Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale‐Up Model Validation and Missing Data on a Micro‐Bioreactor System

Abstract

Multivariate data analysis (MVDA) is a highly valuable and significantly underutilized resource in biomanufacturing. It offers the opportunity to enhance understanding and leverage useful information from complex high‐dimensional data sets, recorded throughout all stages of therapeutic drug manufacture. To help standardize the application and promote this resource within the biopharmaceutical industry, this paper outlines a novel MVDA methodology describing the necessary steps for efficient and effective data analysis. The MVDA methodology is followed to solve two case studies: a “small data” and a “big data” challenge. In the “small data” example, a large‐scale data set is compared to data from a scale‐down model. This methodology enables a new quantitative metric for equivalence to be established by combining a two one‐sided test with principal component analysis. In the “big data” example, this methodology enables accurate predictions of critical missing data essential to a cloning study performed in the ambr15 system. These predictions are generated by exploiting the underlying relationship between the off‐line missing values and the on‐line measurements through the generation of a partial least squares model. In summary, the proposed MVDA methodology highlights the importance of data pre‐processing, restructuring, and visualization during data analytics to solve complex biopharmaceutical challenges.

Spectroscopy integration to miniature bioreactors and large scale production bioreactors–Increasing current capabilities and model transfer

“…The future era of Industry 4.0 envisions an intelligent data-driven manufacturing environment incorporating numerous advanced on-line analytics. 19 The application of these tools across the scales will enable this vision to become a reality while leveraging large amounts of data generated in early development for commercial scale monitoring and control. A prototype of the 50 L SUB (Sartorius Stedim Biotech) was also evaluated.…”

“…Here we present an integrated solution to a miniature bioreactor system (ambr®15) that not only enables fully automated setup for spectral acquisition but also facilitates model transfer across scales by using a spectral probe head with the same optical path despite varying sample presentation; an integrated setup to ambr®15 (static) through to on‐line monitoring at 50 L scale (non‐static) that can be applied to commercial scale vessels. The future era of Industry 4.0 envisions an intelligent data‐driven manufacturing environment incorporating numerous advanced on‐line analytics 19 . The application of these tools across the scales will enable this vision to become a reality while leveraging large amounts of data generated in early development for commercial scale monitoring and control.…”

A rational approach to improving titer in Escherichia coli‐based cell‐free protein synthesis reactions

“…69 3.5 | MVDA to maximize product titer MVDA was selected to evaluate the screening design based on its proven ability within the biopharmaceutical sector to leverage useful information from complex data sets and uncover useful correlations that are not always obvious from univariate analysis. 70 The DoE methodology implemented is a systematic approach enabling the relationship between process operation and process output to be determined while reducing the required number of experiments to understand these key relationships. The face-centered composite DoE was selected as it is the most appropriate design when factors investigated cannot be extended beyond the factorial points which was the case in this experiment.…”

Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation

“…MVDA and modeling approaches are often used in the context of upstream process development, [ 9,11,17 ] likely as it is the key step for determining the maximum achievable product quality and quantity but also as a large amount of potentially useful data is generated at this stage. For instance, sensors available at upstream level produce data online (e.g., for temperature, pH, aeration, stirrer speed) that are traditionally employed to enable standard process control.…”

“…However, besides the sensor data, an increasing number of applications uses concentration data in the process analysis. [ 9,11,17,18 ] For example, MVDA was exploited for process monitoring (golden batch), [ 18,19 ] development, [ 9,11,20 ] scale‐up, [ 4 ] or reproducibility, and robustness studies together with more standard frequentist statistics. [ 17 ]…”