The level of anti-D antibodies in human immunoglobulin products for intravenous administration (IVIG) is controlled by the direct haemagglutination method prescribed by the European Pharmacopoeia (Ph. Eur.) that requires 2 control reference reagents. The World Health Organization (WHO) positive control International Reference Reagent (IRR; 02/228) with a nominal titre of 8 defines the highest acceptable titre, while the negative control preparation (02/226) has a nominal titre of <2. Working reference preparations (04/132 and 04/140) were subsequently established as Biological Reference Preparations (BRPs) for the Ph. Eur., and for distribution by the United States Food and Drug Administration (US FDA) and the National Institute for Biological Standards and Control (NIBSC). Due to diminishing stocks of these working reference preparations across the 3 institutions, a joint international study was organised to establish harmonised replacement batches. Sixteen laboratories contributed data to the study to evaluate positive and negative candidate replacement batches (13/148 and 12/300, respectively) against the WHO positive and negative control IRRs and the current working reference preparations (BRPs). The results show that the candidate reference preparations (13/148 and 12/300) are indistinguishable from the corresponding IRRs and current BRPs. The candidate preparations 13/148 and 12/300 were adopted by the Ph. Eur. Commission as Immunoglobulin (anti-D antibodies test) BRP batch 2 and Immunoglobulin (anti-D antibodies test negative control) BRP batch 2 with nominal haemagglutination titres of 8 and <2, respectively. The same materials were also adopted as NIBSC and US FDA reference preparations, thus ensuring full harmonisation.

OBJECTIVE: To evaluate the effects of the Enhancing Quality Using the Inspection Program (EQUIP) on quality control (QC) and quality assurance (QA) at an academic medical center.
METHODS: EQUIP audit logs for technologist image quality review as well as mammography unit QA and QC formed the basis for study data. One randomly selected screening mammogram was evaluated by the lead interpreting physician (LIP) using EQUIP criteria for each technologist for each imaging site worked, initially semiannually and then monthly. One randomly selected screening mammogram interpreted by each interpreting physician (IP) for each imaging site was evaluated on a semiannual basis. Quarterly, the LIP reviewed QA and QC logs for each mammography unit with deficiencies further investigated.
RESULTS: Of 214 965 eligible screening mammograms performed, 5955 (2.8%) underwent EQUIP image quality review. Five were found to be technically inadequate (0.08%, 5955/214 965). The LIP identified 20 significant interpretive differences compared with the clinical interpretation resulting in 10 biopsies and 7 previously undetected malignancies, with supplemental cancer detection rate of 1.2/1000 cases reviewed. Two hundred ninety mammography unit QA/QC reviews identified 31 potential deficiencies, 29 of which were due to human documentation error (93.4%).
CONCLUSIONS: EQUIP review of both IP and technologists\' quality and mammography unit QA/QC logs as performed identified few deficiencies. EQUIP policies should be evaluated at each institution and modified to best utilize resources and provide opportunities for meaningful quality improvement. Although not an EQUIP focus, supplemental cancer detection was observed as might be expected with double reading.

BACKGROUND: On-site monitoring is a crucial component of quality control in clinical trials. However, many cast doubt on its cost-effectiveness due to various issues, such as a lack of monitoring focus that could assist in prioritizing limited resources during a site visit. Consequently, an increasing number of trial sponsors are implementing a hybrid monitoring strategy that combines on-site monitoring with centralised monitoring. One of the primary objectives of centralised monitoring, as stated in the clinical trial guidelines, is to guide and adjust the extent and frequency of on-site monitoring. Quality tolerance limits (QTLs) introduced in ICH E6(R2) and thresholds proposed by TransCelerate Biopharma are two existing approaches for achieving this objective at the trial- and site-levels, respectively. The funnel plot, as another threshold-based site-level method, overcomes the limitation of TransCelerate\'s method by adjusting thresholds flexibly based on site sizes. Nonetheless, both methods do not transparently explain the reason for choosing the thresholds that they used or whether their choices are optimal in any certain sense. Additionally, related Bayesian monitoring methods are also lacking.
METHODS: We propose a simple, transparent, and user-friendly Bayesian-based risk boundary for determining the extent and frequency of on-site monitoring both at the trial- and site-levels. We developed a four-step approach, including: 1) establishing risk levels for key risk indicators (KRIs) along with their corresponding monitoring actions and estimates; 2) calculating the optimal risk boundaries; 3) comparing the outcomes of KRIs against the optimal risk boundaries; and 4) providing recommendations based on the comparison results. Our method can be used to identify the optimal risk boundaries within an established risk level range and is applicable to continuous, discrete, and time-to-event endpoints.
RESULTS: We evaluate the performance of the proposed risk boundaries via simulations that mimic various realistic clinical trial scenarios. The performance of the proposed risk boundaries is compared against the funnel plot using real clinical trial data. The results demonstrate the applicability and flexibility of the proposed method for clinical trial monitoring. Moreover, we identify key factors that affect the optimality and performance of the proposed risk boundaries, respectively.
CONCLUSIONS: Given the aforementioned advantages of the proposed risk boundaries, we expect that they will benefit the clinical trial community at large, in particular in the realm of risk-based monitoring.

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 medias, and products (SCP) from a pharmaceutical industry facility. From 2018-2020, a total of 1,078 samples from 857 different lots of SCP were analyzed and 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 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%, which can be attributed to a contamination during the sampling or a wrong filtering. 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 negative bacteria, respectively, 31 yeasts, and six 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.

