Driving Data Quality and Ensuring Compliance Using Modern CDS Solutions

Published on: 

The most safe and effective therapies demand the highest data quality.

Pharmaceutical organizations are responsible for ensuring their therapeutic products meet strict criteria to assure safety and efficacy. The best way to achieve this is through accurate and reliable data obtained from a repertoire of analytical tools throughout the manufacturing process. Recognizing the importance of this process, global regulatory agencies have set strict guidelines to ensure data quality and integrity (1–4), defined by FDA as data that are complete, consistent, and accurate (1).

The road to high-quality data is not exempt from obstacles along the way. The growing complexity of novel therapeutic modalities is complicating the manufacturing process, the volumes of data being generated are expanding exponentially, and all of this is occurring in a regulatory landscape that shifts significantly and at short notice. Failing to ensure high quality data, however, has profound consequences for pharmaceutical organizations and patients—from costly product recalls and reputation damage to ineffective or even unsafe medicines reaching the point of care.

In light of this, several enabling tools and strategies have emerged to help pharmaceutical companies navigate a path to better data. One of the most important innovations that have transpired are advanced chromatography data systems (CDS) that help organizations meet dynamic regulatory requirements while also increasing operational efficiency.

Advanced compliance tools shorten the path to data excellence

CDS are generally defined as the tools that integrate with chromatographic equipment (and, more recently, mass spectrometry systems) to collect, process, and store associated data. The 1970s saw the first iteration of CDS platforms, which grew rapidly in complexity and capability over the proceeding decades.

Today, the latest systems integrate with a suite of analytical instruments beyond chromatography and support pharmaceutical manufacturers to achieve greater data quality. They do so by optimizing operations across five key areas: data acquisition, audit trails, data investigation, data reporting, and system access and permissions.

Optimizing data acquisition—getting quality data, the first time

Ensuring high-quality data acquisition starts with effective validation and control of analytical instrumentation. The latest generation of CDS are helping here in several critical ways.

Quelling qualification concerns. Accurate, reliable results are not possible unless instrumentation, equipment, and software have been properly qualified. Accordingly, as with manufacturing equipment, analytical equipment qualification is demanded by regulatory bodies.

Considering this, some modern CDS now have functionality and tools that streamline qualification processes while better ensuring compliance to regulatory requirements. For example, comprehensive qualification procedures, both for the CDS and analytical instruments, are now being built into modern CDS platforms. For instruments, these procedures span installation qualification (IQ), operation qualification (OQ), and performance qualification (PQ). They can accommodate a wide range of instruments and vendors and, in some CDS, are also fully automated. Automating the process better ensures compliance with regulatory requirements while also expediting the process. With such tools, qualification can now be achieved in a matter of minutes.

With the latest systems, qualification results are stored in electronic format inside a secure folder architecture so that no data is ever lost, and a complete historical record of executed procedures is always available.

Improving instrument control. While most mass spectrometry and chromatography workflows are broadly the same, they can differ in key details like instrument conditions, sequence structure, and how results are calculated. This can be a source of data quality loss, as tweaking the workflows manually is time-intensive and thus can lead to errors. Advanced CDS are addressing this issue with automated workflow procedures. With them, analysts can create sequences based on defined structures that comprehensively capture all aspects of a workflow and align with regulatory requirements.

Sequence execution control delivers added data confidence in some CDS solutions. In this case, software checks the instrument configuration, methods, and sequences, and prevents a sequence from starting if any issues are detected. With this being run prior to all injections, only correct and consistent injections can proceed, meaning generated data are more reliable.

Delivering with system diversity. The challenges mentioned previously can be complicated further by the fact labs often operate multiple instruments from different vendors. Idiosyncrasies in instrument operation and the additional burden on staff to accommodate them can open new avenues for data quality loss. A broader trend seen in modern CDS solutions, however, is enhanced compatibility with a range of analytical systems from different manufacturers. The Thermo Scientific Chromeleon CDS , for example, can accommodate more than 540 instrument modules from over 21 different manufacturers.

