automatic note beyond good laboratory...

Journal of Automatic Chemistry, Vol. 14, No. 5 (September-October 1992), pp. 189-191

Technical note

Beyond Good Laboratory Practice

Dick Le VittAnalytical Group Quality Manager, Hewlett-Packard Co.

Introduction

’Good Laboratory Practice’ (GLP) refers to a set ofgovernment regulations for non-clinical testing. Thephrase was first used in a regulatory context in NewZealand, where in 1972 the Testing Laboratory Actspecified conditions for planning, performing and record-ing studies so that results are reliable. Later, the US Foodand Drug Administration (FDA), followed by the En-vironmental Protection Agency (EPA), developed GLPregulations covering chemical safety and efficacy testing.The Organization for Economic Cooperation and Deve-lopment (OECD) and then the European Community(EC) required member states to adopt GLPs that areclosely related to those of the FDA and EPA. Manynations, including the UK, Germany, France, Italy andJapan, are in accord with the OECD/EC guidelines.

The primary motive behind these regulations is publicsafety. Government agencies around the world depend onindustrial laboratories for test results on medicines,pesticides, and other products that may have harmfuleffects on people or the environment. Unlike pureresearch, the work of these laboratories is not indepen-dently duplicated; agencies must trust in the initialaccuracy of studies that are funded by industrialsponsors.

This trust was violated in the 1970s. Evidence of fraudand error prompted the FDA, EPA and OECD to controltesting practice and improve the quality and integrity oftest data. A series of GLPs were created that have beenrefined and extended through the years. The experienceofthe agencies and participating laboratories has resultedin a set of truly good practices that have value for anytesting enterprise. GLPs are particularly useful whencritical decisions depend on the accuracy and trust-worthiness of results.

Despite the success of GLPs, however, they do notcompletely cover the notion of ’good’ in a modernlaboratory. This article highlights some limitations of theoriginal GLP regulations. The new EPA Good Auto-mated Laboratory Practices are described, and it isexplained how these, combined with state-of-the-artquality techniques, can carry laboratory managers beyondgood laboratory practice.

Scope of GLP

In regulatory language, good laboratory practice is amanagement system to ensure that experimental studiesproduce authentic results. These results must be indepen-

dently verifiable by a detailed audit of experiment plans,observational methods and raw data. To be in com-pliance with GLP regulations, a study must:

(1) Follow a documented experiment plan or protocol.

(2) Be conducted by appropriately qualified people.(3) Conform to documented Standard Operating Pro-

cedures (SOPs).(4) Be conducted in appropriate facilities.

(5) Use carefully calibrated and maintained equipment.

(6) Have original observations- raw data- archived andavailable for inspection.

When verifying compliance to GLPs, agencies relyheavily on two levels of inspection. The first level isinternal to the regulated laboratory. An independentQuality Assurance Unit (QAU) must review each studyfor compliance with GLP requirements. All reviews mustbe reported to management and documented along withrecords of discrepancies found and corrective actiontaken. The second level is an agency inspection. Anagency may perform a detailed inspection of anylaboratory over which it has regulatory jurisdiction.Included in the audit is a review of the internal QAU. Inaddition, an agency may audit an experiment in depthfrom raw data through final report.

These requirements define ’goodness’ from a regulatorypoint of view, but, as broad as they seem, they areincomplete. The regulations do not address all aspects ofquality assurance in the laboratory.

The limitations include:

(a) No guidance on the quality of the science. ThoughGLPs require a documented protocol, they havenothing to say about the effectiveness of the protocol,the power of the statistical methods used or theefficiency of the experiment design. Under GLPregulations, it is possible to conduct precisely a set ofuseless tests.

(b) Little attention to process improvement. GLPsrequire that all deviations from experimental proto-cols or SOPs be documented along with the rationalefor change. Instrument problems, human errors andretests must be recorded as well. However the GLPstreat these as isolated events to be logged for auditpurposes, not as measures of process performance. Aprocess management focus would encourage statisti-cal process control methods. Process thinking leads toroot cause analysis of problems and systematicelimination of error in future experiment runs.

