In-line conditioning (IC) is a form of dilution where a process buffer is formulated in-line from concentrated stock solutions of acids, bases, and salts that are mixed with the correct amount of water-for injection (WFI). This new buffer preparation strategy must prove its equivalency to buffers made the traditional way (i.e., weighing salts, stirring in water, titrating with acid or base). In this paper, such a demonstration is presented using two control modes: (1) ratio control with flow feedback; and (2) pH/conductivity feedback. To obtain the necessary parameters for an error propagation analysis, a robustness study has been performed. Our analysis showed that with low incoming variability, or when the uncertainty of the stock solutions is below 2%, the two modes of control give comparable performance. When the uncertainty increases, so does the uncertainty of ratio control with flow feedback, more with respect to conductivity than pH, while the precision of pH/conductivity feedback remains at the same level. The choice of control should therefore take into consideration the critical process parameters, their tolerances, and the input variability in the stock solution concentration. In situations where there are higher variabilities in stock solution concentrations or process temperatures, this study suggests that pH/conductivity feedback might be a better option.
Tag: <span>critical process parameters</span>
Accepting any identified and evaluated risk is “taking a smart risk.” The acceptance decision, before or after mitigation, is a complex and sometimes difficult choice that is based on the information generated during the ICH Q9 quality risk management (QRM) exercise along with many subjective viewpoints impacted by previous experience, knowledge, risk appetite, and bias. This paper provides an approach for understanding and making acceptance decisions centered around the risk-rating methods that define the severity (harm) and uncertainty (likelihood) of the risk’s consequence occurring. It also builds on concepts developed in the first two parts of this QRM series to provide an overall framework for identifying, evaluating, managing, and accepting a wide variety of biopharmaceutical development and manufacturing risks…
This paper, the second in a three-part series on ICH Q9 quality risk management (QRM), uses a process-based risk structure to identify product quality risks from variability in input parameters and process behavior. This paper outlines a method to identify the three types of input parameters and how they can be placed into an ICH Q8 defined design space structured to clearly categorize and control the input parameters such that they can be evaluated for their impact on product critical quality attributes (CQAs). Based on their placement in the well-structured design space, the parameters are rated using a risk severity and uncertainty index to calculate a risk rating for review and acceptance. The process-based risk structure can also be used to mitigate the likelihood of the risk consequence by modifying the processes to manage the uncertainty of the input parameters and control the process’s behavior…