Deep Transformers for Computing and Predicting ALCOA+Data Integrity Compliance in the Pharmaceutical Industry

Kavasidis, Isaak; Lallas, Efthimios; Leligou, Helen C.; Oikonomidis, Georgios; Karydas, D.M.; Gerogiannis, Vassilis C.; Karageorgos, Anthony

doi:10.3390/app13137616

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…Artificial Intelligence (AI) and Machine Learning (ML) methods have already shown great results in supporting those pharma manufacturing systems in multiple ways, such as process monitoring auditing, and control, data mining and processing, digital transformation integration with other technologies (e.g. blockchain), [12], [13], and prediction potential [14]. Digital Twin (DT) solutions have also been successfully applied, facilitating the transformation of pharma manufacturing environments [15].…”

Section: Motivation-backgroundmentioning

confidence: 99%

A Network Modeling and Analysis Approach for Pharma Industry Regulatory Assessment

Charitou,

Lallas,

Gerogiannis

et al. 2024

IEEE Access

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Regulatory compliance in the pharmaceutical industry is challenging, requiring dedicated resources and meticulous control over production processes to ensure adherence to established regulatory guidelines, specifically ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, and Available) principles. This paper introduces an innovative approach to assess pharma regulatory compliance, utilizing a network model of the production process. The model dynamically configures production line characteristics based on manufacturing process data, overcoming complexity and scalability challenges. Purpose: The main purpose is to address the challenges of regulatory compliance in the pharmaceutical industry by introducing a novel approach using a network model. The research question involves assessing the effectiveness of this model in ensuring compliance with ALCOA+ principles. Methods: The approach involves dynamic configuration of the network model parameters based on manufacturing process data. Network analysis methods are then applied to evaluate the conformity of manufacturing process data to ALCOA+ principles. Results: Testing the proposed approach on a real dataset from a representative pharma production line demonstrates its effectiveness in assessing pharma regulatory compliance. The results highlight the potential of network modelling in managing data quality and integrity within the regulatory framework. Conclusions: The study concludes that the network model offers a strategic solution for evaluating and ensuring regulatory compliance in pharmaceutical manufacturing. The approach shows promise in addressing the complexities of data management within the stringent regulatory framework of the industry.

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Section: Motivation-backgroundmentioning

confidence: 99%

A Network Modeling and Analysis Approach for Pharma Industry Regulatory Assessment

Charitou,

Lallas,

Gerogiannis

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

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Cybernetic Model Design for the Qualification of Pharmaceutical Facilities

Tabasevic,

Milanovic,

Spasojevic Brkic

et al. 2024

Applied Sciences

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In this paper, an integrated cybernetic model for managing qualification activities when commissioning pharmaceutical facilities is created, focusing on defining critical factors that provide all the prerequisites for the start of the production process. An eight-year research and work on complex projects in the pharmaceutical industry is integrated into a scientific research endeavor focused on the qualification of pharmaceutical facilities. The newly designed cybernetic model for the qualification of pharmaceutical facilities is flexible and adaptive and has the most adequate elements so far recognized in practice and enables the qualification of smart facilities, in accordance with the concept of Pharma 4.0. Additionally, it meets the requirements of the regulatory authorities; therefore, it constantly initiates the search for better solutions and process improvements. Moreover, it is universal and, thus, applicable to all reconstructions in the pharmaceutical industry. The application of the designed model has been implemented in practice and has shown outstanding results, as it combines diversity and sustainability in project management. Also, the model focuses on indicating aspects that include risk management, scientific approach, experimental testing, numerical simulations, as well as the possibility of optimization and energy saving.

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Deep Transformers for Computing and Predicting ALCOA+Data Integrity Compliance in the Pharmaceutical Industry

Cited by 2 publications

References 28 publications

A Network Modeling and Analysis Approach for Pharma Industry Regulatory Assessment

A Network Modeling and Analysis Approach for Pharma Industry Regulatory Assessment

Cybernetic Model Design for the Qualification of Pharmaceutical Facilities

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