Quality Assurance Journal

The Quality Assurance JournalVolume 14, Issue S1 p. S53-S56 Keyword IndexFree Access Keyword Index First published: 18 March 2011 https://doi.org/10.1002/qaj.463AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume14, IssueS1Supplement: Abstracts of the Society of Quality Assurance 27th Annual Meeting, San Antonio, Texas, USA, 27 March – 1 April 2011April 2011Pages S53-S56 RelatedInformation

The discovery and development of a new drug costs around $1 billion, and it may take approximately 10 years for the drug to reach the marketplace. Drug discovery and development are the processes of generating compounds and evaluating all of their properties to determine the feasibility of selecting one new chemical entity (NCE) to become a safe and efficacious drug. Among many criteria, obtaining experimental pharmacokinetics (PK) data from laboratory animals in the nonclinical stage is critical to evaluating a drug candidate before it can qualify to be tested in the clinical trials for safety and efficacy evaluation. A key parameter in pharmacokinetics is the plasma or tissue concentration of the new drug after its administration to laboratory animals. Therefore, developing an accurate and fast analytical method for measuring the concentrations of a compound in plasma or tissue is the first step toward yielding the PK of a compound. As the drug candidate moves down in the pipeline, the requirements for PK information differ at the different stages of drug discovery and development, leading to the introduction of “fit for purpose” analytical strategies that provide the appropriate level of bioanalytical support for the purpose at hand, while simultaneously minimizing resource expenditures. Due to its superior sensitivity and selectivity, liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) has become the analytical technique primarily used by bioanalytical laboratories performing analyses for preclinical and clinical studies. The increased number of biological agents used as therapeutics—in the form of recombinant proteins, monoclonal antibodies, vaccines, and so on—has prompted the pharmaceutical industry to review and refine aspects of the development and validation of bioanalytical methods for the quantification of these therapeutics in biological matrices in support of preclinical and clinical studies. Most of these methodologies are used in quantitative assays supporting PK and toxicokinetic parameters of the therapeutic agents. To date, an alternative approach for dealing with macromolecules is to utilize ligand-binding assays (LBAs) to generate data. LC–MS/MS has played an incredible role in drug metabolism and pharmacokinetics studies at various drug discovery and development stages since its introduction to the pharmaceutical and biotechnology industries. This paper will elaborate on the most recent advances in sample preparation and separation, along with the mass spectrometric aspects of high-throughput quantitative bioanalysis of drugs and metabolites in biological matrices. Recently introduced techniques such as ultra-performance or ultra-speed liquid chromatography (UPLC or USLC), with small particles (sub-2 mm) and monolithic chromatography, offer improvements in speed, resolution, and sensitivity compared to conventional chromatographic techniques. Hydrophilic interaction chromatography (HILIC) on silica columns with low aqueous/high organic mobile phase is emerging as a valuable supplement to the reversed-phase LC–MS/MS. Sample preparation formatted to 96-well and/or 384-well plates has allowed for semi-automation of off-line sample preparation techniques, significantly improving throughput. On-line solid phase extraction (SPE) utilizing column-switching techniques is rapidly gaining acceptance in bioanalytical applications to reduce both the time and labor required for generating bioanalytical results. Extraction sorbents for on-line SPE extend to an array of media, including large particles for turbulent-flow chromatography, restricted access materials, monolithic materials, and disposable cartridges utilizing traditional packings such as those used in Spark Holland (Symbiosis) systems. Bioanalytical laboratories in the pharmaceutical and life science industries are constantly under pressure to reduce overall time for discovery and development. This pressure is often accompanied by an increase in the number of biological samples requiring PK analysis and a decrease in the desired quantitation levels. Hyphenated techniques are examples of new tools adopted for developing fast and cost-effective analytical methods. LC–MS/MS has led to major breakthroughs in the field of quantitative bioanalysis since the 1990s due to its inherent specificity, sensitivity, and speed 1, 2. It is now generally accepted as the preferred technique for quantitating small-molecule drugs, metabolites, and other xenobiotic biomolecules in biological matrices. The use of LC–MS/MS has grown exponentially in the last decade due to its unmatched sensitivity, extraordinary selectivity, and rapid rate of analysis. Typical workflow of bioanalysis from sample generation to data delivery is summarized in Figure 1. Samples from biological matrices are usually not directly compatible with LC–MS/MS methodologies. Sample preparation has traditionally used such approaches as protein precipitation (PPT), liquid–liquid extraction (LLE), or SPE. Manual operations associated with these processes—in particular with LLE and SPE—are fairly labor intensive and time consuming. Parallel sample processing in a 96-well format using robotic liquid handlers has significantly shortened the time analysts have to spend in the laboratory for sample preparation. An alternative sample extraction method that has generated a lot of interest in recent years is the direct injection of plasma using an on-line extraction method. A major advantage of on-line SPE over off-line extraction techniques is that the sample preparation step is embedded into the chromatographic separation and thus eliminates most of the sample preparation time traditionally performed at the bench, potentially resulting in more variability from batch to batch and analyst to analyst. Steep gradients and relatively short columns were first utilized in early applications of high-throughput LC–MS/ MS assays to reduce run times. A better understanding of how matrix effects can compromise the integrity of bioanalytical methods has re-emphasized the need for adequate chromatographic separation of analytes from endogenous biological components in quantitative bioanalysis using the LC–MS/MS technique. New developments from chromatographic techniques such as UPLC and USLC with sub-2 mm particles and monolithic chromatography show promise in delivering higher speed, better resolution, and increased sensitivity for high-throughput analysis while minimizing matrix effects. LC–MS/MS applications for quantitative bioanalysis have been documented in hundreds of articles in just the past several years, and a number of reviews dealing with one or more aspects of quantitative LC–MS/MS bioanalysis have been published 3-6. Zhang and colleagues described an integrated sample collection and handling process for bioanalysis 7. The sample handling process consumes a significant portion of the resources of a bioanalytical laboratory. One of the primary purposes for analytical automation is the possibility of analyzing a huge number of samples, either simultaneously or sequentially, without any human intervention. When large amounts of samples are analyzed, mainly in routine analyses, the importance of the correct identification of each sample in order to obtain the appropriate correlation between the sample analyzed and the results obtained is obvious. One of the most efficient and reliable ways to identify a sample, from collection to result, is by using bar codes. A bar code can best be defined as an “optical Morse code.” Series of black bars and white spaces of varying widths are printed on labels to identify items uniquely. The bar code labels are read with a scanner, which measures reflected light and interprets the code into numbers and letters that are passed on to a computer. All bar codes have start/stop characters that allow the bar code to be read from both left to right and right to left. Unique characters placed at both the beginning and end of each bar code, the stop/start characters provide timing references, symbol identification, and direction of reading information to the scanner. By convention, the unique character on the left of the bar code is considered the “start,” while the character on the right of the bar code is considered the “stop.” Besides the linear bar code, today's new bar codes are two- dimensional, electronic, or small computer chips, which sort data in inconspicuous places like a credit card. This technology has been included in clinical and bioanalytical analyzers, where large amounts of samples are analyzed daily and a correct assignation of the results obtained is mandatory. Automation for sample handling meets the business needs of improving productivity and reducing the documentation required for compliance. Procedural elements involved in maintaining bioanalytical data integrity for good laboratory practices and regulated studies are discussed by James and Hill 8. The elements can be divided into three areas. First, there are elements ensuring the integrity of the analyte until analysis, through correct sample collection, handling, shipment, and storage procedures. Incorrect procedures can lead to loss of analyte due to instability, addition of analyte through contamination or instability of related metabolites, or changes in the matrix composition that may adversely affect the performance of the analytical method. Second, the integrity of the sample identity must be maintained to ensure that the result that is reported relates to the individual sample that was taken. Possible sources of error include sample mix-up or mislabeling or errors in data handling. Finally, there is the overall integrity of the documentation that supports the analysis, along with any pre-study validation of the method. This includes a wide range of information, from paper and electronic raw data through standard operating procedures, analytical procedures, and facility records to study plans and final reports. These are critical, allowing an auditor or regulatory body to reconstruct the study. A laboratory information management system (LIMS) is a preferable database and handling system that maintains a detailed pedigree for each sample by capturing processing parameters, protocols, stocks, tests, and analytical results for the sample's complete life cycle. Project and study data are also maintained to define each sample in the context of the research tasks it supports. LIMS is required for each analytical pipeline to track all aspects of sample handling. One strategy for high-throughput bioanalytical analysis is to use well-established instrumentation; rigorous, standardized techniques; and automation wherever possible to minimize or replace manual tasks. Automation results in greater performance consistency over time and more reliable methods transfer from site to site. Automated 96-well plate technology is well established and accepted and has been shown to effectively replace manual operations. The 96-well instruments can execute automated off-line extraction and sample cleanups. Automated SPE, LLE, and PPT all can be performed in 96-well format. Adequate sample preparation is a key aspect of quantitative bioanalysis; without it, bottlenecks can occur during high-throughput analysis. Sample preparation techniques in 96-well format have been well adopted in high-throughput quantitative bioanalysis with a number of applications. Techniques that can use the format include LLE, SPE, and PPT. Typically, liquid transfer steps, including preparation of calibration standards and quality control samples as well as the addition of the internal standard, have been performed automatically using robotic liquid handling workstations for parallel sample processing (Hamilton STAR, refer to robot liquid handler system in Figure 2). The increasing demand for high throughput causes a unique situation of balancing cost versus analysis speed as each sample preparation technique offers unique advantages. Dilute-and-shoot and PPT are among the most popular and effective techniques due to their simplicity. Sample preparation with PPT is widely used in the bioanalysis of plasma samples. The method has been extended to the quantitation of drug and metabolites from whole blood. Koseki and colleagues developed a sensitive and specific LC–MS/MS method for the simultaneous determination of cyclosporine A (CsA) and its three main metabolites (AM1, AM4N, and AM9) in human blood 9. Mixed-mode polymer-based sorbents (e.g., Waters Oasis MCX cartridge) were introduced in the late 1990's for the isolation of drugs with ionizable functional groups from biological fluids. The extraction procedures can be a generic protocol or can be optimized if better sample cleanup is desired. The use of SPE often gives superior results to those with a PPT approach but may not be as cost effective as PPT due to the labor and material costs associated with the procedures. Mallet and colleagues described a novel 96-well SPE plate that was designed to minimize the elution volume required for quantitative elution of analytes 10. The plate was packed with 2 mg of a high-capacity SPE sorbent that allows loading of up to 750 mL of plasma. The novel design permitted elution with as little as 25 mL solvent, enabling the plate to offer up to a 30-fold increase in sample concentration. The evaporation and reconstitution step that is typically required in SPE is unnecessary due to the concentrating ability of the sorbent. Yang and colleagues developed a sensitive mElution SPE–LC–MS/MS method for the determination of M+4 stable isotope labeled cortisone and cortisol in human plasma 11. In the method, analytes were extracted from 0.3 mL of human plasma samples using a Waters Oasis HLB 96-well mElution SPE plate with 70 mL methanol as the elution solvent. The lower limit of quantitation was 0.1 ng/mL and the linear calibration range was from 0.1 to 100 ng/mL for both analytes. Several related formats have recently appeared using miniaturized packed-bed formats as well as an adaptation of disk technology for the 96-well format. The ultimate goal for the 96-well approach is to add a higher level of parallel sample preparation using essentially a further miniaturized SPE format. By extracting multiple 96-well format SPE plates, up to four 96-well plates or 384 samples may be prepared and then analyzed within 24 hours by one person with one LC–MS/MS system, a significant improvement in sample throughput using commercially available equipment and supplies. Future developments should further increase throughput significantly. LLE gives excellent sample cleanup but poses engineering challenges for use in an automated high-throughput fashion. Several groups have developed different approaches to solve the mixing and phase separation problems typically observed in a 96-well LLE method. By using vigorous vortexing after well-controlled heat-sealing, or repeated aspiration and dispensing by robotic liquid handler, common extraction solvents such as methyl-t-butyl ether (MTBE) or ethyl acetate (EA) may be used in the routine extraction of plasma, blood, or tissue samples. Another approach is on-line SPE for the automated preparation of samples prior to LC–MS/MS analysis. This approach uses a commercial device that combines an auto-sampler and a solvent delivery unit to aliquot multiple liquid samples into a flowing stream of solvent. The solvent has preconditioned an in-line SPE cartridge. After conditioning, the SPE cartridge retains the targeted analytes, while the relatively weak solvent elutes unretained salts and polar matrix components to waste. An empirically optimized sequence of increasingly stronger solvents is then used to further elute weakly retained unwanted sample components. A final elution with high performance liquid chromatography (HPLC) mobile phase elutes the targeted analytes off the SPE cartridge and onto an analytical HPLC column for LC–MS/MS analysis. The on-line SPE technique offers speed, high sensitivity by the preconcentration factor, and low extraction cost per sample, but typically requires the use of program-controlled switch valves and column reconfigurations. The on-line technique can be fully automated, however, offering real-time high-throughput sample analysis. One commercial automated on-line SPE system is the Symbiosis system, manufactured by Spark Holland (Figure 3). It includes an auto-sampler (Reliance), two binary HPLC pumps, an on-line SPE unit with two high-pressure solvent delivery pumps, and a combined valve system to direct fluid for different steps of SPE. At the beginning of each run, an on-line SPE cartridge is loaded into the unit. After a conditioning step with high organic solvent and an equilibrium step with low organic aqueous solution, a sample is injected onto the cartridge and washed with aqueous solution. Proteins and other matrix materials from the sample are removed during the washing step. Analyte of interest is then eluted onto the analytical column and detected by mass spectrometry or tandem mass spectrometries. During the sample elution step, a second sample is loaded to a new on-line SPE cartridge for the next analysis. In this parallel mode, the sample analysis cycle time approximates the LC run time without the time required for the SPE procedures. Because the on-line SPE cartridge is disposable and each sample uses a new cartridge, the carry-over problem from the extraction cartridge is eliminated.

