PDA Journal of Pharmaceutical Science and Technology最新文献

The ability to deliver safe medications and vaccines, free of microbial contamination, is dependent on modern methods to identify bacteria that range from biochemical to genotypic in nature. Conventional genotypic methods for bacterial identification generally rely on small parts of the genome, but advancements in DNA sequencing technologies have reduced the time and cost needed to sequence whole genomes. In order to increase resolution in microbial identification and reduce time-to-result compared to conventional methods, we have developed a workflow for rapid identification of bacteria using Oxford Nanopore DNA sequencing that can reconstruct whole genome sequences with >99.8% accuracy to reference sequences. The reconstruction of an entire genome sequence enables high-resolution identification of bacteria. For example, the platform can rapidly differentiate closely related strains of Shigella and E. coli through taxonomic assignment using average nucleotide identity (ANI) and multi-locus sequence typing, and investigations into sub-strain-level differentiation using ANI are ongoing. The analysis of genome data normally requires a highly trained analyst, but work is underway to automate these computational processes as part of Merck's patent-pending ViruScreen platform which enables multi-omic analytical research through an easy-to-use web portal.

Environmental monitoring (EM) programs have long relied on action and alert limits as the primary drivers for response and investigation. While these regulatory thresholds remain essential (and required), they are not sufficient on their own to provide a complete picture of cleanroom control. This poster challenges the industry's traditional focus on excursions and proposes a more holistic, modern approach to EM data trending-one that aligns with the evolving expectations outlined in EU Annex 1.Key tools such as Contamination Recovery Rate (CRR), percentile-based evaluations, and a combination of methods offer richer insights into process capability, operator consistency, and potential contamination risks-even when results remain within accepted limits. These statistical approaches acknowledge the semi-quantitative nature of microbiological data and accommodate its non-normal distributions more effectively than conventional means and standard deviations.Attendees will gain a deeper understanding of how to integrate these techniques into existing EM programs, transforming environmental data from a compliance checkbox into a meaningful indicator of cleanroom health and performance.

Automated microbial detection systems are becoming increasingly essential in pharmaceutical manufacturing to enhance efficiency, accuracy, and compliance with Good Manufacturing Practice (GMP) standards. The APAS Independence System, developed by Clever Culture Systems, was evaluated at Bristol Myers Squibb (BMS) to assess its suitability for environmental monitoring applications. The APAS system is capable of automated plate counting of both 55 mm and 90 mm plates, following an offline incubation at routine EM parameters. The study compared APAS automated colony counts to traditional manual counts for compendial strains and environmental monitoring samples across multiple weeks of EM sampling. Performance metrics for both plate sizes were analyzed, such as accuracy, precision, false negative and false positive rates, and robustness. The APAS system demonstrated high accuracy (≥70%) and reliability for most tested organisms. Therefore, the system is being implemented for GMP use, with a validation approach that combines vendor validation of the Artificial Intelligence with site specific instrument qualification. This evaluation provides insight into the capabilities and limitations of automated colony detection, informing future digitalization strategies in microbiological quality control.

The regulations are evolving, for example EMA Annex 1, earlier versions did not mention trends, the current draft version mentions it 23 times. WHO, and FDA also talk about trending, root cause analysis, investigation, and using the data for these purposes. PDA technical report 13, the new revision, even states that automation for data management for environmental mentoring is "essential". Trending the data is now a regulatory requirement but what trends should we use, and when? This presentation will include a review of key regulations as they relate to the need to use our data for trending including how often and what events should trigger trending, root cause analysis, and investigations. Also, which trend tools should we use for the different contamination control processes, cut off method, Control charts (Shewhart, etc.), Quantiles, Percentiles, Weibull Distribution, Scatter plot, Regression analysis for slope (upward/downward trends).The last 20 years we have been collecting data and regulations like Annex 11/21 CFR part 11, and more recently the data integrity guidelines, have facilitated that the data is accurate and reliable. Now this regulatory evolution is mandating the use of the data for the betterment of our processes, process control, product quality and patient safety.

