Data Integrity In Regulated Laboratories
Data Integrityin RegulatedLaboratoriesAPRIL 2019Understanding the Scope of Data IntegrityR.D. McDowallUsing Data Process Mapping to Identify Integrity GapsR.D. McDowallWhat Is the Problem with Hybrid Systems?R.D. McDowallSponsored byThis custom ebook is sponsored by Agilent and presented in partnership with LCGC
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INTRODUCTIONAccording to R.D. McDowall, director of RD McDowall Limited in the UK(1), “Data integrity requires more than just ensuring that the calculatednumbers of an analysis are complete, consistent and accurate.” Rather,laboratories have many aspects to consider that all fall under the umbrellaof data integrity, from how to identify data gaps to taking the problems associated withhybrid computerized systems seriously.These data integrity topics (plus many more) were covered in a recently published LCGCNorth America multipart series* authored by McDowall on “Data Integrity in RegulatedLaboratories”; the first three articles in this six-part series are reprinted here.Building on a previous discussion of data integrity (2), the first article explores a fourlayer data integrity model that demonstrates the scope of a data integrity and datagovernance program. Next, McDowall discusses how data process mapping is a vital stepfor identifying data integrity gaps within chromatography data system processes. Here, healso explores ways to eliminate these gaps.The third installment of this series focuses on hybrid computerized system, namely, whatthey are, why they are problematic, and what laboratories can do to transition to fullycomputerized systems.References1. R .D. McDowall, LCGC North Am. 37(1), 44–51 (2019).2. M.E. Newton and R.D. McDowall, LCGC North Am. 36(5), 330–335 (2018).*Parts IV–VI of this series will be presented in future issues of LCGC.
anding the Scope of Data IntegrityUnderstanding theScope of DataIntegrityR.D. McDowallData integrity requires more than justensuring that the calculated numbers ofan analysis are complete, consistent andaccurate. There is much more to consider.The full scope of a data integrity and datagovernance program can be presented andexplained in a simple diagram.Welcome to “Data IntegrityFocus,” a six-part series ondata integrity in regulatedlaboratories that will alsobe of use to other readers working underquality standards such as ISO 17025 (1).We will explore some selected topics indata integrity and data governance. Tobegin, we will discuss the scope of a dataintegrity program.Last year in LCGC North America, MarkNewton and I wrote a six-part series on dataintegrity in the regulated chromatographylaboratory (2–7) in which we reviewed thewhole analytical process. In Part 1, we introduced briefly a four-layer model to explainthe scope of data integrity (3). In this firstpart of “Data Integrity Focus,” I would like to5APR IL 2 0 1 9 L C G Cgo into more detail of the model so that youcan understand the different strands of adata integrity program. Note the use of theword “program.” Data integrity has manystrands of work; it is not a single project.There are multiple projects that come underthe umbrella of a program. Let me explainthe data integrity model in more detail sothat you can see why.Data Integrity Within a Quality SystemOver the past decade, pharmaceuticalregulation has focused on the developmentof a pharmaceutical quality system (PQS)based on the ISO 9000 quality management system (QMS) following the publication of the International Council for Harmonization (ICH) Q10 guidance (8) and theupdate of EU GMP Chapter 1 (9). In a PQS,senior management have overall responsibility and accountability for all activitiesand data generated (8,9). Although dataintegrity has always been implicit in regulations, section 1.9 of EU GMP Chapter 1was updated so that work performed byImages under license from stock.adobe.comDataIntegrityModel
izedSystemsUnderstanding the Scope of Data IntegrityQuality Control laboratories must proceedas follows: “(iv)Records are made, manuallyand/or by recording instruments,which demonstrate that all therequired sampling, inspecting andtesting procedures were actuallycarried out. Any deviations are fullyrecorded and investigated (9).”Implicit in this definition is that the recordsgenerated have adequate quality and integrity. As an aside, EU GMP Chapter 4 on documentation and Annex 11 for computerizedsystems are being revised to emphasizedata integrity (10).Data Integrity ModelTo understand the scope of data integrity,a four-layer model has been developed,covering development, production, qualitycontrol (QC), and quality assurance (QA).The full GMP model is discussed in mybooks (11,12) and the initial discussionof the analytical portion was presentedin