Good Laboratory Practice Always Sets the R&D Standard

Good Laboratory Practice (GLP) is the quality framework that determines whether non-clinical research data can be trusted, reconstructed, and used confidently in pharmaceutical development. Even as drug discovery technologies diversify, GLP remains fundamental to the integrity of the evidence connecting laboratory experiments with regulatory decisions.

Image

Why Is Good Laboratory Practice Fundamental in Drug R&D?

Drug discovery depends on evidence accumulated across many experimental systems.

Researchers generate data from biochemical assays, cell-based studies, pharmacology, toxicology, analytical testing, and in vivo models.

The scientific value of those results depends not only on what an experiment shows, but also on whether the experiment was properly designed, controlled, recorded, and reported.

GLP provides the organisational and procedural framework for that reliability.

The Organisation for Economic Co-operation and Development (OECD) defines GLP as a quality system covering the conditions and organisational processes under which all non-clinical health and environmental safety studies are planned, performed, monitored, recorded, reported, and archived.

The GLP framework therefore extends across the entire study lifecycle rather than focusing only on the final lab results.

This is most important for pharmaceutical R&D becauseGLP does not establish that a drug candidate is effective. Nor does it independently determine whether a biological hypothesis is correct.

Instead, GLP provides the controls needed to ensure that the data generated during regulated non-clinical studies are credible, traceable, and suitable for review.

GLP should be positioned around data integrity, reproducibility, standard operating procedures (SOPs), trained personnel, suitable facilities, equipment control, documentation, and independent quality assurance.

This makes GLP particularly important as drug candidates move towards the clinical development stage. Promising pharmacological test results are only useful when it can be established how the data were generated and demonstrate that the underlying study was appropriately controlled.

The GLP System Connects People, Processes, and Data

GLP is sometimes treated as a documentation exercise. But that interpretation is too narrow.

The GLP framework creates an interconnected quality system involving personnel, study protocols, facilities, equipment, test systems, SOPs, raw data, quality assurance, reporting, and archiving. All of these components are fundamental elements of laboratory operation.

Each element addresses a different source of uncertainty.

Personnel must have the appropriate qualifications, training, responsibilities, and supervision; equipment must be suitable, maintained, and calibrated; study protocols define how experiments should be conducted, while SOPs establish consistent procedures for recurring laboratory activities, and test articles, specimens, reagents, and controls must remain identifiable and traceable.

The system also requires deviations to be documented and assessed rather than silently absorbed into the study record.

Image

Data integrity starts at the point of generation

Documentation is particularly important because laboratory data can lose evidentiary value when its provenance becomes unclear.

Attention should be paid to the recording of raw data, observations, calculations, analytical results, deviations, equipment information, and other study records throughout the study lifecycle. It also identifies the ALCOA principles for Good Documentation Practice: Attributable, legible, contemporaneous, original, and accurate.

For R&D teams, data integrity is not something that can be added immediately before a regulatory submission. It must be built into the way experiments are designed and executed.

That means knowing who generated a result, when it was generated, which instrument or method was used, which procedure governed the activity, whether deviations occurred, and where the original record resides.

This becomes increasingly important as research organisations operate across multiple laboratories, external partners, contract research organisations (CROs), and technology platforms.

View interview How AI Is Opening New Opportunities in Fertility Care for Pharma

How AI Is Opening New Opportunities in Fertility Care for Pharma

"If I had to get great minds in a room, I'd be looking at it from a tech perspective. How can we take all this knowledge, apply it to policies that can be easily distributed?” Clara Toombs, Fertilia AI.

Image

FDA Warning Letters Show Where GLP Breaks Down

The strongest insights come from examining actual regulatory failures rather than describing GLP only as a theoretical framework.

A review of eight U.S. Food and Drug Administration (FDA) warning letters, issued following inspections between 2019 and 2024, involving GLP compliance in preclinical research facilities categorised violations, assessed their regulatory basis, and examined associated corrective and preventive actions (CAPA).

The findings provide a practical picture of where laboratory quality systems can fail.

Five areas each accounted for 12% of the identified violations: failures by the Quality Assurance Unit (QAU) to fulfil its responsibilities, documentation deficiencies, non-compliance with study protocols, problems involving animal handling, and deficiencies in data capture within final study reports.

Failures involving study director responsibilities and SOP deficiencies each represented 10% of violations. Inadequate personnel qualifications accounted for nine per cent, while specimen, reagent, and control management represented six per cent. Record and specimen retention accounted for five per cent. 

These findings are significant because they show that GLP weaknesses are distributed across the laboratory system.

A laboratory can therefore possess sophisticated analytical instrumentation and highly capable scientists while still producing a study with regulatory weaknesses if responsibilities, procedures, documentation, or quality oversight fail.

GLP compliance is consequently a systems problem, not an equipment problem.

The FDA’s current description of non-clinical laboratory inspections reinforces this principle. Facilities conducting regulated non-clinical studies are inspected for compliance with 21 CFR Part 58, which establishes requirements for areas including personnel, facilities, equipment, protocols, study reports, and quality assurance oversight. 

