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Getting started with a PAT framework enables automation with digital technologies.
Industry 4.0 applications can help businesses boost their manufacturing processes, making them faster, more responsive, integrated, and productive. Despite these key advantages, adopting new Industry 4.0, or Pharma 4.0, solutions within bio/pharmaceutical manufacturing lines can be particularly challenging. However, the implementation of advanced digital technologies can overcome these issues and deliver benefits to pharmaceutical companies.
The future of manufacturing is digital, and several industries have already taken large strides in their digital transformation journeys. Although processing industries and pharmaceutical manufacturing are not at the forefront of these trends, digital technologies that support Pharma 4.0 applications can drive considerable improvements. These technologies promote innovation that can increase the productivity and efficiency of key operations while future-proofing them. The broad range of automated solutions available can be used in a variety of settings to achieve different goals. Pharmaceutical companies can thus confidently embark on digital transformation journeys that address their most pressing requirements or applications that are most in need of upgrades.
Although there are a multitude of technologies available, most of them share the need for an advanced, quality-compliant process analytical technology (PAT) framework where they can be implemented. Embracing PAT is a quality-by-design (QbD)-driven approach that aims to deliver products of consistent and high quality by leveraging the power of data.
More precisely, this smart manufacturing strategy relies on the timely characterization and analysis of raw and in-process materials achieved by measuring their critical quality attributes (CQAs). These CQAs are then controlled with critical process parameters (CPPs) to define the optimum operating conditions and adjust them in real-time. Therefore, PAT supports in-depth process understanding and data-driven control while also laying the foundation for quality assurance, continuous process verification, and material tracking and tracing.
Early on in the evaluation of a suitable automation project, businesses should consider whether the process will require revalidation, as illustrated in Figure 1. If so, they should determine to what extent the return on investment (ROI) of the total project, including any revalidation costs, meets corporate objectives.
Even in cases where revalidation is not an option or the ROI is not attractive enough, pharmaceutical companies can still leverage digital technologies and PAT-like environments to improve quality control activities in off-line analytical laboratories. These tasks generally take considerable portions of manufacturing cycle times and manpower.
Key automated technologies can cut the downtime associated with these quality control activities and, in turn, optimize the speed and productivity of the entire production process. For example, robots can speed up testing by performing repetitive tasks, such as liquid or solid sample collection and delivery to off-line testing facilities. There, samples can be automatically prepared and analyzed. Additionally, businesses can implement serialization and data management systems to create data repositories that enhance data integrity for material tracking and tracing.
Once the environment in which digital technologies are to be implemented has been selected—R&D, manufacturing, or testing facilities—pharmaceutical companies should define their goals. This definition, in turn, helps to select the solutions that are most suited to addressing these ambitions.
To succeed in the implementation of PAT and digital technologies, companies should start with small projects, ideally where there is already a level of process understanding available and it is clear to see how the process can be optimized. In addition, it is generally recommended to initially use PAT and data-driven solutions to solely monitor processes, without including control activities. This strategy helps to simplify the requirements of the project and streamline implementation while gathering more knowledge on CQAs, CPPs, and the entire production process, as illustrated in Figure 2.
After supervision activities have been improved, pharmaceutical companies can determine whether automated, quality-centric control strategies could further enhance production activities. If automated control model building is used, then generally these are highly cost effective, but they may generate less process knowledge.
Once this groundwork has been completed, companies should define the technologies that can complement PAT in their applications. For example, continuous processing applications can greatly benefit from effective and accurate dynamic flow modeling for material tracing. These tools can monitor product movement throughout continuous processes, coordinate sampling and rejection systems, and identify raw materials in the final products. As a result, dynamic flow modeling supports closed-loop feedback process control at the core of good manufacturing practices (GMP), quality assurance, and regulatory compliance strategies. As continuous processing and flow chemistry applications become increasingly popular, a PAT knowledge management platform needs to feature advanced mathematical predictive models, combined with extensive empirical data, to accurately forecast product and material movement.
When handling complex processes, advanced data processing strategies can provide a solid foundation for effective control. In particular, multivariate control—the ability to direct the control of more than one CPP in concert with others in order to control the desired CQA value—is valuable. It is possible to model such functionality with automated intelligent control, in which the control algorithm is automatically derived, indicating the optimum CQA values that should be delivered. By adopting this type of multivariate control, it is possible to do more than simply keep CPPs as close as possible to their set-points, as it can drive a CQA value from its current state to the desired state. Ultimately, this strategy can help bio/pharmaceutical producers make their processes more flexible and adaptable while enhancing end-product quality and process efficiency.
A digital twin can simulate process orchestrations in a virtual environment to test if the selected conditions and operating parameters are effective or need to be refined without running physical processes, which are resource, cost, and time consuming. This functionality can be helpful during commissioning, particularly for complex manufacturing and quality prediction functions (e.g., predictive dissolution).
To ensure consistency across different plants or R&D laboratories, a common platform used by each plant together with a secure Cloud-based environment is ideal. This approach also supports advanced Big Data analytics and mining for next-level PAT applications. In effect, Cloud computing facilitates interconnectivity and offers near unlimited space for storage and data processing power on demand. The common platform allows knowledge, orchestrations, models, and intellectual property developed in one location to be shared in a traceable and GMP-compliant way with another location.
It is crucial to select a PAT framework that can support the necessary digital technologies. In addition, it is important to consider analytical instruments and sampling solutions. In particular, it is fundamental to check the ability of these technologies to converge to a central PAT knowledge management platform, where all data and the insights they produce are revealed, stored, visualized, and processed.
Businesses should not be limited by vendor-specific components when building effective Pharma 4.0 applications; a multi-vendor, multi-instrument PAT knowledge management platform is beneficial. Compatibility should also be evaluated across the entire network infrastructure, particularly when enterprise-wide digital technologies are applied. When setting up industrial Internet of things (IIoT) applications that merge the operational technology domain, typical of the shop floor, with information technology of higher-level systems, the PAT platform should be able to link both worlds. In practice, this means that the software should be able to seamlessly interconnect with Edge devices and other key nodes, such as virtual machines.
To utilize Cloud computing, a PAT knowledge management system should support data transfer to, or run within, these environments. Additionally, information must be transmitted in accordance with existing regulations, such as regulations on electronic signatures and records. If advanced cybersecurity is a top priority, a data pump system that transfers information from a PAT platform to the Cloud should be based on “push” methods, as they support controlled access. These operate by periodically sending data from each monitored system to a central, Cloud-based data lake. Push architectures prevent a network from opening to areas that should be restricted.
Independent of the network components utilized, it is crucial to evaluate latency and reliability. As PAT-driven Pharma 4.0 applications rely on real-time communications with machines and instruments on the factory floor to adjust their operations on the fly, time-critical data should be transmitted in a timely manner. Otherwise, delays could compromise productivity and end-product quality, ultimately reducing the benefits of digital strategies.
Speed should remain a top priority to effectively control operations. The creation of highly effective networks that rely on advanced communications technologies is key to overcoming these challenges and setting up successful IIoT environments for pharmaceutical operations.
The future of pharmaceutical manufacturing lies in digital technologies. Players in the sector should therefore begin to embrace them to maintain a competitive edge. To succeed, it is crucial that companies select a suitable PAT knowledge management platform and implement it using these best practices.
Martin Gadsby, is director at Optimal Industrial Technologies, firstname.lastname@example.org.
Volume 46, Number 2
When referring to this article, please cite it as M. Gadsby, “How to Effectively Implement Pharma 4.0 Technologies,” Pharmaceutical Technology 46 (2) 2022.