PCA (or more generally MVA) applied to this kind of data is sometimes referred to as Quantitative Structure Activity Relationships (QSAR) and has helped some companies to significantly reduce the time and effort required to isolate suitable candidates for further development.
Formulation of suitable products: Stabilizing the candidate into a suitable matrix for manufacturing and delivery is best approached using DoE and, in particular, excipient screening and mixture designs. Excipient screening designs allow the formulation scientist to select the best components that will preserve the nature of the candidate, while mixture designs allow for the development of the best combination that will not only stabilize the candidate, but also protect it during subsequent manufacturing processes.
Clinical trials have traditionally been the domain of univariate statistical approaches (in particular clinical statistics) where statistical significance is assessed for parameters such as efficacy and major side effects. The tools of MVA can be used to compliment the findings generated by clinical trial statistics to further confirm and accelerate key findings through this phase of product development.
The ability to incorporate demographic, age, sex and patient history into predictive or exploratory models is a unique feature of the MVA method, and approaches such as the L-PLS model can provide an overall picture of the patient groups, disease markers and the candidate properties to better assess the effect of the therapy on specific patient groups. Figure 3 provides an example of the L-PLS model structure and an example output.
Through the use of MVA tools for monitoring and controlling bioprocesses, manufacturers worldwide have realized significant cost savings through proactive quality control. During the scale up and technology transfer of a process from R&D to full scale manufacturing, the use of DoE is a critical strategy for assessing the effect of changing process and equipment variables. This allows the definition of the Design Space, which defines the most effective control strategy for the process. Multivariate Statistical Process Control (MSPC) uses multivariate exploratory and predictive models and integrates them into the entire data collection and process control system.
This allows manufacturers to be more innovative in their approach to quality combining in-line process analytics into single or holistic process models that better assess the quality of production than single measurements in isolation. Two particular processes that are commonly used in biotherapy manufacture are fermentation and lyophilization. Some applications of MVA to these are discussed in the following sections.
MVA FOR FERMENTATION MONITORING
For many years manufacturers have been challenged with the development of suitable models for monitoring the progress of batch processes, fermentation being one such process. These batch models aim to establish a process trajectory and associated limits around the trajectory that define the bounds of acceptable product quality.
Methods exist that unfold batch data and use so-called “maturity indices” to model the process. However, the major drawback of these methods is that they assume linear relationships in the processes, which are fundamentally incorrect and have only partially solved the batch problem. Other approaches use time warping to distort the time scale and align batch trajectories. Again, these approaches also suffer fundamentally as they distort the chemistry or biology of the system and hence do not describe the true state of the process.
Relative Time Mapping (RTM) addresses the shortcomings of the previously defined methods by keeping the chemistry/biology of the system intact, while at the same time, providing the usual batch trajectory plots and associated diagnostics that have become synonymous with this type of analysis. Figure 5 provides some typical outputs from a RTM Batch Modeling process.
Whether batch models or traditional Statistical Process Control (SPC) charts are used to assess the progress of a bioprocess, there are many diagnostics available in multivariate models that can be used to determine the onset of process failure.
EARLY EVENT DETECTION
The term Early Event Detection (EED) is being increasingly used to describe the application of Multivariate Statistical Process Control for the detection of process faults. The diagnostics from these models can be fed back into the manufacturing control systems using protocols such as OPC to automate process adjustments and therefore maximize the quality of the final product.