Artificial intelligence and machine learning applications in biopharmaceutical manufacturing

Rathore, Anurag S.; Nikita, Saxena; Thakur, Garima; Mishra, Somesh

doi:10.1016/j.tibtech.2022.08.007

Cited by 31 publications

(11 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…29,[33][34][35][36] Additionally, several methodologies for reducing computation burdens are emerging such as model linearization, and reinforcement learning. [37][38][39] To fully implement the QbC, an additional requirement is to develop a systematic framework that enables the integration of operation control based on hierarchical process automation principles.…”

Section: Quality By Controlmentioning

confidence: 99%

See 1 more Smart Citation

Current challenges and recent advances on the path towards continuous biomanufacturing

Drobnjakovic

Hart²,

Kulvatunyou

et al. 2023

Biotechnology Progress

View full text Add to dashboard Cite

Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever‐evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end‐to‐end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

show abstract

Section: Quality By Controlmentioning

confidence: 99%

“…Different variations of model predictive control have also shown excellent performance for various unit operations 29,33–36 . Additionally, several methodologies for reducing computation burdens are emerging such as model linearization, and reinforcement learning 37–39 …”

Section: Quality Control and Process Monitoringmentioning

confidence: 99%

Current challenges and recent advances on the path towards continuous biomanufacturing

Drobnjakovic

Hart²,

Kulvatunyou

et al. 2023

Biotechnology Progress

View full text Add to dashboard Cite

show abstract

“…In industry, this evolution has been reflected in mechanization and digitalization to manage processes in real-time (the fourth industrial revolution) [1] through the application of algorithms, data compilation methods, and data processing. These technologies have various applications in the different operational areas of companies; for instance, machine learning (ML) plays an optimal role in the design, monitoring, and control of manufacturing, stimulating integrated and continuous processes [2]. Likewise, it is used by marketing specialists in the segmentation of customers based on predictions [3].…”

Section: Introductionmentioning

confidence: 99%

A bibliometric study with statistical patterns of industry 4.0 on business management in the decade

Torres-Salazar,

Cruzado-Yesquén,

Alvarez-Vasquez

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In search of perpetual sustainability, companies need a continuous line of innovation, incorporating new technologies to optimize production; the adoption of Industry 4.0 is marking an important milestone in the deployment of business functions in all areas, adapting from human resources management to manufacturing operations. By leveraging these tools, operational areas can be interconnected to drive productivity. Thus, this research performs a descriptive bibliometric analysis of the influence of Industry 4.0 on business management from 2013 to 2022, using the contribution of physics through the implementation of data mining tools, such as Bibliometrix, whose foundation is given by mathematical and statistical models; in such a way, articles indexed in Scopus and Web of Science were analyzed, focusing on the indicators of citations, bibliography, abstract and keywords. The results showed an exponential growth of articles on Industry 4.0, with manufacturing as the central area of interest, especially in artificial intelligence and big data applications. The most cited articles highlight its role in process optimization. Overall, this bibliometric analysis suggests that the adoption of Industry 4.0 has a positive impact on decision-making, improving the direction of business management.

show abstract

“…While we still rely largely on an iterative, one-factor-at-a-time (OFAT) procedure based on experiments that explore a limited process space, model-based process optimization is widely adopted in process development in other industries 2 and, with an increasing prevalence, in developing bioprocesses. [3][4][5][6][7] Response surface methodology (RSM) models, for example, are suitable for model-based cell culture process optimization as they can be used to predict the final productivity or product quality responses based on process inputs such as temperature, pH, and other process conditions. 6,8 Creating an RSM model requires data from properly designed experiments such that the effects of process inputs on productivity and product quality responses can be estimated systematically.…”

Section: Introductionmentioning

confidence: 99%

“…Process conditions and the feeding strategy for nutrient supplementation critically affect the growth and metabolic behaviors of cells, which in turn determine the final product titer and quality. While we still rely largely on an iterative, one‐factor‐at‐a‐time (OFAT) procedure based on experiments that explore a limited process space, model‐based process optimization is widely adopted in process development in other industries 2 and, with an increasing prevalence, in developing bioprocesses 3–7 . Response surface methodology (RSM) models, for example, are suitable for model‐based cell culture process optimization as they can be used to predict the final productivity or product quality responses based on process inputs such as temperature, pH, and other process conditions 6,8 .…”

Section: Introductionmentioning

confidence: 99%

Efficient optimization of time‐varying inputs in a fed‐batch cell culture process using design of dynamic experiments

Luo¹,

Stanton²,

Sharp³

et al. 2023

Biotechnology Progress

View full text Add to dashboard Cite

In cell culture process development, we rely largely on an iterative, one‐factor‐at‐a‐time procedure based on experiments that explore a limited process space. Design of experiments (DoE) addresses this issue by allowing us to analyze the effects of process inputs on process responses systematically and efficiently. However, DoE cannot be applied directly to study time‐varying process inputs unless an impractically large number of bioreactors is used. Here, we adopt the methodology of design of dynamic experiments (DoDE) and incorporate dynamic feeding profiles efficiently in late‐stage process development of the manufacture of therapeutic monoclonal antibodies. We found that, for the specific cell line used in this article, (1) not only can we estimate the effect of nutrient feed amount on various product attributes, but we can also estimate the effect, develop a statistical model, and use the model to optimize the slope of time‐trended feed rates; (2) in addition to the slope, higher‐order dynamic characteristics of time‐trended feed rates can be incorporated in the design but do not have any significant effect on the responses we measured. Based on the DoDE data, we developed a statistical model and used the model to optimize several process conditions. Our effort resulted in a tangible improvement in productivity—compared with the baseline process without dynamic feeding, this optimized process in a 200‐L batch achieved a 27% increase in titer and > 92% viability. We anticipate our application of DoDE to be a starting point for more efficient workflows to optimize dynamic process conditions in process development.

show abstract

Artificial intelligence and machine learning applications in biopharmaceutical manufacturing

Cited by 31 publications

References 78 publications

Current challenges and recent advances on the path towards continuous biomanufacturing

Current challenges and recent advances on the path towards continuous biomanufacturing

A bibliometric study with statistical patterns of industry 4.0 on business management in the decade

Efficient optimization of time‐varying inputs in a fed‐batch cell culture process using design of dynamic experiments

Contact Info

Product

Resources

About