Guest Column | November 10, 2017

When And How To Implement Data Integrity Practices In The Product Development Lifecycle

By Kip Wolf, X-Vax Technology, @KipWolf

Data integrity is frequently discussed in many industries. It is more formally defined within the life sciences industry through regulation (e.g., current good manufacturing practices [cGMPs], good laboratory practices, good clinical practices, the application integrity policy) and in guidance documents from health agencies, including the FDA and WHO. These regulations set the expectations for companies to ensure data integrity and traceability by establishing and monitoring the effectiveness of their quality systems.

Demonstrating the integrity and security of data is essential for a successful health agency inspection and maintaining product quality. The increased reliance on electronic/automated systems and expansion of regulatory violations in recent years have led to greater scrutiny of data integrity by inspectors. Still, there remains a great deal of confusion as to how to implement practices to best ensure data integrity. This confusion frequently stems from the assumption that data integrity is something to “turn on” at a particular stage of the product development life cycle. As an alternative, we have seen positive results from considering individual elements of data integrity and maturing those elements within an evolving quality management system (QMS) along the product development life cycle.

Data Integrity Starts With Governance Within An Evolving Quality Management System

The recently issued final version of the WHO guidance on data integrity (Annex 5, Guidance on Good Data and Record Management Practices, part of the WHO Technical Report Series No. 996, 2016) states “[l]eadership is essential to establish and maintain a company-wide commitment to data reliability as an essential element of the quality system.” A quality culture begins and grows best when leadership recognizes early the importance of data integrity through simple governance within the most basic of QMSs (e.g., supplier management during manufacture of clinical trial material).

Data integrity is established (well or poorly) with the earliest capture of development data that will later inform clinical research. It then extends to chemistry, manufacturing, and controls (CMC); regulatory review and approval; and, ultimately, commercial production. The application of quality-related activities and (later) a formal quality assurance function is frequently misunderstood in relation to the product development life cycle. Specifically, there remains confusion regarding the necessary evolution of maturity of these activities and functions from clinical trials through commercial supply and how and when appropriate levels of rigor and maturity should be applied to ensure both regulatory compliance and improved probability of successful regulatory filing and product launch. As the product development evolves throughout the life cycle, the QMS also must evolve and mature, with data integrity practices likewise expanding as the QMS becomes more robust.

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Leadership As A Critical Success Factor To Evolve QMS Along The Product Life Cycle

The QMS should evolve over the life cycle of product development, manufacturing, and distribution (and ultimately divestiture or retirement). Inspired, capable, and intelligent leadership will identify this need for maturity and teach, mentor, and assure management and operational staff that the level of QMS is efficient, effective, and sufficient given the relative risks and regulatory compliance requirements. As we know, regulatory compliance is relative, and different levels of control are necessary at different stages in the life cycle. As stated in an earlier example, supplier management during manufacture of clinical trial material is important, while formally defining the procedures for annual product review will be necessary only after commercial supply is established.

Quality practices evolve by incrementally establishing management control to ensure product safety, identity, strength, purity, and quality throughout the product life cycle, beginning with the quality control principles during the manufacture of investigational drugs (i.e., interpreting and implementing CGMP consistent with good scientific methodology). The confusion of when and how to apply various elements of a robust QMS related to CGMP frequently leads to poor data management practices and reduced data integrity. This frequently results in (at a minimum) increased variable costs related to inefficiencies involving authoring, review, and approval of content that is intended to be included in the trial master file and/or the CMC sections of module three of the common technical document. At worst, these types of issues can lead to a negative impact on patient safety and/or regulatory filing approval.

When leadership establishes a basic and consistent approach to managing data and simple but robust data integrity practices early in the product development life cycle, real value can be realized in terms of data quality and improved quality culture. Early establishment of simple actions and implementation of routine monitoring and auditing to identify any need for remediation pay significant dividends later in terms of higher probability of regulatory filing approval and greater confidence in product quality.

Future articles in this series will discuss specific data integrity improvement opportunities within individual quality systems (e.g., document control, records management, materials management) with specific operational examples (e.g., master batch record design, efficient review of executed batch records, improved schedule adherence).

About The Author:

Kip Wolf is a senior managing consultant at Tunnell Consulting, where he leads the data integrity practice. Wolf has more than 25 years of experience in quality assurance and regulatory affairs, GMP and IT compliance, technical operations, and product supply. His areas of expertise include business transformation, new business development, organizational change leadership, and program/project management. He has held various management positions at some of the world’s top life sciences companies. Wolf can be reached at