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Regulatory Matters


Ludwig Huber and Wolfgang Winter

he first three parts of this series described the requirements of the FDA rule on electronic signatures and records. We focused on data security and data integrity, demonstrating how access to the system and to critical system functions could be limited to authorized personnel and how the integrity of the data could be assured at the time of data analysis and evaluation (1­3). We discussed how the creation, modification, and deletion of records are recorded in a computer-generated audit trail. According to a column by Barbara Immel, records and validation issues were among the top

. . . procedures and controls shall include the following: (b) The ability to generate accurate and complete copies of records in both human readable and electronic form suitable for inspection, review, and copying by the agency. . . . (c) Protection of records to enable their accurate and ready retrieval throughout the records retention period. (5)

Implementing 21 CFR Part 11 in Analytical Laboratories Part 4, Data Migration and Long-Term Archiving for Ready Retrieval

The fourth installment of the series describes the need for, and how to achieve, long-term data archiving and retrieval of electronic records and explains which types of data should be stored. Recommendations for selecting media and software ensure not only long-term storage, but "ready retrieval" for data reprocessing as required by the FDA rule.

Wolfgang Winter is worldwide product manager, data systems, and corresponding author Ludwig Huber is worldwide product marketing manager, HPLC, at Agilent Technologies GmbH, PO Box 1280 D-76337, Waldbronn, Germany, +49 7243 602 209, fax +497243 602 501, [email protected],

problem areas discovered in domestic and international preapproval inspections in 1998 (4). But 21 CFR Part 11 contains more than what we have discussed. It also requires ensuring data integrity through the entire retention period detailed in the applicable rules for good laboratory practices (GLP), good clinical practices (GCP), and current good manufacturing practices (CGMP). Regulations in Part 11 refer only to the use of electronic records and signatures, not to which records need to be retained or for how long. Those guidelines can be found in previously published regulations called predicate rules (a previously published set of rules such as GLPs, GCP, or CGMPs that mandate what records must be maintained, the contents of those records, whether signatures are required, and how long the records must be retained). Long-term archiving and ready retrieval of records throughout the mandated lifetime is probably the requirement most difficult to implement.

Archiving and Retrieval

Long-term storage and ready retrieval are mentioned in paragraph 11.10 (b) and (c) of 21 CFR, Part 11.

Implementation challenge. The difficulty in implementing this part of the rule is caused by a combination of three challenges. Records must be stored and available in electronic form. Data can be printed, but printed material is not a substitute for an electronic record, and the electronic record must not be deleted. The "typewriter excuse" is now invalid (3,6). Records must be stored as "complete and accurate" copies. A complete copy includes metadata, such as processing parameters and audit trail logs (3). In chromatography, metadata include integration parameters and calibration tables (2). Metadata allow reviewers to obtain the original results from the raw data. Records must be readily available throughout the entire retention period. Inspectors want to be able to replay data using the same process the system operator used when the data were initially generated. Although that is usually possible shortly after the analysis was carried out, it is more difficult several years later. The retention period is specified in the predicate rules and local legislation and may be up to 10 years or more. FDA reasoning. At the time the proposed rule was prepared, industry comments suggested alternative wording that would make providing electronic copies optional, suggestions such as that companies could provide FDA with paper copies only (5). Dissenters to the rule argued that providing FDA with electronic copies was unnecessary, unjustified, and impractical considering the different types of computer systems that might be in use. In the preamble to the rule and at conferences, FDA and its representatives defended their position with three primary reasons that suggested why paper printouts were no substitute for electronic records. You cannot print on paper everything that's available in electronic form. FDA wants to use the same tools to evaluate the data that the operator used to create the data. For example, FDA wants to

Regulatory Matters

be able to reintegrate chromatograms using original integration parameters to see whether they make sense. FDA wants to take advantage of modern electronic search tools, which are expected to make inspection work more efficient. Without such tools, inspections would take longer to complete, resulting in delays in the approval of new medical products. To operate effectively, the agency must function on the same technological plane as the industries it regulates. Archiving and replaying data in laboratories is usually easy as long as the same computer hardware, operating system (OS), and application software are still in use. Processing parameters are stored in the same folder as raw data. The original raw data and the processing parameters can be reloaded, and the data can be reprocessed. The problem occurs when new application software, a new operating system, or an entirely new computer system is loaded, and the original system is retired.

