– Enabling solutions for biopharmaceutical development and production –
TOKYO – (BUSINESS WIRE) –# Bioprocess–Yokogawa Electric Corporation (TOKYO: 6841) announces that it has acquired all of the shares of Insilico Biotechnology AG (“Insilico”), a developer and provider of bioprocess software and services based in Stuttgart, Germany. Fostering the development of the bioeconomy is one of the priority issues of Yokogawa’s Sustainable Development Goals, and with this in mind, the company aims to leverage this acquisition to create comprehensive bioprocess solutions that support biopharmaceutical development up to the making.
Driven by developments such as the COVID-19 pandemic, demand continues to grow for biopharmaceuticals that have fewer side effects and can be used to treat patients with rare and incurable diseases. Unlike the production of chemically synthesized general-purpose pharmaceuticals, the overall cost of biopharmaceuticals is higher, and the complex cell culture process required to efficiently and stably obtain the target proteins requires stringent quality control measures that require significant challenges with mass production.
The cells grown in a bioreactor are immense in number. As each of these cells generates the material that forms the active ingredients of pharmaceuticals, it is necessary to monitor their individual metabolic reactions. Real-time visualization and analysis of all environmental factors such as changes in pH and dissolved oxygen concentration is also very important. As such, it has been extremely difficult to achieve cell production by controlling complex cell reaction systems with a large number of defined parameters.
Insilico’s digital twin technology uses an advanced hybrid model formed from a mechanistic model* 1 unique characteristics of an intracellular metabolic network and data-driven model* 2 built from process data using machine learning process. In addition to dramatically speeding up what until now was a multi-year development process, prediction and simulation also provide a deep understanding of the metabolic process. And since this solution allows the construction of metabolism models for bacteria and many other types of cellular organisms, it can also be used in a wide variety of applications related to food, chemicals and others. products using biotechnology.
Also in manufacturing, Insilico’s digital twin technology enables real-time analysis of process data, enabling constant prediction of crop performance, gentle detection of nutritional components that cannot be directly measured, and the early detection of process anomalies and the provision of advice to operators. By deploying this problem-solving technology, product quality can be stabilized, which contributes to efficient mass production.
Klaus Mauch, CEO of Insilico Biotechnology AG, said: “There are high expectations for this merger between our cutting edge digital twin software technology for bioprocesses and Yokogawa pharmaceutical production system solutions. I believe that through Yokogawa’s global network, we will be able to expand our sales channels and make a great contribution to the biopharmaceutical industry.
Hiroshi Nakao, Vice President of Yokogawa and Head of Life Business Corporate Headquarters, said, “I have no doubts that the innovative digital twin technology offered by Insilico, which has been proven to work with major biopharmaceutical companies, will accelerate the digital transformation in the bioprocess. industry. We will take advantage of our engineering technology and develop our business for the commercialization of bioprocesses.
Presentation of Insilico Biotechnology AG
Creation date: 2001
Location: Stuttgart, Germany
CEO: Klaus Mauch
Number of employees: 29
Activity: Development of software based on digital twins and provision of services for bioprocesses
* 1 Mechanistic model: a model developed on the basis of the fundamentals of the reaction or mechanism involved, a deep knowledge and understanding of the process is therefore required to build the model. The resulting model has variables and parameters that can be interpreted physically, and advanced generalization is possible. However, a high precision physical model requires high development and computational costs.
* 2 Data-driven model: Unlike mechanistic models, no knowledge of the fundamentals of the process involved is required. The advantages of this include simple implementation and relatively low development and computational costs. However, the drawbacks include difficulties in interpreting the data after running the forecast or simulation and generalizing the results. Another disadvantage of this technique is to require large volumes of process data to build the model.
Yokogawa provides advanced measurement, control and information solutions to customers in a wide range of industries including energy, chemicals, materials, pharmaceuticals and food. Yokogawa addresses customer concerns regarding production, asset and supply chain optimization with the effective application of digital technologies, enabling the transition to stand-alone operations.
Founded in Tokyo in 1915, Yokogawa continues to work for a sustainable society through its 17,500 employees in a global network of 119 companies in 61 countries.
For more information, visit www.yokogawa.com
The names of companies, organizations, products, services, and logos contained herein are registered trademarks or trademarks of Yokogawa Electric Corporation, Insilico Biotechnology AG or their respective owners.
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