Operational Excellence: More Data or Smarter Approach?

The authors focus on operational excellence in manufacturing of biotechnology therapeutic products in the QbD paradigm.
Jun 01, 2011
Volume 24, Issue 6

Anurag Rathore, PhD
Achieving operational excellence is a growing goal among biopharmaceutical enterprises. Companies are investing heavily in software, data marts, and data integration to achieve operational excellence. The authors explore approaches for meeting operational-excellence goals, and discuss how to manage collected data with a risk-based approach for improved process understanding.

Operational excellence is achieved when each and every employee can see the flow of value to the customer, and fix that flow when it breaks down (1). To reach this goal, and thereby to achieve continuous improvement at all levels of organization, one needs to make the best possible use of available resources (e.g., data, information). Only when there is complete visibility of existing manufacturing processes and systems within an organization can this goal be achieved. In the biomanufacturing industry, complete visibility means effective communication of process data and product information.

Quality by design (QbD) is defined in the the International Conference on Harmonization's Q8 Guideline as "a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management" (2). In a typical QbD approach for development of biological products, decision on the Target Product Profile (TPP) is followed by a risk- and science-based assessment of product attributes with respect to their effect on the product's safety and/or efficacy (3, 4). This assessment results in identification of the so-called critical quality attributes (CQAs). Next, the process is designed to consistently deliver these CQAs within the acceptable limits.

A robust control strategy that can ensure consistent process performance in view of the various sources of variability that exist in biotechnology manufacturing is central to successful implementation of QbD. Once the manufacturing process and the control strategy are in place, validation is performed to demonstrate the effectiveness of the control strategy, and the product is filed for approval. After approval, ongoing monitoring is performed to ensure robust process performance over the life cycle of the product (2, 3). The approaches needed to make these decisions require sufficient access to process and product information that is otherwise distributed within a myriad of sources. Thus, the need for efficient information-sharing has become more crucial to industry in the QbD paradigm.

This article is the 23rd in a series in BioPharm International about the "Elements of Biopharmaceutical Production" (see full list at http://BioPharm-International.com/). This article focuses on operational excellence in manufacturing of biotechnology therapeutic products in the QbD paradigm, including the use of efficient and effective data and knowledge management. The authors discuss key concepts and present a related case study.


Data hoarding

In an attempt to achieve operational excellence, biopharmaceutical companies often spend significant amounts of resources on building data-collection and data-capture systems, leaving fewer resources for the more important task of performing analysis of the collected data. Collecting more doesn't enable doing more. Rather, the task adds to the complexity of finding a needle in haystack. Although data collection is an important step toward continuous improvement, the inability to visualize data and share its analysis across the organization is a key deficiency in current industry practice.

Complex analysis tools

Complex statistical tools for performing advanced analysis of manufacturing process data have become the norm in biotechnology manufacturing (5). As the complexity of process data and analysis increases, however, training an entire organization's staff on how to best use these tools is not feasible. Many organizations, therefore, opt to create a specialized team that is trained to perform such analysis. As a result, organizaitons end up being segregated into groups of staff who on one side, have hands-on scientific experience with the product or process but no knowledge of the analytical tools associated with them, and on the other side, staff who know how to use the statistical tools but do not have hands-on scientific experience with the product or process. This situation is not desirable.Appropriate analysis requires both sides of expertise.

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