RESUMO
BACKGROUND: The control of the microbial contamination of pharmaceutical products (PP) is crucial to ensure their safety and efficacy. The validity of the monitoring of such contamination depends on the uncertainty of this quantification. Highly uncertain quantifications due to the variability of determinations or the magnitude of systematic effects affecting microbial growth or other analytical operations make analysis unfit for the intended use. The quantification of the measurement uncertainty expressing the combined effects of all random and systematic effects affecting the analysis allows for a sound decision about quantification adequacy for their intended use. The complexity of the quantification of microbial analysis uncertainty led to the development of simplified ways of performing this evaluation. OBJECTIVE: This work assesses the adequacy of the simplified quantification of the uncertainty of the determination of the microbial contamination of PP by log transforming microbial count and dilution factor of the test sample whose uncertainty is combined in a log scale using the uncertainty propagation law. METHODS: This assessment is performed by a parallel novel bottom-up and accurate evaluation of microbial analysis uncertainty involving the Monte Carlo method simulation of the Poisson log-normal distribution of counts and of the normally distributed measured volumes involved in the analysis. Systematic effects are assessed and corrected on results to compensate for their impact on the determinations. Poisson regression is used to predict precision affecting determinations on unknown test samples. RESULT: Simplified and detailed models of the uncertainty of the measurement of the microbial contamination of PP are provided, allowing objective comparisons of several determinations and those with a maximum contamination level. CONCLUSIONS: This work concludes that triplicate determinations are required to produce results with adequately low uncertainty and that simplified uncertainty quantification underevaluates or overevaluates the uncertainty from determinations based on low or high colony numbers, respectively. Therefore, detailed uncertainty evaluations are advised for determinations between 50 and 200% of PP's maximum admissible contamination value. HIGHLIGHT: User-friendly tools for detailed and simplified evaluations of the uncertainty of the measurement of microbial contamination of PP are provided together with the understanding of when simplifications are adequate.
Assuntos
Contaminação de Medicamentos , Método de Monte Carlo , Incerteza , Preparações Farmacêuticas/análise , Contagem de Colônia MicrobianaRESUMO
Recently, rapid microbiological methods (RMM) have often been used to determinate the potency of antibiotic drugs. Since all the standard and sample preparations are assayed into the same analytical conditions, it is expected that the correlations among the inhibitions zone sizes are not negligible. However, the procedures adopted in uncertainty estimations do not consider the correlation of data. The aim of this work was to study the impact of the correlation of data in the measurement uncertainty and, consequently, in the risk of false conformity decisions. RMM for the determination of the potency of cephalosporin antibiotics in pharmaceutical products were performed using an agar diffusion method. The shared analytical effects on inhibition resulted in correlation of data, which significantly decreased the combined measurement uncertainties, and therefore, the risk of false conformity decisions. Due to the lognormal distribution of potency values, measurement uncertainties were reported as a multiplicative uncertainty factor (UF). A MS-Excel spreadsheet is provided as supplementary material and may be used to estimate the measurement uncertainty and the risk of false conformity decisions for results obtained from RMM.