Quality Assurance Must Remain Independent

Quality Assurance (QA) occupies a distinctive position within GLP because it provides independent oversight of study conduct.

A Quality Assurance Unit is responsible for verifying compliance with GLP requirements, approved protocols, SOPs, and applicable regulatory expectations. Its activities include inspections, audits, review of records, identification of deficiencies, and follow-up of corrective and preventive actions.

This independence is important.

The team conducting a study has a direct interest in generating and interpreting the experimental results. QA has a different responsibility of assessing whether the study was conducted according to the defined quality system.

That separation creates an internal challenge mechanism.

For pharmaceutical R&D, the value of QA independence extends beyond inspection readiness.

Independent QA can identify process weaknesses before they propagate into larger development programmes. It can expose recurring deviations, weak documentation practices, inadequate training, equipment problems, or gaps between written procedures and actual laboratory practice.

Therefore, training, role clarity, SOP quality, and QA oversight should be treated as development infrastructure rather than administrative overhead. 

Image

GLP Is Becoming More Important as R&D Gets More Complex

Modern drug discovery increasingly combines conventional pharmacology with complex biological models, advanced analytical platforms, automated experimentation, outsourced research, and large-scale data generation.

It also requires large cross-collaborative, interdisciplinary scientific teams.

That complexity increases the number of points at which evidence can become difficult to reconstruct.

A study may involve several instruments, operators, software systems, sample transfers, analytical methods, and external laboratories. Each hand-off introduces another requirement for traceability.

The OECD’s GLP framework already addresses this broader systems perspective. Its GLP principles cover responsibilities across test facility management, study directors, study personnel, QA personnel, facilities, equipment, SOPs, raw data, reports, and archives. 

The framework also supports international acceptance of appropriately generated non-clinical safety data. OECD’s Mutual Acceptance of Data system links regulatory confidence to studies conducted according to OECD Test Guidelines and GLP principles at facilities subject to appropriate compliance monitoring. 

For global pharmaceutical organisations, that harmonisation has strategic value.

A development programme may generate evidence across countries, CROs, specialist laboratories, and internal research sites. Consistent GLP systems help create a common quality language across those environments.

This is particularly relevant to outsourced R&D. Sponsors cannot assume that transferring a study to a CRO transfers the underlying quality responsibility. They still need confidence that the study design, execution, data, deviations, records, and final report can withstand regulatory scrutiny.

Pharmatica’s wider analysis of data integrity and ALCOA principles in pharma manufacturing explores the same underlying issue from a manufacturing perspective: Increasingly connected pharmaceutical environments require data to remain attributable, complete, traceable, and reliable throughout its lifecycle. The same principle applies to non-clinical R&D.

Read Maximise Pharma Innovation With Quality-by-Design

Maximise Pharma Innovation With Quality-by-Design

Find out how Quality-by-Design improves reliability and compliance in pharma and how you can maximise innovation through quality-based pharma development systems.

Image

From GLP Compliance to Better R&D Decisions

GLP is fundamentally a quality system, but its strategic importance reaches further.

Reliable laboratory evidence improves the foundation on which development decisions are made.

If data cannot be reconstructed or its provenance is uncertain, the problem is not confined to regulatory compliance. It can affect candidate selection, interpretation of safety signals, study repetition, programme timelines, and confidence in downstream evidence.

This makes GLP relevant to the economics of pharmaceutical R&D.

Poorly controlled studies can generate more than inspection findings, in the form of duplicated experiments, delayed decisions, additional investigations, and uncertainty about whether a result reflects genuine biology or a flaw in experimental execution.

Conversely, a well-controlled study creates a stronger evidentiary foundation for deciding what happens next.

GLP supports that reliable, reproducible, traceable research through structured procedures, documentation, qualified personnel, safety controls, and QA oversight. It is part of the infrastructure that makes experimental evidence decision-ready.

The next phase of laboratory transformation will add further complexity as automation, advanced data systems, and artificial intelligence enter non-clinical workflows.

Those technologies may change how GLP processes are performed, monitored, and documented, but the underlying requirement remains constant: Pharmaceutical R&D needs evidence that can be trusted.

That technology question warrants deeper examination in its own right.

What Comes Next for Good Laboratory Practice?

GLP remains the foundation of pharmaceutical R&D because it connects experimental execution with data integrity, quality assurance, and regulatory confidence.

The most consequential GLP weaknesses in pharma R&D often arise from fundamental operational failures, including documentation, protocol adherence, personnel responsibilities, SOPs, QA oversight, and data capture.

The strategic opportunity for pharmaceutical organisations is to treat GLP as research infrastructure. Stronger systems can make laboratory evidence more traceable, reproducible, and usable across the entire drug development lifecycle.

Pharmatica tracks the scientific, operational, and regulatory systems shaping pharmaceutical R&D, connecting evidence and emerging practice to the decisions that determine development quality and strategic impact.

Pharmatica: Insight. Connection. Impact.