Challenges in Implementing Digital Twin in the Pharmaceutical Industry

Digital Twin is one of many digitalization techniques enabling the pharma industry to revolutionize their processes with data-driven and automatic ones. It helps its users tackle modern-day challenges, facilitating its customers with cost effectiveness, less time to produce, patient-centric, and quality drugs. Digital Twin adds value to the pharma industry to achieve their goals without depending on older and conventional techniques.

The digital Twin model can be applied to all products or processes, enabling users to visualize it by mapping different parameters to improve the design process.

What is the digital twin?

A Digital Twin is the digital representation of a physical entity that could be a process or product having some biological fingerprint or characteristics. The digital representation can take and understand the same parameters that are understandable and applicable to the physical entities. 

The main advantage of a digital twin is that it allows its user to check the response of the process or a product in a realistic manner that mimics accurate testing. On the other hand, traditional or paper-based planning relies on personal expertise, historical trends, and design requirements. The result of conventional planning is still being determined and can only be verified once considered in a practical scenario. The system must be redesigned if desired results are not achieved and must be re-tested until desired results are achieved.

Digital twins can be implemented in many industries, including Pharma, to create a virtual representation before going for physical production. This article focuses on digital twins in the pharmaceutical sector.

Digital Twin in the pharmaceutical industry

The digital twin in the pharma industry is the virtualization of the physical process of the pharma industry and the application of physical parameters, design values, and practical scenarios for analyzing their output behavior. It eliminates the need for physical testing by defining acceptable input-output relationships, thus providing valuable benefits such as cost-effectiveness, less time to market, and quality products.

Some approaches by which digital twins are being implemented are discussed in this section.

Process and product improvements

Process and product improvement are the most critical applications of the digital twin in the pharma industry. The pharma industry commonly responds to various feedback types, such as market demand, regulatory guidelines, and complaints. Digital twin allows testing different process parameters to analyze their behavior without physically creating products and processes.

Digital Twin replicates the desired process in a virtualized model, applying real-world parameters. The digital twin takes the data and models the corresponding behavior of the process.

Training

Training is an essential element of the pharma industry and includes safety, regulatory regulations, and production processes. The digital twin aids in training by creating equipment, instruments, processes, or a virtualized product, allowing the participants to test and enhance their skills. Digital twins also enable the safe application of scenarios without losing process, product, instrument, and equipment. 

On the contrary, traditional training systems are complete of limitations, such as

● Planning a training event depends on the availability of a specific process, component, or product. Especially in times of high demand, it becomes more complex and is often delayed. 

● The number of participants at a given time is limited and requires multiple sessions due to the unavailability of resources or a participant needing to be more relaxed in their routine responsibilities.

Manufacturing operations

Digital twins can replicate manufacturing operations virtually and allow pharmaceutical personnel to verify various product parameters before taking them into physical production. If the product parameters suffer any deviation, it is immediately detected in the virtual model, allowing the personnel to alter product parameters to save the ongoing production process. 

In traditional systems, the product characteristics or parameters can only be verified after the completion of a production process or after a specific stage, and for some tests, it can take days to produce.  

To eliminate deviation, the manufacturing process is re-started after the testing procedure and until desired results are achieved. This approach can also result in product wastage if deviation cannot be eliminated by re-manufacturing.

R&D

R&D is a critical pharma industry unit used to test different product and process parameters. It aims to verify and define the product & process parameters of a newly manufactured drug. 

Digital twin allows the pharma industry to apply different parameters virtually to test, verify, and finalize the product parameters before actually taking it into a laboratory environment. It will enable a maximum number of quality tests to occur in less time and to test in the worst-case scenario. Its main advantage is that it reduces the time to market by compiling test results in less time. 

In traditional systems, R&D involves several manual tests, which consume time and limit the latest capacity. The tests are not conducted in a single location, resulting in decentralized data collection, and it takes more time to compile the results. As a result, it increases the time to market for a specific product.

Challenges in implementing a digital twin

Let's discuss some challenges when implementing digital twins in the pharma industry.

Uncertain goals or requirements

The biggest challenge when implementing digital twins is the uncertainty of goals and requirements. It means there needs to be more clarity in a pharma organization regarding its capabilities, goals, what problems it can solve, and the specific requirements for its implementation. 

A pharmaceutical organization could be attracted to digital twins due to its famousness and buzzword. However, uncertain goals might fade its prospects for a successful run. 

For its success, always have a clear picture of what it can offer you, what problems you want to solve, and what your goals and requirements are. The main reason is that Digital Twin is built on digitalization techniques that can be customized for organizational requirements.

I do not know how to do it.

Not knowing how to implement a digital twin for your organization could lead to out-of-specification solutions. Because digital twin can be customized to a particular organization's requirements and experts deploy the digital twin according to the specific requirements of a particular organization, but if done without proper planning, it can result in uncertain goals.

Always plan when implementing a digital twin solution, and describe how a digital twin will be implemented. Some questions, such as

● Whether it will be implemented during production or shutdown

● What department of process will be implemented first i.e. production, quality or maintenance

● How to resolve any discrepancy if occurs during the implementation process

● How to keep the implementation within the regulatory regulations. Because if digital twin model can exceed regulations, it can be of no use and the entire investments made will be wasted.

The above mentioned and like these are good reference points to start implementing a digital twin solution.

Unavailability of resources

If enough resources are not prepared for the digital twin, the implementation will be not be helpful and succeed. 

Like other new projects or upgrades, Digital Twin is a resource-intensive technology with various resources for success. Examples of common resources include human resources, hardware, and software. 

When resources for digital twins are dedicated, it creates an ecosystem that benefits the entire organization and helps accelerate to the operational stage.

Starting big

Another challenge a pharma organization faces is implementing the digital twin at a significant scale or the entire manufacturing plant. The major drawback of this approach is that its result cannot be verified, and it could take time to interpret the results into meaningful information. This condition makes it challenging to determine the system's acceptability, configuration, and the benefits it offers over traditional systems. 

Another disadvantage is that it pressures the project or implementation team since all resources are dedicated to implementing digital twins and cannot be put into routine operations,, thereby affecting the entire production plans and market orders

When starting to implement digital twins in pharma organizations, always start small. Select a single component of production and begin as a pilot project. Upon completion, analyze the results for gaps, efficiency improvement, and impact on the production process. Depending on the achieved results, plan for further implementation at large scale by considering points such as changing or retaining the project characteristics, requirements, and acceptance criteria.

Advantages

Let's discuss some advantages of digital twins in the pharmaceutical industry.

● Organizations can visualize the process and its respective output with the digital twin. If there is any discrepancy in the process, it can be detected early in a virtual environment. This helps to minimize downtime by planning ahead of any breakdown factors.

● Planning increases the operational efficiency of the entire organization and prevents losses due to unplanned maintenance, unavailability of necessary parts, product failure, and the process of quality testing and enhancement.

● The digital twin also helps to enhance product quality by providing a mechanism to pinpoint faults in a product and timely elevate quality to its maximum level. This in turn increases the customer trust and increased market footprint

● Implementing digital twins can help reduce product time to market by accurately checking product characteristics in less time, preventing time-consuming testing and results, and shifting to the manufacturing stage in less time.

About the Author:

Muhammad Asim Niazi

Muhammad Asim Niazi has a vast experience of about 11 years in a Pharmaceutical company. During his tenure he worked in their different departments and had been part of many initiatives within the company. He now uses his experience and skill to write interested content for audiences at PharmaSources.com.