Benefits here expand beyond data quality and include reduced training requirements, greater ease-of-use with a more consistent end-user experience, and streamlined administration and IT infrastructure (although these could be argued to indirectly impact data quality, too, by reducing process complexity and thus risk of error).

On the trail to better data

Beyond the analytical instrument, its sequence and the results it generates, data reliability, and confidence require a comprehensive audit trail. An audit trail can be defined as a secure, computer-generated, time-stamped electronic record that allows for reconstruction of the course of events relating to the creation, modification, or deletion of an electronic record (1). Essentially, they constitute rich information on who did what, when, and why, and are a highly effective means of detecting data integrity issues. For this reason, audit trails are a regulatory requirement and come under heavy focus from inspectors.

While their importance is evident, building, reviewing, and maintaining compliant audit trails is not so straightforward. For success, audit trails must be set up and configured correctly, undergo time-intensive reviews from quality assurance departments, and easily demonstrate when non-desirable activities have occurred.

Tracking the who, what, and when. Modern CDS solutions can now comprehensively track the who, what, and when of pharmaceutical manufacturing and testing operations. The most advanced solutions accomplish this by tracking data, covering everything from instrument configuration and data processing to system administration (Figure 1).


These tools also facilitate easier review using intuitive filtering options, type-as-you-go or drag-and-drop searching functions, and data grouping capabilities, simultaneously with helping create rich and compliant audit trails.

Tracking the why. Tracking the ‘why,’ or the intent, of actions across pharmaceutical testing operations has been considerably more difficult than tracking the who, what, and the when. However, it is a problem that requires serious attention, as the reason for an action provides critical context to data and helps ensure data integrity. Without knowing the ‘why’ behind an action, those reviewing the data cannot fully reconstruct and understand the events that have taken place, with some resulting in a misunderstanding.

Thankfully, modern CDS solutions have significantly eased the path to accurate and reliable ‘why’ capture, primarily by enhancing traditional CDS comment functionality.

To help capture the ‘why’ of an action, most standard CDS let users add free form comments. These CDS may even provide a list of default or acceptable comments for the user to select from. However, freeform comments are uncontrolled and can be inaccurate or misleading. Further, the selection of acceptable comments given to end users often spans all possible comments, not just those relevant to the action undertaken. Thus, an acceptable comment can easily be attributed to the wrong action.

Modern CDS solutions overcome this by forcing users to use default or acceptable comments that are tied to specific actions; only the comments applicable to the action in question are available for selection. These pre-approved, action-specific comment options can also be aligned with what is deemed acceptable in a standard operating procedure, meaning users cannot deviate from business acceptance criteria, and compliance is simplified. Importantly, selected personnel can also be given the authority to override default comments if the available options aren’t suitable. Carefully controlling and enforcing comments in this way ensures they provide true, complete, and accurate context to actions. Ultimately, this increases clarity, reliability, speed, and confidence when it comes to review or investigation.

On top of enforcing action-specific comments, modern CDS also ensure user attribution of actions is accurate, namely by requiring input of a password and ID before actions can be completed. While user-session timeouts offer a certain degree of safety in ensuring that actions are attributable to the correct user, there is stilla window of time where another user could complete an action under the wrong login. Passwords and ID requirements eliminate this risk. This feature is important, as performing an action under the wrong user-session, even if accidental, is considered fraudulent activity by regulators. Regulators could mark such activity as an audit observation at the very least, and the broader reliability of data could be thrown into question.

Empowering thorough and efficient data quality monitoring

A recent and significant change in regulatory behavior means it is now up to the end-user to defend their data and prove that there are no irregularities. For this, pharmaceutical companies need to be able to monitor and investigate their data easily and at all times.

Within the modern CDS toolkit, there are several solutions that can help pharmaceutical organizations meet this new obligation. Given the heavy workload of quality assurance and quality control personnel, these tools also often prioritize ease-of-use and minimize training requirements.

First, advanced version control capabilities allow clearer and faster data change comparisons, whether changes are additions, deletions, or modifications. Such version tracking is accompanied by clear visualizations of changes, and users can revert to previous versions where a change is not acceptable or accurate, meaning issues can be resolved before they have a chance to grow. In some cases, visual comparison features can directly compare versions on a single screen, side by side, providing deeper insight into change, and supporting a clearer justification of changes to regulators. To avoid becoming a source of error itself, the review of comparison panes can be done in read-only mode so that further changes to the data are not possible.