(c) An emphasis on inspections to guarantee test quality.Like any other complex human.activity, inspections

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D. Le Vitt Beyond good laboratory practice

are subject to error. It is unrealistic to assume thatQAUs or agencies are capable of detecting all defectsin the large-scale studies done by today’s chemicaland pharmaceutical industries. QAUs can typicallydo no more than sample the work of their labs. TheFDA and EPA, though they aim for consistency,cannot completely eliminate the effects of individualdifferences among their inspectors. The outcome ofan audit is partly a function of the people who do theinspecting. Quality emerges when people strivecontinually to improve their tools and processes.

The process approach to quality

During the 1970s and early 1980s when the GLPs werebeing formulated, industrial firms in the USA andEurope used a policy ofquality management grounded inaudits and inspections by third party organizations, theQA or QC units, who did not report to production. Thisapproach became widespread in the years following theSecond World War and was adopted by many manufac-turers, including Hewlett-Packard.

In the meantime, another approach to quality gainedstrength among the Japanese. This quality managementstrategy originated with Walter Shewhart, a US statisti-cian, and came to post-WarJapan through the teachingsof Deming and Juran. In the following decades, theJapanese demonstrated the superiority of statisticalprocess control. This resulted in the belated recognitionof Deming and Juran in the West and led to a revolutionin our thinking about quality. Unfortunately, this revolu-tion came too late to influence the authors of GLPregulations.

Under the process approach, work is broken down intostages, each with suppliers (upstream process steps) andcustomers (downstream process steps). Errors are treatedas data, not as cause for blame. Suppliers and customersbecome partners working together for better results. Thisquality strategy can yield dividends that accumulate likecompound interest. The strategy is so powerful that it isoften called ’Total Quality Control’ or ’TQC’ by itsadvocates.

In the TQC method, the workers themselves are theprimary inspectors. Workers are encouraged to takeownership for quality. They track incoming qualitylevels, check their own results, and statistically analyseand correct the causes of process problems. In this way,the intrinsic error rate of the process is steadily drivendown. As error rates are reduced, the need for indepen-dent inspectors is diminished.

This it not to suggest that independent audits should beeliminated. In regulated laboratories, the QAUs andagencies will always be important safety nets for publicprotection. However, even in regulated industries, TQCis a better strategy than quality assurance throughinspections alone. Inspections add cost to any operation.TQC saves cost by eliminating the sources of error.

Of course, process management is not the only factor inJapan’s extraordinary success. However, it is an import-ant element in the productivity and quality that nation

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has achieved. Hewlett-Packard’s (HP’s) experience veri-fies this. In the early 1980s YHP, HP’s Japanesesubsidiary, won the Deming Prize. This achievementinspired other organizations in the company to examinetheir approach to quality. What followed was a transfor-mation. Hardware failure rates were reduced by a factorof 10 in a decade. Productivity increased: costs arisingfrom waste and rework were reduced. Independentinspections were also eliminated from the majority ofHP’s operations.

With the advent of Europe 1992, many laboratorymanagers have become interested in ISO 9000 as aframework for quality management. ISO 9000 is a set ofstandards that define requirements for a quality system.Although the EC has not universally endorsed ISO 9000,it has adopted the standards as European Norms, and hasissued directives affecting a few industries. Customerinterest in these standards caused a number of com-

panies, including Hewlett-Packard, to pursue ISO 9000certification.

ISO 9000 and GLPs are very much alike in theirapproach to quality. ISO 9000 is generic, to suit virtuallyany industry, while the GLPs are far more specific.Neither gives attention to statistical process manage-ment. Therefore adoption of ISO 9000 in a laboratorywill not naturally lead to the changes signalled byDeming and Juran.The challenge for laboratories is to work within therequirements of GLP to build processes that are bothefficient and mistake-proof. People being error prone asthey are, automation is important to this task.

