PDA Journal of Pharmaceutical Science and Technology最新文献

Microbiological contamination may cause microbial proliferation and consequently additional problems for pharmaceutical companies through production stoppage, product contamination, investigations of process deviations, out-of-specification results, and product disposal. This is one of the major concerns of the regulatory health agencies. Microbiological load (bioburden) may represent a potential risk for patients if the sterilization process is not effective and/or due to the production of toxins. Although bioburden can be eliminated by terminal sterilization or filtration processes, it is important to monitor the amount and determine the identity and characteristics of the microorganisms present prior to final processing. The application of microorganism identification systems is crucial for identifying the type of contamination, which can be extremely useful for investigating. The aim of this study was to evaluate the profiles of microorganisms identified in bioburden assays from solutions, culture media, and products (SCP) from a pharmaceutical industry facility. From 2018-2020, a total of 1078 samples from 857 different lots of SCP were analyzed and the isolated microorganisms were identified. A prefiltering step was included after March 2020 in order to reduce the bioburden before sterilizing filtration. Criteria for the definition and management of the microorganisms identified were evaluated after an integrative bibliographic review, and three groups were proposed (critical, objectionable, and nonobjectionable microorganisms). For the samples that did not include prefiltering (n = 636), 227 (35.7%) presented microbial growth. For those that included prefiltering, before prefiltering (n = 221), 60.6% presented microbial growth, and after prefiltering, this value was reduced to 4.1%. Microbial growth recovered after prefiltration is likely to be attributed to contamination during sample collection or filtration. From the samples that presented microbial growth, 678 microorganisms were identified as bacteria and 59 as molds and yeasts. A total of 120 microorganisms (56 and 27 Gram-positive and Gram-negative bacteria, respectively, 31 yeasts, and 6 filamentous molds) could not be identified, and the remaining microorganisms were classified as objectionable (n = 507; 82.2%), nonobjectionable (n = 103; 16.7%), and critical (n = 7; 1.1%). Most of the bioburden species (>80.0%) were considered objectionable microorganisms. A process for classification and management of bioburden analysis results based on a literature review of pathogenic and physiological characteristics of the microorganisms was proposed.

Visual inspectors' ability to detect foreign matter in injections must be qualified for each product type or bracketing group, as stated in the United States Pharmacopeia <1790>. The common defect criterion is ″visible″ However, this qualitative lower rejection limit is based on the premise that the nature of human inspection at one site is globally comparable to that at all other sites. If not, the ″visible″ foreign matter can vary among inspector groups for each product, leading to quality differences between sites. Inspectors are trained and qualified using ″visible″ foreign matter samples from their site; therefore, inspectors' ability to detect foreign matter can differ due to different standards. Japanese pharmaceutical companies import injections and perform secondary visual inspections because variations in ″visible″ ability arise due to individual differences among regions. Currently, there is no universal method for comparing ″visible″ rejection zones among manufacturing sites for different products because product-specific standard samples for qualifications cannot be universalized. Therefore, instead of comparing the product-specific ″visible″ standard sample, this study proposes a novel universal particle standard challenge kit to compare sites' or inspectors' inner ″visible″ detection ability. Our results suggest that the proposed kit could evaluate the comparability of ″visible″ at each site and for each inspector but not on sites' ″visible″ standard samples. The primary challenge in implementing a universal standard lies in the inherent bias of inspectors already acclimated to specific products. This study found that an inspectors' foundational ability can be evaluated by canceling the bias via rapid and effective training in cases of simple standard samples. Our results suggest that using a universal standard kit to assess an inspector's fundamental ability to detect ″visible″ foreign matter is feasible. The study does not aim to replace the inspectors' product qualifications but to compare their inner ability for risk assessment.

Data from cell culture processes contain myriad parameters arriving sequentially in phases which may hold vital information for optimizing process runs and ameliorating manufacturing yield. This study analyzed temporal process data from 249 cell culture production batches of an active pharmaceutical ingredient at Roche's Location A manufacturing facility. The titer manufactured is utilized for Roche's Product X, a prescription drug that can treat adults with cancer. We aim to optimize the upstream production phase titer in Chinese hamster ovary cell manufacturing by identifying the most influential features. A phase-incremental (PI) decision tree method is proposed for feature selection and interaction exploration, being model and loss function agnostic while promoting early feature importance for prediction and process control. In this case study, the method is applied to Ensemble of Gradient Boosting Machines, using adjusted R-squared as the penalized loss function. The result leads to better process understanding and enables earlier control in the manufacturing.

