1010 USP39-NF34 ANALYTICAL DATA INTERPRETATION AND TREATMENT£¨ÖÐÓ¢ÎÄ£©

<1010> ANALYTICAL DATA¡ªINTERPRETATION AND TREATMENT

·ÖÎöÊý¾ÝµÄ½âÊͺʹ¦Àí INTRODUCTION

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This chapter provides information regarding acceptable practices for the analysis and consistent interpretation of data obtained from chemical and other analyses. Basic statistical approaches for evaluating data are described, and the treatment of outliers and comparison of analytical procedures are discussed in some detail.

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[NOTE¡ªIt should not be inferred that the analysis tools mentioned in this chapter form an exhaustive list. Other, equally valid, statistical methods may be used at the discretion of the manufacturer and other users of this chapter.] [×¢£º±¾ÕÂËùÁеIJ¢·ÇÊÇËùÓеķÖÎö¹¤¾ß¡£¸ù¾ÝÉú²úÉÌºÍÆäËûʹÓÃÕßµÄÉ÷ÖØÅжϣ¬Ò²¿ÉʹÓÃÆäËûµÄһЩµÈЧͳ¼Æ·½·¨¡£]

Assurance of the quality of pharmaceuticals is accomplished by combining a number of practices, including robust formulation design, validation, testing of starting materials, in-process testing, and final-product testing. Each of these practices is dependent on reliable test procedures. In the development process, test procedures are developed and validated to ensure that the manufactured products are thoroughly characterized. Final-product testing provides further assurance that the products are consistently safe, efficacious, and in compliance with their specifications.

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Measurements are inherently variable. The variability of biological tests has long been recognized by the USP. For example, the need to consider this variability when analyzing biological test data is addressed in Analysis of Biological Assays <1034>. The chemical analysis measurements commonly used to analyze pharmaceuticals are also inherently variable, although less so than those of the biological tests. However, in many instances the acceptance criteria are proportionally tighter, and thus, this smaller allowable variability has to be considered when analyzing data generated using analytical procedures. If the variability of a measurement is not characterized and stated along with the result of the measurement, then the data can only be interpreted in the most limited sense. For example, stating that the difference between the averages from two laboratories when testing a common set of samples is 10% has limited interpretation, in terms of how important such a difference is, without knowledge of the intralaboratory variability.

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This chapter provides direction for scientifically acceptable treatment and interpretation of data. Statistical tools that may be helpful in the interpretation of analytical data are described. Many descriptive statistics, such as the mean and standard deviation, are in common use. Other statistical tools, such as outlier tests, can be performed using several different, scientifically valid approaches, and examples of these tools and their applications are also included. The framework within which the results from a compendial test are interpreted is clearly outlined in General Notices and Requirements 7. Test Results. Selected references that might be helpful in obtaining additional information on the statistical tools discussed in this chapter are listed in Appendix G at the end of the chapter. USP does not endorse these citations, and they do not represent an exhaustive list. Further information about many of the methods cited in this chapter may also be found in most statistical textbooks.

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PREREQUISITE LABORATORY PRACTICES AND PRINCIPLES

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The sound application of statistical principles to laboratory data requires the assumption that such data have been collected in a traceable (i.e., documented) and unbiased manner. To ensure this, the following practices are beneficial. ÍêÈ«ÕýÈ·µØÓ¦ÓÃͳ¼ÆÔ­ÀíÓÚ·ÖÎöʵÑéÊÒµÄÊý¾Ý£¬ÐèÒª¾ß±¸ÏÂÁУ¬¼´ÕâЩÊý¾ÝÒÔÒ»ÖÖ¿ÉÒÔËÝÔ´£¨Èç¼Ç¼²¢´æµµ£©²¢ÎÞÆ«Òеķ½Ê½ÊÕ¼¯¡£×ñÊØÏÂÁй淶ÊǷdz£ÓÐÒæÓÚÈ·±£»ù±¾¼Ù¶¨ÒªÇóµÄ¡£

Sound Record Keeping ±£´æÍêºÃÎÞÎóµÄ¼Ç¼

Laboratory records are maintained with sufficient detail, so that other equally qualified analysts can reconstruct the experimental conditions and review the results obtained. When collecting data, the data should generally be obtained with more decimal places than the specification requires and rounded only after final calculations are completed as per the General Notices and Requirements.

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Sampling Considerations

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Effective sampling is an important step in the assessment of a quality attribute of a population. The purpose of sampling is to provide representative data (the sample) for estimating the properties of the population. How to attain such a sample depends entirely on the question that is to be answered by the sample data. In general, use of a random process is considered the most appropriate way of selecting a sample. Indeed, a random and independent sample is necessary to ensure that the resulting data produce valid estimates of the properties of the population. Generating a nonrandom or ¨Dconvenience¡¬ sample risks the possibility that the estimates will be biased. The most straightforward type of random sampling is called simple random sampling, a process in which every unit of the population has an equal chance of appearing in the sample. However, sometimes this method of selecting a random sample is not optimal because it cannot guarantee equal representation among factors (i.e., time, location, machine) that may influence the critical properties of the population. For example, if it requires 12 hours to manufacture all of the units in a lot and it is vital that the sample be representative of the entire production process, then taking a simple random sample after the production has been completed may not be appropriate because there can be no guarantee that such a sample will contain a similar number of units made from every time period within the 12-hour process. Instead, it is better to take a systematic random sample whereby a unit is randomly selected from the production process at systematically selected times or locations (e.g., sampling every 30 minutes from the units produced at that time) to ensure that units taken throughout the entire manufacturing process are included in the sample. Another type of random sampling procedure is needed if, for example, a product is filled into vials using four different filling machines. In this case it would be impor-tant to capture a random sample of vials from each of the filling machines. A stratified random sample, which randomly samples an equal number of vials from each of the four filling machines, would satisfy this requirement. Regardless of the reason for taking a sample (e.g., batch-release testing), a sampling plan should be established to provide details on how the sample is to be obtained to ensure that the sample is representative of the entirety of the population and that the resulting data have the required sensitivity. The optimal sampling strategy will depend on knowledge of the manufacturing and analytical measurement processes. Once the sampling scheme has been defined, it is likely that the sampling will include some element of random selection. Finally, there must be sufficient sample collected for the original analysis, subsequent verification analyses, and other analyses. Consulting a statistician to

identify the optimal sampling strategy is recommended.

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Tests discussed in the remainder of this chapter assume that simple random sampling has been performed. ±¾ÕÂÏÂÃæËùÌÖÂ۵ļì²âʵÑé¶¼ÊÇÕë¶Ô¼Ù¶¨²ÉÓÃÁ˼òµ¥Ëæ»ú³éÑùµÃµ½µÄÑù±¾¡£

Use of Reference Standards

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Where USP or NF tests or assays call for the use of a USP Reference Standard, only those results obtained using the specified USP Reference Standard are conclusive for purposes of demonstrating conformance to such USP or NF standards. While USP standards apply at all times in the life of an article from production to expiration, USP does not specify when testing must be done, or any frequency of testing. Accordingly, users of USP and NF apply a range of strategies and practices to assure articles achieve and maintain conformance with compendial requirements, including when and if tested. Such strategies and practices can include the use of secondary standards traceable to the USP Reference Standard, to supplement or support any testing undertaken for the purpose of conclusively demonstrating conformance to applicable compendial standards. Because the assignment of a value to a standard is one of the most important factors that influences the accuracy of an analysis, it is critical that this be done correctly.

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