FDA Guidelines

ability to access old records. Many in the industry are concerned that old computer hardware will have to be retained. Section 71 of the Part 11 summary states, "The agency notes that . . . persons would not necessarily have to retain supplanted hardware and software systems provided they implemented conversion capabilities when switching to replacement technologies" (8). Similarly, Paul Motise, senior staffer and consumer safety officer in CDER's Office of Compliance, said during a conference in


is translating data from one format or storage device to another. It is needed when a company adopts a new system that is incompatible with the one it used previously.

selected (because the stored digital information needs to last for 10 years or more) and how records will be read, reevaluated, and printed if the software used to generate the original result is obsolete. This article discusses alternative ways to meet the requirement for "ready retrieval." We use chromatographic data as examples. We approach the problem by dividing the solution into two parts: the selection of storage media and considerations for longterm storage, and the selection of software to read and reevaluate the data. Before addressing those considerations, we first discuss the types of data that need to be archived and retrievable. We also briefly discuss the storage requirements for signal and spectral detectors in chromatography.

Types of Records to Store

FDA has stated clearly in an industry guide and several conferences that Part 11 regulations extend beyond the retirement of a computer system. For example, in the following passage, FDA refers to the use of computers in clinical trials.

Recognizing that computer products may be discontinued or supplanted by newer (possibly incompatible) systems, it is nonetheless vital that sponsors retain the ability to retrieve and review the data recorded by the older systems. This may be achieved by maintaining support for the older systems or transcribing data to the newer systems. . . . FDA expects to be able to reconstruct a study. This applies not only to the data, but also how the data were obtained or managed. Therefore, all versions of application software, operating systems, and software development tools involved in processing of data or records should be available as long as data or records associated with these versions are required to be retained. Sponsors may retain these themselves or may contract for the vendors to retain the ability to run (but not necessarily support) the software. Although FDA expects sponsors or vendors to retain the ability to run older versions of software, the agency acknowledges that, in some cases, it will be difficult for sponsors and vendors to run older computerized systems. (7)

The question is how to achieve that

Berlin, "The agency did not expect companies to save computer hardware and software for the sole purpose of recreating events. We anticipated that it would be possible to make an accurate and complete copy of those electronic records." Consequences for laboratories. From those comments, we conclude that records must be kept electronically and be retrievable whether or not the computer system used for original data entry and evaluation is still in use, and it is the user's responsibility to make sure that data can be retrieved in its original form. That can be achieved by keeping older systems' hardware and software available or by converting data to a new system. Two primary considerations in selecting a method for retrieving data in its original form are the type of storage media

FDA's guidance for using computers in clinical trials describes which types of information need to be stored and migrated to new systems. It is important to generate accurate and complete copies of study data and collateral information relevant to data integrity. That information would include, for example, audit trails and computational methods used to derive the data. Any data retrieval software, script, or query logic used for the purpose of manipulating, querying, or extracting data for generating reports should be documented and maintained for the life of the report. The transcription process must be validated (7). Types of data. In analytical measurements, we typically have three types of data: raw or original data, processed data, and metadata. When the definition of raw data for computerized systems was discussed a few years ago, the recommendation was that a user could decide whether raw data were the original data captured on the computer or the first computer printout. At that time, raw data could be defined and stored on paper. Part 11 has changed that. Raw data are created when saved the first time on a durable storage device, typically a computer's hard drive. In chromatography, raw data are usually area slices calculated from predefined time slices and from the intensity of the signal (Figure 1). From raw data, the software calculates peak areas and amounts, and the results are processed data (in long division, "1,000 5" would be the raw data, the work you had to show on your paper in fourth grade math

