Data in Pharmaceutical R&D: 5 Steps to Ensuring Quality

Producing high-quality, reliable, and reproducible data is extremely important in the non-GxP research environment.

These data go on to form the basis of all subsequent steps in pharmaceutical development. If data quality is lost, all subsequent GxP steps will be impacted. This is also the case for incorrect R&D (research and development) data. Data quality, when lost, cannot be regained at a later stage. Pharmaceutical companies must establish a quality culture throughout their value chain. This includes pre-commercial research until the latter GxP stages (GLP, GCP, GMP).

This blog suggests five actions to ensure R&D data quality and promote a quality culture. While GxP standards are common throughout the drug product lifecycle, they may not be required for early discovery research. This stage has no direct impact on human health. Imposing GxP regulations on non-GxP research could hinder creativity, slow innovation, and limit early discovery. Instead, implementing pragmatic, risk-based, and science-driven research quality standards can ensure data integrity. Aligning the scope and requirements of discovery activities enables optimized resource deployment.

1. Emphasis on a risk-based approach

Emphasizing a risk-based approach is crucial in pharmaceutical development. It ensures optimal quality and mitigate potential risks while determining the criticality of the data and their impact. Adopting risk-based approaches and methodologies allows the industry to effectively balance resources, business needs, and process burden, maximizing assessment impact. One of these approaches is e.g., critical thinking. The risk-based approach involves systematically identifying, assessing, and addressing risks throughout all stages of drug development, including R&D. Through an increased understanding, it aims to safeguard the safety, quality, and efficacy of pharmaceutical products. In line with the WHO’s good practices for research and development facilities of pharmaceutical products, these risk-based principles are increasingly applied from early research through development. Included are, among others, quality management principles, personnel and training, outsourced activities. The level of effort should be commensurate to both the risk level and the stage of the drug product lifecycle. For example, it should be tailored to the R&D stage. The criticality of the data should also be kept in mind, when conducting quality system activities.

The pharmaceutical industry can improve decision making, optimize resource allocation, and enhance product safety. By implementing the risk-based approach within the R&D domain, this goal can be accomplished. The approach likewise acknowledges the criticality of the data as applicable.

2. Data Integrity Principles

Data integrity provides characteristics to build and preserve confidence in the reliability of the data throughout the entire data lifecycle. During its lifecycle, data should not be altered in an unauthorized manner. The principles around data integrity are summarized in the ALCOA concept. Basic data qualities are described as attributable, legible, contemporaneous, original, and accurate. The concept has been extended to the characteristics complete, consistent, enduring, and available (ALCOA+). Many life science

companies rely upon the ALCOA+ framework to ensure data integrity. It is the gold standard set by regulators worldwide for maintaining compliance with data integrity regulations. As an example, the U.S. FDA (Food and Drug Administration) 21 CFR Part 11.

In pre-GxP R&D, defining expectations and committing to a minimum set of characteristics for data (including metadata), highlights their significance as valuable resources. At the same time, an adequate level of data integrity will be ensured, as e.g., by adhering to ALCOA+. Subsequently, based on these data, conclusion and interpretation of scientific results are being drawn. Therefore, being an extremely important resource for pharmaceutical organizations.

3. Data Governance and Data Culture

Data Integrity as it is described with the ALCOA+ principle is focusing on document-based human-to-human interaction. This can be protocols, summary reports etc. However, all-encompassing data integrity is not only achieved by adherence to ALCOA+ principles. Rather it is an outcome of a strong data culture. A prerequisite for its development and upkeep is a data governance program. Clear rules for data management and stewardship unlock the full potential on the way to useful and high-quality data. Data ownership and responsibilities should be defined as well.

A framework that focuses on the switch from document-centric (ALCOA+) to data-centric compliance can be found in the FAIR principle. Through FAIR, data should be Findable, Accessible, Interoperable and Reusable within an organization. It aims to unlock the immense value that comes with data, driving the business performance. Scattered and siloed data sets will be overcome. Instead, the value of data will be maximized, and data efficiency will be promoted.

Taking meaningful data governance and data culture together, key processes in data analysis can be improved. The importance of data integrity but also the emphasis on a strong data culture is reflected in the transition of paradigms. Document-centric principles will be enhanced by introducing data-centric compliance methodologies like FAIR. This will contribute to unlock the value of data and boost workflows in drug discovery and R&D

4. Research Quality System

The research quality system plays a vital role for R&D activities. It ensures that the necessary standards and requirements are met.

Vital components that the quality system should address are listed below. The effort level should align with the risk-based approach. Consideration should be given to the drug product lifecycle stage and the data criticality.

– Effective management and governance

– Research documentation and data management

– Method and assay qualification

– Materials, reagents, and sample management

– Facility, equipment, and computerized system management

– Personnel and training records management

– Appropriate handling of outsourcing and external collaborations

The quality system should also cover when data are intended for submission for approval in marketing authorization applications. All batch data, results, and associated information must adhere to defined standards. Documentation of analytical procedures developed by R&D facilities should be thorough to facilitate successful transfer when required. Implementing a robust research quality system helps organizations uphold data integrity. Furthermore, regulatory compliance and the reliability of R&D activities are ensured and maintained.

5. Quality Mindset

Moving forward in the research and development of pharmaceutical drugs and medicines, cultivating a quality mindset is essential. Promoting a quality culture involves raising awareness, providing onboarding, and training. In addition, scientists engaged in the process should be provided with mentoring opportunities. As specified in the PIC/S Guidance on Data Integrity, a strong quality culture is in the responsibility of senior management. The quality culture should be reflected in an open and transparent work environment. Meaning that empowering individuals within the organization encourages a sense of ownership and responsibility for maintaining high-quality standards. Fostering a positive error culture is crucial as it promotes early error detection. Mistakes are seen as opportunities for learning and improvement. Employees are empowered to seek out errors instead of hiding them out of fear of retribution. Moreover, offering incentives for quality adherence and excellence can boost individuals’ motivation to prioritize quality in their work.

By embracing these principles and practices, the R&D area of pharmaceutical companies can foster a robust quality mindset. This ensures the development of safe and effective medicines for the benefit of patients.


We hope you found the information valuable and insightful. Now, we’d like to encourage you to take action reaching out to us or sharing the article!

GxP-CC can support you in the quality processes of your R&D department and making sure that all necessary implementations comply with applicable quality guidelines. Contact us today to get started.

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