Spectroscopy (13). The four layers areshown in Figure 1 and described below fora regulated laboratory and QA only:Foundation: Right CorporateCulture for Data IntegrityThe foundation goes across all elements inan organization and is the data governancelayer. The elements here for data integrityare management leadership, data integritypolicies including data ownership, stafftraining in these procedures, management6APR IL 2 0 1 9 L C G Creview including quality metrics and the establishment, and maintenance of an openculture with ethical working by all staff.Level 1: Right Instrumentor System for the JobAnalysis requires analytical instrumentsand computer applications to ensure dataquality, and data integrity instruments mustbe qualified and software including spreadsheets must be validated. Included here arecalibration, point-of-use checks, or systemsuitability test samples to confirm that theanalytical instrument or laboratory computerized system is within user specificationsbefore use.Level 2: Right AnalyticalProcedure for the JobFor a laboratory, this is validation or verification of analytical procedures underactual conditions of use. What is notcovered in current regulations or guidanceis method development, which will determine the robustness of the procedure;this is the subject of a draft United StatesPharmacopeia (USP) general chapter 1220 (14) on analytical procedure lifecycle management (APLM).Level 3: Right Analysis forthe Right Reportable ResultHere, process development and productionprovide the laboratory samples for analysisthat are taken to demonstrate adequateproduct quality and conformance with theproduct specification in the marketing authorization (MA). It is this level where thework of the three layers below is essential
izedSystemsUnderstanding the Scope of Data IntegrityFigure 1: A Data Integrity Model. Reproduced with Permission from TheRoyal Society of Chemistry (11).for work to be performed ethically and correctly and where deviations occur they areinvestigated (9).This is an overview of the data integritymodel. We will now look in more detailat each level of the model for a regulatedlaboratory.Quality OversightAlthough shown on the left of Figure 1 because of the sample link between production and quality control, the QA functionis pervasive throughout the data integritymodel to provide quality oversight of bothproduction and laboratory operations,such as ensuring compliance with regulations, policies, and procedures as well asperforming data integrity audits and dataintegrity investigations.7APR IL 2 0 1 9 L C G CFoundation Level: Data GovernanceThe first level is called the FoundationLevel for a very specific reason: Data integrity and data governance start with seniormanagement involvement. Without it, anywork at the levels above will be wasted. Asshown in Figure 2, the Foundation Levelhas several elements that are essential fordata integrity, which are explained below.
izedSystemsUnderstanding the Scope of Data IntegrityFigure 2: Data governance functions at the foundation level.Adapted from reference (11) with Permission.Management Leadershipand Involvement with the PQS Senior management leadership for dataintegrity that includes a communicationprogram for all staff about the importance of data integrity and the impact itcan have on patients and the organization if instances of noncompliance arefound. Generation and use of quality metricsfor monitoring data integrity. This is thesubject of the two papers by Newtonand McDowall (7,15) and will not be discussed further here. As part of the Pharmaceutical Quality8APR IL 2 0 1 9 L C G CSystem, a review of the effectiveness ofthe data governance and data integrityprojects within the overall program. EUGMP Chapter 1 (9) mandates that management must review the QMS and dataintegrity is part of that review process.Policies and Procedures Needto Be in Place, Including: Writing a data integrity policy with initialand ongoing training in its contents.After being trained, all members of staffshould sign a commitment to data reliability for their work. Although the EMA
