process-analytical-technologies-imaging-and-data-intelligence-for-regenerative-medicine-manufacturing