Qualification Strategy Research Articles

Maintaining a Sterile Environment: Validation and Qualification Strategies for Heating, Ventilation, and Air Conditioning Systems Adhering to Current Good Manufacturing Practices in Pharmaceutical Facilities.

This article presents a validation and qualification on heating, ventilation, and air conditioning(HVAC) systems. An HVAC system is required for suitable temperature maintenance, continuous air flow, and also keeping the air fresh, which ultimately helps in the prevention of cross-contamination and air accumulation and also ensures the availability of cool air on the premises. The quality of air ventilation in the pharmaceutical business has a considerable impact on worker safety, material efficacy, including raw materials, in-process items, and final products, and machinery. It ensures the optimal quality of air, as directed by the regulatory guidelines. Three degrees of validation are important for HVAC systems: installation qualification (IQ), performance qualification (PQ), and operational qualification (OQ). Air variations per hour, air circulation velocity, and air circulation pattern, pressure differential, recovery test for temperature and humidity, temperature and humidity uniformity, filter leak test, particle count, loss of utility test, compliance test, filter integrity test, and fresh air determination are some of the parameters to be assessed for the HVAC system validation.The validation tests that are mentioned in this article have acceptance criteria and procedures for conducting the tests that are provided by the current good manufacturing practices (cGMP) guidelines.

Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India

Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geo-environmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.

A novel biomarker assay qualification strategy for rare human matrices - a case study of biomarkers in aqueous humor.

Background: Measuring pharmacodynamic biomarkers near the therapeutic site of action presents considerable challenges for sites with limited matrix volume or difficult access. Bioanalytical method qualification requires theuse of numerous matrix samples, which is problematic for rare matrices. The aim of this study was to design and implement a streamlined, fit-for-purpose strategy for qualification of biomarker assays in rare matrices. Materials & methods: A multiplexed biomarker immunoassay was developed in human aqueous humor. Results: Our strategy was successfully implemented, providing characterization of assay performance while reducing number of samples in assay qualification. Our assay was used in clinical trial support for an ophthalmic drug candidate. Conclusion: Our results indicate this approach can be applied to other earlystage drug development programs facing similar challenges.

Perceptions of community health agents about violence against elderly of people

Violence against the elderly is a serious public health problem, which has been growing and interfering with the quality of life of this population group. In view of this, this article aims to investigate the perceptions of community health agents (CHA) about violence against elderly people and their knowledge about this topic. To this end, one exploratory and descriptive research was carried out with a qualitative approach based on interviews with seven health agents, whose contents were analyzed using the MAXQDA Analytics Pro 2020 Software. This search resulted in two categories: “weaknesses in working with violence to the elderly” and “the power of the multidisciplinary team, of home visits, qualified listening and teaching-service integration” with their respective subcategories. In speech of the CHAs, the lack of knowledge of the coping network was emphatically pointed out, which is a great weakness, proving to be a challenge to be overcome. In short, community agents are at an impasse in recognizing the Network to combat violence against the elderly, with a notable lack of training and ongoing education to deal with this social problem. It is necessary to develop qualification strategies for CHWs, and also for the institutions participating in the Network to combat the elderly to be strengthened, enhancing the monitoring of proposed actions in cases of violence.

Obesity Current Research and Future Hope

Obesity is an all-encompassing community health concern characterized by overconsumption of carcass fat, superior to adverse strength effects, containing cardiovascular ailments, diabetes, and various cancers. This abstract supports a short survey of current research styles and future prospects in the battle against obesity. Current research exertions are directed at understanding the complex interaction of historical, environmental, and behavioral determinants relating to corpulence. Genetic studies have identified abundant susceptibleness genes, peeling away the hereditary facets of corpulence. Meanwhile, material determinants such as obesogenic diets and lazy behaviors stretch to be key extents of investigation. Novel healing attacks and behavior qualification strategies are energetically being examined. Approaches involve the growth of anti-corpulence drugs, bariatric abscission, and attacks address gut micro biota. Promising findings are arising in the fields of accuracy, cure, personalized digestive approvals, plant structure, and individualized absorption. The future hope in the fight against corpulence lies in the unification of science and mathematical strength tools. Mobile devices, wearable devices, and telehealth manifestos offer creative ways to monitor and control corpulence. The big dossier science of logical analysis and artificial intelligence can help healthcare providers and cases form cognizant resolutions for personalized burden administration.

