The field of cell and gene therapy (CGT) is maturing at a rapid rate. Manufacturing remains a major challenge and manufacturing failures and limitations are a notable source of CGT products not meeting specification or being held back from regulatory approvals. To ensure that products remain compliant and competitive throughout their lifecycle, CGT process development requires both ongoing advancement of fit‐for‐purpose technologies to allow for and support well‐designed development studies, and a methodology that enables these studies to occur effectively during focused product development campaigns. We describe a framework for ongoing innovation in CGT process development, manufacturing, and clinical testing, based on the quality by design (QbD) approach. We propose use of the concepts of process discovery, process characterization, and process development in an iterative way to refine the design space as a product matures. This strategy is enabled by the early implementation of broad and robust analytics, and a modular approach to accelerate optimization. With these strategies, CGT developers can prospectively plan for comparability studies and develop in a stage‐appropriate manner, to allow for ongoing innovation and improvements from discovery through to commercialization.

Next-generation sequencing (NGS)-based methods are revolutionizing biology. Their prevalence requires biologists to be increasingly knowledgeable about computational methods to manage the enormous scale of data. As such, early introduction to NGS analysis and conceptual connection to wet-lab experiments is crucial for training young scientists. However, significant challenges impede the introduction of these methods into the undergraduate classroom, including the need for specialized computer programs and knowledge of computer coding. Here, we describe a semester-long, course-based undergraduate research experience at a liberal arts college combining RNA-sequencing (RNA-seq) analysis with student-driven, wet-lab experiments to investigate plant responses to light. Students derived hypotheses based on analysis of RNA-seq data and designed follow-up studies of gene expression and plant growth. Our assessments indicate that students acquired knowledge of big data analysis and computer coding; however, earlier exposure to computational methods may be beneficial. Our course requires minimal prior knowledge of plant biology, is easy to replicate, and can be modified to a shorter, directed-inquiry module. This framework promotes exploration of the links between gene expression and phenotype using examples that are clear and tractable and improves computational skills and bioinformatics self-efficacy to prepare students for the “big data” era of modern biology.

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