A Contamination Control Strategy (CCS) is a strategy that focuses on how to prevent contaminations with microorganisms, particles and pyrogens and manages risks to medicinal product quality and patient safety within a GMP facility. A CCS should reflect on all proactive and retrospective data within a sterile and/or aseptic and/or non-sterile manufacturing environment, to identify all sources of contamination and associated hazards and/or control measures. It should outline associated Quality Risk Assessments (QRAs), Critical Control Points (CCPs) and suggest necessary control measures. An effective way to perform QRA for CCS is adopting the Hazard Analysis Critical Control Point (HACCP) methodology, a proactive risk assessment tool. This tool can be ideally used to monitor all CCPs associated with various sources of contamination.To keep the CCS effective, it is highly recommended to have the CCS reviewed by a multidisciplinary team and updated periodically, and to re-review it as necessary in the event particular issues arise. An annual review of the CCS effectiveness can be performed by constructing an assessment tool in the form of a report. The report provides an overview of all proactive and retrospective Quality Management System (QMS) related data that may have an impact on the CCS. This report then acts as a tool to drive continual improvement of the manufacturing and control methods, as required per EudraLex Volume 4 GMP guidelines Annex 1. This article describes a practical application of setting up an annual CCS assessment within a pharmaceutical manufacturing company to keep the CCS effective. This assessment will facilitate the process of reviewing the effectiveness of all control measures over time and facilitates escalation of issues that are not under control.

The detectability size threshold of visible particles ("visibility" size) in the context of visual inspection of parenteral drug products has been an elusive target for several decades. The current common sense, also reflected in official guidelines, dictates that particles of different shapes and morphologies have different "visibility" size thresholds, which can range between hundreds and thousands of micrometers. This study demonstrates experimentally for the first time that it is possible to define a single, shape- and morphology-independent detectability size threshold, identical across particles of various types, provided that observation conditions and product attributes are kept constant. We propose that, based on the physiology of human visual perception instead of single-dimension measures of particle size (e.g., diameter or length), such a single size-threshold requires the use of area-based size parameters (such as "equivalent circular diameter", or ECD. The experimental results reported here clearly demonstrate that the "visibility" thresholds for particles of various morphologies converge on a single ECD value. In addition, the data reported here show that product attributes, such as container configuration, fill volume, etc. influence the threshold of visibility. Collectively, the findings reported in this paper provide substantial evidence and scientific rationale, as well as unanticipated prospects for standardization of visual inspection qualification practices, ultimately leading to improvement of pharmaceutical product quality.

Topical ophthalmic solutions, suspensions, and emulsions are typically packaged in opaque or semi-transparent plastic dropper bottles. This packaging provides resistance to breakage and the controlled drop size needed in ophthalmic container systems. Recent changes to USP <771> Ophthalmic Products-Quality Tests have impacted the particulate and foreign matter testing requirements for ophthalmic products dosed via topical application. The USP <771> chapter instructs that topical products undergo visual inspection for particulate matter as described in USP <790> Visible Particulates in Injections Visual inspection for particulates in the filled unit is not feasible due to the lack of package transparency, and therefore alternative test strategies are needed to evaluate the acceptability of the batch. Aspects of this visual inspection approach include: a statistically based sampling plan for the batch, a destructive testing process, and acceptance limits based on manufacturing process capability supported with benchmark testing of competitor products to confirm manufacturing performance. Overall, the visual inspection program should include: historical trending; process monitoring; and upstream life cycle controls for facilities, raw materials, components, and product contact equipment to meet current regulatory expectations and good manufacturing practices.

This review paper explores the transformative impact of Artificial Intelligence (AI) on Continued Process Verification (CPV) in the biopharmaceutical industry. Originating from the CPV of the Future project, the study investigates the challenges and opportunities associated with integrating AI into CPV, focusing on real-time data analysis and proactive process adjustments. The paper highlights the importance of aligning AI solutions with regulatory standards and offers a set of comprehensive recommendations to bridge the gap between AI's potential and its practical, compliant, and safe application in pharmaceutical manufacturing. Emphasizing transparency, interpretability, and risk management, the research contributes to establishing best practices for AI implementation, ensuring the highest quality pharmaceutical products while meeting regulatory expectations. The conclusions drawn provide valuable insights for navigating the evolving landscape of AI in pharmaceutical manufacturing. This paper serves as a guideline for implementing AI, Machine Learning and Deep Learning models to the pharma industry. Nevertheless, the specific algorithms used in the CPV of the Future are not relevant for our paper (Good Practices), since we have to generalize the process independent of the algorithm.