Quantitative Microbial Risk Assessment of Pharmaceutical Products

Monitoring of microbiological quality in the pharmaceutical industry is an important criterion that is required to justify safe product release to the drug market. Good manufacturing practice and efficient control on bioburden level of product components are critical parameters that influence the microbiological cleanliness of medicinal products. However, because microbial dispersion through the samples follows Poisson distribution, the rate of detection of microbiologically defective samples lambda (λ) decreases when the number of defective units per batch decreases. When integrating a dose-response model of infection (P_inf) of a specific objectionable microbe with a contamination module, the overall probability of infection from a single batch of pharmaceutical product can be estimated. The combination of P_inf with detectability chance of the test (P_det) will yield a value that could be used as a quantitative measure of the possibility of passing contaminated batch units of product with a certain load of a specific pathogen and infecting the final consumer without being detected in the firm. The simulation study can be used to assess the risk of contamination and infection from objectionable microorganisms for sterile and non-sterile products.LAY ABSTRACT: Microbial contamination of pharmaceutical products is a global problem that may lead to infection and possibly death. While reputable pharmaceutical companies strive to deliver microbiologically safe products, it would be helpful to apply an assessment system for the current risk associated with pharmaceutical batches delivered to the drug market. The current methodology may be helpful also in determining the degree of improvement or deterioration on the batch processing flow until reaching the final consumer. Moreover, the present system is flexible and can be applied to other industries such as food, cosmetics, or medical devices manufacturing and processing fields to assess the microbiological risk of the processed and manufactured batch.