Blend Controls Boost Liquid Chromatography Performance
Adaptive PAT Strategy Reduces Reagent Variability, Dramatically Improving Liquid Chromatography Performance
Traditional large scale pharmaceutical manufacturing operations use manual or fixed automation to prepare large volumes of reagent, storing them in large tanks. Such production-scale mixing of process fluids is much more difficult than at the bench-scale, where solutions and reagents typically are developed and optimized.
Many individual mixing protocol steps, such as the addition of large volumes of water, can achieve no better than 2% accuracy. Measurement variability is another problem, reflecting variations in instrument resolution, sampling error, and intrinsic temperature and concentration gradation within tanks. Together, these factors can add another 2-3% to total process variability.
Further, variability in raw material quality only increases total process variability, which, in turn is reflected in variable purity, potency, yield and recovery for final product. Such variability is a key contributor to many pharmaceutical companies' need for a "hidden factory," or an extensive non-value-adding infrastructure, to ensure product quality and safety (Figure 1).
Figure 1. Chromatography process variability starts with buffer or solution make-up; effects of that variability ripple through the entire process.
Some manufacturers have used over-sized equipment, processing an entire batch as one lot to minimize variability within a manufacturing campaign. However, this increases the capital required for both equipment and liquid chromatography resin. It also increases risk, since the manufacturer must, in effect, put all of its eggs in one basket. It also requires larger, heavier equipment that is more difficult for operating staff to handle. Finally, this approach eliminates the opportunity for gaining improved process knowledge from multiple runs.
"Blending as Usual" Falls Short
Consider some representative automated blending approaches (Figures 2 and 3) that are widely used today. At first glance, both appear seductively simple and self-explanatory. However, neither approach addresses the variability inherent in concentrated reagent feedstocks today, which typically ranges from 2-5%.
Figure 2. Blend control strategies that entail ratio control do little to overcome the variability inherent in reagent feedstocks.
Using ratio control, the first approach shown in Figure 2, one would expect that extremely accurate (0.1%) mass flowmeters would permit equally accurate blending. This is not the case, however, because blending accuracy depends heavily upon where within the total flow range the equipment operates and what percentage blend composition is being made. Outside of a limited "sweet spot" within which blend composition still varies by 3-5%, variation worsens still.
Figure 3.Use of a digital proportioning valve, likewise, does little to prevent variability.
The second approach shown in Figure 3, using a digital proportioning valve, typically adds 4%-5% to feedstock variation. One manufacturer has developed an "upgrade" to help reduce this variability. This upgrade involved measuring the error profile and creating an equipment- and process-specific table of offsets. The unit's programmable controller was instructed to use these offsets to modify the target process value. Although this upgrade compensated for the systematic mechanical errors introduced by the blending configuration, it could not react to random equipment variations or differences in feedstock. In addition, developing the table is extremely labor intensive. Neither of these approaches provides adequate process control.