The Risk Knowledge Infinity (RKI) Cycle Framework was featured as part of the ICH-sanctioned training materials supporting the recent issuance of ICH Q9(R1) Quality Risk Management To support ICH Q9(R1) understanding and adoption, this paper presents a case study on the application of the RKI Cycle, based on an underlying out-of-specification investigation. This case study provides a stepwise walk-through of the cycle to illustrate how key concepts within the ICH Q9(R1) revision can be achieved through better connecting quality risk management and knowledge management with a framework such as the RKI Cycle.

Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions. This work details the development and use of a platform for monitoring the performance of a production-level ML model operating in Mayo Clinic. In this paper, we aimed to provide a series of considerations and guidelines necessary for integrating such a platform into a team\'s technical infrastructure and workflow. We have documented our experiences with this integration process, discussed the broader challenges encountered with real-world implementation and maintenance, and included the source code for the platform. Our monitoring platform was built as an R shiny application, developed and implemented over the course of 6 months. The platform has been used and maintained for 2 years and is still in use as of July 2023. The considerations necessary for the implementation of the monitoring platform center around 4 pillars: feasibility (what resources can be used for platform development?); design (through what statistics or models will the model be monitored, and how will these results be efficiently displayed to the end user?); implementation (how will this platform be built, and where will it exist within the IT ecosystem?); and policy (based on monitoring feedback, when and what actions will be taken to fix problems, and how will these problems be translated to clinical staff?). While much of the literature surrounding ML performance monitoring emphasizes methodological approaches for capturing changes in performance, there remains a battery of other challenges and considerations that must be addressed for successful real-world implementation.

Prostate magnetic resonance imaging (MRI) stands as the cornerstone in diagnosing prostate cancer (PCa), offering superior detection capabilities while minimizing unnecessary biopsies. Despite its critical role, global disparities in MRI diagnostic performance persist, stemming from variations in image quality and radiologist expertise. This manuscript reviews the challenges and strategies for enhancing image quality in prostate MRI, spanning patient preparation, MRI unit optimization, and radiology team engagement. Quality assurance (QA) and quality control (QC) processes are pivotal, emphasizing standardized protocols, meticulous patient evaluation, MRI unit workflow, and radiology team performance. Additionally, artificial intelligence (AI) advancements offer promising avenues for improving image quality and reducing acquisition times. The Prostate-Imaging Quality (PI-QUAL) scoring system emerges as a valuable tool for assessing MRI image quality. A comprehensive approach addressing technical, procedural, and interpretative aspects is essential to ensure consistent and reliable prostate MRI outcomes.

Calibration of an analytical measurement procedure is an important basis for the reliability of patient results. Many publications and as well as procedures on how to estimate quality control and interpret those results have been become available over the years. In this publication we are focusing on the critical part of the calibration as there are no clear communication or guidelines on how to perform it. Usually only the recommendation of the reagent or instrument manufacturer is available. We would like to point out this gap to invite for a discussion and improvement of the current situation.

Small RNA (sRNA) profiling of Extracellular Vesicles (EVs) by Next-Generation Sequencing (NGS) often delivers poor outcomes, independently of reagents, platforms or pipelines used, which contributes to poor reproducibility of studies. Here we analysed pre/post-sequencing quality controls (QC) to predict issues potentially biasing biological sRNA-sequencing results from purified human milk EVs, human and mouse EV-enriched plasma and human paraffin-embedded tissues. Although different RNA isolation protocols and NGS platforms were used in these experiments, all datasets had samples characterized by a marked removal of reads after pre-processing. The extent of read loss between individual samples within a dataset did not correlate with isolated RNA quantity or sequenced base quality. Rather, cDNA electropherograms revealed the presence of a constant peak whose intensity correlated with the degree of read loss and, remarkably, with the percentage of adapter dimers, which were found to be overrepresented sequences in high read-loss samples. The analysis through a QC pipeline, which allowed us to monitor quality parameters in a step-by-step manner, provided compelling evidence that adapter dimer contamination was the main factor causing batch effects. We concluded this study by summarising peer-reviewed published workflows that perform consistently well in avoiding adapter dimer contamination towards a greater likelihood of sequencing success.

The extraction process plays a crucial role in the production of Tibetan medicines. This study focused on assembling a set of online near-infrared (NIR) spectroscopy detection devices for the extraction of medicinal herbs. The original infrared device was transformed into an online detection system. After evaluating the stability of the system, we applied online NIR spectroscopy monitoring to the flavonoid contents (total flavonoids, quercetin-3-O-sophoroside, and luteolin) of Meconopsis quintuplinervia Regel. during the ultrasonic extraction process and determined the extraction endpoint. Nine batches of samples were employed to construct quantitative and discriminant models, half of the remaining two batches of samples are used for external verification. Our research shows that the residual predictive deviation (RPD) values of total flavonoids, quercetin-3-O-sophoroside and luteolin models exceeded 2.5. The R values for external verification of the three ingredients were above 0.9, with RPD values generally exceeding 2 and RSEP values within 10 %, demonstrating the model\'s strong predictive performance. Most of the extraction endpoints of the flavonoid components in M. quintuplinervia ranged from 18 to 58 min, with high consistency between the predicted extraction endpoints of the external validation, suggesting accurate determination of extraction endpoints based on predicted values. This study can provide a reference for the online NIR spectroscopy quality monitoring of the extraction process of Chinese and Tibetan herbs.