Summary Ethics committee supervision is an essential part of conducting clinical trials and is required to ensure compliance with the international recommendations including the Declaration of Helsinki, International Conference on Harmonisation Good Clinical Practice (ICH GCP), Directive 2001/20/EC of the European Parliament and Council and WHO recommendations etc. No clinical trial can be initiated without prior review and approval from an ethics committee. In addition to expert evaluation of planned clinical trials, ethics committees are responsible for providing regular monitoring of clinical trial compliance with the international and national ethic, moral and legal aspects throughout the whole process of the trial conduct. Copyright © 2011 John Wiley & Sons, Ltd.

Summary This article provides an advisory approach regarding how to avoid an FDA Warning Letter following the receipt of a significant Form FDA 483. The focus is on the 483 response as it applies to GCP but can be applied to the response to the Warning Letter itself as well as a wider scope of GXP (GMP, GLP, GCP) inspection and audit responses. Copyright © 2011 John Wiley & Sons, Ltd.

Sampling is an inevitable means of testing in quality inspections. However sampling is always associated with sampling risks that inspectors have to control. This paper aims to reveal the reliability and efficacy of the commonly used formula, ‘Square root of N plus one’, in lot acceptance sampling. An Operating Characteristic Curve is exploited to test the validity of the sampling plan and it offers a further dimension to unveil the unreliability of it. The probability of accepting a defective lot is increased substantially from larger to smaller lot sizes with the deployment of the √N + 1 rule as a sampling plan. Using a sampling plan blindly from recognized sources and pursuing a traditional practice does not ensure that the sampling plan is statistically valid. Regardless of the source of the sampling plan and how it is indexed, it is the actual acceptable quality level (AQL) and lot tolerance percent defective (LTPD) of the sampling plan that describes its protection and determines validity. Copyright © 2011 John Wiley & Sons, Ltd.

SummaryPoor drug quality is a global concern as it can cause treatment failure, adverse effects, increased morbidity, mortality and development of drug resistance. This study was aimed to identify factors affecting drug quality problems. Odds ratio (OR) and 95% confidence intervals (CIs) were used through a logistic regression model to determine association between 4 M factors (man, machine, materials and methods) and drug quality problems (physical appearance changes, labeling problems, packaging problems and packing problems). We found that two factors, man and material, were mainly associated with drug quality problems. Among other man factors, ‘inspector checking the finished products’ was the leading cause for labeling problems. Likewise, aluminum foil was responsible for packaging problems among other material factors. We conclude that drug manufacturers should consider using detector and barcode systems to reduce human errors and use good quality packaging materials to reduce drug quality problems. Copyright © 2011 John Wiley & Sons, Ltd.

A risk-based approach to method validation takes into consideration the phase of drug development when determining the level of validation necessary to show that the method is suitable for its intended use. Both the United States Pharmacopeia (USP <1225>) and the International Conference on Harmonisation (ICH Q2[R1] and Q3B[R2]) specify the validation elements required by method type, focusing mainly on the validation of chromatographic methods 1-3. These same principles are applied to the validation of non-chromatographic methods such as those used to determine pH, moisture, or inorganics. Although each generic validation element is defined in these documents, method type and drug-specific factors, as well as phase of drug development, must be considered when designing the validation experiments for a given small-molecule method 4. For every phase of product development, the analytical method must demonstrate specificity. The method must have the ability to unambiguously assess the analyte of interest while in the presence of all expected components, which may consist of degradants, excipients/sample matrix, and sample blank peaks. The sample blank peaks may be attributed to things such as reagents or filters used during the sample preparation. For identification tests, discrimination of the method should be demonstrated by obtaining positive results for samples containing the analyte and negative results for samples not containing the analyte. The method must be able to differentiate between the analyte of interest and compounds with a similar chemical structure that may be present. For a high-performance liquid chromatography (HPLC) identification test, peak purity evaluation should be used to assess the homogeneity of the peak corresponding to the analyte of interest. For assay/related substances methods, the active peak should be adequately resolved from all impurity/degradant peaks, placebo peaks, and sample blank peaks. Resolution from impurity peaks could be assessed by analyzing a spiked solution with all known available impurities present or by injecting individual impurities and comparing retention to that of the active. Placebo and sample matrix components should be analyzed without the active present in order to identify possible interferences. If syringe filters are to be used to clarify sample solutions, an aliquot of filtered sample diluent should be analyzed for potential interferences. If the impurities/degradants are unknown or unavailable, forced degradation studies should be performed. Forced degradation studies of the active pharmaceutical ingredient (API) and finished product, using either peak purity analysis or a mass spectral evaluation, should be performed to assess resolution from potential degradant products. The forced degradation studies should consist of exposing the API and finished product to acid, base, peroxide, heat, and light conditions until adequate degradation of the active has been achieved. An acceptable range of degradation may be 10% to 30% but may vary based on the active being degraded. Over-degradation of the active should be avoided to prevent the formation of secondary degradants. If placebo material is available, it should be stressed under the same conditions and for the same duration as the API or finished product. The degraded placebo samples should be evaluated to ensure that any generated degradants are resolved from the analyte peak(s) of interest. Evaluation of the forced degraded solutions by peak purity analysis using a photodiode array detector or mass spectral evaluation must confirm that the active peak does not co-elute with any degradation products generated as a result of the forced degradation. Another, more conservative, approach for assay/related substances methods is to perform peak purity analysis or mass spectral evaluation on all generated degradation peaks and verify that co-elution does not occur for those degradant peaks as well as the active peak. The standard deviation of the response (σ) is based on either the magnitude of the analytical background response (measurement of blank samples) or residual standard deviation of a regression line or standard deviation of Y intercepts of regression lines for calibration curves generated using samples containing the analyte in the range of the detection limit. The slope (S) is based on the calibration curve of the analyte. An acceptable approach to determine the detection limit of an HPLC-related substances method or a gas chromatography (GC) residual solvent method involves preparing triplicate placebo samples spiked with the analyte of interest at an estimated detection limit concentration. Duplicate injections are made of each spiked placebo preparation. The analyte must be detected in all six chromatograms with a signal-to-noise ratio of approximately 2:1 or 3:1. The quantitation limit must be determined for related substances methods and for methods that measure residual levels of solvents or other by-products associated with the manufacture of a drug substance or product. The quantitation limit must also be determined for analytical methods associated with cleaning validations. The quantitation limit is typically defined as the lowest concentration of active in the presence of placebo (or vehicle), where the peak area response is greater than a signal-to-noise ratio of 10:1. The standard deviation of the response (σ) is based on either the magnitude of the analytical background response (measurement of blank samples) or residual standard deviation of a regression line or standard deviation of Y intercepts of regression lines for calibration curves generated using samples containing the analyte in the range of the quantitation limit. The slope (S) is based on the calibration curve of the analyte. Once the quantitation limit has been identified, further studies are required to verify that it is the lowest concentration that displays acceptable levels of precision, accuracy, and linearity. An acceptable approach to evaluate the accuracy and precision at the quantitation limit for an HPLC-related substances method or a GC residual solvent method is to prepare six placebo samples spiked with the analyte of interest at the quantitation limit concentration. The analyte should have a signal-to-noise ratio of approximately 10:1 with adequate recovery compared to theoretical content and precision in response (relative standard deviation [RSD] ≤ 20%) 5. The determined quantitation limit must not be greater than the reporting level of the method. The linearity solutions are prepared by performing serial dilutions of a single stock solution; alternatively, each linearity solution may be separately weighed. The resulting active response for each linearity solution is plotted against the corresponding theoretical concentration. The linearity plot should be visually evaluated for any indications of a non-linear relationship between concentration and response. A statistical analysis of the regression line should also be performed, evaluating the resulting correlation coefficient, Y intercept, slope of the regression line, and residual sum of squares. A plot of the residual values versus theoretical concentrations may also be beneficial for evaluating the relationship between concentration and response. The determined relative response factors can then be utilized for sample analysis to accurately correct for differences between impurity and active response to generate a reliable concentration for each impurity. Establishment of an appropriate qualification/validation protocol requires the assessment of many factors, including phase of product development, purpose of the method, type of analytical method, and availability of supplies, among others. Repeatability reflects the closeness of agreement of a series of measurements under the same operating conditions over a short interval of time. For a chromatographic method, repeatability can be evaluated by performing a minimum of six replicate injections of a single sample solution prepared at the 100% test concentration. Alternatively, repeatability can be determined by evaluating the precision from a minimum of nine determinations that encompass the specified range of the method. The nine determinations may be comprised of triplicate determinations at each of three different concentration levels, one of which would represent the 100% test concentration. Intermediate precision reflects within-laboratory variations, such as different days, different analysts, and different equipment. Intermediate precision testing can consist of two different analysts, each preparing a total of six sample preparations, as per the analytical method. The analysts execute their testing on different days, using separate instruments and analytical columns. The use of experimental design for this study could be advantageous, because statistical evaluation of the resulting data could identify testing parameters (i.e., brand of HPLC system) that would need to be tightly controlled or specifically addressed in the analytical method. Results from each analyst should be evaluated to ensure a level of agreement between the two sets of data. Acceptance criteria for intermediate precision are dependent on the type of testing being performed. Typically for assay methods, the RSD between the two sets of data must be ≤2.0%, while the acceptance criteria for impurities is dependent on the level of impurity and the sensitivity of the method. Intermediate precision may be delayed until full ICH validation, which is typically performed during late Phase 2 or Phase 3 of drug development 4. However, precision testing should be conducted by one analyst for early phase method qualification. Reproducibility reflects the precision between analytical testing sites. Each testing site can prepare a total of six sample preparations, as per the analytical method. Results are evaluated to ensure statistical equivalence among various testing sites. Acceptance criteria similar to those applied to intermediate precision also apply to reproducibility. Accuracy should be performed at a minimum of three concentration levels. For drug substance, accuracy can be inferred from generating acceptable results for precision, linearity, and specificity. For assay methods, the spiked placebo samples should be prepared in triplicate at 80%, 100%, and 120%. If placebo is not available and cannot be formulated in the laboratory, the weight of drug product may be varied in the sample preparation step of the analytical method to prepare samples at the three levels listed above. In this case, the accuracy study can be combined with method precision, where six sample preparations are prepared at the 100% level, while both the 80% and 120% levels are prepared in triplicate. For impurity/related substances methods, it is ideal if standard material is available for the individual impurities. These impurities are spiked directly into sample matrix at known concentrations, bracketing the specification level for each impurity. This approach can also be applied to accuracy studies for residual solvent methods where the specific residual solvents of interest are spiked into the product matrix. If individual impurities are not available, placebo can be spiked with drug substance or reference standard of the active at impurity levels, and accuracy for the impurities can be inferred by obtaining acceptable accuracy results from the active spiked placebo samples. Accuracy should be performed as part of late Phase 2 and Phase 3 method validations. For early phase method qualifications, accuracy can be inferred from obtaining acceptable data for precision, linearity, and specificity 4. Stability of the compound(s) of interest should be evaluated in sample and standard solutions at typical storage conditions, which may include room temperature and refrigerated conditions. The content of the stored solutions is evaluated at appropriate intervals against freshly prepared standard solutions. For assay methods, the change in active content must be controlled tightly to establish sample stability. If impurities are to be monitored in the method sample, solutions can be analyzed on multiple days and the change in impurity profiles can be monitored. Generally, absolute changes in the impurity profiles can be used to establish stability. If an impurity is not present in the initial sample (day 0) but appears at a level above the impurity specification during the course of the stability evaluation, then this indicates that the sample is not stable for that period of storage. In addition, impurities that are initially present and then disappear, or impurities that are initially present and grow greater than 0.1% absolute, are also indications of solution instability. During Phase 3 validation, solution stability, along with sample preparation and chromatographic robustness, should also be evaluated. For both sample preparation and chromatographic robustness evaluations, the use of experimental design could prove advantageous in identifying any sample preparation parameters or chromatographic parameters that may need to be tightly controlled in the method. For chromatographic robustness, all compounds of interest, including placebo-related and sample blank components, should be present when evaluating the effect of modifying chromatographic parameters. For an HPLC impurity method, this may include a sample preparation spiked with available known impurities at their specification level, or, alternatively, a forced degraded sample solution can be utilized. The analytical method should be updated to include defined stability of solutions at evaluated storage conditions and any information regarding sample preparation and chromatographic parameters, which need to be tightly controlled. Sample preparation and chromatographic robustness may also be evaluated during method development. In this case, the evaluations do not require repeating during the actual method validation. Establishment of an appropriate qualification/validation protocol requires assessment of many factors, including phase of product development, purpose of the method, type of analytical method, and availability of supplies, among others. There are many approaches that can be taken to perform the testing required for various validation elements, and the experimental approach selected is dependent on the factors listed above. As with any analytical method, the defined system suitability criteria of the method should be monitored throughout both method qualification and method validation, ensuring that the criteria set for the suitability is appropriate and that the method is behaving as anticipated.