Regulatory Matters

Examples of short life-spans are the 8-inch floppy disk, the 5.5-inch floppy disk, tape cartridges, hard sectored disks, and seventrack tapes. Not only do the media disappear from the market, but the appropriate drives capable of reading the information on those media disappear also. Damaged data. Another problem with digital media is that it is difficult to determine when the data are damaged. Because of our lack of experience with the longevity of digital media and the risk of media and drive obsolescence, records should be copied to new media, either to the same type of media or to the new media the company has selected. The chain of media storage must be unbroken throughout the entire lifetime of the records. More difficult challenges in the storage and retrieval of digital information lie in the software's ability to make the information available in human readable form.

Software to Read/Interpret Records

Signal (mV)

Raw data (area slices)

Metadata processing parameters, integration parameters, calibration tables


Peak area

Processed data Peak area, amount

Figure 1. Raw data, metadata, and processed data in chromatography

class would be the metadata, and "200" would be the processed data.). The parameters used for calculating the processed data from the raw data are metadata. In chromatography, metadata are mainly integration parameters and calibration factors. The predicate or applicable regulatory rules define what type of data must be archived. For example, some rules require archiving raw data; others do not. Spectral data. UV-visible (UV-vis) diode array detectors in HPLC and mass spectrometers are both spectral detectors that add a third dimension of wavelength or mass to run time and signal intensity characteristics. Spectra are acquired during the run and used to confirm a compound's identity and to check a peak's purity. Those runs can generate a large amount of data. Although storage capacity is no longer much of a problem, we need to be creative in handling all that data in a database. With a long chromatogram (for example, one hour with a few peaks), acquiring all spectra during the entire run would be a burden because the only spectra needed are from a peak's elution. We can do that with appropriate diode array detector (DAD) firmware (a combination of hardware and software with the programming written directly into read-only memory). The benefits of acquiring the spectra during the peak's elution are immediately apparent only when we look at the storage capacity needed for that example: 14 megabytes of storage are needed for that single chromatogram when all spectra are stored for the entire run, but only 400 kilobytes are

needed when we acquire spectra just during a peak's elution. To acquire spectra only during a peak's elution requires a peak detector in the firmware of the DAD, which cannot be accomplished as part of the computer software alone. The type of data stored depends on the applicable predicate rule. Part 11 requires storing all data in electronic form if they are saved on a durable storage device. That includes the original or raw data, metadata, and processed data. Motise makes that clear in his statement about raw data storage in chromatography: "GMPs require you to keep all laboratory data for as long as the batch record must be kept and that includes the chromatographic raw data itself." (9)

Storage Media

Electronic records can be stored on a variety of media, such as computer hard drives, digital tapes, CD ROMs, and digital video disks (DVDs). Selection criteria for the most appropriate media are the information technology (IT) environment, existing practices, storage capacity, anticipated physical life-span, and most important, the anticipated lifetime until obsolescence. When you are determining retention time, obsolescence is more important than the physical life-span of storage media. Based on historical evidence, storage media can be expected to become obsolete within five years. Most types of media may have longer life-spans, but there are no guarantees that they will be available longer than five years. The market makes older storage media obsolete when better new ones are available.

Digital information is recorded as 0s and 1s. Therefore, that information makes sense only with software that understands how to access and convert the sequence of 0s and 1s into meaningful numbers. We all know about that from word processors and presentation programs. To replay an animated presentation requires the original software and selected revisions of that software. If such software is unavailable from the presenter, viewer software is distributed together with the presentation file. Similarly, text documents lack correct headers, footnotes, and sometimes formatting such as paragraph types, highlighting, and indentation when reprocessed with something other than the original word processing software. Similarly, if we transform a spreadsheet into a table, we need the formula that related the cells of the table to each other. The situation is similar with records acquired from analytical instruments such as chromatographs and spectrometers. The software from chromatographic data systems integrates peaks and calculates amounts of analyte by comparing the results of the unknown sample with known standards. If you want to obtain the same results years after an analysis was done, you still need the same software. Before Part 11 was issued, a common practice was to save the information in a more generic file format like the

