computerized system validation (CSV) aims to ensure that software systems in regulated environments, such as the pharmaceutical industry, adhere to strict regulatory requirements. It plays a crucial role in guaranteei...
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ISBN:
(纸本)9783031434327;9783031434334
computerized system validation (CSV) aims to ensure that software systems in regulated environments, such as the pharmaceutical industry, adhere to strict regulatory requirements. It plays a crucial role in guaranteeing the safety of products or services by verifying that computerizedsystems operate according to the specified guidelines. However, CSV is a complex and resource-intensive process that poses challenges for small and medium-sized enterprises in particular, making it challenging to be implemented effectively. In this paper, we investigate the potential of Robotic Process Automation (RPA) as a solution to partially automate CSV and address these challenges. We present an ongoing project where we apply RPA to CSV and discuss its effectiveness in reducing the time and effort associated with manual CSV activities. We also describe the challenges that we encountered during the implementation of RPA in the context of CSV. Our research highlights the possibility of extending the application of RPA beyond simple data entry and verification tasks, allowing for the automation of entire complex processes.
In the pharmaceutical industry, it is essential to ensure the safety and efficacy of medicinal products. Therefore a robust quality assurance framework is needed. This manuscript examines the impact of GAMP 5 and data...
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In the pharmaceutical industry, it is essential to ensure the safety and efficacy of medicinal products. Therefore a robust quality assurance framework is needed. This manuscript examines the impact of GAMP 5 and data integrity (DI) on quality assurance, while also highlighting the role of quality by design (QbD) principles. GAMP 5 is a widely used framework for validating automated systems that establishes quality assurance practices. DI guarantees the reliability of data collected throughout various stages of drug development. The integration of QbD principles promotes a systematic approach to development that emphasizes a deep understanding of critical quality attributes, risk management, and continuous improvement. With their implementation, organizations are able to meet regulatory requirements and provide safe medications to patients worldwide.
[...]in our facility, it is standard to run the last robot run of the day overnight, utilizing assay time when the last laboratory technician has already left.[...]a typical standard ELISA robot with integrated plate ...
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[...]in our facility, it is standard to run the last robot run of the day overnight, utilizing assay time when the last laboratory technician has already left.
[...]a typical standard ELISA robot with integrated plate washers and a reader allows the increase of throughput tenfold, with just one laboratory technician required to feed the system with samples and reagents.
[...]the focus on data traceability became decisive to proceed with automated systems.
With regard to the flexibility and complexity of robotic systems, our facility made good experience not to design a platform to cover the entire workload of the assays.
Since many assays require a defined interruption, such as over-night incubation, we tend to split the assay between platforms at exactly these interruption points.
[...]the laboratory personnel may develop resentments against the robots, not seeing the potentials, but only the errors and immaturities.
Background: With the rapid increase in utilization of imaging endpoints in multicenter clinical trials, the amount of data and workflow complexity have also increased. A Clinical Trial Imaging Management system (CTIMS...
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Background: With the rapid increase in utilization of imaging endpoints in multicenter clinical trials, the amount of data and workflow complexity have also increased. A Clinical Trial Imaging Management system (CTIMS) is required to comprehensively support imaging processes in clinical trials. The US Food and Drug Administration (FDA) issued a guidance protocol in 2018 for appropriate use of medical imaging in accordance with many regulations including the Good Clinical Practice (GCP) guidelines. Existing research on CTIMS, however, has mainly focused on functions and structures of systems rather than regulation and compliance. Objective: We aimed to develop a comprehensive CTIMS to meet the current regulatory guidelines and various required functions. We also aimed to perform computerized system validation focusing on the regulatory compliance of our CTIMS. Methods: Key regulatory requirements of CTIMS were extracted thorough review of many related regulations and guidelines including International Conference on Harmonization-GCP E6, FDA 21 Code of Federal Regulations parts 11 and 820, Good Automated Manufacturing Practice, and Clinical Data Interchange Standards Consortium. The system architecture was designed in accordance with these regulations by a multidisciplinary team including radiologists, engineers, clinical trial specialists, and regulatory medicine professionals. computerized system validation of the developed CTIMS was performed internally and externally. Results: Our CTIMS (AiCRO) was developed based on a two-layer design composed of the server system and the client system, which is efficient at meeting the regulatory and functional requirements. The server system manages system security, data archive, backup, and audit trail. The client system provides various functions including deidentification, image transfer, image viewer, image quality control, and electronic record. computerized system validation was performed internally using a V-model and ex
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