by Giuliana Miglierini

The long waited new PIC/S guideline PI 041-1 has been finally released on July 1st; the document defines the “Good Practices for Data Management and Data Integrity in regulated GMP/GDP Environments”, and it represents the final evolution of the debate, after the 2nd draft published in August 2016 and the 3rd one of November 2018.
While maintaining the previous structure, comprehensive of 14 chapters for a total of 63 pages, some modifications occurred in the subchapters. The Pharmaceutical Inspection Co-operation Scheme (PIC/S) groups inspectors from more than 50 countries. PIC/S guidelines are specifically aimed to support the inspectors’ work, providing a harmonised approach to GMP/GDP inspections to manufacturing sites for APIs and medicinal products.

Data integrity is a fundamental aspect of inspections
“The effectiveness of these inspection processes is determined by the reliability of the evidence provided to the inspector and ultimately the integrity of the underlying data. It is critical to the inspection process that inspectors can determine and fully rely on the accuracy and completeness of evidence and records presented to them”, states the Guideline’s Introduction.
This is even more true after the transformation impressed by the pandemic, resulting in a strong acceleration towards digitalisation of all activities. The huge amount of data produced every day during all aspects of the manufacturing and distribution of pharmaceutical products needs robust data management practices to be in place in order to provide adequate data policy, documentation, quality and security. According to the Guideline, all practices used by a manufacturer “should ensure that data is attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available”. This means also that the same principles outlined by PIC/S may be used also to improve the quality of data used to prepare the registration dossier and to define control strategies and specifications for the API and drug product.
The guidance applies to on-site assessments, which are normally required for data verification and evidence of operational compliance with procedures. In the case of remote (desktop) inspections, as occurred for example during the pandemic period, its impact will be limited to an assessment of data governance systems. PIC/S also highlights that the guideline “is not intended to provide specific guidance for ‘for-cause’ inspections following detection of significant data integrity vulnerabilities where forensic expertise may be required”.

The impact on the entire PQS
PIC/S defines data Integrity as “the degree to which data are complete, consistent, accurate, trustworthy, and reliable and that these characteristics of the data are maintained throughout the data life cycle”.
This means that the principles expressed by the guideline should be considered with respect to the entire Pharmaceutical Quality System (and to the Quality System according to GDPs), both for electronic, paper-based and hybrid systems for data production, and fall under the full responsibility of the manufacturer or the distributor undergoing the inspection.
The new guidance will represent the baseline for inspectors to plan risk-based inspections relative to good data management practices and risk-based control strategies for data, and will help the industry to prepare to meet the expected quality for data integrity, providing guidance on the interpretation of existing GMP/GDP requirements relating to current industry data management practices without imposition of additional regulatory burden. PIC/S highlights that the new guidance is not mandatory or enforceable under the law, thus each manufacturer or distributor is free to voluntarily choose to follow its indications.

Principles for data governance
The establishment of a data governance system, even if not mandatory, according to PIC/S would support the company to coherently define its data integrity risk management activities. All passages typical of the data lifecycle should be considered, including generation, processing, reporting, checking, decision-making, storage and elimination of data at the end of the retention period.
“Data relating to a product or process may cross various boundaries within the lifecycle. This may include data transfer between paper-based and computerised systems, or between different organisational boundaries; both internal (e.g. between production, QC and QA) and external (e.g. between service providers or contract givers and acceptors)”, warns PIC/S.
Chapter 7 specifically discusses the Good document management practices (GdocPs) expected to be applied, that can be summarised by the acronyms ALCOA (Attributable, Legible, Contemporaneous, Original, Accurate) and ALCOA+ (the previous plus Complete, Consistent, Enduring and Available).
Data governance systems should take into consideration data ownership and the design, operation and monitoring of processes and systems. Controls should include both operational (e.g. procedures, training, routine, periodic surveillance, etc) and technical features (e,g, computerised system validation, qualification and control, automation or other technologies to provide control of data). The entire organisation should commit to the adoption of the new data culture, under a top-down approach starting from the Senior management and with evidence provided of communication of expectations to personnel at all levels. Sections 6 of the guideline provides some examples in this direction. The ICH Q9 principles on quality risk management should be used to guide the implementation of data governance systems and risk minimisation activities, under the responsibility of the Senior management. Efforts in this direction should always be commensurate with the risk to product quality, and balanced with other quality resource demands. In particular, the risk evaluation should consider the criticality of data and their associated risk; the guideline provides an outline of how to approach the evaluation of both these factors (paragraphs 5.4 and 5.5). Indication is also provided on how to assess the effectiveness of data integrity control measures (par. 5.6) during internal audit or other periodic review processes.
Chapter 8 addresses the specific issues to be considered with respect to data integrity for paperbased systems, while those related to computerised systems are discussed in Chapter 9. As many activities typical of the pharmaceutical lifecycle are normally outsourced to contract development & manufacturing organisations (i.e. API manufacturing, formulation, analytical controls, distribution, etc.), PIC/S also considered in the guideline the aspects impacting on the data integrity of the overall supply chain (Chapt. 10). “Initial and periodic re-qualification of supply chain partners and outsourced activities should include consideration of data integrity risks and appropriate control measures”, says the guideline.

The regulatory impact of data integrity
Recent years have seen the issuance of many deficiency letters due to problems with data integrity,. Approx. half (42, 49%) of the total 85 GMP warning letters issued by the FDA in 2018, for example, included a data integrity component.
The new PIC/S guideline provides a detailed cross-reference table linking requirements for data integrity to those referring to the other guidelines on GMPs/GDPs for medicinal products (Chapter 11). Guidance on the classification of deficiencies is also included in the document, in order to support consistency in reporting and classification of data integrity deficiencies. PIC/S notes that this part of the guidance “is not intended to affect the inspecting authority’s ability to act according to its internal policies or national regulatory frameworks”.
Deficiencies may refer to a significant risk for human or animal health, may be the result of fraud, misrepresentation or falsification of products or data, or of a bad practice, or may represent an opportunity for failure (without evidence of actual failure) due to absence of the required data control measures. They are classified according to their impact, as critical, major and other deficiencies.
Chapter 12 provides insight on how to plan for the remediation of data integrity failures, starting from the attention required to solve immediate issues and their associated risks. The guideline lists the elements to be included in the comprehensive investigation to be put in place by the manufacturer, as well as the corrective and preventive actions (CAPA) taken to address the data integrity vulnerabilities. A Glossary is also provided at the end of the guideline.