Multivariate Data Analysis for Biotechnology, Bio-processing

Powerful MVA and DoE methods are giving biotech companies greater insights from complex data

By Brad Swarbrick, vice president business development, CAMO Software

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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.

An extension of MSPC is the use of Hierarchical Models (HM). These models provide an excellent way of classifying the state of discrete phases of processes such as fermentation and adapt to changing conditions as they occur. HMs can be set up as Classification — Classification, Classification — Prediction and Projection — Prediction models which can be adapted to applications such as analysis of raw materials, process monitoring and quality-control applications.

Near Infrared (NIR) spectroscopy has been used for many years with multivariate predictive and exploratory models for the rapid, non-destructive assessment of product quality. One common application of the NIR method is the quantitative analysis of residual moisture in lyophilized products.

Lyophilization is a common method used in the manufacture of biopharmaceutical products as it uses low temperatures to remove residual moisture, thus preserving the structure of the active components and allowing their storage at room temperature. The traditional method of analysis for residual moisture in lyophilized product is Karl Fischer (KF) titration which is a destructive test and can only be applied to a small number of samples.

Replacement of the KF method with NIR not only results in non-destructive testing, but also allows for 100% inspection systems to be put in place. These systems use MVA predictive models to transform the NIR spectrum into a single value for residual moisture (or other properties) and are used to accept and reject product as it is being manufactured.

In one case, a biopharma manufacturer saved about $1 million by using the NIR method combined with PCA to validate the performance of a new freeze dryer. They also developed a quantitative Partial Least Squares Regression (PLSR) model to replace the KF method in the laboratory. This method saves them $1,000 per sample and provides more confidence when releasing the batch to market.

Although initiatives such as Process Analytical Technology (PAT) have been used by many manufacturers globally to assess product and process quality at the point of manufacture, not every process measurement can be replaced at the point of manufacture. Quality Control (QC) operations are still vital in the final release stage of some, if not all, products.

Due to the high variability in many biological assays, DoE and MVA can be used to design and refine the analytical methods used in the QC laboratory and has been successfully applied to the optimization of chromatographic methods, the refinement of sampling procedures and the analysis of complex data produced by mass spectrometers.

Another advantage of combining spectroscopic analysis with MVA methods is in stability studies. Since the NIR method is non-destructive and is sensitive to changes in the product and its matrix, the same sample can be assessed over the entire timeframe of the study. Where applicable, this avoids the destruction of product, and the results are completely representative as the same sample is being assessed each time.

MVA and DoE are fast becoming essential tools for all process development and monitoring applications. Bioprocesses provide an excellent but challenging application area. Modern manufacturing execution systems and control platforms produce a massive amount of data that requires the tools of MVA to fully “data mine” the most important information and make real-time quality decisions.

From raw material analysis to final product release, MVA models can be integrated into the total Quality Management System (QMS), allowing manufacturers to realize the benefits of the Quality by Design (QbD) initiative.

Multivariate data analysis and DoE are powerful tools ideally suited for understanding the complex behavior and relationships in biological systems. These methods can be used across the full biotech product lifecycle, from discovery and development, to scale up, production and quality control. Today’s leading MVA and DoE solutions can be seamlessly integrated with other systems including process equipment, laboratory and spectroscopy instruments, enabling faster and more informed decision making.

Leading biotechnology companies that implement and exploit the power of MVA and DoE can realize substantial benefits including lower development and production costs, improved product quality and compliance, technology transfer, faster time to market and ultimately increased business value.

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