SummaryPseudomonas is one among the many bacteria that contribute to the deterioration of fish. Other microorganisms such as Salmonella sp., coagulase positive Staphylococcus and thermotolerant coliforms can also be found. The presence of this microbiota is always related to raw materials, poor handling, or badly designed plants. In this study the presence of these indicators was evaluated. The monitoring was conducted during the reception of the fish, chilling and storage of the product at −18 °C. As part of the monitoring, preventive measures for each Critical Control Point associated with the program of Hazard Analysis and Critical Control Point were established. The results showed that there was significant difference (p < 0.05) between the samples. The resistance of these microorganisms involved in getting the frozen fillets confirmed the need for more effective control and should be subject to a regulatory program like ISO 22000. This program specifies the requirements needed for a system that can monitor and master hazards and ensure that food is safe for human consumption. Copyright © 2011 John Wiley & Sons, Ltd.

SummaryThis is the fourth and final part of the Principles in Quality Assurance series. Previous articles discussed the need for steering principles, the definition and application of GLP concepts, and the larger context of a model for effective Quality Assurance. The preceding paper proposed the use of the “Grip/Build/Engage” (GBE) model as a structure to improve the impact of Quality Assurance, giving an overview as well as some details of the “Grip” phase ‐ defining story, structure and standards for Quality Assurance. This current part will review the other components of the GBE model and how Quality Assurance engage to produce the maximum positive impact. Copyright © 2011 John Wiley & Sons, Ltd.

For this final issue of The Quality Assurance Journal we would like express our sincere gratitude to everyone who has supported the Journal over the years. Thanks are due to all the authors, editors, production staff and Editorial Board members, and to all who have purchased and read the Journal – we hope you have benefited from the fine papers that have been published. Particular recognition is merited for those people who shared a vision of a professional Quality Assurance journal and were prepared to take action to make this a reality. In this category we would especially like to thank Elliott Graham and everyone associated with the Society of Quality Assurance who were instrumental in the Journal's evolution over the years. Please note that this Editorial is an “au revoir” and not an “adieu”, there being an important distinction between the two salutations. It is our hope that the quality profession will realize that there is still a need for an internationally respected publication to foster the global development and improvement of Quality Assurance. Such a journal is a key element in generating and disseminating new ideas and avoiding professional stagnation. We hope that the QA community will be prepared to invest in a new Quality Assurance Journal; a journal that will improve upon its predecessor and light the way for Quality Assurance in the future. Until that time, we thank you for your support and bid you au revoir.

Pfizer's quality control (QC) laboratory in Puurs, Belgium, has been applying lean techniques to reduce the total throughput lead time for products across the entire supply chain for several years. In 2010, the QC laboratory met an ambitious target to cut standard lead times. Following that, the lab moved on to the new Pfizer global manufacturing initiative called lean laboratory, which involves adopting new ways of working and a deeper internal customer engagement to achieve real-world results. The lab began its lean journey in 2007, engaging in several agile manufacturing projects and end-to-end value stream assessments. In 2009, the lab set its sights on reducing lead times by 25% for eight major products. In 2010, having met that target, it looked to the next level of quality improvement, the Pfizer lean laboratory initiative. A global model, lean lab is currently being rolled out across Pfizer's entire manufacturing organization; the Puurs lab was included in implementation at the first 12 sites. What has allowed the lab to set, meet, and reach new improvement targets is a lean approach that emphasizes broader colleague involvement and engagement to establish a highly effective and collaborative approach to problem solving. At one time, for example, a project lead might have said, “I will determine how to improve results, and I will have it all worked out for you to implement tomorrow.” This method may have worked in that it generated some degree of improvement, but it did not always produce the optimum solution. Because the decision was made without seeking the insight of the colleagues charged with improving results, it didn't have buy-in from the very individuals who would ultimately carry out the process. The lean concept invites colleagues to give input and bring forward ideas for consideration that may challenge the “usual” way of working. Just because a process has always been done a certain way does not necessarily mean that is the best way. As expected, the colleagues who perform the work involved in carrying out a specific process, in this case test analysis, have the greatest insight into where, why, and how frequently they were able to identify non-value added activities and their resulting impact on process robustness. For example, colleagues pointed out that test results might wait up to two days before being double-checked, as required by an independent analyst. Their suggestion: optimize the process to enable double-checking within 24 hours; this waiting period was soon whittled down to between 12 and 24 hours. They created a spaghetti diagram that mapped the movement path within a laboratory during the execution of a test (Figure 1). This visual evidence clarified the amount of non-value added activity that existed, while making it easy to see how co-locating materials and equipment needed for a particular test into one area would minimize wasted movement and time. In order to stick to this tighter time frame, colleagues took on the responsibility of planning, scheduling, and organizing their individual workflows. In this way they operated as self-directed teams, fulfilling the promise of a previously launched initiative. Analyst teams now operate as if they're running their own business; by scheduling their work, colleagues know what's coming in and are able to organize workflow without the involvement of a supervisor. Daily huddles enable them to discuss, review, and iron out any issues. Achieving a 25% reduction in lead times involved many changes to the value stream map (see Figure 2). One area targeted for improvement was the creation of a continuous workflow to execute post-analysis tasks, such as calculations, data input into the laboratory information management system, and double-checking and approval activities. There were also many opportunities to eliminate extra approval steps and wait times. The collective efforts of the laboratory teams not only helped achieve the target, but also maintained an on-time delivery rate to customers of greater than 90% for the eight products, which represent 60% of all manufactured semi-finished lots. This notable success could be considered the harvesting of low-hanging fruit. Now the lab teams are stepping up efforts as part of the Pfizer global manufacturing lean laboratory initiative. Through a multi-phase approach, the teams expect to improve quality, productivity, and effectiveness by looking at not only the value stream but also the volume of products coming into the lab in order to level the workflow and use standard work teams. In cases with high predictability in the incoming volume of work, the company applies a lean technique called rhythm wheels, which allows it to set a fixed schedule for executing certain tests such as weekly pH testing. The train testing method is better suited to less predictable incoming workloads, especially in cases in which testing is dependent upon maximum capacity being reached or approaching deadlines. Because leveling the workflow enables the company to work with standard teams, it can plan ahead more accurately for the time and equipment needed to perform and analyze the required tests. Embracing lean has also yielded another significant benefit: increased customer engagement and transparency. The process is not yet entirely transparent, but the company has made substantial progress toward that goal. Throughout 2010, the Puurs lab continued to stretch its targets for reducing total throughput lead times. The lab's overall goal for 2010, in addition to reducing lead times for its initial group of eight high-volume products, is to achieve a similar reduction across 80% of all the products and volume handled by Puurs QC. Establishing a reduced time schedule for all products is among its targets for 2011–2012. Toward that end, the lab has established a benchmark: to match or beat its sterility testing lead time; 13 or 18 working days within a climate-controlled environment is typical, depending on the particular sterile product. Once this goal is accomplished, the lab can assure its customers of results for any product within two weeks. Another quality initiative focuses on those troublesome testing processes that result in atypical outcomes and lead to additional analyses and follow-up QA investigations. Atypical outcomes may be caused by a number of factors, including broken equipment, test methodology, or flawed sample preparation. Regardless of their cause, the result of such occurrences is always rework and the potential for detailed and sometimes lengthy laboratory investigations. The subsequent impact of such delays affects customer service levels. Getting it right the first time, which optimizes processes, achieves a smoother workflow, and minimizes outcomes that require QA investigations, is the focus of this initiative. The quality improvements accomplished to date, like those the lab is working toward, require a new and different mindset and collaborative approach to problem solving. The most important element is the early engagement of key colleagues involved in the process, giving them both responsibility and accountability for making improvements that support overall lab objectives. These lean efforts incorporate best practices that are in place throughout Pfizer's global manufacturing network. Reducing lead times means nurturing a culture of continuous improvement that focuses on the customer and advances the company's collective efforts in quality, productivity, and effectiveness.