An Optimization Method of Flexible Manufacturing System Reliability Allocation Based on Two Dimension-Reduction Strategies

As increasingly extensive applications of flexible manufacturing systems (FMSs) arise, their reliability allocation has been a research hot spot. But, since FMSs are always composed of transfer and buffer devices, production machines, and complex control systems, the large number of basic elements makes the number of variables and constraints of reliability-allocation optimization increase greatly, which leads to the difficulty and inefficiency of optimization. To solve the above problem, two dimension-reduction strategies are proposed for the FMS reliability optimization with low cost and a high level of reliability as the objectives, and they are the reliability-weight double-threshold qualification strategy (RWTS) and the bi-level optimization strategy (BLOS), respectively. Based on these two strategies, an overall reliability-allocation optimization model and a bi-level reliability-allocation optimization model are established based on the FMS reliability evaluation presented in our previous work, and their algorithms based on particle swarm optimization (PSO) are presented. In terms of their contributions, for the RWTS, thresholds of reliability and the weight index of each basic element are set to dynamically reduce the number of variables in each iteration of the optimization; for the BLOS, the overall reliability-allocation optimization problem for transitioning from the FMS to basic elements can be transformed into simpler allocation optimizations from the FMS to subsystems and from subsystems to basic elements, which have fewer variables, and this can largely improve the optimization convergence performance. Through applying this to a box-parts finishing FMS, compared with the traditional optimization method, the high efficiency and the good allocation effect of the optimization based on these two strategies for improving convergence speed are verified by the simulation results. The proposed method has great significance for FMS design due to its limited cost but high-reliability requirement.

Testing needs for the development and qualification of a breeding blanket for DEMO

The purpose of this paper is to critically review the testing and qualification strategy for the DEMO breeding blanket in Europe, identifying the potential risks and weaknesses and recommending, where necessary, the changes required to strengthen or accelerate the programme. Based on the information presented in this paper, a risk mitigation strategy is required to reduce the technological, licencing, and regulatory risks associated with the performance and reliability of the breeding blanket technologies for DEMO. An attractive mitigation option, which was originally proposed more than 30 years ago but not implemented, is to perform testing and qualification of breeding blanket technologies and design concepts in a dedicated nuclear plasma device that serves as a 14 MeV volumetric neutron source.

Practical Characterization Strategies for Comparison, Qualification, and Selection of Cell Viability Detection Methods for Cellular Therapeutic Product Development and Manufacturing.

Cellular therapy development and manufacturing has focused on providing novel therapeutic cell-based products for various diseases. The International Organization for Standardization (ISO) has provided guidance on critical quality attributes (CQAs) that shall be considered when testing and releasing cellular therapeutic products. Cell count and viability measurements are two of the CQAs that are determined during development, manufacturing, testing, and product release. The ISO Cell Counting Standard Part 1 and 2 addressed the needs for improving the quality of cell counting results. However, there is currently no guidance on the qualification and selection of a fit-for-purpose cell viability detection method. In this work, we present strategies for the characterization and comparison of AO/PI and AO/DAPI staining methods using the heat-killed (HK) and low temperature/nutrient-deprived (LT/ND) cell death models to evaluate the comparability of cell viability measurements and identify potential causes of differences. We compared the AO/PI and AO/DAPI staining methods using HK and LT/ND-generated dead cells, investigated the staining time effects on cell viability measurements, and determined their viability linearity with different mixtures of live and dead cells. Furthermore, we validated AO/PI and AO/DAPI cell viability measurement with a long-term cell proliferation assay. Finally, we demonstrate a practical example of cell viability measurement comparison using AO/PI and AO/DAPI on antibiotic-selected transduced Jurkat and THP-1 cells to select a fit-for-purpose method for functional genomics screening. The proposed strategies may potentially enable scientists to properly characterize, compare, and select cell viability detection methods that are critical for cellular therapeutic product development and manufacturing.

Neutron irradiation and electrical characterisation of the first 8” silicon pad sensor prototypes for the CMS calorimeter endcap upgrade

As part of its HL-LHC upgrade program, the CMS collaboration is replacing its existing endcap calorimeters with a high-granularity calorimeter (CE). The new calorimeter is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic and hadronic compartments. Due to its compactness, intrinsic time resolution, and radiation hardness, silicon has been chosen as active material for the regions exposed to higher radiation levels. The silicon sensors are fabricated as 20 cm (8”) wide hexagonal wafers and are segmented into several hundred pads which are read out individually. As part of the sensor qualification strategy, 8” sensor irradiation with neutrons has been conducted at the Rhode Island Nuclear Science Center (RINSC) and followed by their electrical characterisation in 2020-21. The completion of this important milestone in the CE's R&D program is documented in this paper and it provides detailed account of the associated infrastructure and procedures. The results on the electrical properties of the irradiated CE silicon sensors are presented.