Regulatory Matters

analytical data interchange (ANDI) format (10,11). Analysts could reload, display, and print chromatograms and reports, together with information on the operator's name, analytical methods, and calibration data across data systems from different vendors. The disadvantage of that approach is that it did not allow replaying the data on a different system to yield the same result. That disadvantage makes the practice of saving information in a generic file format unacceptable for the current interpretation of Part 11. Inspectors want to be able to reprocess information and generate results in the same way as the original operator. That means we need to store and retrieve raw data, processing parameters, and other "transformations" such as method specific calculations, calibration tables, and recalibration history data. As long as the same data system software is used, that poses little problem. Instrument vendors usually take care that the same results are obtained when moving from one revision to another, as long as the software is on the same platform (the same hardware and software). Ideally, processing parameters are stored in a folder together with raw data and results. Typically, problems arise if the vendor moves to a new platform or if the company selects a new vendor to meet business or technical requirements, or because the previous vendor has gone out of business. If the system is retired, the company may no longer have the software to reprocess older data. Alternative solutions. In theory several solutions are available for reprocessing older data. In practice, each has severe limitations. Before Part 11, four solutions existed, and the first was to print and save digital information as hard copy. With Part 11 in effect, three solutions remain: using data and software standards so that data can be shared between different software packages; keeping old computer hardware, application software, and operating systems; and migrating data to new systems in a supervised, controlled process.

Data and Software Standards

· · · ·

1 signal Peak spectra (apex, slopes) A1 spectra during a peak A1 spectra during a run UV-vis diode array, run one hour, 10 peaks

290 32 400 14,000

kb kb kb kb 14,000 Kb 400 Kb

Figure 2. Storage requirements for signal and spectral detectors with different spectral

acquisition modes

Using data and software standards means that the user of a system generates raw data and analytical results using a specific set of processing parameters. The data format is generic, agreed to by different vendors, and is therefore interchangeable. Some attempts

at standardizing processing parameters have been made through the Analytical Instrument Association (AIA, Alexandria, VA), which developed the ANDI protocols. Their efforts are insufficient for compliance with Part 11 -- the current limitations include missing spectral functionality, for example. Now, the American Society for Testing and Materials (ASTM, West Conshohocken, PA) committee is working on further definitions, but no results are in sight. Standard functions. The real difficulty for the standards approach is finding a common set of data processing functions, rather than agreeing on a standard data file format. The standards approach works only if all software products have the same functionality or feature set and probably require the same algorithms (a formula or set of steps for solving a particular problem) for data evaluation. To survive in the market, products (including software) must introduce proprietary functionality. If "ready retrieval" requires all software be able to regenerate the results, that also means all software must have the same functionalities. To secure and extend their market positions, vendors will always introduce new functionalities requested by users to increase their productivity. That natural behavior conflicts with the generic standards approach. Whereas the standards approach can work for well-established and static techniques, standards will be difficult to achieve for the emerging technologies used when developing new drugs.

Even though instrument vendors intend to work on developing standards, no such generic solution is available now. Without an available generic standard, system portability (using a program on a different computer without modification) and a software vendor's track record for migrating legacy systems (inherited systems from earlier platforms) should be the selection criteria for a new data system. Designing portable software. The key challenge in using portability as the data retention solution is the technical feasibility of data migration. Visiting research fellow at the University of Surrey, R.D. McDowall (McDowall Consulting, Bromley, UK) cautions that "Although many vendors adhere to the netCDF format, there are small differences that may prevent a full match between systems and the results they produce" (12). The migration must be carefully considered when moving data between systems from different vendors, and when systems are replaced with new ones from the same vendor. "So, the data when retrieved must be able to be reanalyzed with the same results. To do this you may need to have the original software or the software will need to ensure that historical data can be imported and reprocessed to obtain the same results," suggests McDowall (13). Portable algorithms. By applying state-of-theart software design methodology, a vendor can prevent most of the difficulties associated with changing software and hardware platforms. Proper encapsulation (combining elements to make a new entity)

Regulatory Matters

chromatography results to be recorded using freely definable chromatograms and evaluation parameters, and those results are stored in a binary checksum protected register, which counts the number of bits transmitted and included with the transmission so that the receiving program can check to see whether the same number of bits arrived. When operational qualification is necessary after a revision, the update reruns the verification test and compares the newly determined results with the prerecorded results. The outcome of that comparison is documented in an appropriate system verification report (Figure 4).

