Biopharma analytics with JMP: A data-driven approach in monoclonal antibody

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Monoclonal antibodies (mAbs) have transformed the therapeutic landscape, offering targeted treatments for cancer, autoimmune disorders, and rare genetic diseases. Unlike small molecules, mAbs are large, highly specific proteins that bind precisely to disease-associated targets, minimizing off-target effects and improving patient outcomes.

In the biopharmaceutical industry, Chinese Hamster Ovary (CHO) cells remain the gold standard for mAb production due to their human-compatible post-translational modifications, high productivity, and regulatory familiarity. However, CHO-based systems are complex, with inherent variability driven by gene expression, metabolic shifts, and culture conditions. This variability can impact critical quality attributes (CQAs) such as glycosylation, aggregation, and bioactivity, making robust process understanding essential.

Figure 1: Typical lifecycle

From development to commercialization: The mAb life cycle
The path from cell line development to commercial manufacturing involves a series of interconnected stages:

• Cell line development: Gene insertion, clone screening, and stability assessment over 60–80 generations.
• Upstream process development: Optimization of media, feeds, and bioreactor parameters to maximize yield and quality.
• Downstream process development: Purification strategies such as Protein A chromatography and impurity removal.
• Analytical development: Potency, purity, and stability testing with clearly defined product specifications.
• Scale-up and tech transfer: Bench-to-plant transitions with process characterization and validation.
• Regulatory filing and commercialization: CMC documentation, continued process verification (CPV), and life-cycle management.

Figure 2: An overview of CPV

Why data-driven decisions are critical
In CHO-based bioprocessing, even minor shifts in pH, temperature, or feed strategy can significantly alter CQAs. Advanced analytics enable scientists to identify critical process parameters (CPPs), assess process robustness, and anticipate deviations before they affect product quality.

Figure 3: Decision tree for designating parameter criticality

Applying JMP across the workflow
JMP offers an integrated statistical environment for biopharma R&D teams, including:

• Design of experiments (DOE): Screen and optimize process variables efficiently.
• Multivariate analysis (MVA): Detect relationships between process parameters and CQAs.
• Principal component analysis (PCA): Simplify complex data sets for clone or process evaluation.
• Control strategy development: Implement univariate and multivariate monitoring to maintain process control.
• Stability and comparability studies: Use control charts, tolerance intervals, and simulation to ensure product consistency.

Ensuring ongoing process control
CPV frameworks, supported by JMP’s control charting and trend analysis tools, help detect shifts early – whether it’s a single-point outlier (Nelson Rule 1) or subtle drift over time (Rule 5 or 6). This vigilance reduces the risk of product deviations and supports regulatory compliance.

Rule #	Description	Indication	Sensitivity	Example mAb upstream process cause to investigate
1	One point outside the control limits (UCL or LCL).	Strong indication of an out-of-control process.	High (detects large shifts)	A single batch with significantly lower mAb titer than usual. Could be due to a major contamination event, a sudden equipment malfunction (e.g., impeller failure), or a critical raw material issue.
2	Nine or more consecutive points on the same side of the centerline.	Shift in the process average.	Moderate (detects sustained shifts)	mAb titer consistently trending downward over several batches. Could be due to gradual degradation of a key media component, a subtle change in cell line performance over passages, or a consistent environmental drift (e.g., slight temperature change in the incubator).
3	Six or more consecutive points continually increasing or decreasing.	Trend in the process.	Moderate (detects systematic changes)	Lactate production is steadily increasing over several batches. Could indicate a shift in cell metabolism due to changes in media composition, suboptimal feeding strategy development, or accumulation of inhibitory byproducts.
4	Fourteen or more consecutive points alternating up and down.	Oscillation, suggesting over-adjustment or interacting factors.	Low (less common in many processes)	Dissolved oxygen (DO) levels show a consistent up-and-down pattern with each feed addition cycle, suggesting unstable control loop tuning or an interaction between feed rate and oxygen demand.
5	Two out of three consecutive points are more than two standard deviations from the centerline (on the same side).	Smaller shift in the process average.	Moderate to High (more sensitive than Rule 1)	Two out of three recent batches with moderately higher than average glycosylation levels (e.g., %G0F). Could indicate a minor change in culture conditions, like pH or temperature setpoints, or a slight variation in feed composition.
6	Four out of five consecutive points are more than one standard deviation from the centerline (on the same side).	Even a smaller shift or increasing variability.	Moderate (sensitive to smaller changes)	Four out of five recent pH measurements were slightly below the target range in the bioreactor. It could be the beginning of a control system drift, a minor issue with the pH probe, or subtle changes in media buffering capacity.
7	Fifteen consecutive points all within one standard deviation of the centerline (on either side).	Reduced process variation, potentially artificial or due to data issues.	Low (indicates lack of expected variation)	Very little variation in cell viability measurements across many batches, raising suspicion about the accuracy or sensitivity of the assay or potentially indicating an unusually stable (and possibly non-representative) process.
8	Eight consecutive points exist with none within one standard deviation of the centerline (on either side).	Bimodal distribution or sampling from multiple processes with different means.	Low (indicates "zone avoidance")	Bimodal distribution of cell growth rates observed across different bioreactors running in parallel, possibly due to subtle differences in their operating conditions or seed train history.

Conclusion
For biopharma principal scientists and managers, integrating statistical analysis into every stage of the mAb life cycle is no longer optional – it’s essential. JMP provides a comprehensive toolkit for transforming complex bioprocess data into insights, enabling faster, more reliable decision making from early development through commercial supply.

Visit the JMP Marketplace today to download the comprehensive – and complimentary – biopharma toolkit!

Acknowledgement: Co-authored with Prithwish Dey (@prithwishdey), General Manager of Aragen Life Sciences, whose contributions were key to this work.