Applying Machine Learning to the Visual Inspection of Filled Injectable Drug Products.

With machine learning (ML), we see the potential to better harness the intelligence and decision-making abilities of human inspectors performing manual visual inspection (MVI) and apply this to automated visual inspection (AVI) with the inherent improvements in throughput and consistency. This article is intended to capture current experience with this new technology and provides points to consider for successful application to AVI of injectable drug products. The technology is available today for such AVI applications. Machine vision companies have integrated ML as an additional visual inspection tool with minimal upgrades to existing hardware. Studies have demonstrated superior results in defect detection and reduction in false rejects, when compared with conventional inspection tools. ML implementation does not require modifications to current AVI qualification strategies. The utilization of this technology for AVI will accelerate recipe development by use of faster computers rather than by direct human configuration and coding of vision tools. By freezing the model developed with artificial intelligence tools and subjecting it to current validation strategies, assurance of reliable performance in the production environment can be achieved.

Evaluation of Soil Physico-Chemical Properties in Pre and Post Flood Conditions at Thatta Sindh Pakistan

The heavy rainfall caused biggest ever natural rain flood in River Indus in the history of Pakistan during 2010. The deltaic district Thatta of Sindh Pakistan hit by flood with breach came out at Dyke near Thatta city on right bank of River Indus. The soil sampling was carried out in month July 2010 prior to flood for routine soil analysis. After flood again soil samples from 13 same sites of pre flood spots were collected at two depths, 0-15 and 15-30 cm. Aim was to observe soil physic-chemical properties and nutrient status of pre-flood and post flood conditions. Soil sand pH and SAR were found significantly higher in post-flood conditions whereas clay and OM were found high in pre-flood conditions. The nutrients status of N, P, Ca and Mg were depleted however K remained satisfactory in post-flood conditions. In post flood conditions the pH and SAR were positive and significantly correlated with each other indicated disperse of soil. The information obtained from this study will be helpful to develop flood qualification strategies and proper management of soil.

Implementation of a fully integrated continuous manufacturing line for direct compression and coating at a commercial pharmaceutical facility – Part 1: Operational considerations and control strategy

We implement a fully integrated continuous manufacturing (CM) line for direct compression and coating of a pharmaceutical oral solid dosage form in a commercial production facility. In this first paper of a two-part series, we describe process design and operational choices made to introduce CM using infrastructure originally intended for batch operations. Consistent with lean manufacturing principles, we select equipment, facilities, and novel process analytical technologies that meet production agility goals alongside an existing batch process. Choices address process risks, are aligned with existing quality systems, yet allow exploration of CM agility benefits in commercial operations. We outline how operating procedures, control schemes, and release criteria from the historical batch process are adapted for CM with modified lot and yield definitions based on patient demand. We devise a hierarchy of complementary controls including real-time process interrogation, predictive residence time distribution models of tablet concentration, real-time product release testing using automated tablet NIR spectroscopy, active rejection and diversion, and throughput-based sampling. Results from lots produced under normal operational conditions confirm our CM process provides assurance of product quality. Qualification strategies to achieve lot size flexibility aims are also described. Finally, we consider CM extensions to formulations with differing risk profiles. Further analysis of results for lots produced under normal operational conditions is provided in part 2 (Rosas et al., 2023).

Corporate Education and Industry 4.0: Strategies for (Re)Qualification of the Workforce

Corporate Education and Industry 4.0: Strategies for (Re)Qualification of the Workforce

Meaningful Learning Theory and Science Teaching Through Research: Interfaces From a Narrative Literature Review

