It is estimated that new drugs take an average of 8–10 years to hit the shelves, costing around $ 1 billion [1,2]. With the growing demand for bio-based drugs such as monoclonal antibodies, fusion proteins, antibody-drug conjugates, vaccines, and the more recent cell and gene therapies, these timelines are simply unsustainable. The recent COVID-19 pandemic presents the perfect example of the urgent need to reduce these timelines, especially when facing a human health crisis. Moreover, the pandemic has shown us that the current labor-intensive manufacturing set-ups with extensive supply chains can be easily disrupted. While accelerated regulatory approvals are an effective step towards this goal, the transition to “Intelligent Biopharmaceutical Production” with the integration of digital and automated technologies into manufacturing set-ups can drastically reduce production time and manual intervention, thereby accelerating the drug development process. Figure 1 illustrates the different aspects of Intelligent Biopharmaceutical Production and the various stages in which it can be incorporated.
Owing to its biological origins, the manufacturing of biopharmaceuticals is so complex and the volume of data obtained so large, particularly with the implementation of process analytical technologies (PAT), it is the paragon of ‘big data’ — a characteristic feature of Industry 4.0, which is now being adapted to Pharma 4.0 [3]. Machine learning (ML), artificial intelligence (AI), and statistical tools such as multivariate analysis are invaluable assets that not only collate and contextualize large data sets but also provide intuitive predictions for greater efficiency [4–6]. These approaches are revolutionizing the development of biopharmaceuticals at every stage: from target identification (molecular docking and dynamics) to clone development (clone selection and construction) to upstream processing (medium/feed optimization and bioreactor control) to downstream processing (selection of resins and optimizing purification parameters) and even formulation design and in vivo studies [7–10]. Upstream processing possibly has the greatest potential for automation and digitalization. Computational methods such as metabolic flux analysis along with the design of experiments and multivariate analysis have gained great impetus for improving feeding strategies and medium compositions to enhance product titres and control critical quality attributes (CQAs) [11–13]. Similarly, computational fluid dynamics (CFD) is also being routinely adopted for understanding bioreactor geometries and mixing properties [14]. AI-ML approaches are finding applications in even the most niche spaces of biopharmaceutical production such as controlling glycosylation [15]. With new technologies such as continuous processing, on-line/at-line monitoring, and PAT, being integrated into biopharmaceutical manufacturing, the amount of data being generated will only increase, in turn necessitating digitalization.
Possibly the most ambitious of all digitalization endeavors is the development of ‘digital twins’ — virtual replicas of physical processes that can mirror not only a single unit operation but an entire manufacturing set-up as well. Digital twins are finding their way into manufacturing facilities for systematizing process development, suggesting experimental designs, and managing data. This effort is being spear-headed by companies such as Cytiva, Sartorius, Yokogawa, and Korber, among several others, for integration into biopharmaceutical development. These digital twins utilize historical and real-time data to predict key performance indicators such as product titre and critical quality attributes (CQAs), which directly aid in reducing untoward incidents leading to batch failure or delays, in turn significantly cutting development costs and increasing process robustness [16]. The implementation of such digital technologies can improve the overall efficiency and flexibility of the manufacturing process, as well as maintain consistency in product quality, which is a key regulatory requirement.
In 2015, the Government of India, via the Confederation of Indian Industry (CII), launched the ‘Smart Manufacturing’ platform, to promote and develop excellence in Industry 4.0 within the Indian industry, including the pharmaceutical/biopharmaceutical industries [17]. Recently, Infosys provided the services of its XR platform to a leading pharmaceutical company for the creation of a digital twin of their vaccine production lab [18]. These developments indicate that India is also embracing the vision of a digitalized future, which is of great importance considering its role among the largest manufacturers of pharmaceuticals in the world. The transition to digitalized and intelligent manufacturing of therapeutics will be a vital breakthrough for science, and humanity at large.
Register for the Biosimilar Workshop 2023, to be held on 2–3 February 2023 in Goa, India, to learn more about emerging technologies in biopharmaceutical production from global leaders. The event includes a dedicated scientific workshop on ‘Industry 4.0 Next Generation Opportunities in Biomanufacturing’ which will introduce the concepts of intelligent biomanufacturing with inspiring talks from the domain leaders. Attendees get a chance to hear from visionaries from academia and industry, learn about the latest research in the biopharma domain from the poster sessions and gain visibility and networking opportunities for start-ups.
Registration link:
https://bit.ly/Registration2023GOA
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References:
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