Keeping Old Hardware in Museums

Figure 3. Regression test tool used to qualify results from Cerity (Agilent Technologies)

against prerecorded test specifications

of the algorithmic portions of a system from the user interface and other operating system­dependent layers of the physical system lessens the dependency on a particular system environment or operating system. That method of structuring software enables portable algorithms that vendors can use across generations of systems or with new ones specifically designed for a different operating system. Agilent Technologies (then called Hewlett-Packard) successfully used that approach when designing the Generic Integration Engine (GENIE) integration algorithm almost 20 years ago and for the algorithms for peak identification, quantification, and calibration first used in Agilent ChemStations in 1991. Portable algorithms allow a software vendor to test the algorithms separately from the rest of the system and to test them under a different operating system. For example, Agilent tested its quantitative algorithms under both Windows (Microsoft, Richmond, WA) and Unix (Bell Labs, Murray Hill, NJ) long before Windows NT became dominant. Using the same algorithms in established data systems like ChemStations and in the new Cerity networked data systems (Agilent) is a prerequisite for enabling the migration of legacy electronic records. Portable algorithms allow vendors to continue maintenance, providing users with a viable support path for the future.

Designing laboratory­specific applications that use a component­based software platform was recently discussed in the literature (14,15). Revalidation and regression testing. The described encapsulation approach allows the test engineering teams employed by vendors to develop powerful computer-based, automated regression test suites (software that tests changes to computer programs to make sure that the older programming works with the new changes). Even in the early stages of a software product's life cycle, those test suites allow a vendor to perform extensive software testing on a particular module. Known input can be fed into the module interfaces, and the resulting output can be compared with predefined test specification results (Figure 3). Regression test software can automatically determine whether the result is within the defined acceptance limits, and it can flag deviations. An invaluable tool during systems development and testing, the same approach can be applied effectively and consistently to other aspects of system validation, particularly for the revalidation or requalification of a system, such as for a revision update supplied by the vendor. System verification tools. For chromatography data systems, our company has implemented a "system verification" utility in the ChemStations. The utility allows

The second method for data retention and ready retrieval is to keep a generation of computer hardware, application software, and operating systems in some kind of a computer museum. Old computers run the original software to access, reevaluate, and display original results. The only advantage to this approach is that it makes your company independent of vendors. It can be a temporary solution if a specific vendor goes out of business. Problems associated with the museum solution are well known. Computer chips have a limited lifetime. Integrated circuits decay because of processes such as metal migration and dopant diffusion. Obsolete computers are difficult to keep running at a reasonable cost for a long period of time. Even if large companies could afford it, access would be limited to one or a few sites in the world, which would prohibit ready retrieval. So computer museums are an unreliable solution to data retention and ready retrieval.

Data Migration to New Systems

Data migration entails making sure either that new systems can process data generated on older systems or that data can be converted to work on a new system. Typically that approach works well when a single vendor is involved, the time periods are short, and the software remains on the same platform. Vendors usually make sure that data are backward compatible so data from a legacy system can be processed on new systems. That practice (and sometimes its lack) is also well known from office programs. For example, newer versions of word processing software typically can read