We discuss relationships of consistency and coherence in teaching and learning processes at the interface of Meaningful Learning Theory and Science Teaching through Research. For this, we analyze works that, explicitly or implicitly, have carried out such amalgamation in the 2013, 2015 and 2017 editions of ENPEC/ABRAPEC, an environment that promotes and disseminates academic knowledge of relevance and impact; procedurally, we used Discursive Textual Analysis in a descriptive approach to a narrative review, in which the search for sources is not systematic and is usually less comprehensive, although useful as a primary exploration of the research field. Five argumentative categories were identified: (i) authors of reference regarding the TAS; (ii) methodological approachs; (iii) scientific literacy and its potential contributions to science teaching; (iv) the student as an active/participating subject in their learning; and the (v) use of digital technologies as a qualification strategy for science teaching. The results indicated that the most frequent authors were Ausubel, Novak and/or Gowin; the methodological approaches of the works were not mainly defined, although actions/instruments were mentioned; as for scientific literacy, there was an emphasis on students, noting that the mere appropriation of media resources does not underlie the foundations of the specialized knowledge that teaching of a scientific nature requires; in relation to the student as an active/participative subject, this could be seen induced by the use of methodologies in the configuration of space-times that favor reflection/problematization of knowledge and actions; as for digital technologies, they were recurrently recommended in didactic approaches, mediated directively by the teacher with an emphasis on participation, cooperation and research.

On the use of wearable sensors as mobility biomarkers in the marketing authorization of new drugs: A regulatory perspective.

The loss of mobility is a common trait in multiple health conditions (e.g., Parkinson's disease) and is associated with reduced quality of life. In this context, being able to monitor mobility in the real world, is important. Until recently, the technology was not mature enough for this; but today, miniaturized sensors and novel algorithms promise to monitor mobility accurately and continuously in the real world, also in pathological populations. However, before any such methodology can be employed to support the development and testing of new drugs in clinical trials, they need to be qualified by the competent regulatory agencies (e.g., European Medicines Agency). Nonetheless, to date, only very narrow scoped requests for regulatory qualification were successful. In this work, the Mobilise-D Consortium shares its positive experience with the European regulator, summarizing the two requests for Qualification Advice for the Mobilise-D methodologies submitted in October 2019 and June 2020, as well as the feedback received, which resulted in two Letters of Support publicly available for consultation on the website of the European Medicines Agency. Leveraging on this experience, we hereby propose a refined qualification strategy for the use of digital mobility outcome (DMO) measures as monitoring biomarkers for mobility in drug trials.

Formal analysis of the application programming interface of the PVS verification system

A formal specification of the Application Programming Interface provided by the PVS verification system is developed. The specification documents the modes of operation and the communication patterns necessary to interact with PVS to trigger parsing, type-checking, and interactive theorem proving. The primary goal is to explore the use of formal methods as part of a toolchain for rigorous documentation of the functionalities of PVS. The developed specification can be used to track API design changes and to inform possible enhancements that can be introduced in future versions of PVS. The same specification can also be used as a reference by developers of client applications that use PVS as a back-end, to verify the correctness of communication patterns used by the client to interact with PVS. It is a first step towards a possible tool qualification strategy for PVS.

Using machine learning to predict dimensions and qualify diverse part designs across multiple additive machines and materials

One of the most critical decisions in a factory is whether to accept or reject manufactured parts, where the acceptance criteria usually includes part geometry and the presence of defects. Additive manufacturing (AM) produces parts having geometric defects that depend upon the part size and shape as well as manufacturing process parameters. There is a need to develop methods that can effectively predict these defects and aid the part qualification process. This study addresses two problems: first, the prediction of part geometry; and second, the use of these predictions to qualify parts. To mimic an AM production scheme, we produced 405 parts across nine builds, using two machines, three polymer materials, and two different arrangements of part designs within a build. For each of three part designs, we identified and measured five critical features per part for a total of 2025 feature measurements. We developed a data model that describes design information and manufacturing process information using both continuous and categorical variables and used this data to train a support vector regression (SVR) machine learning (ML) model to predict part geometry. The models are trained on a random selection of the 405 parts and predict the geometry of unsampled parts with high accuracy. The SVR models predict feature geometry to within 53 µm, which is close to the manufacturer reported process repeatability and much smaller than the 180 µm standard deviation of the data. An exploration of the data model shows that categorical information about the part design significantly improves the model prediction compared to models that do not capture this information. In the second part of the study, we leverage the same data model to develop a part qualification tool and explore three strategies for accepting or rejecting parts based on ML predictions. The strategies are based on: SVR feature geometry predictions and tunable acceptance thresholds, direct feature classifications with a part acceptance threshold, and direct part classifications. In all cases, the only model inputs are the manufacturing parameters and feature descriptors. The SVR-based part qualification strategy achieves the highest accuracy at 81% and is more data efficient than the other approaches. The research demonstrates the utility of ML in predicting AM part geometry and accurately classifying parts, and provides opportunities to better understand geometry variability in production AM.