izedSystemsUnderstanding the Scope of Data IntegrityQ&A (16) notes that there is no regulation for a data integrity policy, guidancedocuments issued by the Medicines andHealthcare Products Regulatory Agency(MHRA), the World Health Organization (WHO), and the PharmaceuticalInspection Co-operation Scheme (PIC/S)guidances (17–21) all note that one isneeded. Good documentation practices coveringpaper, hybrid, and electronic processescoupled with training in the procedure.This should include defining a flexible analytical data life cycle that was discussedin a recent column (22) with more detailof this approach in my book (11). Interpretation of analytical data, such aschromatographic integration, comparisonof spectra using libraries, what are analysts allowed to do, and when and whatspecifically activities are prohibited. Training in these procedures withdemonstrable understanding of the contents evidenced by using questionnairesor practical execution of the procedure.Who Does What? The roles and responsibilities of all staffinvolved in a data governance and dataintegrity program including data ownership need to be documented and theinformation transmitted to all staff (11). Roles and responsibilities must be reinforced by appropriate sections in eachindividual’s job or position description. Incorporation of data integrity goalsinto personnel objectives must be completed.9APR IL 2 0 1 9 L C G CQuality Culture and theWorking Environment S enior management needs to create anopen quality culture where there are standards for expected behavior. This is probably the most difficult task in the wholedata integrity program: It does not consistof a single e-mail, but an ongoing task toaffect a culture change in an organization.It is not an event but a journey. ISPE haspublished a Cultural Excellence report(23) and there is also an abridged sectionon corporate culture in the recent GAMPGood Practice Guide on Data integrity –Key Concepts (24). Gemba walks where management getsto see issues in the laboratory first handrather than filtered by subordinates (23).It is also an opportunity to influence staffdirectly by promoting the open culture andowning up to mistakes. Accordingly, there is an expected behaviorand an open culture where mistakes canbe admitted without blame. Admittingmistakes is also a regulatory expectationas noted in the Analyst Responsibilitiessection of the FDA OOS Guidance (25)that states:“If errors are obvious, such as thespilling of a sample solution or theincomplete transfer of a samplecomposite, the analyst shouldimmediately document what happened. Analysts should not knowingly continue an analysis theyexpect to invalidate at a later timefor an assignable cause (i.e., analyses should not be completed for thesole purpose of seeing what results
izedSystemsUnderstanding the Scope of Data Integritycan be obtained when obvious errors are known).”Outsourcing Work Any outsourced work requires an assessment of the outsourcing organization’sor laboratory’s data governance and dataintegrity status before technical and quality agreements are written and signed.Level 1: Integrated Instrument Qualification and Computer ValidationThere is little point in carrying out ananalysis if an analytical instrument is notadequately qualified, or the software thatcontrols it or processes data is not validated. Therefore, at Level 1, the analyticalinstruments and computerized systemsused in the laboratory must be qualified forthe specified operating range, and validatedfor their intended purpose, respectively.There are the following sources: USP 1058 for Analytical InstrumentQualification, 2018 version (26) G AMP Good Practice Guide for Validation of Laboratory Computerised Systems, 2nd Edition (27) V alidation of Chromatography Data Systems, 2nd Edition (12)These documents provide guidance andadvice on these two interrelated subjects.Indeed, the new version of USP 1058 integrates instrument qualification and computer validation for analytical equipment (26)and the integrated approach is discussed inmore detail in recent publications (12,28–30)A user requirements specification must bewritten for both instruments and softwareto define the intended use and against10APR IL 2 0 1 9 L C G C“There is little point incarrying out an analysis ifan analytical instrument isnot adequately qualified, orthe software that controlsit or processes data is notvalidated.”which the instrument will be qualified andthe software validated. Where the softwareapplication must be configured to protectelectronic records generated by the system,this must be reflected in the validationdocuments for the application software. Byimplementing suitable controls to transfer,mitigate, or eliminate any record vulnerabilities so that they can be adequately protected and ensure data integrity. Burgessand McDowall in an earlier LCGC seriesabout an ideal chromatography data system(CDS) discussed some of the architecture,workflow and compliance requirements forensuring data integrity (31–34).Failure to ensure that an analytical instrument is adequately qualified or software isadequately validated means that all workin the top two levels of the data integritymodel is wasted, as the quality and integrityof the reportable results is compromised byunqualified instrumentation and unvalidatedand uncontrolled software.Assessment, remediation, and long-termsolution of paper processes and computerized systems are also included in this levelof the model.