Regulatory Matters


A recent article in BioPharm's sister publication LCGC Europe proposed seven steps for data migration and system retirement (12). The article suggests that companies inventory the existing system and which departments use it, perform a risk assessment, write a system retirement plan including roles and responsibilities, gather information about the current hardware and software, write a system decommissioning and data migration plan, execute the work, and report the work in a retirement report. The team chartered to define the system migration needs to make wellinformed decisions on which data need to be migrated and which do not. Writing the decommissioning and retirement plan and executing the work will be the biggest items on the to-do list. We propose the following refinements to the data migration plan. Develop a migration policy and strategy for your company. Develop an active implementation plan with time schedules and checkpoints. Define data and metadata for all system categories. Try to reduce the amount of data to be archived. For example, save spectra only during a peak's elution instead of during the entire run. Adapt the calculations and report formats used in the data systems so they produce the results mandated in your standard operating procedures. Prevent results that are irrelevant for laboratory purposes and that result in more overhead when migrating to a newer system. The latter requires flexibility in the data system. Define the type of data to be retained. For example, define whether raw data must be archived. When running the analysis, save the processing parameters in the same directory as the data (both the raw data and the results). Validate the proper functioning of the previous step by retrieving data and metadata and reprocessing the analysis. Include backward compatibility in the user requirements and functional specifications for future revisions and platforms. Select a proper storage media for long-term archiving. Adhere to the prescribed storage conditions. For example, when archiving on tape, regular tape retensioning is required. Develop and implement a procedure to check the integrity of data at regular intervals. Again, save the processing parameters in the same directory with the data. Before you retire a system, make sure that the data can be accurately processed on the new systems. Results should be within the limits as specified during the original analysis.

Figure 4. The system verification test for ChemStations (Agilent Technologies) shows that such tests can include the complete data path, which includes the digital data acquisition using a prerecorded chromatogram stored in the 1100 array detector.

formatted text documents written on older versions. Compatibility. Vendors play a major role here. The ideal scenario would be for the new software product, which can be either an update of an existing product or a new software platform, to automatically guarantee full compatibility with previous models, either directly or after data conversion. Full compatibility means that the new product must have all the functionality that the old product had. New functions can be added, but previous functions must not be removed. Checking validity. With every software revision, either of an application or an operating system, the validity of previously recorded data files should be checked. That is best done by using a few sets of raw data and the associated processing parameters acquired from real samples. Again, the software vendor can help. Ideally, validitycheck software should be provided with a revision that will automatically compare results generated on the new product with those from the older one. The validity check software must also be validated. In a validity check, results are calculated and compared with previously specified acceptance limits. Specifying such limits is important and can prevent later trouble. For example, the acceptance values for chromatographic peak areas and amounts should be in a range of 0.05 to 0.1%, which is about the best precision of the analytical results. Specifying better accuracy, for example up to seven digits, is unnecessary

and could cause problems at slightly different rounding algorithms. Migration procedures. Although less than ideal, validated migration procedures now seem to be the only viable solution to data retention and ready availability. Vendors can help reduce the burden of the migration process by providing conversion routines and software to check the validity of the conversion on data sets specified by the user. Those functions should be built into products, not developed as an afterthought. Companies purchasing software are advised to include those functions in their user

requirements and functional specifications list. The company should also develop test data sets that represent typical samples and use those to test the compatibility of the new software with the older versions. Performing such tests should be part of the change control procedures when changing an existing system and it should be part of the migration strategy.

Compliance for Older Data

One question that frequently comes up is what to do with data and metadata that were recorded or that will be recorded until all

Regulatory Matters

necessary administrative and technical controls required to implement Part 11 have been developed. Part 11 became effective on 20 August 1997. Before that date, it is inapplicable, and records generated by a computer could be stored on paper. Part 11 is not retroactive. From 20 August 1997 on, all records generated by a computer and stored on a durable storage device must be recorded and archived electronically. Most laboratories did not have, and many still do not have, the procedures and tools in place to comply with Part 11. Some software is unable to electronically store metadata together with the raw and processed data. In its compliance policy guide, FDA makes it clear what level of compliance it expects (16). FDA representatives have also made it clear in numerous discussions that they expect administrative controls to be in place, including procedures and policies. Technical controls can take a little bit longer, but the implementation process should be a "best effort." FDA expects an active implementation plan with a time schedule and checkpoints. Investing time in an effective migration procedure is important because with each migration cycle, the amount of digital information will increase almost exponentially.

