The U.S. Food and Drug Administration has cited multivariate data acquisition and analysis tools as one of the key initiatives of PAT. In many manufacturing settings, the multivariate data acquisition infrastructure is in place, but presently the tools that are capable of handling the enormous amount of data that is collected and stored are under-utilized. This significant opportunity for improving process understanding and detection and diagnosis of operating problems currently exists. The need for rapid analysis methods that serve this situation is paramount. More specifically, the recognition of specific process responses similar to previous occurrences of the same conditions would be beneficial if they could be quickly located in the historical data. This would allow for the improved comparison of different batches, thus reducing the development time for new processes.

An improved pattern-matching technique is proposed for multivariate data analysis, utilizing an extension of partial least squares (PLS) analysis. While this approach is of specific relevance in the biopharmaceutical industry, it can analyze data from a wide range of systems. The proposed approach takes an arbitrary set of multivariate data and quickly recognizes similar patterns in a large historical database. This pattern matching strategy is based upon a modified PLS similarity factor. This methodology requires that a PLS model be generated for each data set of interest. The data for each batch is first divided into two categories: process variables (X) and quality variables (Y). The process variable data are used to explain the variation captured in the quality variable data. One of the resulting features of the PLS model is a weighting matrix (W) that relates X and Y. W serves as a figurative “fingerprint” for each batch. Using a similarity factor approach, rapid screening can be performed that quantitatively compares the W matrices of two data sets, yielding the relative similarity of the batches. Conveniently, these similarity factors are bound between zero (completely dissimilar) and one (identically similar).

This proposed approach has been applied to industrial fed-batch cell culture data for ~100 batches in a process development setting. This methodology was shown to accurately characterize regions of similarity and dissimilarity between batches for a multitude of different operating conditions. For example, this approach is able to recognize similarity between batches for the following phenomena: lactate build-up, elevated dissolved CO₂ conditions, low glutamate levels, etc.

Also, the performance of the modified PLS similarity factor is compared to a number of alternative similarity factors (e.g., principal component analysis, PCA, similarity factors) and shown to be superior. Furthermore, the new approach possesses the ability to diagnose the underlying causes for similarity and/or dissimilarity between two datasets.

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