How Pharma Companies Can Reduce Waste, Time & Costs By Adopting Digital Twins

Digital Twins are a new class of enterprise software that uses streaming data from IoT devices to monitor, analyze, and predict the operation of equipment and processes. As part of process optimization, they have been around as a concept for years now, but the streaming data and real-time data science computation has made it possible to harness their potential only now.

Manufacturing, automotive, residential, and commercial industries are expected to contribute the largest share of the market’s growth in Digital Twins adoption during the period of 2021–26. The market size for Digital Twins is predicted to grow by 40 percent CAGR between 2020–25. Up to 91% of all IoT Platforms will contain some form of digital twinning capability by 2026.”

The Digital Twin market is expected to grow at a high CAGR, it is still fragmented in terms of implementation as well as sourcing. Asset management (Predictive and Operational twins) and Simulation twins are the most prominent Digital Twin solutions adopted across various industries.

What is a Pharma Digital Twin?

Artificial Intelligence (AI) has disrupted almost every industry and the pharmaceutical and healthcare sector is no exception. The role of AI in the pharma industry has given rise to multiple use cases to automate operations such as manufacturing, sales and marketing, and even logistics.

Under pharma manufacturing, the rise of technology has produced technologies such as Digital Twins or Virtual simulations.

A digital twin is a virtual representation, simulation, or mapping of a physical entity. This entity could be a process, plant, object, system, or any other abstraction.

Digital Twins technology has gained popularity for more than a decade and IoT adoption, hyper-automation, and real-time data flow are driving its relevance today for business optimization.

It’s an emerging enterprise software class using IoT technologies to monitor, analyze, simulate, and control pharma business processes.

The new decade demands personalized treatments

Every industry is increasingly developing products that meet the needs of customers. This applies to the pharmaceutical industry as well, where products are now adapted for smaller groupings of patients as well as the individual — what is called personalized medicine.

Thus, medical information in a digital format is crucial for giving patients the best treatment and products.

With the development of longitudinal biomarker data, digital twins of patients increase in sophistication, and it is now time to consider how to use those data to develop digital twins of real people.

Unlike physical models, digital twins can be adjusted to almost any situation with more accuracy and patient specificity over time.

A Digital Twin technology for the pharma and healthcare industry simulates each patient’s attributes that affect a drug’s fate in his or her body, creating a computerized model of each person.

Optimal drug-dosing regimens are determined based upon a patient’s specific needs, to achieve maximum therapeutic benefit with a minimum amount of side effects.

Digital Twins Accelerate the Drug Development Process

There are several application areas for digital twins in biopharmaceutical research and development. Right from building disease models, running simultaneous modeling, and simulation experiments, to perform model-based safety and efficacy assessments in-silico.

This can lead to a decrease in time and cost for the needed research experiments, reduce the need for animal/human testing and enable precision medicine.

By collecting and analyzing data, it is possible to reduce the time necessary to bring a drug to market. Making improvements in manufacturing and scaling up a drug development process is possible with digital twins.

Although the biopharmaceutical industry is still in the early phases of adopting digital twin technology, there are already early examples of organizations using digital twins for simulating the progression of disease in a particular organ system, predicting drug exposure, and reducing the need for placebo treatment arms in clinical trials

Digital Twins Streamline Medical Product Manufacturing

As medical delivery systems, like complex drug products, become more sophisticated, digital models are increasingly being used to design and optimize them.

Digital platforms are often used to design complex drugs. Large portions of the design are then reused in multiple drug variants.

A digital model permits quick assessment of whether a new variant is compatible with existing manufacturing equipment (an example of design for manufacturing).

Many aspects of the pharmaceutical and healthcare manufacturing processes can be streamlined through digital twins, including minimizing the amount of process qualified operators required to establish robust control strategies.

Here are some of the common applications:

• Pharmaceutical companies use digital twins to set acceptance criteria and normal operating ranges during process characterization studies, aligned with the FDA’s Quality by Design initiative.
• Biopharma companies conduct process performance qualification (PPQ) to determine the necessary number of PPQ batches for validation, using digital twin models to justify fewer PPQ runs.
• Digital twins can be used by biopharma companies to set alert limits, predict changes, and adapt to trends during continual process verification (CVP).

There is also a reduction in waste and experimentation time. Furthermore, digital representations have been employed to ensure that experiments are of high quality and meet industry standards around quality by design.

As part of a data-driven industry, the solution can also be used to optimize machine quality and process quality.

Gramener’s digital twins have enabled clients to predict quality in pharma manufacturing and to get a high production yield.

A global drug manufacturer struggled to maintain high & consistent production of the Golden Batch. Our Digital Twins solution helped the client to establish a relationship between operational and material parameters and improve production yield for 10 compounds.

The solution is based on advanced statistical analysis and machine learning models.

Digital Twins for Better Biopharma Logistics Efficiency

For logistics companies to remain competitive, they must provide security and speed, while also remaining cost-effective. Connected logistics companies in the pharma sector might be able to meet these expectations by offering digital twins, which allow logistics providers to provide complete visibility into a product’s lifecycle to their partners.

In addition to this, logistics companies can increase operational efficiency by using the digital twin design element. Logistics providers can make better-informed decisions about optimizing the flow of materials and monitoring supply chain processes by using this technology.

Through the digital twin process, assets and good status can also be tracked in real-time throughout the supply chain. Logistic providers can benefit from this by reducing waste, enhancing inventory control, optimizing warehouse space utilization, material tracking, serialization, and quality assurance.