July / August 2019

The Digital Twin: Creating Efficiencies in a Virtual World

Andrew Whytock
Digital representation of a chemical API process

The Pharma 4.0™ Special Interest Group is focusing on key technologies that will modernize pharmaceutical manufacturing and facilitate digital transformation. These technologies include digital twins, augmented reality, artificial intelligence, big data and analytics, mobiles, cloud, advanced robotics, and three-dimensional (3D) printing. This first article of a series about these enabling technologies discusses the digital twin.

How are pharmaceutical companies getting value from their data, both in production and development? What technology is needed to build a 3D model of a plant or equipment so that a process can be pretested or programmed? How do we simulate a complex production line or process so that we can build it quickly and efficiently? The answers to these questions lie in the creation of a virtual world, and, more specifically, the creation of a digital twin (a virtual replica of a physical entity).

Digital twins are widely used in the pharmaceutical world, from the creation and modeling of manufacturing processes to enabling the analysis of how a medicine will work inside the human body. The common factor in these various applications is using software to create a virtual replica and then performing simulations on that model.

In drug development, companies create digital twins to create models and then analyze and predict how a process or material will behave. For example, how will materials react together in a machine or how will a device, such as an inhaler, distribute a drug substance?

Bausch + Ströbel Plant
German manufacturer Bausch + Ströbel plans to make engineering at least 30% more efficient by 2020 (source: Siemens).

In production, a digital twin can be useful for entities ranging from individual machines to entire production lines. Complex production routes can be calculated, tested, and programmed with minimal cost and effort in a very short time. Simulation and testing of a production environment can optimize the design of operations or identify and prevent potential failures. Capital investment can start later in relation to the clinical trial process and closer to actual commercialization, obviating the need to invest significantly before market authorization. The digital twin can also permit virtual com-missioning, revealing potential defects and enhancing engineering efficiency by 30%.

Digital representation of a chemical API process
Digital representation of a chemical API process (source: Siemens).

The objective of the digital twin is to constantly gather operational data from products or production. Information such as the status of a piece of equipment or energy data can be continuously monitored, making it easier to perform predictive maintenance, prevent downtime, or optimize energy consumption.

The digital twin can also permit virtual commissioning, revealing potential defects and enhancing engineering efficiency by 30%.

Pharmaceutical companies are increasingly employing digital twins to develop solutions for the digital design of products, the digital engineering of plants, and the use of digital tools to simulate and monitor performance. There is enormous potential for the pharmaceutical industry to maximize benefits from the numerous use cases that the digital twin can offer, and it is reassuring to see that many companies are already investing in the different tools and skill sets that are needed.

Part of Pharma 4.0™ Series

Wearable Devices & Biometrics Improving Efficiency in GxP Operations

Wearable Devices & Biometrics Improving Efficiency in GxP Operations

As the old saying goes, “Time is money.” In today’s industrialized world, this adage is profoundly true. Manufacturers can no longer afford to overlook operational excellence. A new production philosophy called “Lean manufacturing” has been developed to save as much time as possible during manufacturing processes. In some industries, such as the automotive sector, Lean has almost been perfected. However, in pharma, we are still seeking perfection. Despite recent efforts, there is still plenty of room for improvement.