Querying and trending functionalities further build out the modern CDS data monitoring toolkit. CDS with these functionalities can streamline the search for certain types of activity with appropriate search criteria, for example, pinning down all manual integrations or all activity by a specific user in a specified time frame. If the CDS has trending capabilities, graphs and charts can simplify pattern detection, providing a clearer, more succinct summary for regulators (Figure 2). Further, with fuller visibility of trends in, for example, user-behavior, laboratories can better implement corrective action before regulatory review.

Even with these features, finding all the events that could have influenced a data’s integrity during review can be a very complex and difficult task. This is because some events are not explicitly tracked or visible from within the audit trail itself, and so searching multiple audit trail entries is needed. Because these events are not prominently displayed or are problematic to identify, they may go overlooked.

Manual integrations and frequent sequence restarts are common examples of such events, with the latter being particularly difficult to identify. To identify and track a sequence restart, for example, a detailed investigation is needed, where a reviewer must look at the sequence start and abort entries and then confirm whether it was the same user carrying out the actions. Such actions could collectively represent a non-conformity when mapped against business procedures. On the other hand, a thorough investigation could show that the actions were justified.

The latest CDS solutions can search across audit trails and combine specific, related entries, treating multiple operations as one to highlight specific events. This makes it possible to generate a real-time notification when these events occur, alerting team members to actions and operations that are not initially visible from the dataset or object they are reviewing.Further, this trail of events, as with standard audit trails, can be easily searched, queried, and reported on. With this critical additional information to hand, a reviewer has greater visibility, can better recognize patterns, and can be reassured that a more complete and considered audit trail review has taken place.

Straightforward reporting, easier validation

The data analysis journey ends with the reporting of results for product batch release. The traditional reporting processes itself, however, can sabotage data quality. For example, in such processes, data are typically exported to external spreadsheets manually, which, aside from being time consuming, is prone to data transcription errors. Changes to reporting templates and spreadsheets also often cannot be tracked in traditional approaches, so opportunities to detect errors are reduced. Then, when source data change, it can be difficult to ensure reported data reflect the updated results. Further, the use of independent systems for analysis and reporting entails extra software validation burden.

More recent CDS address these common pain points by enabling reporting from within the software. With a single system encompassing both data analysis and reporting, there is no need to manually export results, and software validation effort can be reduced. Critically, all changes and versions can then be tracked within the CDS to ensure complete visibility. If any changes start to invalidate a report, then the creation of a fresh one is enforced before submission. Managing the electronic report as it goes through review and final sign-off is also made more robust, namely through unique user-specific digital signatures at successive submission, review, and approval stages.

With less error in electronic reports, better-tracked changes, and enforced control of document review, batch results become more consistent and compliant.

Advanced CDS tools: better data for a safer world

Delivering safe and effective medicines to patients is the greatest responsibility of pharmaceutical organizations, but it can’t be achieved without high-quality data. Several trends are convoluting the path to data excellence, such as an explosion of novel therapies, more complex manufacturing processes, and regulations that shift at short notice.

Among the solutions addressing these challenges is a new generation of advanced CDS. CDS have come a long way since their inception more than five decades ago, maturing considerably into the advanced solutions seen today. With the latest platforms, pharmaceutical organizations can better drive quality and efficiency at all stages of the data collection, analysis, and reporting journey while better weathering regulatory change. The result is more reliable data, higher quality medicines, and, ultimately, a safer world.


1. FDA, Data Integrity and Compliance With Drug CGMP: Questions and Answers Guidance for Industry (December 2018).

2. FDA, CFR Part 11, Electronic Records; Electronic Signatures - Scope and Application (September 2003).

3. MHRA, ‘GXP’ Data Integrity Guidance and Definitions (March 2018).

4. EMA, Guidance on Good Manufacturing Practice and Good Distribution Practice: Questions and Answers(2018).

About the author

Peter Zipfell is product marketing manager, Thermo Fisher Scientific.