The scene is a psychiatrist's office; the dusty rays of afternoon sunlight stripe a brown leather couch where we see the shadowy outline of a figure waiting for a consultation. The psychiatrist arrives and settles in an adjacent chair, beginning the session in classic fashion. “Please relax and get comfortable. Now, tell me about your childhood.” The patient begins with his background. “I came from a safety testing environment with poor standards, weak management, lack of process or protocol, inadequate training, and flawed research. This was compounded by suspicions of corruption and fraud that lead to long investigations by the FDA and subsequent legal action. Another part of me was born from unethical clinical research, and yet another persona was derived from the deaths of many people who were exposed to improperly manufactured medicines.” The psychiatrist acknowledges and comforts the patient. “Well,” he says. “Tell me about your fears and anxieties, what are you afraid of?” “I'm terrified of the FDA and their investigators. I worry constantly about 483 s, about getting any observations when they come to inspect us or our partners. Just thinking about Warning Letters makes me break out in a rash, and I panic whenever I hear about FDA enforcement actions.” “Is there anything else you would like to tell me?” “No one respects me doctor. I'm seen as an administrative overhead, a problem to be circumvented and, at best, a necessary evil.” The doctor turns away for a minute, gently stroking his pointed movie-psychiatrist beard. “Hmm…” he begins, “I'm afraid it's worse than I had thought.” “Tell me doctor” the patient pleads “what's wrong with me?” “You have multiple personalities, each born out of trauma, fear and suspicion. In addition, you suffer from irrational fears and anxieties, hearing the voices of imaginary agency personnel in your head. All these symptoms cause you to focus overly on details and anything that could, in your wildest fantasies, cause a 483 observation. You like to control others, and have inclinations to punish those whose work you believe will lead to a compliance deficiency. Your perspective is obscured by the fear of failure and your need to report audit findings, which you see as a justification for your existence. The voices in your head push you to constantly threaten your colleagues with what might happen. All these symptoms cause your co-workers to shun you, diminishing the possibility of developing meaningful relationships based on trust.” The patient takes a deep breath and reclines further into the couch as if to avoid the doctor's words. “What can I do to get better?” “You will never improve unless you shake off the past and begin to see your role in a new light.” “How can I do that doctor?” “By examining your paradigms and changing them one-by-one. Move away from being an enforcer to being a service provider. Open your mind to different ways of achieving a compliant outcome. Don't interrogate; try appreciative inquiry, building collaborative partnerships with your colleagues to help them produce new products that will benefit society. Progress beyond reviewing the details of data samples to employing a risk-based, intelligent review strategy, recognizing patterns and gaps to identify the real compliance risks before they become major problems. Simplify, mentor and entertain. Yes, entertain, the best way of teaching, influencing and changing patterns of behavior. Stop being terrorized by the imaginary FDA voices in your head, and think instead about possibilities and new perspectives. Become who you are destined to be - GxP Quality Assurance for the 21st century - and make a positive difference in your ailing industry. Be the catalyst for rational change and build mutually beneficial, trusting relationships with other stakeholders.” “Oh, and relax, enjoy your work.” The stage goes dark and the curtain falls. The audience leave, squinting as they emerge into the day-lit street, pondering the psychiatrist's words, at least until they get back to their offices.

In the pharmaceutical industry qualification of HVAC systems is done by using a risk based approach. Failure mode effect analysis (FMEA) concepts were used for risk assessment of a HVAC system to determine the scope and extent of qualification and validation in this present work. The HVAC is the “direct impact” system in the aseptic practice which directly affects the product quality and regulatory compliance. The level of risk associated with the HVAC system was assessed based on the impact and severity of the probable risk in aseptic practice in sterile manufacturing. On completion of the risk assessment, control and measures developed and recommended actions for unacceptable risk were identified for improved cGMP compliance and qualification of the system upgrades. After completion of the risk assessment the recommended actions were extended and verified against the qualification stages of the HVAC system. Finally, the HVAC system was subjected to a performance qualification (PQ) study. All of the tests were performed and a report was generated. On evaluation of the data collected during PQ, it was found that the HVAC system met all the specified design criteria and complied with the entire cGMP requirement. Hence the system stands validated for PQ. Copyright © 2011 John Wiley & Sons, Ltd.

The Quality Assurance JournalVolume 14, Issue 3-4 p. 61-64 Research ArticleFree Access Principles and Practices of Analytical Method Validation: Validation of Analytical Methods is Time-consuming but Essential Chung Chow Chan, Chung Chow Chan Employed at CCC Consulting chungchow@rogers.com Search for more papers by this author Chung Chow Chan, Chung Chow Chan Employed at CCC Consulting chungchow@rogers.com Search for more papers by this author First published: 08 December 2011 https://doi.org/10.1002/qaj.477Citations: 2 Editor's Note: This article is excerpted from a chapter that appeared in Pharmaceutical Manufacturing Handbook: Regulations and Quality, which was edited by Shayne Cox Gad, PhD. The book was published in 2008 by John Wiley & Sons Inc. Dr. Chung Chow Chan is employed at CCC Consulting. Reach him at chungchow@rogers.com This article was previously published in December/January 2010 in Pharmaceutical Formulation & Quality. AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Validation of an analytical procedure is the process by which it is established, by laboratory studies, that the performance characteristics of the procedure meet the requirements for its intended use. All analytical methods intended to be used for analyzing any clinical samples will need to be validated. Validation of analytical methods is an essential but time-consuming activity for most analytical development laboratories. It is therefore important to understand the requirements of method validation in more detail and the options that are available to allow for optimal utilization of analytical resources in a development laboratory. There are many reasons for the need to validate analytical procedures. Among them are regulatory requirements, good science, and quality control requirements. The Code of Federal Regulations (CFR) 211.165e explicitly states that “the accuracy, sensitivity, specificity, and reproducibility of test methods employed by the firm shall be established and documented.” Of course, as scientists, we would want to apply good science to demonstrate that the analytical method used had demonstrated accuracy, sensitivity, specificity, and reproducibility. Finally, management of the quality control unit would definitely want to ensure that the analytical methods that the department uses to release its products are properly validated for its intended use so the product will be safe for human use. Current Good Manufacturing Practices The overarching philosophy in current good manufacturing practices of the 21st century and in robust modern quality systems is that quality should be built into the product, and testing alone cannot be relied on to ensure product quality. From the analytical perspective, this will mean that analytical methods used to test these products should have quality attributes built into them. To have quality attributes built into the analytical method will require that fundamental quality attributes be applied by the bench-level scientist. This is a paradigm shift that requires the bench-level scientist to have the scientific and technical understanding, product knowledge, process knowledge, and/or risk assessment abilities to appropriately execute the quality functions of analytical method validation. It will require three things: the appropriate training of the bench-level scientist to understand the principles involved with method validation and to be able to validate an analytical method and understand the principles involved with the method validation; proper documentation and understanding and interpreting data; and cross-functional understanding of the effect of their activities on the product and the customer (the patient). It is the responsibility of management to verify that skills gained from the training are implemented in day-to-day performance. Cycle of Analytical Methods The analytical method validation activity is not a one-time study. This is illustrated and summarized in the life cycle of an analytical procedure in Figure 1. An analytical method will be developed and validated for use to analyze samples during the early development of an active pharmaceutical ingredient or drug product. As drug development progresses from Phase 1 to commercialization, the analytical method will follow a similar progression. Figure 1Open in figure viewerPowerPoint Life Cycle of an Analytical Procedure The final method will be validated for its intended use for the market-image drug product and transferred to the quality control laboratory for the launch of the drug product. However, if there are any changes in the manufacturing process that have the potential to change the analytical profile of the drug substance and drug product, this validated method may need to be revalidated to ensure that it is still suitable to analyze the API or drug product for its intended purpose. The typical process that is followed in an analytical method validation is as follows: Planning and deciding on the method validation experiments. Writing and approval of method validation protocol. Execution of the method validation protocol. Analysis of the method validation data. Reporting the analytical method validation. Finalizing the analytical method procedure. The method validation experiments should be well planned and laid out to ensure efficient use of time and resources during execution of the method validation. The best way to ensure a well-planned validation study is to write a method validation protocol that will be reviewed and signed by the appropriate person (e.g., laboratory management and quality assurance). Validation Parameters The validation parameters that will be evaluated will depend on the type of method to be validated. Analytical methods that are commonly validated can be classified into three main categories: identification, testing for impurities, and assay. Table 1 lists the ICH recommendations for each of these methods. Execution of the method validation protocol should be carefully planned to optimize the resources and time required to complete the full validation study. For example, in the validation of an assay method, linearity and accuracy may be validated at the same time as both experiments can use the same standard solutions. A normal validation protocol should contain the following minimum contents: objective of the protocol; validation parameters that will be evaluated; acceptance criteria for all the validation parameters evaluated; details of the experiments to be performed; and draft analytical procedure. The data from the method validation data should be analyzed as the data are obtained and processed to ensure a smooth information flow. If an experimental error is detected, it should be resolved as soon as possible to reduce any impact it may have on later experiments. Analysis of the data includes visual examination of the numerical values of the data and chromatograms followed by statistical treatment of the data if required. Upon completion of all the experiments, all the data will be compiled into a detailed validation report that will conclude the success or failure of the validation exercise. Depending on the company's strategy, a summary of the validation data may also be generated. Successful execution of the validation will lead to a final analytical procedure that can be used by the laboratory to support future analytical work for the drug substance or drug product. The minimal information that should be included in a final analytical procedure is: Rationale of the analytical procedure and description of the capability of the method. Revision of analytical procedure should include the advantages offered by the new revision. Proposed analytical procedure. This section should contain a complete description of the analytical procedure in sufficient detail to enable another analytical scientist to replicate it. The write-up should include all important operational parameters and specific instructions, such as preparation of reagents, system suitability tests, precautions, and explicit formulas for calculation of the test results. List of permitted impurities and their levels in an impurity assay. Validation data. Either a detailed set or summary set of validation data is included. Revision history. Signature of author, reviewer, management, and quality assurance. References 1 International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. ICH Guideline Q2(R1): Validation of analytical procedures: text and methodology. Geneva, Switzerland; 1994. Available at: www.ich.org/LOB/media/MEDIA417.pdf. Accessed December 10, 2009. Google Scholar 2 International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. ICH Guideline Q6A: Specifications: test procedures and acceptance criteria for new drug substances and new drug products: chemical substances. Geneva, Switzerland; 1999. Available at: www.ich.org/LOB/media/MEDIA430.pdf. Accessed December 10, 2009. Google Scholar 3 International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. ICH Guideline Q7: Good manufacturing practice guide for active pharmaceutical ingredients. Geneva, Switzerland; 2000. Available at: www.ich.org/ LOB/media/MEDIA433.pdf. Accessed December 10, 2009. Google Scholar 4 U.S. Food and Drug Administration. Center for Drug Evaluation and Research. Guidance for industry: quality systems approach to pharmaceutical CGMP regulations. FDA. Available at: www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm070337.pdf. Accessed December 11, 2009. Google Scholar 5 U.S. Food and Drug Administration. Title 21 Code of Federal Regulations (21 CFR Part 211). Current good manufacturing practice for finished pharmaceuticals. Available at: www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=211.194. Accessed December 11, 2009. Google Scholar 6 CC Chan, YC Lee, H Lam, et al., eds. Analytical Method Validation and Instrument Performance Verification. Hoboken, N.J.: John Wiley & Sons, Inc; 2004. Wiley Online LibraryGoogle Scholar 7 United States Pharmacopeial Convention. General chapter <1225>: Validation of compendial procedures. Rockville, Md.: United States Pharmacopeial Convention. Google Scholar 8 U.S. Pharmacopeial Convention. General Chapter <1226>: Verification of compendial procedures. Rockville, Md.: United States Pharmacopeial Convention. Google Scholar Citing Literature Volume14, Issue3-4July-December 2011Pages 61-64 FiguresReferencesRelatedInformation

