As cell and gene therapies (CGTs) move from experimental therapies to real-world treatments, developers face one major challenge:

How do you build a robust, reproducible manufacturing process around something as complex as living cells or gene vectors?

The answer, at least the one industry has tested, lies in Quality by Design (QbD). Before the introduction of Quality by Design (QbD), pharmaceutical development largely followed a Quality by Testing (QbT) model. In this reactive approach, product quality was verified by testing the final product, often without a deep understanding of how process variables influenced outcomes. Manufacturing changes or scale-up often introduced risk, and regulatory filings lacked the transparency to explain why processes worked—only that they did.

To modernize this model, regulatory agencies like the FDA and ICH (International Council for Harmonisation) introduced QbD in the early 2000s through ICH guidelines Q8 (Pharmaceutical Development), Q9 (Quality Risk Management), and Q10 (Pharmaceutical Quality System). The goal: move toward a proactive, science- and risk-based framework where quality is built into every step of product development and manufacturing.

QbD starts by defining the Quality Target Product Profile (QTPP)—the ideal safety, efficacy, and performance profile of the final product. Developers then identify Critical Quality Attributes (CQAs), followed by the Critical Material Attributes (CMAs) and Critical Process Parameters (CPPs) that influence them. Using experimental tools like Design of Experiments (DoE) and statistical analysis, a design space is established to define acceptable process ranges. The final output is a control strategy ensuring consistent product quality through ongoing monitoring.

Table below summarizes definitions, examples and a few references.

This post outlines how QbD applies across four critical stages of process qualification: Engineering Runs, Pilot Runs, Comparability Runs, and Process Performance Qualification (PPQ). Along the way, we’ll explore CQAs, CPPs, CMAs, DoE, and the Control Strategy Document, with examples pulled from real FDA-reviewed BLAs like Kymriah and Zolgensma.

Engineering, Pilot, Comp, and PPQ Runs in CGT Manufacturing

In cell and gene therapy, the transition from development to commercial manufacturing relies on structured process qualification phases. Each phase generates evidence to support a robust, repeatable, and regulatory-compliant process.

Engineering runs are early development-stage batches using non-GMP or development-grade materials. Their purpose is to explore the relationship between Critical Process Parameters (CPPs) and Critical Quality Attributes (CQAs), define initial process ranges, and support assay development. These runs are essential for mapping the process design space and informing risk assessments.

In this phase, you:

• Explore design space through small-scale or non-GMP runs
• Identify relationships between CPPs and CQAs
• Test feasibility of assays, sampling, and tech transfer

Pilot runs simulate full-scale GMP conditions, using qualified materials and final equipment. They verify that the process performs consistently at scale and allow teams to finalize in-process controls, batch record formats, and material handling protocols. QbD Angle: Pilots test the “edges” of your design space — ensuring robustness.

• Use near-GMP conditions and materials
• Confirm SOPs, process timing, equipment readiness
• Finalize critical parameters for full-scale runs

Comparability runs are triggered by process changes (e.g., scale, equipment, material sources). They use side-by-side or matched-run data to confirm that CQAs remain unaffected, as required by FDA comparability guidance. Example: Zolgensma’s BLA included comparability data from multiple manufacturing facilities with matching CQA outcomes and validated assays.

PPQ runs are the final validation stage, often requiring three consecutive full-scale GMP batches. These runs demonstrate that the control strategy maintains product quality across variability in manufacturing.

• Requires ≥3 GMP lots (or justified statistical model)
• Confirms repeatability and control strategy effectiveness
• Includes IPCs, batch records, and assay results

Example: Kymriah’s BLA submitted three GMP PPQ lots that met viability, potency, and purity specs under commercial conditions.

