Verification and Validation of Computational Models Used in Biopharmaceutical Manufacturing: Potential Application of the ASME Verification and Validation 40 Standard and FDA Proposed AI/ML Model Life Cycle Management Framework

Bideault, Gautier; Scaccia, Anthony; Zahel, Thomas; Landertinger, Robert W.; Daluwatte, Chathuri

doi:10.1016/j.xphs.2021.01.016

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…For the model calculation, cloud computing (Chen et al, 2020), hybrid modeling (Chen et al, 2020; Lin et al, 2022), or some AI approaches (Tang et al, 2023) can significantly reduce computation time, enabling real‐time feedback control. Finally, considerations should be given to the industrial application of DT (O'Connor et al, 2016), including verification and validation of mathematical model and control strategies (Food, 2016) as well as life‐cycle management (Bideault et al, 2021). Furthermore, how to integrate the current laboratory‐scale DT into the commercial manufacturing Supervisory Control and Data Acquisition system (Chen, Chen, et al, 2022; Feidl et al, 2020) to enhance the process‐wide control still requires further research.…”

Section: Resultsmentioning

confidence: 99%

Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation

Shi,

Chen,

Zhong

et al. 2024

Biotech & Bioengineering

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Digital twin (DT) is a virtual and digital representation of physical objects or processes. In this paper, this concept is applied to dynamic control of the collection window in the ion exchange chromatography (IEC) toward sample variations. A possible structure of a feedforward model‐based control DT system was proposed. Initially, a precise IEC mechanistic model was established through experiments, model fitting, and validation. The average root mean square error (RMSE) of fitting and validation was 8.1% and 7.4%, respectively. Then a model‐based gradient optimization was performed, resulting in a 70.0% yield with a remarkable 11.2% increase. Subsequently, the DT was established by systematically integrating the model, chromatography system, online high‐performance liquid chromatography, and a server computer. The DT was validated under varying load conditions. The results demonstrated that the DT could offer an accurate control with acidic variants proportion and yield difference of less than 2% compared to the offline analysis. The embedding mechanistic model also showed a positive predictive performance with an average RMSE of 11.7% during the DT test under >10% sample variation. Practical scenario tests indicated that tightening the control target could further enhance the DT robustness, achieving over 98% success rate with an average yield of 72.7%. The results demonstrated that the constructed DT could accurately mimic real‐world situations and perform an automated and flexible pooling in IEC. Additionally, a detailed methodology for applying DT was summarized.

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Section: Resultsmentioning

confidence: 99%

Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation

Shi,

Chen,

Zhong

et al. 2024

Biotech & Bioengineering

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“…Moreover, the American Society of Mechanical Engineers and the FDA developed a framework for the validation of computational models for specific use in medical device development [114]. Bideault et al performed a use case in bioprocess engineering based on this regulatory framework [115] . The fundamental problem in the validation of machine learning models in bioprocessing is that they learn and adapt during the process [41,115].…”

Section: Discussionmentioning

confidence: 99%

Promoting Sustainability through Next-Generation Biologics Drug Development

et al. 2022

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The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using “digital twins” can maximize efficiency while improving sustainability. As part of this review, we investigate how the World Health Organization’s 17 sustainability goals can apply toward next-generation drug development. We analyze the state-of-the-art laboratory leadership, inclusive personnel recruiting, the latest therapy approaches, and intelligent process automation. We also outline how modern data science techniques and machine tools for CMC help to shorten drug development time, reduce failure rates, and minimize resource usage. Finally, we systematically analyze and compare existing approaches to our experiences with the high-throughput laboratory KIWI-biolab at the TU Berlin. We describe a sustainable business model that accelerates scientific innovations and supports global action toward a sustainable future.

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“…Universal Immune System Simulator for Tuberculosis is a unique, bespoke ABM for the evolution of TB in the lung after RUTI vaccination ( 12 , 18 , 19 ), which can be considered as a computational model for RUTI vaccination. As such, under ASME V&V 40-2018 it is necessary to define its Context of Use under the Question of Interest [see, e.g., ( 20 , 21 ), and references therein]. Based on the taxonomy proposed in Viceconti et al ( 22 ), this process is carefully carried out in Curreli et al ( 23 ) for using UISS-TB in augmented clinical trials.…”

Section: Methodsmentioning

confidence: 99%

Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

Kiagias

Russo

Sgroi

et al. 2021

Front. Med. Technol.

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We propose a Bayesian hierarchical method for combining in silico and in vivo data onto an augmented clinical trial with binary end points. The joint posterior distribution from the in silico experiment is treated as a prior, weighted by a measure of compatibility of the shared characteristics with the in vivo data. We also formalise the contribution and impact of in silico information in the augmented trial. We illustrate our approach to inference with in silico data from the UISS-TB simulator, a bespoke simulator of virtual patients with tuberculosis infection, and synthetic physical patients from a clinical trial.

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Verification and Validation of Computational Models Used in Biopharmaceutical Manufacturing: Potential Application of the ASME Verification and Validation 40 Standard and FDA Proposed AI/ML Model Life Cycle Management Framework

Cited by 10 publications

References 8 publications

Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation

Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation

Promoting Sustainability through Next-Generation Biologics Drug Development

Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

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