Many business crises bubble up to the executive suite in slow motion. In many cases, they brew for years before they come to a boil. Unfortunately, by the time they're visible, a great deal of damage may have been done. In the biopharmaceutical industry, we are accustomed to problems caused by blockbuster drugs coming off patent when their hoped-for replacements have failed to navigate Phase 3 clinical trials and reach market. With the cost of a new drug reaching Phase 3 clinical trials nearing $1 billion, such dislocations can cause a severe financial crisis. But the causes of other crises can be far more subtle and insidious. One of the most difficult to recognize and ameliorate is the organizational disconnect. Nearly everyone in business has experienced at least one, although they often chalk it up to politics: a poor manager, a coworker with personality problems, and so forth. There are certainly plenty of problems like these in every corporation. But an organizational disconnect is not really linked to individual personality conflicts-those manifest later. It usually has its roots in two separate groups that are supposed to work together, but that, because they have different goals, responsibilities, and cultures, often end up on opposite sides of the same issue. Take, for example, the well-known disconnect between sales and marketing that plagues many companies. Sales people want high quality leads that help them bring in revenue. After all, that's how they're judged. Marketing wants to generate those leads, but it does so by understanding customer needs, positioning the company's products and services appropriately, and then branding those offerings in customers' minds. It's a complex exercise not often appreciated by sales. So it's not surprising that two groups that should be working in harmony often find themselves in conflict. Another familiar disconnect that came to a crescendo in the 1990s was between information technology (IT) and other areas of the business. IT was often focused on the latest and greatest technology. Business wanted applications that would give a competitive advantage in the marketplace. IT did not understand the language of business, while-for their part-business managers often failed to understand the complexity and risk inherent in developing game-changing technology. Instead of cooperating to achieve shared goals, the two sides often found themselves at odds, fighting while critical enterprise projects ran far over budget and sometimes failed completely-at a cost of millions. In recent years, biopharmaceutical firms have found that they, too, confront an organizational disconnect: the one between their quality assurance (QA) and IT teams. As in prior disconnects, the QA and IT departments fail to recognize that, ultimately, they share the same goals. Without a common understanding, the two groups often clash over responsibilities, accountability, and agreement on the extent of regulatory exposure. Companies sometimes get the bad news about QA and IT when the company fails an inspection, which leads to costly remediation and even drug recalls. But early signs of the QA/IT disconnect can usually be seen in the high cost of IT compared with other industries. Alinean (an IT investment analytics firm in Orlando, Fla.) published IT spending metrics on 21,000 companies operating in 37 industry sectors. It found that overall IT spending averaged 3.3% of revenue in 2006. Yet a 2004 study of big pharma conducted by Alinean for Baseline magazine found that their IT spending ranged from 4.7% to 5.7% of annual revenue per company-in some cases twice as much as other industries. Numerous other surveys point to the same truth: Pharmaceutical firms spend more than others on IT. The traditional explanation is that IT costs far more in pharma because these firms must comply with Food and Drug Administration (FDA) regulations. But that begs the question: Why can't regulatory compliance be an everyday part of the business? Must it always turn into a fire drill? Every department-sales, marketing, QA, or IT-has its own unique personality or culture. IT professionals love technology-the newest software, the most powerful or adroit hardware. That's why they entered the profession. But, while they are excited by hot solutions and systems, they generally tend to be allergic to process and documentation, not to mention the adoption of standard operating procedures. Consider Joe, an IT professional and team supervisor with three reports, all of whom work in a specialized area. They could be qualifying hardware and validating software in a server consolidation effort, or they might be managing those same infrastructure systems 24/7 once they are released to the corporate data center. Joe and his team are highly educated and speak in a language replete with an alphabet soup of acronyms describing technical protocols. And, because they are often viewed as a cost center, their responsibilities escalate continuously while their budgets remain flat. In short, Joe has to do more and more with less and less. As he and his team are pushed to the breaking point trying to meet project due dates or keeping balky systems running far past their obsolescence, they begin to practice a form of triage that is common in many IT environments. Often, the first thing to go is good documentation and, with it, adherence to corporate policies and standard operating procedures (see “Documentation and Compliance are King” later in this article). As the documentation grows increasingly cryptic, only Joe and his team really know what it means. If they leave, the company will have little or no institutional knowledge about its IT systems. Or worse–imagine an FDA inspector reading their materials during an inspection. It's easy to appreciate the problem with this approach. Joe will have to serve as a translator, and that's not going to go over well. At the very least, the inspector is likely to cite the biopharm for noncompliance and insist on corrective action (i.e., rework) that must be performed to remediate the situation. Many companies encourage their IT departments to adopt best practice methodologies like the IT Infrastructure Library (ITIL), CobiT, and Six Sigma or to use Project Management Institute practices to ensure that project rollouts run smoothly. But that doesn't mean professionals like Joe and his team have the know-how or time to implement these practices. Indeed, it's not surprising if Joe comes to feel that process and documentation are irrelevant as his team rushes from one crisis to another, dousing fires along the way. If your only choice were to let critical systems crash or become a process maven, what would you do? Developing a good systems practice (GSP) requires more than adopting a standard, best-practice approach to managing IT systems (both infrastructure and applications). GSP requires QA and IT to meet with customers on a regular basis to ensure they are meeting agreed service levels and that these are accomplished in a way that maintains compliance. Each quarter, senior and operational QA and IT managers should meet with a sponsoring senior business executive. This meeting should focus on strategy, infrastructure, application needs, capacity planning, consolidation opportunities, and, of course, compliance. The meeting is all-important because it provides IT an opportunity to play a proactive role in supporting the business. GSP enables IT and QA to position themselves as proactive players supporting management's business strategy. At its best, GSP reporting provides a high-level overview or executive dashboard to management that enables business executives to collaborate with QA and IT in a way that fundamentally changes the relationship among all three parts of the organization. Joe's predicament is all the more bitter because many of his compliance procedures come from two QA departments: one in the business and one in the IT department itself. Both interpret corporate policy into standard operating procedures designed to mitigate regulatory exposure. Why is Joe bitter? Because no one in either QA department really knows how to qualify, validate, or manage systems against a risk-based analysis that identifies critical systems and is comprehensive yet economical. Joe is frustrated because QA often asks him to do things that are unnecessary while ignoring the critical issues needed to comply. So he either has to do both or engage in a running battle until he convinces the powers-to-be that they're missing the point. Of course, every war has two sides and two stories to tell, and the QA/IT disconnect is no different. Like Joe, Judy is a well-educated professional with more than a dozen years of experience managing QA teams throughout the organization; first in manufacturing, then in drug distribution and labeling, and now in IT. Judy does her best to translate corporate QA policy into something Joe and his team will understand, then trudges into the computer room to confront her longtime nemesis. She knows the drill. Joe will fidget impatiently at the conference room table while she goes over a book of procedures she wants him to follow and document during his server consolidation project. Business people sometimes get the bad news about QA and IT when the company fails an inspection, which leads to costly remediation and even drug recalls. But early signs of the QA/IT disconnect can usually be seen in the high cost of IT compared to other industries. She suspects half the steps she's insisting on may be unnecessary make-work-Joe has explained it to her time and again. But Judy is constrained by two key issues. First, she is unfamiliar with a risk-based analysis that would allow Joe to tailor the company's QA procedures to address the concerns of FDA inspectors. Second, she does not agree with her counterparts in the business department as to what actually constitutes compliance in the eyes of FDA inspectors. Judy is IT savvy. She's adept with Excel and expert in crafting PowerPoint presentations. But if her desktop goes down or her software malfunctions, she'S at a complete loss. And she knows that dealing with desktops is a far cry from managing data centers or consolidating thousands of servers running advanced software. Judy is painfully aware that she and Joe don't share the same language or culture, even though-in the end-she's confident they share the same goals. They both want to comply with FDA regulations in the most efficient manner possible, they never want to fail an inspection, and they desperately want to escape the combative, crisis-oriented atmosphere to which they seem perpetually doomed. Biopharms must comply with the FDA's IT regulations covering electronic records or those governing the validation and qualification of software and hardware systems. They must also comply with the myriad rules that control manufacturing systems and drug marketing, labeling, and distribution, as well as financial systems if they are publicly traded. As a result, a large biopharm can have numerous QA groups spread throughout the organization, each focusing on vastly different issues. One danger in this scenario is that as people move from one group to another, they begin to apply QA practices inappropriate in their new domain. A common mistake made in many biopharms is to apply manufacturing compliance processes to the governance of IT regulations, even though manufacturing and IT have little in common. QA “grew up” in manufacturing and helped develop the Good Manufacturing Practice (GMP) guidance documents that ensure quality in drug manufacturing. It was a signal achievement, and it is understandable that QA would want to transfer the same principles to IT operations and projects. But manufacturing and IT are completely different disciplines, particularly in the biopharmaceutical industry where drugs are made in batch mode but IT systems run continuously. Try to merge the two, and you will find much is lost in translation. Reliable documentation and record keeping provide the key to avoiding failed inspections. Nearly all IT practitioners admit documentation is not on their list of favorite things to do, no matter how much their managers complain. But QA professionals depend upon it. Sound documentation is the cornerstone of regulatory compliance. Without it, IT professionals are often forced to remediate systems again and again, essentially repeating the same work they could have completed in the first pass. So how do you get IT practitioners to document their work and systems at a best practice, good systems practice (GSP) level? The best way is to show them, usually through small wins at first, how their workload gradually diminishes as they adopt and repeat GSP and other quality-assured project management processes and procedures. Good records and reliable audit trails will enable them to move on quickly to more interesting challenges. IT needs its own quality management paradigm. For example, it would be far better to develop an ITIL-based good systems practice to manage IT infrastructure and to adopt and modify Project Management Institute methodologies to manage projects like validation and qualification in FDA-regulated environments. It'S important to note that, while there are several excellent best practice approaches to managing or deploying information technology, virtually none has been optimized to meet FDA regulations. It is high time individual companies-and the biopharm industry as a whole-begin to develop a set of IT best practices designed to meet their unique regulatory and compliance challenges. Did Joe and Judy ever stop bickering and learn to work collaboratively, meeting each other's needs? Surprisingly, the answer is yes. It wasn't easy for Joe to admit that process, documentation, and standard operating procedures were not only important but also in his best interests. It meant giving up some long-cherished beliefs and behaviors, not to mention the cowboy mentality with which many IT professionals are afflicted: the “Stand back everybody, I'm here to save you, and it's all black magic” approach to managing IT systems and projects. Joe realized this approach was unsustainable. He and his team were working ever-longer hours, and as their morale suffered the quality of their work, as measured by indisputable quantitative benchmarks such as systems availability, projects delivered on-time and on budget, and audits passed-was clearly falling. But that didn't mean Joe had to accept Judy's over-the-top approach to compliance, with all its make-work that had nothing to do with true compliance. IT systems and projects could be managed with far less work, if only Judy and her organization would listen. For her part, Judy was more than willing to sit down with Joe and his team, bringing in others as needed to work toward developing common protocols that could address the interests of both sides. Judy even went so far as to recruit a champion, a senior business executive to whom both QA and IT ultimately reported (see previous section “GSP Affects the Whole Business”). He agreed to provide the top-down push to support their grassroots effort. Indeed, this executive had long been concerned about the high budget allocations and diminishing quality of IT, not to mention the fear that someday the company would fail a critical audit and suffer severe financial penalties. He would provide corporate cover and chair quarterly meetings with the QA and IT teams as they developed state-of-the-art compliance and operating procedures that eschewed the nonessential while adopting or developing best practices that saved time and money. Like any change, the first steps were the most difficult. Joe sometimes still chafed at what he called “all the process talk,” and Judy worried the IT cowboy was still lurking behind every corner. But, with weekly meetings, each group began to understand the other and gradually learned to collaborate, ultimately adopting and developing a set of best practices for QA and IT. Judy didn't miss the daily fighting. And Joe and his team learned that, in time, standard operating procedures and sound documentation cut their workloads so much that they could get home most nights.