Looking Ahead

by a discussion of biometrics. In computer security, biometrics refers to authentication techniques that rely on measurable physical characteristics that can be automatically checked, such as fingerprints, retinas and irises, voice patterns, facial patterns, and hand measurements for system access and electronic signatures.

Acknowledgments The authors would like to thank Risto Peltonen and Michael Beck of Agilent Technologies in Waldbronn, Germany, for their help in configuring and running the Cerity regression test suites. References (1) L. Huber, "Implementing 21 CFR Part 11 in Analytical Laboratories: Part 1, Overview and Requirements," BioPharm 12(11), 28­34 (1999). (2) W. Winter and L. Huber, "Implementing 21 CFR Part 11 in Analytical Laboratories: Part 2, Security Aspects for Systems and Applications," BioPharm 13(1), 44­50 (2000). (3) W. Winter and L. Huber, "Implementing 21 CFR Part 11 in Analytical Laboratories: Part 3, Ensuring Data Integrity in Electronic Records," BioPharm 13(3), 45­49 (2000). (4) B. Immel, "GMP Issues: Step Up to the Responsibility of QA and QC," BioPharm 13(2), 58­59,70 (2000). (5) Code of Federal Regulations, Food and Drugs, Title 21, Part 11, Sections 11.10(a) and 11.10(b), "Electronic Records; Electronic Signatures; Controls for Closed Systems" (U.S. Government Printing Office, Washington, DC, 1999). Also Federal Register 62(54), 13429­13466. Available at r11_99.html. (6) B. Immel, "GMP Issues: An Electronic Eye Opener," BioPharm 12(6), 60­63 (1999). (7) Center for Biologics Evaluation and Research, Guidance for industry: Computerized Systems Used in Clinical Trials (FDA, Washington, DC, April 1999). Also Federal Register

©Reprinted from






(13) (14)


The final installments of "Implementing 21 CFR Part 11 in Analytical Laboratories" will discuss the importance of appropriate computer control of analytical instruments to ensure compliance with Part 11, followed


64(89). Available at compliance_ref/bimo/ffinalcct.htm Code of Federal Regulations, Food and Drugs, Title 21, Part 11, Summary, "Electronic Records; Electronic Signatures; Controls for Closed Systems" (U.S. Government Printing Office, Washington, DC, 1999). Also Federal Register 62(54), 13446. Available at P. Motise, "FDA Requirements for Computers in Analytical Laboratories," paper presented at the ECA Conference, Berlin, September 1999. Available at conferences/august99.htm E1947-98 Standard Specification for Analytical Data Interchange Protocol for Chromatographic Data (American Society for Testing Materials, West Conshohocken, PA, 1999). Available at E1948­98 Standard Guide for Analytical Data Interchange Protocol for Chromatographic Data (American Society for Testing Materials, West Conshohocken, PA, 1999). Available at R.D. McDowall, "Chromatography Data System V: Data Migration and System Retirement," LCGC Europe 13(1), 30­35 (2000). R.D. McDowall, "Just e-sign on the Bottom Line?" LCGC Europe 13(2) 79­86 (2000). T.A. Rooney, "Computers in Chemistry -- Chromatography Data Systems Just Got Easier: New Networked Software Delivers Flexibility and Ease-of-Use," Today's Chemist at Work, 9(2) (ACS Publications, Washington D.C., 2000), pp. 17­24. Available at L. Doherty, J. Welsh, and W. Winter, "A Networked Data System for Specific Chromatography Applications," Am. Lab. 32(3), 50­58 (2000). Also available as Agilent Technologies (Palo Alto, CA) publication number 5980­0231E. Compliance Policy Guide: 21 CFR Part 11; Electronic Records, Electronic Signatures (CPG 7153.17) (FDA, Washington, DC, 13 May 1999). Available at ora/compliance_ref/cpg/cpggenl/ cpg160-850.htm. BP



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