Figure 1 Optimization of the quality by design approach for gene therapy products: A case study for adeno-associated viral vectors https://doi.org/10.1016/j.ejpb.2020.08.002

Design of Experiments (DoE)

DoE is a powerful statistical tool used in the Quality by Design (QbD) framework to systematically investigate how process parameters affect product quality. Rather than changing one variable at a time (OVAT), DoE evaluates multiple variables simultaneously, allowing developers to understand interactions, optimize performance, and build robust manufacturing processes.

In the context of QbD, DoE supports the identification and control of Critical Process Parameters (CPPs) that impact Critical Quality Attributes (CQAs). For example, in cell therapy, DoE might be used to examine how MOI, incubation time, and media composition together influence transduction efficiency and cell viability. Common DoE methods include factorial designs, response surface methodology (RSM), and Plackett-Burman screening designs. These help define the design space—a multidimensional region of operating conditions shown to yield acceptable product quality. DoE not only accelerates development by reducing the number of experiments needed, but it also provides statistically meaningful insights to justify control strategies during regulatory submissions. When implemented early, DoE de-risks scale-up, tech transfer, and PPQ by ensuring that the process is data-driven and predictable, aligning with ICH Q8 and FDA expectations.

The Control Strategy Document

The Control Strategy Document serves as the regulatory and operational backbone in cell and gene therapy manufacturing, aligning your process with quality expectations from agencies like the FDA. Take CAR T as an example, According to the FDA’s “Considerations for the Development of CAR T Cell Products”, sponsors must clearly define Critical Quality Attributes (CQAs) and design Critical Process Parameters (CPPs) to consistently safeguard those CQAs. The document should include:

• In‑process controls (IPCs) and release criteria, such as viability thresholds and transgene expression assays.
• Analytical comparability plans for process or material changes, a key element emphasized in the guidance
• Continuous chain-of-identity and stability assurances, including consistency in leukapheresis procedures, vector titer characterization, and process control strategies

Together, these FDA guidelines inform a Control Strategy Document that integrates CQAs, CPPs, IPCs, assay validation, stability plans, and comparability frameworks—organized to ensure safe, reproducible, and compliant CGT manufacturing.

Figure 2 Control strategy implementation options from doi: 10.1208/s12248-014-9598-3

Final Thoughts

QbD isn’t just regulatory jargon. For cell and gene therapy, it’s your blueprint for making something reproducible from something inherently variable. From first engineering runs to PPQ filing, QbD offers a powerful structure for building—and defending—your process.

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Human embryonic stem cells (hESC) and induced pluripotent stem cells (iPSC) can differentiate into various cell lineages including bone, cartilage, fat, muscle, and neurons. In recent years, there has been growing interest in using hESC and iPSC for developing cell therapies and tissue engineering because of their differentiation capacity. For instance, Fate Therapeutics focuses on cancer and immunological illnesses by developing programmed cellular immunotherapies using iPSC-derived NK and T-cells. Whereas, Sana Biotechnology aims to develop engineered cells as therapeutics using pluripotent stem cells to replace any missing or damaged cells in the body. 

To achieve high reproducibility production, it is critical to monitor and track the migration, proliferation, and differentiation of these cells in vitro (and in vivo). Accordingly, various assays exist to check bio-markers in transcription and translation levels. In this post, we introduce a few most popular methods that are widely used to study genomic expression profiles. These include microarray analysis, RT-qPCR, and RNA sequencing. Each of these techniques offers advantages and has limitations that are briefly summarized in Table 1.

Table 1. Summary of popular techniques that are used to study gene expression profile of iPSC and hESC.