Instrument qualification is a frequently cited deviation in regulatory observation and warning letters. A serious violation could even shut down a production line. One of the main reasons for noncompliance is that qualifying instruments and validating the related software is a complex specialty area that has lacked clear guidelines and terminology. “Historically, qualification of lab instruments was seen as a barrier,” said Paul Smith, European validation program manager at PerkinElmer Life and Analytical Sciences. “Because qualification is a specialist subject, that made it mysterious, and people didn't know what to do to qualify an instrument. It was something that had to be done and that was specialized and that delayed the introduction of a product.” Efforts to change the situation are underway, including the release in 2008 of the United States Pharmacopoeia (USP) General Chapter <1058> guidelines on analytical instrument qualification (AIQ). USP <1058> originated at a 2003 conference of the American Association of Pharmaceutical Scientists (AAPS). A USP committee took recommendations, for example, to more strictly define use of the terms “validation” (for manufacturing processes, analytical procedures, and software procedures) and “qualification” (for instruments), as well as to classify instruments into three major groups based on their complexity. They were careful to complement existing and widely used good automated manufacturing practice (GAMP) principles and procedures set by the International Society for Pharmaceutical Engineering (ISPE). GAMP covers all aspects of production and is more complex than USP <1058>, which is suited to instrument qualification but not software validation. “When we were developing this chapter [<1058>], our committee was concerned with the GAMP guidance,” said Horacio Pappa, PhD, senior scientific liaison in USP's Documentary Standards/General Chapters Division. “We made it easy to perform but did not contradict what is in the GAMP guidance.” Dr. Pappa explained that GAMP is for more sophisticated instruments used with a computer or on a network. USP <1058> applies easily to commercial off-the-shelf instruments. In addition, GAMP is a voluntary standard and contains specific steps, while USP <1058> is guidance. “There are not too many details on how to qualify [instruments],” Dr. Pappa said of USP <1058>. “There is no intent to make it a standard. No one certifies you for <1058>.” Neither is it meant to verify software. There is room within USP <1058> for more specifics, however, and analytical chemists, contract research organizations, and others who perform qualification testing must be aware of them. While the USP general chapters from <1000> through <1999> are informational, allowing alternative approaches to be used, the general chapters from <1> through <999> are requirements that must be met in order for equipment to pass inspection. And <1058> refers to other USP chapters that designate specific techniques that are requirements, such as <21>, which addresses thermometers. USP is mandatory by law under the Food, Drug, and Cosmetic Act, Dr. Pappa said. “If it [the applicable chapter] is under <1000>, [it is] more likely the Food and Drug Administration [FDA] will enforce it,” he said. “Instrument guidance is one piece of the whole in terms of the government assuring any drug or drug product manufactured in the United States and Europe meets certain standards of safety, purity, and quality.” One of the biggest benefits of USP <1058> is its classification of instruments, said Bob McDowall, PhD, principal of McDowall Consulting, whose consulting company outsources service, writes testing documentation, and trains staff to qualify equipment. USP <1058> goes further to qualify analytical equipment than an earlier proposal by the Pharmaceutical Analytical Sciences Group (PASG), Dr. McDowall said. “PASG was a forerunner of <1058>, but <1058> goes further in trying to classify instruments into various groups,” he said. “There is more system suitability testing and method validation in <1058>.” USP <1058> classifies systems and operations into three groups: A, B, and C. Category A includes standard equipment set to the vendor's specifications and with no measurement capability; inspectors can check it by observation. Such equipment includes centrifuges, sonic baths, vortex mixers, and magnetic stirrers, Dr. McDowall said. Category B includes standard instruments that have measurement values requiring calibration. Such instruments include balances, pH meters, thermometers, and pumps. Category C includes more complex instruments and computerized or networked systems that require full qualification and specific function and performance tests. Examples include dissolution, high-performance liquid chromatography, and spectrometers. Where the categories become unclear, Dr. McDowall said, is in how the instruments are used. When a sonic bath from the A group is used to dissolve material, simply observing that the material has dissolved keeps it in group A. But if a method requires a specific temperature and flasks with materials that are located in specific areas of the bath, the use is different, and the sonic bath moves into the more stringent group B requirements needing calibration. USP <1058> describes the AIQ process for assuring an instrument is suited to its intended use and serves as the underpinning of a USP data quality triangle (see Figure 1) for all other phases of analytical work. The three activities layered on top of AIQ are analytical method validation, system suitability tests, and, at the top, quality control checks. Each layer is intended to add to overall quality. Analytical method validation is documented evidence that an analytical procedure is suitable for its intended use. System suitability tests verify that the system will perform to the criteria for a given procedure, and the tests are performed along with sample analysis. Other chapters have more details. USP general chapter <621> Chromatography, for example, has more information on system suitability tests related to chromatographic systems. The AIQ process is broken down into four stages known as the “4Qs” (see Table 1). They are design qualification (DQ), installation qualification (IQ), operational qualification (OQ), and performance qualification (PQ). DQ, which defines the functional and operational specifications of the instrument and associated software, is performed before buying a new instrument. Dr. McDowall said that it is typically either done poorly or not at all. IQ, which is performed upon installation of a new system as well as on existing unqualified systems, establishes that the instrument is delivered as designed and is specified and installed properly. OQ, which is done after installation or major repair, documents that the instrument will run according to its operational specification in the intended application. And PQ documents that the instrument performs consistently to specification and its intended use and is performed at specified intervals for each instrument. In September 2009, the FDA tightened the regulations by which it handles a 483 inspection report, narrowing the time for a complete response to within 15 working days before it elevates a problem to a warning letter. Once issues are resolved, the letter is closed out. The whole process—from warning letter to resolution—is posted on the FDA's website, Dr. McDowall explained. “Standards are done by internal audit, contract manufacturing organization or contract research organization external audit, or inspection by the FDA. If you are not in compliance, your company proposes corrective actions and a time period to do them. It's up to the inspector to approve it,” he said. Depending on how serious the violation is, a whole lab could not be qualified or a piece of equipment needing cleaning could put the entire manufacturing line into question, he said. “It potentially could shut down a production line.” “If <1058> were more prescriptive, you'd expand it a lot and lose the benefits of its simplicity,” Smith said. “What I'd like to see is the people who wrote <1058> and GAMP get together.” He added that an organization that decides to use <1058> needs to harmonize around it. “The benefits of a similar structure are beneficial in an audit,” Smith said. “If a procedure is outlined in a general chapter, it is easier for a manufacturer to follow that,” added Dr. Pappa. “The FDA is familiar with it, so it is a ‘known currency.’” Although Dr. Pappa said there is no current effort at USP to harmonize with GAMP, he didn't rule out the possibility when a new expert committee overseeing the chapter takes over at the end of June 2010. “It's a five-year cycle, so they could look at GAMP,” he said.

