Quality assurance stability, control

The design of an assay is, in large measure, prospective quality assurance. The factors that are likely to affect the results of the assay must be defined and controlled to the greatest extent possible. Once the general outlines of an assay have been established, key features should be examined, including optimization of sample preparation, sample stability, choice of standards, assay range, assay repeatability, optimization of separation, and optimization of detection. 

The validation process begun in Phase I is extended during Phase II. In this phase, selectivity is investigated using various batches of drugs, available impurities, excipients, and samples from stability studies. Accuracy should be determined using at least three levels of concentration, and the intermediate precision and the quantitation limit should be tested. For quality assurance evaluation of the analysis results, control charts can be used, such as the Shewart-charts, the R-charts, or the Cusum-charts. In this phase, the analytical method is refined for routine use. 

The vesicle size is an important parameter not only for in-process control but particularly in quality assurance, because the physical stability of the vesicle dispersion depends on particle size and particle size distribution. An appropriate and particularly quick method is laser light scattering or diffraction. Laser light diffraction can be applied to particles > 1 pm and refers to the proportionality between the intensity of diffraction and the square of the particle diameter according to the diffraction theory of Fraunhofer. 

In general, the pharmaceutical industry has used the term quality control in reference to the laboratory function in which finished pharmaceuticals or APIs are tested for stability and release. The term quality assurance has replaced quality control as defined above. The terms (QAU) will be used throughout this article in reference to a proactive quality control unit. 

Quality control/quality assurance Specification of raw materials Stability testing... 

The application of quality control procedures to ensure that satisfactory analytical performance of enzyme assays is maintained on a day-to-day basis is complicated by the tendency of enzyme preparations to undergo denaturation with loss of activity. This maltes it difficult to distinguish between poor analytical performance and denaturation as possible causes of a low result obtained for a control sample introduced into a batch of analyses. Assured stability within a defined usable time span is therefore the prime requirement for enzyme control materials, as it is for enzyme calibrators. However, specifications for the two types of materials can differ in other respects. Because the function of a calibrator is to provide a stated activity under defined assay conditions, it is not necessary for it to show sensitivity to changes in the assay system identical to those of the samples under test therefore within certain Umits, enzymes from various sources can be considered in the search for stability. However, it is the function of a control to reveal small variations in reaction conditions, so it must mimic the samples being analyzed. The preparation of enzymes from human sources is not by itself a guarantee of an effective control. For example, human placental ALP is very stable, but it differs significantly in kinetic properties from the liver and bone enzymes that contribute most of the ALP activity of human serum samples it is therefore not an ideal enzyme for use in control material for the determination of ALP. 

Quality Control Data. Data obtained from assays of blood gas and pH control materials may be handled in the same way as data from other clinical chemistry determinations (i.e., mean, SD, and coefficient of variation, and control and confidence limits for construction of Levey-Jennings plots). As stability of commercial aqueous control materials is generally several months, vendors often provide data reduction programs that standardize and simplify documentation. However, the resulting reports are temporally delayed and are most useful for meeting accreditation requirements as opposed to real-time corrective or preventive action. They are however useful to compare long-term performances with other laboratories. Equally important features of quality assurance to an active blood gas service are the sixth sense of practiced operators for detecting subtle manifestations of deterioration of instrument performance and the suspicion of trouble expressed by clinicians. 

Is there any evidence that the process is unstable with respect to the amount of variability inherent in the process Can the process be considered reliable to meet potency requirements From the control chart for subgroup standard deviation, the process appears stable. The control chart for the subgroup average certainly detected the potency differences among the batches. Whether these process shifts in potency are of practical significance to manufacturing and quality assurance may depend on the chemical stability of the product and the ability of the process to remain at the current level of variability, namely, the standard deviation of 3%. 

Finally, in this section, it is useful to agree on what the minimum acceptable shelf life for the product should be. The product will need to be stable enough to allow time for quality control (QC) testing and quality assurance (QA) release after manufacture distribution to wholesalers, pharmacists and doctors and with acceptable time for storage until prescribed and used by patients. Normally, a minimum three-year shelf life at room temperature is targeted. However, if the treatment is very novel, it may be possible to justify a shorter shelf life and/or storage at lower temperatures, if stability is likely to be a problem. 

To improve process stability and resulting component performance further development of simulation tools is needed. One challenging topic will be to develop inline working process simulation tools which allow the process to be controlled based on inline acquired material data. In addition to the development of inline quality assurance tools and new analysis methods an increased computing power is required. The latter might also allow more physical aspects to be included in the simulation. In the future the complexity of such simulation tools will increase as well as the results. 

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