Assessing the impact of automation in pharmaceutical quality control labs using a digital twin

2022 ◽  
Vol 62 ◽  
pp. 270-285
Author(s):  
Tiago Coito ◽  
Miguel S.E. Martins ◽  
Bernardo Firme ◽  
João Figueiredo ◽  
Susana M. Vieira ◽  
...  
2019 ◽  
Vol 102 (3) ◽  
pp. 801-809
Author(s):  
Ana Carolina Kogawa ◽  
Ana Elisa Della Torre Pires ◽  
Hérida Regina Nunes Salgado

Abstract Background: Atorvastatin, a lipid-regulating drug, was the best-selling drug in the world in the early 2000s. Thus, monitoring of this drug is important because it is accessible to a large portion of the population. In addition, its quality control is fundamental to provide quality medicines. Method of analysis can be the first step in the rational use of pharmaceuticals. Objective/Methods: In this context, a critical review of analytical methods present in the literature and official compendia for the pharmaceutical quality control of atorvastatin was made. Results: Among the analytical methods most used in the evaluation of atorvastatin, HPLC is highlighted, followed by HPLC coupled to MS, and spectrophotometry in UV. Tablets are the most studied pharmaceutical samples, and plasma is the most studied biological matrix. In the literature, studies with atorvastatin-based pharmaceutical products are more common than biological materials. Acetonitrile is the organic solvent most commonly used in the methods surveyed to evaluate atorvastatin. Conclusions: Currently, awareness of the impact that the analytical choice has on the health of the operator and the environment is growing. Therefore, the suitability of existing methods for the determination of atorvastatin can be made to adhere to the current analytical chemistry. In this way, the analytical, environmental, and human consciousness will remain united. Highlights: Although the literature shows interesting methods from an economic and environmental point of view, such as UV, Vis miniaturized, and TLC, they can still be improved to meet the requirements of the current sustainable analytical chemistry.


2019 ◽  
Vol 2019 (5) ◽  
pp. 32-38
Author(s):  
Валентина Косенко ◽  
Valentina Kosenko ◽  
Алла Трапкова ◽  
Alla Trapkova ◽  
Светлана Тарасова ◽  
...  

The article conducts the analysis of system errors detected by Roszdravnadzor by conducting state quality control of circulating medicines, as well as weaknesses in pharmaceutical quality management systems of the manufacturers, that can influence the quality of manufactured drugs.


2020 ◽  
Author(s):  
Ali Al-Yacoubb ◽  
Will Eaton ◽  
Melanie Zimmer ◽  
Achim Buerkle ◽  
Dedy Ariansyaha ◽  
...  

Author(s):  
Erin Polka ◽  
Ellen Childs ◽  
Alexa Friedman ◽  
Kathryn S. Tomsho ◽  
Birgit Claus Henn ◽  
...  

Sharing individualized results with health study participants, a practice we and others refer to as “report-back,” ensures participant access to exposure and health information and may promote health equity. However, the practice of report-back and the content shared is often limited by the time-intensive process of personalizing reports. Software tools that automate creation of individualized reports have been built for specific studies, but are largely not open-source or broadly modifiable. We created an open-source and generalizable tool, called the Macro for the Compilation of Report-backs (MCR), to automate compilation of health study reports. We piloted MCR in two environmental exposure studies in Massachusetts, USA, and interviewed research team members (n = 7) about the impact of MCR on the report-back process. Researchers using MCR created more detailed reports than during manual report-back, including more individualized numerical, text, and graphical results. Using MCR, researchers saved time producing draft and final reports. Researchers also reported feeling more creative in the design process and more confident in report-back quality control. While MCR does not expedite the entire report-back process, we hope that this open-source tool reduces the barriers to personalizing health study reports, promotes more equitable access to individualized data, and advances self-determination among participants.


2021 ◽  
Vol 11 (10) ◽  
pp. 4602
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.


2021 ◽  
Vol 429 ◽  
pp. 119937
Author(s):  
Paola Valentino ◽  
Serena Martire ◽  
Fabiana Marnetto ◽  
Luca Mirabile ◽  
Antonio Bertolotto

Trudy MAI ◽  
2021 ◽  
Author(s):  
Andrey Dement`ev ◽  
Anton Bannikov ◽  
Konstantin Arsen`ev ◽  
Alexey Shiryaev ◽  
Alexey Basak

2021 ◽  
Author(s):  
Senthil Krishnababu ◽  
Omar Valero ◽  
Roger Wells

Abstract Data driven technologies are revolutionising the engineering sector by providing new ways of performing day to day tasks through the life cycle of a product as it progresses through manufacture, to build, qualification test, field operation and maintenance. Significant increase in data transfer speeds combined with cost effective data storage, and ever-increasing computational power provide the building blocks that enable companies to adopt data driven technologies such as data analytics, IOT and machine learning. Improved business operational efficiency and more responsive customer support provide the incentives for business investment. Digital twins, that leverages these technologies in their various forms to converge physics and data driven models, are therefore being widely adopted. A high-fidelity multi-physics digital twin, HFDT, that digitally replicates a gas turbine as it is built based on part and build data using advanced component and assembly models is introduced. The HFDT, among other benefits enables data driven assessments to be carried out during manufacture and assembly for each turbine allowing these processes to be optimised and the impact of variability or process change to be readily evaluated. On delivery of the turbine and its associated HFDT to the service support team the HFDT supports the evaluation of in-service performance deteriorations, the impact of field interventions and repair and the changes in operating characteristics resulting from overhaul and turbine upgrade. Thus, creating a cradle to grave physics and data driven twin of the gas turbine asset. In this paper, one branch of HFDT using a power turbine module is firstly presented. This involves simultaneous modelling of gas path and solid using high fidelity CFD and FEA which converts the cold geometry to hot running conditions to assess the impact of various manufacturing and build variabilities. It is shown this process can be executed within reasonable time frames enabling creation of HFDT for each turbine during manufacture and assembly and for this to be transferred to the service team for deployment during field operations. Following this, it is shown how data driven technologies are used in conjunction with the HFDT to improve predictions of engine performance from early build information. The example shown, shows how a higher degree of confidence is achieved through the development of an artificial neural network of the compressor tip gap feature and its effect on overall compressor efficiency.


Sign in / Sign up

Export Citation Format

Share Document