The ability to control and correct processes in today's quality management systems (QMS) is key to maintaining a high level of compliance within an organization. Whether tracking incoming customer complaints, identifying nonconforming materials from production, or using corrective and preventive actions (CAPA) to remedy system problems, defining quality management processes can improve quality, reduce legal liability, and make compliance a competitive advantage. Among all the processes associated with managing a quality and compliance management system, CAPA is a particularly prominent feature. In many cases, if an adverse event is found in the system, a CAPA is generated. Regardless of the scope or severity of the event, CAPA becomes the ultimate catchall for adverse events in the system. The result? Hundreds, if not thousands of CAPAs, with varying degrees of severity, assigned in the order they were created, like a pile of virtual papers stacking up in the quality assurance manager's inbox. This becomes a problem when the critical CAPAs—those events that have the most impact on the business as a whole—become lost in the pile, virtually invisible, hidden among non-critical, immediately correctable events. This article will examine how using a risk assessment model in a QMS can help filter the needles from the haystack by identifying those critical events, mitigating the risk, and preventing recurrence of adverse events. Risk management is not new to the quality and compliance industry. Risk has always been a prominent feature in standards such as ISO 13485, ISO 14971, and ISO 31000. Risk management methods are typically defined as the ability to detect and then evaluate and control potential risks. When applying risk management to adverse events, we will look at how evaluating and controlling risks can help to filter CAPAs and streamline business processes. So what is risk? When you actually sit down to define and quantify risk, you'll find it is not an easy task. Many organizations have varying degrees of what is considered a risk and spend plenty of time and money putting risk metrics in place. Most use standard risk criteria, such as severity and frequency, and assign an action based on severity and frequency levels. These levels are often displayed graphically in a heat map called a risk matrix (Figure 1). The risk matrix is a typical chart that helps decision makers visualize the severity and frequency of an event and provides a risk level based on these two (or more) criteria. A quantitative method like this removes some of the subjectivity that can occur when looking at an event and assessing the risk. With a well-defined set of risk criteria that can be calculated into a risk matrix, companies can now use risk to help make decisions on events that come into the QMS. However, it is important to note that not all risk matrices are perfect—they must be vetted and tested. Many companies go back to historical data and apply the new risk matrix to old events to see if the matrix is making the proper decisions. If not, the matrix must be fine-tuned to meet your risk standards. Companies often fine-tune risk levels to fit into a broad regions of the matrix—generally acceptable risk, generally unacceptable risk, and as low as reasonably practicable. These broad regions will help to further define how you approach the varying levels you may encounter as you assess the risk of events. To better illustrate how the process of defining and utilizing risk in a QMS can benefit an organization and reduce CAPAs, let's take a look at a case study of a life sciences company faced with the challenge of too many CAPAs. We will take the case of a leading Fortune 1000 life sciences company. For these kinds of manufacturers, handling complaints from customers, patients, and health care professionals is part of the business. This particular manufacturer receives more than 10,000 complaints per month, and hundreds, with varying degrees of severity, are forwarded to the quality department for investigation. The following problem arose within the company's CAPA system: Any time a quality-related complaint came in, a CAPA was immediately issued. Soon the CAPAs began to overrun the process, and it became impossible to dedicate the time and resources to handle every CAPA in an effective manner. This manufacturer desperately needed a better solution to ensure that adverse events were reported in a timely manner to the U.S. Food and Drug Administration (FDA). The quality department of this manufacturer needed to filter and prioritize incoming complaints more effectively. Previously, complaints had only been flagged as critical if a member of the complaint management team reviewed the complaint and gave it a priority level. This was a subjective measure, however, and the criteria for the severity of complaints was hit or miss. An objective measure was needed to assess the complaint and designate a consistent corresponding action. The company decided to utilize the concept of risk assessment, so that it could immediately correct non-critical events and take action to correct any critical events. The risk-based method for the company is two-fold. A special subject-matter group first determines the risk criteria (severity, probability, and so on), and the risk matrix then determines the corresponding actions to be taken based on those criteria. Risk that is within tolerance levels is immediately corrected, and the process ends there. If the risk is intolerable, actions are automatically generated within the system—e.g., deciding whether or not the risk is reportable to the FDA or if a CAPA is needed. The CAPA process in itself is directly tied to the risk level. The risk level is inherited into the CAPA, and direct links to the risk assessment and the complaint are displayed on the CAPA form. The CAPA then generates the action plan, which determines the root cause and corrective action of the complaint, much like in any other CAPA process. The difference for this company lies in the CAPA-effectiveness phase of the process. For an action to be truly corrective to the process, it must be effective. Previously, this company's effectiveness measures had been subjective: the manager of the CAPA checked the process and confirmed whether it had worked or not. If the problem related to the complaint was visibly corrected, it was considered effective. There was no objective way to ensure that the event had been corrected within the company's compliance standards. To ensure true objective effectiveness, the company chose to use risk assessment as a mitigation tool. To mitigate risk at the effectiveness phase, the company incorporated a second risk assessment to measure the risk as a result of the corrective action taken. By implementing an additional risk measure, the effectiveness is assessed and the risk level is recorded. The subject-matter group looks at the actions taken and determines the new severity and frequency of the action to ensure that it is within acceptable risk tolerances. If the risk has been reduced and is within acceptable parameters, it is effective. If the risk has not been reduced, or even if the risk has been reduced but not to acceptable parameters, it must be reworked until it is corrected. Using risk mitigation as a measure of effectiveness is extremely valuable for ensuring that the CAPA has solved the problem to within compliance standards. By implementing a risk-based approach to its process, the manufacturer has effectively identified critical events and dedicated resources to solving the quality problems that affect the business most. As more and more complaints enter the system, a risk history builds. The benefit is that, based on previous complaints and their risk levels, the company has a growing base of knowledge about events with similar risk levels. This becomes a crucial reference point for the quality management process, because any event with similar risk can be handled in a similar fashion, further streamlining the process. By referring to the base of knowledge, the company can take action much more quickly and handle complaints more efficiently. For example, if a complaint is related to product line A, the company can review previous events with similar parameters across all product lines and uncover any past events with the same conditions. Based on a history of past events, the company can make more informed decisions by drawing on previous actions taken. Using risk history will not necessarily make the decision, but it will provide a library of objective actions to help with new complaints. Furthermore, the company is now able to report on the overall risk history of a particular product, enabling it to make a decision on whether this product incurs too much risk and requires a rework, redesign, or recall. The case of the life sciences manufacturer cited above is just one example of how risk assessment is applied to the QMS. The underlying concept of applying a quantitative risk assessment to an event is prevalent in other aspects of a QMS. For instance, if a process or design change is required, you can assess the total business effect or risk of that change from within your change-management process. Managing risk in design enables an organization to identify potential failures early on, reduce occurrences of “in-field” failures, and identify any potential hazards. In addition, handling product lots through nonconforming materials can utilize risk assessment methods to measure the level and severity of any defects. There's no limit to risk assessment's benefits to the QMS. In this article, we covered the broad nature of risk management and how using risk assessment tools will help to drive decision making in a quality management system. Once defined, a risk matrix can provide organizations with a decision-making tool that will help not only to prioritize adverse events but also to define the action to be taken. This tool is designed to help filter events and minimize the corrective actions that can cause a bottleneck in a QMS. Implementing a risk-based CAPA system within a QMS is a bold step toward improving the operational control your business has in managing adverse events. By using the concepts discussed here, your business will be able to streamline the CAPA process, handle events that are immediately correctable, and ultimately focus your quality system on the essential events that have the most impact on your business.