1. Microarray Analysis

Microarray technology, which emerged in the early 1990s, revolutionized genomics by enabling simultaneous analysis of thousands of genes. Microarray was originally developed in two main forms. The cDNA microarrays by Patrick Brown’s team at Stanford and oligonucleotide microarrays by Stephen Fodor at Affymetrix. The latter used synthesized short DNA sequences fixed on a chip, allowing for more precise control over probe placement and a higher degree of miniaturization which made it quickly moved from academic labs to widespread commercialization. Although faced with competition from next-generation sequencing in the 2010s, microarrays have retained a significant role in clinical diagnostics and large-scale studies due to their cost-efficiency and established workflows. Currently, there are several instruments in the market each with unique features and limitations such as Affymetrix, now part of Thermo Fisher Scientific, Nanostring nCounter, and Illumina’s BeadArrays. Regardless of the technology, a microarray workflow typically starts with RNA extraction. Although there are protocols that directly use cell lysis, RNA isolation is advised for better-quality microarray data. Additionally, RNA extraction allows for more robust cDNA synthesis (if needed). Upon isolating RNA (and cDNA synthesis if needed) hybridization is performed where labeled nucleic acid samples (RNA or DNA) are allowed to bind, or hybridize, to complementary DNA probes attached to the microarray chip. There are pre-made panels that contain variant markers relevant to the status of the cell, it is also possible to produce custom panels based on literature. The microarray is then scanned with a laser, which excites the fluorescent dye, allowing the measurement of fluorescence intensity emitted from each spot on the array. The intensity of the fluorescence at each spot on the array is proportional to the amount of target nucleic acid binding to the probe, which reflects the gene expression level of that gene in the sample.

Figure 1. Typical microarray workflow from mRNA to data from here DOI: 10.1128/CMR.00019-09

2. Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)

The development of RT-qPCR occurred in the 1990s, with the first systems introduced by Applied Biosystems in 1996. This technology integrates fluorescent chemistry to provide quantitative results, enabling the real-time accumulation of amplified DNA during the PCR process. RT-qPCR revolutionized genetic analysis by allowing for precise quantification of nucleic acids, making it invaluable in clinical diagnostics, research, and even forensic applications. Its ability to detect and quantify gene expression and genetic mutations rapidly and sensitively underpins its broad adoption across stem cell research.

RT-qPCR workflow involves (optional) RNA extraction followed by reverse transcribing RNA into complementary DNA (cDNA) and then amplifying specific cDNA sequences using PCR. RT-PqCR is widely used to analyze gene expression in embryonic stem cells and induced pluripotent stem cells. For instance for identification of stem cell markers such as Oct4, Nanog, and Sox2 (pluripotency markers. RT-qPCR is also widely employed to evaluate the purity and quality of stem cell populations by quantifying the expression levels of genes associated with undesired cell types or differentiation stages. For instance, the expression of lineage-specific markers (e.g., hematopoietic markers for mesenchymal stem cells) can be assessed to ensure the absence of contaminating cell populations. The main disadvantage of RT-qPCR is its lower throughput (usually limited to <90 markers) and the fact that it can be affected by inconsistencies in PCR. 

Figure 2. Typical RT-qPCR  from here https://www.americanlaboratory.com/

3. RNA-Sequencing (RNA-Seq)

Initially described in 2008, RNA-seq allows researchers to detect both known and novel transcripts, quantify gene expression levels, and identify isoforms, providing a comprehensive view of the transcriptomic landscape. It involves sequencing cDNA libraries generated from stem cell RNA, allowing for the detection of novel transcripts, alternative splicing events, and gene expression levels. The main advantage of RNA-seq is that can detect both known and novel transcripts, providing a more comprehensive (and individual) view of the stem cell transcriptome compared to microarrays or RT-qPCR. Despite the unparalleled potential, RNA-seq workflow can be lengthy, costly, and it requires complex data analysis pipelines which limit its routine usage. Nonetheless, applications of RNA sequencing and its derivatives such as single-cell RNA-seq is rapidly growing in the field. Refer here for more info doi: 10.1101/gr.223925.117

Figure 3. Typical RNA-seq workflow from from here DOI: 10.1007/s11914-022-00726-x

There are other methods to measure gene expressions such as northern Blotting and Massively Parallel Signature Sequencing (MPSS) that are less common due to their limitations and the existence of better alternatives. In future posts, I will review assays that are available to study hESC and iPSC in the protein level.

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