izedSystemsUnderstanding the Scope of Data IntegrityLevel 2: AnalyticalProcedure Lifecycle ManagementUsing qualified analytical instruments withvalidated software, an analytical procedureis developed or established, and then validated or verified. The GMP requirement isthat analytical methods must be verifiedunder actual conditions of use as per 21CFR 211.194(a)(2) (35), and, therefore, be fitfor its intended use.There are several published referencesfor method validation from ICH Q2(R1)(36), FDA validation guidance documents(37,38) and the respective chapters inthe European Pharmacopoeia (EP) andUnited States Pharmacopoeia (USP).However, the focus of these publicationsis validation of an analytical procedurethat has been already developed. Methoddevelopment is far more important, asit determines the overall robustness orruggedness of any analytical procedure,but this process receives little or no attention in these publications. However,this analytical world is changing; followingthe publication in 2012 by Martin et al(39), there is a draft USP 1220 on TheAnalytical Procedure Lifecycle (14), issued for comment. This will mean a movefrom chapters focused only on validation,verification, or transfer of a method to alife cycle approach to analytical chaptersthat encompass development, validation,transfer, and continual improvement ofanalytical methods.A life cycle approach to analytical procedures validation means that definition ofan Analytical Target Profile (ATP) leads togood scientifically sound method develop11APR IL 2 0 1 9 L C G C“A life cycle approach toanalytical proceduresvalidation means thatdefinition of an AnalyticalTarget Profile (ATP) leadsto good scientifically soundmethod development thatends with the definitionof the procedure’s designspace.”ment that ends with the definition of theprocedure’s design space, which now becomes important, as changes to a validatedmethod within the validated design spacewould be deemed to be validated per se.There will be a transition period wherethe old approach is phased out while thenew one is phased in. There is currentlyan ICH initiative that began in 2018 toupdate ICH Q2(R1) (36) to a life cycle approach (40).Verification ofPharmacopoeial MethodsGiven the vague descriptions of most analytical methods in various pharmacopoeias, it isamazing that any laboratory can get a method working at all. In essence, pharmacopoeial methods are unlikely to work as written.One of the reasons is that if a method forhigh performance liquid chromatography(HPLC) is developed using a specific supplier’s C18 column, the only information about
izedSystemsUnderstanding the Scope of Data IntegrityFigure 3: Interaction of the four levels of the data integrity model. Adapted with permission from reference (11). Definition of acronyms: Researchand development (R&D), contract research organization (CRO), analyticalinstrument qualification (AIQ), computerized system validation (CSV), andsystem suitability test (SST).the column that appears in the monograph isa description of the packing and the columndimensions. For gradient methods, there isno information about whether the gradientis formed using a low-pressure or highpressure mixing pump. For these reasons,analytical procedures based on pharmacopoeial “methods” need to be developedand verified under actual conditions of useas required by 21 CFR 211.194(a)(2) (35).The pharmacopoeia simply provides anindication of where to start but the details12APR IL 2 0 1 9 L C G Care left to the individual laboratory to develop, document, and verify.Level 3: Analysis of SamplesFinally, at Level 3 of the data integrity model, the analysis of sample will be undertakenusing the right analytical procedure, using aqualified analytical instrument and processing with validated software applications. Tobe successful, this also requires an openenvironment that enables data to be generated and interpreted, and the reportable