The pharmaceutical industry continues to evolve. New International Conference on Harmonisation (ICH; Q8, Q9, and Q10) guidelines provide science- and risk-based approaches to development, risk management, and quality systems that can empower manufacturers to manage continuous improvement and technical innovation throughout the product life cycle. New manufacturing technologies are being introduced, and increasingly sophisticated approaches to process analytical technology (PAT) are being developed. Key factors drive this evolution, including industry competition, cost containment, and quality and regulatory considerations. New technology initiatives provide capabilities with potential to enhance productivity by improving the process—capability, control, and robustness—reducing cycle times, and improving consistency, while at the same time ensuring compliance. New technologies and advances in PAT-enabled process control can, in combination with strategic use of design space and implementation of quality risk management, enable quality by design (QbD) to achieve a desired state of manufacturing. These new manufacturing paradigms can provide opportunities for significant regulatory flexibility, including real-time release (RTR) and post-approval continuous improvement. Historically, the pharmaceutical industry has applied PAT to further process understanding. Over time, as the technology has grown and become more sophisticated, the potential for PAT-based applications to add value has increased. All of these approaches add value by furthering process understanding, but, like the sophistication of the technology and implementation of PAT, value rises exponentially as the list descends. The jump from process understanding to determining process endpoints is certainly significant, but new PAT-based control strategies can overcome traditional process control limitations, bring new definition to process consistency and efficiency, and extend process capabilities beyond what is possible with conventional control approaches. Newly developed and emergent PAT-based approaches are pushing the boundaries of process design and redefining strategies for process control. As the reliability and performance of PAT systems improve, its potential to serve an integral role in pharmaceutical processes will increase. Within this context, PAT is increasingly used to replace off-line final product tests with at-line or on-line PAT-based release tests, to provide the basis for process control strategy, and to enable continuous quality verification (CQV) and RTR. Process control has traditionally been achieved through tight control of key process parameters at predetermined set points or ranges. The premise for this approach is the assumed or established relationship between process inputs—raw materials properties, process parameters such as temperature or pH, and so on—and critical and key product attributes such as process outputs. This control strategy, however, does not allow for mid-course correction to account for variation in starting materials or process upsets, nor does it allow flexibility within or between production runs to utilize the design space concept. The set points for critical process parameters are commonly determined during development—typically in a design of experiment—and the process validated using a three-batch validation approach. Yet, in reality, after validation, the process will be subject to different sources of input variation that would be transferred directly to process outputs. Variability in quality attributes, therefore, is virtually inevitable. Reducing common cause variation in such a traditionally controlled process can require significant effort. To ensure acceptable process capability, over-processing—over-drying, for example—is typically utilized, commonly resulting in increased costs and cycle times. Thus, control strategies that are based on fixed process parameters can result in higher variability of product attributes. No single definition of advanced process control (APC) exists in the literature, but the phrase as it is currently used describes mathematically advanced control algorithms that use predictive, adaptive, and optimization techniques to control multi-input, multi-output processes. A new concept in the pharmaceutical industry, APC is a mature technology that is commonly used in all other industrial sectors to improve quality, consistency, and process efficiency. Pfizer distinguishes APC as control strategies that utilize PAT, process models, or other techniques to manipulate process parameters (process inputs, Xs) within any required constraints, in order to actively control one or more active pharmaceutical ingredient (API) or drug product attributes (process outputs, Ys) at a set point or within a tight range. In a continuous process, outputs are typically controlled to reach and maintain steady state; thus, simpler steady state control strategies (time invariant) are sufficient. By comparison, in a batch process, outputs will typically follow a time-variant trajectory, necessitating more involved control strategies, including the use of multivariate controller models. One PAT application uses near infrared (NIR) technology to monitor multivariate high shear wet granulation (HSWG) batch trajectory. (HSWG is a particle size enlargement process for maximizing powder handling and uniformity and minimizing dust hazards. Powder is mixed as the powder bed is simultaneously sprayed with binder solution.) Figure 2 illustrates how the variation in raw materials and processing conditions results in separation of NIR trajectories. The performance of the granules during downstream processing can be predicted by the NIR trajectory, with the trajectory for Excipient 1 at X1 = M representing an optimal batch. Figure 3, illustrates the use of PAT-based APC to control the real-time trajectory of a batch with non-optimal raw materials; it would follow the trajectory of the optimal “golden batch” that results in the required granule properties. APC offers a new and promising paradigm for efficiency in pharmaceutical processes while providing tangible quality and business benefits. It can enable higher process capability, maintaining the process attributes close to specification. Improved quality, lower common cause variations, greater product consistency, improved yield, and cycle time improvements are among the benefits these advanced strategies can provide. Currently, at Pfizer, there are a number of APC applications, involving both drug product and API processes and focused on large-scale manufacturing, at different stages of development. They utilize a range of APC technologies, including various latent variable model-based batch control strategies, non-linear APC with hybrid process models, soft sensing, and multi-loop optimal control using quality and business cost functions. These applications aim to address some of the most technically challenging problems in our industry with regard to chemical and physical attributes of API and drug products. CQV (ASTM E2537) is a science-based approach to process validation in which manufacturing process performance is continuously monitored, evaluated, and adjusted as necessary (see Figure 4, below, right). This science-based approach verifies that a process is capable of producing and will consistently produce product meeting its predetermined critical quality attributes. PAT-based CQV provides real-time quality assurance; the desired quality attributes are ensured through continuous assessment during manufacture. Data from each batch are used to validate the process. RTR is the ability to evaluate and ensure acceptable quality of in process and/or final product based on process data, which include a valid combination of material attributes and process control (ICH Q8 [R1]). RTR requires a high level of process knowledge to understand the impact of process parameters and raw materials on product critical quality attributes, as well as to identify and control the sources of variations. Process data composed of larger numbers of continuously gathered samples as compared to samples taken at the beginning, middle, and end of a process serve as the basis for real-time release of the final product. Measurement may be indirect, e.g., blend uniformity using on-line NIR coupled with unit dose weight variation control versus United States Pharmacopeia testing of tablets. Enhanced process understanding, larger number of process samples, and effective process control provide an increased level of quality assurance with RTR. Pfizer defines QbD as designing and developing formulations and manufacturing processes to ensure predefined product quality and understanding and controlling formulation and manufacturing process variables that affect the quality of a product. Pfizer's Next Generation Manufacturing Initiative extends QbD principles to achieve continuous manufacturing of a drug product. Design space and PAT are combined to demonstrate and achieve process control; CQV is used for process validation. The combination of these approaches enables the RTR of a continuously manufactured product. Continuous manufacturing is the combination of multiple unit operations in a manufacturing process into a single integrated system. In a continuous process designed based on QbD principles, sources of variation are defined and controlled, and end product variation is minimized by controlling the process within the design space. Reduced waste and increased containment capabilities are among the environmental, health, and safety benefits afforded by continuous management. Driven by cycle time reduction and capacity enhancement, as well as reduced off-line analytical testing and minimized change over time, resulting efficiencies may be captured in capital and operating cost reductions. The integration of on-line PAT tools supports the development of more advanced process control strategies and CQV that can lead to real-time release. Among the PAT applications enabling CQV and RTR is a PAT-based system for real-time monitoring of blend potency and uniformity in new continuous drug product manufacturing processes. The application utilizes sophisticated PAT signal conditioning, advanced chemometrics, and multivariate calibration models to accurately measure the potency of flowing blends. Apart from enabling CQV and RTR, PAT supports process control strategy by providing fast, real-time measurement that is used by the supervisory control system to maintain the process and the product within the required limits. As the reliability and performance of PAT systems improve, their potential to serve an integral role in pharmaceutical processes will increase. Newly developed and emergent PAT-based approaches are pushing the boundaries of process understanding at Pfizer and redefining process control strategies in batch and continuous processes. Within this context, PAT is increasingly used to replace off-line final product tests with at-line or on-line PAT-based release tests, to enable CQV and RTR, and to provide the basis for advanced process control.

SummarySpreadsheets are widely used for the storage, processing and reporting of data. As these spreadsheets are associated with the conduct of non‐clinical safety studies intended for regulatory submission, it is essential that they should be developed, validated, operated, maintained, retired and archived in accordance with the OECD Principles of GLP.The present document provides guidance, i.e. a basic strategy for GLP‐compliant development and validation of spreadsheets. Different approaches can also be used, as long as they are compliant with the OECD Principles of GLP.The AGIT (Arbeitsgruppe Informations‐Technologie) is a working group consisting of representatives from Swiss industry and Swiss GLP monitoring authorities that proposes strategies relating to information technology issues which can be put into practice by test facilities in order to fulfil GLP regulatory requirements. Copyright © 2011 John Wiley & Sons, Ltd.

Quality is an ever-growing problem in the fields of pharmaceuticals, medical devices, medical diagnostics, and biologics, and its negative impact significantly affects manufacturers as well as their patients, employees, and investors. Furthermore, as the world's dependence on medicine increases-along with the trend toward pervasive personalized medicine-the reins of quality become harder for many firms to hold on to. While this is partly due to poor products, much of the problem arises from the inadequate quality of manufacturers' reporting infrastructure. It is true that regulatory bodies across the globe impose regulations and guidelines for manufacturers to follow for managing, reporting, and resolving complaints, investigations, and adverse events. Unfortunately, many companies simply haven't invested heavily enough in their quality staffs, systems, and procedures to ensure quality products and practices. This situation has to change in order to better protect patients, manufacturers, and investors. The best solution involves better testing, higher quality manufacturing, and a holistic quality management program across the entire product development life cycle. Life sciences manufacturers-whether they produce drugs, devices, diagnostics, or biologics-introduce products to their target market once they have received the proper regulatory approval, a step that typically follows more than 15 years of diligent research, development, and testing. While this approval indicates that the product is safe or medically necessary to a target population, negative incidents can result from the use of accessories and processes related to the product itself, including its formulation, labeling, delivery, or packaging, among other possibilities. These complaints come to manufacturers through call centers established to handle customer issues, via the company's Web site or e-mail address, or from field service during on-site visits by the vendor. By law, manufacturers and service providers are required to have a quality management program in place to act on such problems, but many of these so-called programs are disjointed, fostering inconsistencies and information flow breakdowns. Without a centralized system and processes, critical information can easily be lost or mismanaged. And let's not forget just how serious the implications of erroneous quality management can be to all constituents involved. The negative impact of these mistakes can lead to serious outcomes, including patient harm or death, product withdrawal, and negative financial and brand impact to the organization. To curb this trend toward inconsistent and insufficient quality monitoring practices, regulators across the globe have published regulatory guidances and imposed regulations for quality management. Chief and most pervasive among the regulations is the U.S. Food and Drug Administration's 45 CFR part 46, which ensures protection for human research subjects. Under this regulation, manufacturers in the life sciences field must report all “adverse events that are unanticipated problems and unanticipated problems that are not adverse events,” according to the Office for Human Research Protections and the Department of Health and Human Services. For an event to be deemed an unanticipated problem, it must be unexpected, must be related or possibly related to participation in the research, and must suggest that the research places subjects or others at a greater risk of harm than was previously known or recognized. If the event meets all three of these criteria, then it is considered unanticipated and must be reported. According to the Food and Drug Administration's Center for Drug Evaluation and Research Facts and Figures for fiscal year 2006, the following were the most common drug quality complaints and adverse events reported: When a centralized, automated, and fully integrated quality management process and reporting system is not in place, many issues arise that put a serious strain on a company's quality management efforts. These challenges can lead to some very serious consequences. Other policies exist within each industry subset. In the pharmaceutical industry, the FDA mandates that each company must have a system for implementing corrective and preventive actions resulting from the investigation of complaints, product rejections, non-conformances, and so on. It also requires a structured approach to the investigation process, with the objective of determining root cause, as noted in the FDA's May 2007 guidance document, “Pharmaceutical Quality System.” In the medical device industry, there is a medical device reporting (MDR) regulation that requires the filing of an MDR whenever an adverse event occurs for products undergoing remedial action. The biologics industry also has regulations for quality monitoring. The Bioresearch Monitoring (BIMO) Program was created to monitor all aspects of the conduct and reporting of FDA-regulated research. Given these and a number of other regulations and guidance documents, it is easy to see the challenges manufacturers face in maintaining quality management of their products. The violation of each of these regulations triggers a warning letter from the governing body to the manufacturer, containing instructions on how to become compliant with regard to the specific complaint or adverse event. Depending on the severity of the event and its ubiquity, a warning letter can go as far as initiating a lawsuit, requiring product recall, or recommending post-marketing surveillance, further data research, or the conduct of a phase IV trial, among other possibilities. In the ideal scenario, all of these warnings would be tied directly into the manufacturer's CAPA system so that the company could develop a corrective action plan to address the violations and prevent any future complaints or adverse events. Fortunately, there are comprehensive quality management systems available to automate all of these functions and requirements for manufacturers. These software programs can provide a centralized, consistent, and standardized intelligent mechanism for recording, tracking, and trending customer complaints, investigations, and adverse event reporting according to regulatory standards. Recently, many companies have received warning letters for inconsistencies in their quality management procedures as well as for failure to properly investigate and determine root causes for complaints and adverse events. Companies have also been warned for having deviant processes set up to decide what is reportable as an adverse event and what is not, for late closure of investigations and submission of regulatory reports, and also for inadequate links to a CAPA system. The impact of these deficiencies on any company-and their potential effect on the people who use the company's products-can be quite substantial in this industry. A qualified quality management system helps a manufacturer by automating and streamlining all complaint, investigation, and adverse event-related issues across the organization-with centralization of data. This system keeps the company compliant with ever-increasing regulations, ensures patient safety, maintains a strong bond with customers and the public, and avoids litigation. By implementing a Web-based quality management system, organizations can avoid these problems and, at the same time, improve overall data integrity and consistency, increase work flow efficiency and precision, keep patients safe and investors satisfied, and maintain a positive relationship with customers and the public at large. In short, these systems help life sciences manufacturers avoid negative publicity and maintain credibility in the eyes of the public because potential problems are caught and handled immediately upon receipt. Compliance: Within 15 to 30 days of a complaint of adverse event notification-depending on event severity and type-the manufacturer must report the incident to the regulating bodies. Having a centralized, standardized, and compliance-adhering system to help automate this process makes it far easier to remain compliant, both externally and internally. A qualified quality management system helps manufacturers by automating and streamlining all complaint, investigation, and adverse event-related issues across the organization-with centralization of data. This system keeps the company compliant with ever-increasing regulations, ensures patient safety, maintains a strong bond with customers and the public, and avoids litigation. The law says you need a quality management system to handle all of these issues. Why not arm yourself and your organization with the tool that will ensure the right results for you, your organization, your customers, and your investors?