Risk Management and Quality in Health Services: A Study at the Vereador Valdinar Pereira Basic Health Unit, in Teresina-Pi

This article is about risk management and quality in health services, in the basic unit councilor Valdinar Pereira in Teresina Piaui. It is known that the provision of care in the health area is quite complex, since this area is constantly evolving, resulting from social, scientific, and technological advances. In achieving the purpose of the study, the general objective was outlined, based on knowing the mechanisms adopted in risk management and the organizing processes through preventive work and adequate treatment in the face of impacts in the control of adverse situations, aiming at improving the quality of care of patients/users of the Vereador Valdinar Pereira Basic Health Unit. The methodology consists of bibliographic research. For that, sources were used, in which Cedra et al. [1-4] stood out; and other authors who contributed with the theoretical method. Field research with a quantitative approach was also used, in which the questionnaire consists of 14 (fourteen) open and closed questions, being prepared by the Google Forms application. In view of the results, it was verified that there is a great effort by the managers and teams of the family health program that make up the basic health unit, in promoting expansion and qualification strategies to favour problem-solving in the work process.

UPLC-MS/MS assay for the simultaneous determination of catecholamines and their metabolites at low pg/mg in rat/mouse striatum

Catecholamines and their metabolites act as neurotransmitters in the brain and are important for nervous system function. In the current work, a highly selective and sensitive UPLC-MS/MS assay was developed for quantitation of six catecholamines and their metabolites, including dopamine, norepinephrine, serotonin, 3,4-dihydroxyphenylacetic acid, homovanillic acid and 5-hydroxyindolacetic acid from rat and mouse striatum as pharmacodynamic biomarkers to support neuroscience and pharmaceutical research. A fit-for-purpose strategy for method development, assay qualification and study support were adopted for this assay. A surrogate matrix (brain homogenizing solution absent of targeted analytes) was used for preparation of calibration samples and certain levels of quality control samples to avoid interference from endogenous baselines. Homogenized rodent striatum was derivatized by dansyl chloride to enhance the sensitivity, followed by liquid-liquid extraction with ethyl acetate in 96-well plate format. The lower limit of quantitation (LLOQ) was 0.2 ng/mL in tissue homogenate, equivalent to 3.2 pg/mg in brain tissue, which could be further reduced to ten times lower by changing the re-dissolving and injecting volume in the last sample purification step. Acceptable accuracy, precision, linearity, specificity, recovery, and matrix effect was obtained. Bench-top stability (2 h), freeze-thaw stability (3 cycles at −20 °C), and − 80 °C storage stability (up to 51 days) in both tissue homogenate and surrogate matrix along with autosampler stability (60 h at 4°C) all met acceptance criteria. This assay was successfully applied to measure the six analytes in striatum from mice treated with the neurotoxin 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), an animal model of Parkinsonism for which dosing protocols can vary widely, and further confirmed the metabolic pathway of neurotoxicity by the quantification of catecholamine metabolites. Our study is the first detailed the step-by-step recovery and pointed out the key factors for the assay to simultaneously quantify these six neurotransmitters in rodent striatum with superior sensitivity.

Investigation of Material Properties of Wall Structures from Stainless Steel 316L Manufactured by Laser Powder Bed Fusion

To make powder bed fusion (PBF) via laser beam (-LB) for metals (/M) available for highly regulated components such as pressure equipment according to the Pressure Equipment Directive, system-specific qualification methods need to be established to deal with process- and geometry-dependent inhomogeneous material behavior. Therefore, the material properties of austenitic stainless steel (316L) and their influences on normative acceptable qualification strategies were investigated in this study. Flat tensile test specimens were produced by two manufacturing systems identical in construction and were compared to specimens produced from conventionally rolled sheet material. Specimens were compared in the horizontal and vertical building directions in relation to different slope angles, wall thicknesses and cross-sectional areas. Despite identical process setups, parameters and powder feedstock, differences in mechanical behavior could be seen. Furthermore, the mechanical properties, surface roughness and density showed dependencies on the wall thickness and slope angle. In particular, the influence of wall thickness has not been covered in publications about PBF-LB/M before. These results suggest that geometry- and system-dependent components can be designed based on associated data from qualification processes. Therefore, a new qualification method based on wall structure properties is suggested for standard qualification processes of components with wall structures, such as pressure equipment.