We have developed a next-generation MAT kit based on a reporter assay. In this presentation, we report on the potential for a more flexible MAT test design using our new reagent.In conventional MAT, inflammatory cytokines are typically detected using ELISA as an indicator. In contrast, the LumiMAT pyrogen detection kit detects the transcriptional activity of NF-κB, a transcription factor that induces the expression of inflammatory cytokines, using a luciferase reporter assay. Compared to ELISA-based MAT, the advantages of the reporter assay-based MAT include a significant reduction in cell incubation time due to increased sensitivity (from 24 hours to 3 hours) and the removal of labor-intensive steps (several hours for ELISA to a few minutes for the addition of luminescent substrates).For practical application in pyrogen testing, we conducted preliminary tests in accordance with EP 2.6.30. guidelines on several medicinal products subject to Rabbit Pyrogen Test and confirmed that the results met the requirements specified in the pharmacopoeia. Additionally, our method allows for reduced coefficient of variation, enabling the reduction in the number of replicates (n=4 to n=3) and increasing throughput (from a 96-well plate to a 384-well plate). These features demonstrate the potential for designing more cost-effective test protocols.

We present a rapid sterility testing method targeting ribosomal RNA (rRNA) to enable sensitive and specific detection of microorganisms. The assay follows a simple three-step protocol: (1) activation of microbial cells with concurrent inactivation of DNA, (2) lysis and RNA extraction using magnetic particles, and (3) detection via one-step real-time reverse transcription PCR (RT-PCR). The total assay time is approximately 7 hours. Validation using six compendial organisms listed in USP <71> demonstrated successful detection at a sensitivity of 10 CFU/mL. The method is also compatible with samples containing mammalian cells, maintaining performance in complex matrices. A key feature of this system is the rigorous degradation and removal of residual DNA, effectively reducing the risk of false-positive results that may arise in conventional DNA-targeting assays. By focusing on rRNA, which reflects microbial viability, this method provides a more accurate sterility assessment within a significantly shorter time frame compared to traditional culture-based tests. The approach offers a promising solution for rapid microbiological testing in pharmaceutical quality control and is designed to align with current regulatory expectations for alternative methods.

In the pharmaceutical industry, image analysis plays a crucial role in microbial identification. Traditional parametric algorithms, while effective for tracking microbial growth over time, struggle with differentiating between bacterial and fungal species due to their rigid predefined rules. Machine learning, particularly deep learning, offers a powerful alternative by learning complex patterns from large datasets, enabling more accurate and adaptable classification. The goal here is to explore the limitations of classical algorithms, the advantages of AI-driven approaches, and the methodology for building a robust training dataset to enhance model performance. A case study on automated mold identification on petri dishes will illustrate these concepts in a real-world application.

Biotherapies, and especially cell therapy products, are required to be tested for sterility and mycoplasma. Developing an analytical strategy to test for sterility and mycoplasma can be daunting. There are many variables to consider, including complex matrices with high mammalian cell density and detection of non-viable microbes. Which type of analytical testing can work in an early stage of therapeutic development and then be scaled to meet the challenges later in the production process?Leveraging rapid sterility as well as mycoplasma qPCR-based detection techniques can help provide confidence in the final product by helping to detect potential contamination earlier in the production process.

Sterility testing remains a key bottleneck in the release of cell and gene therapy products (CGTPs), where current compendial methods require up to 14 days, delaying batch release and increasing costs. Symcel's calScreener+ is a novel, commercially available, phenotypic sterility testing system that enables continuous and non-destructive metabolic monitoring via isothermal microcalorimetry, delivering microbial detection results in under three days- offering the fastest reported time-to-detection (TTD) among currently available, growth-based sterility methods evaluated for CGTPs.To demonstrate system applicability, we will present application data showing microbial detection in the presence of high-density eukaryotic cells (10⁶-10⁸ cells/ml), without enrichment or sample processing. This illustrates the method's compatibility with complex, cell-rich matrices typical of CGTPs.The primary focus is validation data from the calScreener+ Three-Day sterility test, covering:Limit of detectionDetection times for fast- and slow-growing organismsSpecificity across 30 relevant microbial speciesComparability to compendial methodsRobustness and ruggedness under varied conditionsA brief overview of the assay workflow will also be included. This method enables earlier contamination detection and supports faster, more efficient batch release-representing a significant advancement for CGT manufacturing.

This session will explore the integration of recombinant Cascade Reagents (rCR) with centripetal microfluidic technology. We will detail our multi-matrix approach for comparing LAL and rCR on different sample types and how we assessed performance metrics such as sensitivity, specificity, and reproducibility across diverse sample types. We also address key technical challenges and practical considerations encountered during this research. Finally, the results of the study will be presented.