izedSystemsUnderstanding the Scope of Data Integrityresult to be calculated, without bias or manipulation of data. Staff should be encouraged to admit any mistakes and there mustbe a no-blame culture in place based on theleadership of senior management from thefoundation level of the model. It is also important not to forget the importance of theoverall pharmaceutical quality system in providing the umbrella for quality such as theinvestigation of out-of- specification results,managing deviations and developing corrective and preventative actions. Figure 3shows an analysis in practice and how thevarious levels of the data integrity modelinteract with each other. There are also thefollowing elements of data governance: performing the work correctly and contemporaneously including any deviations effective and comprehensive second person reviews of the work visibility of errors investigation of aberrant results availability of both paper and electronicrecords for audit or inspection monitoring the development and maintenance of operational data integrity procedures and training. These complete the laboratory levels of thedata integrity model shown in Figure 3 butdon’t forget the quality oversight (checks ofcurrent work plus data integrity audits andinvestigations) shown in Figure 1.Quality Does Not Own Quality AnymoreFigure 3 shows how the various levels ofthe laboratory data integrity model interacttogether. However, without the Foundationlayer, how can the three other layers hopeto succeed? The onus is on trained staff to13APR IL 2 0 1 9 L C G Cact ethically. Also, without qualified analyticalinstruments and validated software, how canyou be assured of the quality and integrityof the data used to calculate the reportableresult? And so on up the levels of the model.It is less important where an individualactivity is placed in the various layers; theprimary aim of this model is to visualize forchromatographers and analytical scientiststhe complete scope of data integrity.If the data integrity model works from thefoundation through the three levels that exist on top, it means that the responsibilitiesfor data integrity and data quality are nowdispersed throughout the laboratory and organization, whilst the overall accountabilityfor quality oversight remains with a qualityassurance function. It is not the role of quality assurance to fix other people’s mistakes.The responsibility for data integrity and dataquality in the chromatography laboratorylies is with the analytical staff performingthe work, showing that quality (that is, thequality control department) does not ownquality anymore. Everyone in the laboratoryand the whole organization does.Data Integrity Guidancesand the Data Integrity ModelWhen the material in the data integrity guidance documents from MHRA, FDA, EMA,WHO and PIC/S (Refs) are compared withthe model, there are several gaps and thereis no mention of: a nalytical instrument qualification and fitness for intended use in comparison witha heavy emphasis on control of computerized systems, nor analytical procedures, including robust
izedSystemsUnderstanding the Scope of Data Integrity“If the data integrity modelworks from the foundationthrough the three levelsthat exist on top, it meansthat the responsibilitiesfor data integrity and dataquality are now dispersedthroughout the laboratoryand organization.”method development and procedurevalidation.All layers of the data integrity model areessential to ensure data integrity in a chromatography laboratory.SummaryIn this column, we have looked at a four-layer data integrity model to cover the wholescope of a data integrity program. The layers are interactive; ensuring data integritydepends on a foundation of data governance, qualified analytical instruments, andvalidated software with properly developedand validated robust analytical procedures.In the next article in this series, we will lookat a way of identifying data integrity vulnerabilities in paper processes and computerized systems.References(1) ISO 17025-2017 General requirements for the competence of testing and calibration laboratories. 