The role of automation

Automated samplers, smart instruments and computersystems are generally thought of as productivity tools.Productivity is a major benefit of these devices. It is lesswidely recognized that these modern labour-savingtechnologies are also quality tools. The sampler thatinjects precisely the same amount time after timeminimizes variation in the process and reduces thechance of manual error. An instrument that remembersmethods, logs data and diagnoses its own health alsoreduces error. And the computer system that reads eachbar code, tracks each sample history, interrogates eachinstrument and prepares the report prevents manymistakes that can occur in a manual operation.

The potential that automation brings for laboratoryquality improvement has only just begun to be exploited.Many of the repetitive tasks and data logging functionshave been taken into the instruments and computersystems. However, these technologies are still not beingused to manage the processes using Total QualityControl.

Process management requries a lot of data. The naturaltool for collecting and analysing this data is the computerin a unified laboratory. In an ideal laboratory, a

Laboratory Information Management System (LIMS)

Do Le Vitt Beyond good laboratory practice

would track all errors and present the results usingcontrol charts, Pareto charts and the other tools ofTQC.The database would permit workers to do multilevelqueries and rapid diagnosis of roots causes. And SOPswould be on-line and ’instrumented’ with process metricsso that a diagnosis could quickly result in changes tostandard procedure.

The possibilities are limitless, more than enough to keepLIMS developers, VARs and system administrators busyfor some time. However, another regulatory guideline hascome along which will actually ease the task of using labsystems for process management. This guideline isGALP, or Good Automated Laboratory Practice.

Good automated laboratory practice (GALP)

For some years, regulators have realized that the originalGLPs do not adequately describe the management ofcomputer systems. To address the issue, the US DrugInformation Association published Computerized DataSystems for Nonclinical Safety Assessment in 1988. Anotherattempt was made to define system requirements by theUK Departments of Health in 1989. In 1990, the EPAresearched data management practices and their suit-ability to GLP. This work led to the GALP recommen-dations which are presently in draft form.

The EPA GALP standard applies the data qualityassurance principles ofGLP to computer systems. GALPdoes not replace the GLP, but clarifies and standardizesthe interpretation of GLP for computer-resident data.The GALP standard is based on six principles bestdescribed by the EPA’s implementation guide:

(1) Data: The system must provide a method of assuringthe integrity of all entered data.

(2) Formulae: The formulae and decision algorithmsemployed by the system must be accurate andappropriate.

(3) Audit: An audit trail that tracks data entry andmodification to the responsible individual is a criticalelement in the control process.

(4) Change: A consistent and appropriate change controlprocedure capable tracking the system operation and

application software is a critical element in thecontrol process.

(5) Standard Operating Procedures (SOPs): Control of eventhe most carefully designed and implemented systemwill be thwarted if appropriate user procedures arenot followed.

(6) Disaster: Consistent control of a system requires thedevelopment of alternative plans for system failure,disaster recovery and unauthorized access.

These are strong principles and their importance toLIMS users and developers should be clear. Also clear isthe bonus for process management that arises from thedemand for data integrity. Data integrity requires audittrails that give insight into errors, retests and other causesfor change. All that has gone wrong is on record. Whensystematically examined, the data kept for GALP com-pliance becomes a powerful mechanism for improving theintrinsic performance of laboratory processes.

Conclusion

GALP imposes special requirements on system users andoperators to guarantee the integrity of data. The require-ments for data entry, audit trails, change control andSOPs mean that histories must be kept ofall transactions.These histories must include all errors, rework, retestsand the like. This information is a gold mine for processmanagement.

The data that must be preserved under GALP includesthe very information needed to track and diagnoseprocess performance. A system managed to GALP willcontain records of all operations that change data,together with the reasons for change. This offers an

unparalleled opportunity for developers and users toapply the techniques of Total Quality Control towardlaboratory management.

Once the initial investment in equipment and training ismade for GALP, the added cost for process data is smalland the returns could be enormous.

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