2017,International Standards Organization: Geneva.(2) M.E. Newton and R.D. McDowall, LCGC North Am.36(5), 330–335 (2018).(3) M.E. Newton and R.D. McDowall, LCGC North Am.36(1), 46–51 (2018).(4) M.E. Newton and R.D. McDowall, LCGC North Am.36(4), 270–274 (2018).(5) M.E. Newton and R.D. McDowall, LCGC North Am.36(7), 458–462 (2018).(6) M.E. Newton and R.D.McDowall, LCGC North Am.36(8), 527–529 (2018).(7) M.E. Newton and R.D. McDowall, LCGC North Am.36(9), 686–692 (2018).(8) ICH Q10 Pharmaceutical Quality Systems. 2008,ICH, Geneva.(9) EudraLex - Volume 4 Good Manufacturing Practice(GMP) Guidelines, Chapter 1 Pharmaceutical QualitySystem. 2013, European Commission: Brussels.(10) Work plan for the GMP/GDP Inspectors WorkingGroup for 2018 2017, European Medicines Agency:London.(11) R.D. McDowall, Data Integrity and Data Governance: Practical Implementation in Regulated Laboratories. (Royal Society of Chemistry Publishing,Cambridge, UK, 2019).(12) R.D. McDowall, Validation of Chromatography DataSystems: Ensuring Data Integrity, Meeting Business and Regulatory Requirements (Royal Societyof Chemistry Publishing, Cambridge, UK, 2nd ed.,2017).(13) R.D. McDowall, Spectroscopy, 31(4), 15–25 (2016).(14) G.P. Martin et al., Stumulus to the Revision Process: Proposed New USP General Chapter: TheAnalytical Procedure Lifecycle 1220 Pharmacopoeial Forum, 43(1), 2017.(15) M.E. Newton and R.D. McDowall, LCGC Europe,30(12), 679–685 (2017).(16) EMA Questions and Answers: Good Manufacturing Practice: Data Integrity. 2016; Availablefrom: http://www.ema.europa.eu/ema/index.14APR IL 2 0 1 9 L C G C
izedSystemsUnderstanding the Scope of Data Integrityjsp?curl pages/regulation/general/gmp q a.jsp&mid WC0b01ac058006e06c#section9.(17) MHRA GMP Data Integrity Definitions and Guidance for Industry 2nd Edition. 2015, Medicines andHealthcare products Regulatory Agency: London.(18) MHRA GMP Data Integrity Definitions and Guidance for Industry 1st Edition. 2015, Medicines andHealthcare products Regulatory Agency: London.(19) MHRA GXP Data Integrity Guidance and Definitions. 2018, Medicines and Healthcare productsRegulatory Agency: London.(31) R.D. McDowall and C. Burgess, LCGC North Am.33(8), 554–557 (2015).(32) R.D. McDowall and C. Burgess, LCGC North Am.33(10), 782–785 (2015).(33) R.D. McDowall and C. Burgess, LCGC North Am.33(12), 914–917 (2015).(34) R.D. McDowall and C. Burgess, LCGC North Am.34(2), 144–149 (2016).(20) WHO Technical Report Series No.996 Annex 5Guidance on Good Data and Records ManagementPractices. 2016, World Health Organization: Geneva.(35) 21 CFR 211 Current Good Manufacturing Practicefor Finished Pharmaceutical Products. 2008, Foodand Drug Administration: Sliver Springs, MD.(21) PIC/S PI-041 Draft Good Practices for Data Management and Integrity in Regulated GMP / GDPEnvironments. 2016, Pharmaceutical InspectionConvention / Pharmaceutical Inspection Co-Operation Scheme: Geneva.(36) ICH Q2(R1) Validation of Analytical Procedures:Text and Methodology. 2005, International Conference on Harmonisation: Geneva.(22) R.D. McDowall, Spectroscopy 33(9), 18–22 (2018).(23) ISPE Cultural Excellence Report. 2017, InternationalSociety of Pharmaceutical Engineering: Tampa, FL.(24) GAMP Good Practice Guide: Data Integrity - KeyConcepts. 2018, International Society for Pharmaceutical Engineering: Tampa, FL.(25) FDA Guidance for Industry Out of SpecificationResults.
Building on a previous discussion of data integrity (2), the first article explores a four-layer data integrity model that demonstrates the scope of a data integrity and data governance program. Next, McDowall discusses how data process mapping is a vital step for identifying data integrity gaps within chroma
EMA: Good Manufacturing Guidance to Ensure the Integrity of Data 23 Q&A 10 Aug 2016 PIC/s: Draft Guidance Good Practices For Data Management And Integrity In Regulated GMP/GDP Environments 41 pages July 2016 MHRA: GXP Data Integrity Draft Guidance 14 pages April 2016 FDA: Draft Guidance: Data Integrity
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