(Boehringer Ingelheim Pharma) - Generic modelling for gaining platform knowledge in upstream development
Speaker: Liliana Montaño Herrera, PhD - Development Biologicals
Abstract:
In this talk, we introduce a novel approach to leverage platform knowledge to support new development projects. Our method utilizes generic hybrid modelling to predict process dynamics and product quality attributes of CHO cultivation processes, regardless of particular product specifics.
By analyzing common trends across different projects, we can improve prediction capabilities and reduce the need for extensive wet-lab experimentation.
(Boehringer Ingelheim Pharma) - Generic modelling for gaining platform knowledge in upstream development
Speaker: Sabine Arnold, PhD - Development Biologicals
Abstract:
In this talk, we introduce a novel approach to leverage platform knowledge to support new development projects. Our method utilizes generic hybrid modelling to predict process dynamics and product quality attributes of CHO cultivation processes, regardless of particular product specifics.
By analyzing common trends across different projects, we can improve prediction capabilities and reduce the need for extensive wet-lab experimentation.
(Cytiva) - Achieving process consistency through in-silico PD
Speaker: Helena Öhrvik, PhD - Senior Product Manager
Abstract:
The biopharma industry faces significant challenges. Among these challenges are price pressure on medicines, biosimilar manufacturers entering the market, and the expectation to reduce time to approval and a focus on consistent product quality. These constraints impose companies to accelerate development cycles, improve manufacturing processes, and implement strategies that ensure processes to deliver the expected product quality.
This presentation describes different examples on how process consistency can be achieved through in-silico process development to mitigate these challenges. We will discuss Cytiva’s recent investments into digital technologies, before going into detail on how scaling between different bioreactors and the navigation in the parameter design space for bioreaction operations can be facilitated with a scaling calculator. The calculator helps to get the process transfer and scale-up right the first time.
These examples of digitalization in the biopharma industry provide useful insights for how biomanufacturers can address the increased cost pressure and accelerated timelines applying digital tools to achieve process consistency.
(Cytiva) - Achieving process consistency through in-silico PD
Speaker: Mark Demesmaeker, PhD - Head of Digital Product Portfolio
Abstract:
The biopharma industry faces significant challenges. Among these challenges are price pressure on medicines, biosimilar manufacturers entering the market, and the expectation to reduce time to approval and a focus on consistent product quality. These constraints impose companies to accelerate development cycles, improve manufacturing processes, and implement strategies that ensure processes to deliver the expected product quality.
This presentation describes different examples on how process consistency can be achieved through in-silico process development to mitigate these challenges. We will discuss Cytiva’s recent investments into digital technologies, before going into detail on how scaling between different bioreactors and the navigation in the parameter design space for bioreaction operations can be facilitated with a scaling calculator. The calculator helps to get the process transfer and scale-up right the first time.
These examples of digitalization in the biopharma industry provide useful insights for how biomanufacturers can address the increased cost pressure and accelerated timelines applying digital tools to achieve process consistency.
(DataHow) - Transfer learning: Faster process developments and more insights
Speaker: Moritz von Stosch, PhD - CIO
Abstract:
Process development is critical to delivering robust processes quickly and with limited technical risk. Quality by Design (QbD) is used to systematically explore process knowledge and improve understanding. However, transferring knowledge between projects and scales is currently limited, and there is potential to accelerate process development.
Advanced machine learning and hybrid modeling approaches can be used to transfer knowledge between scales and projects, and embedding approaches can enable transversal data analysis across process runs with different cell-lines and products. These approaches can improve process development efficiency and reduce timelines.
(ETH Zurich) - Digitalization and optimization of sustainable (bio)processes
Speaker: Prof. Dr. Gonzalo Guillén Gosálbez, Institute for Chemical and Bioengineering, ETH Zürich
Abstract:
In this talk, I will provide an overview of well-stablished as well as emerging computer aided engineering tools applied to the development and optimization of (bio)processes, with a particular focus on cost and environmental impact minimization. I will start by motivating the need to transition towards more efficient and sustainable processes, formally stating the problem of interest and reviewing the main modeling and algorithmic tools in the field.
I will then focus on mathematical programming and machine learning algorithms, as well as their integration with life cycle sustainability assessment methods. I will conclude the talk with some thoughts about potential research directions and industrial trends.
(Merck Serono SA) - Development of a digital twin for monitoring and predictive control of an automated perfusion microbioreactor system
Speaker: Jean-Marc Bielser, PhD - Head of BDC Business Operations
Abstract:
A digital twin application was developed to monitor and control perfusion cell cultures on an automated and high throughput robotic system. This presentation will cover the different steps taken for this development and present the resulting application. The prediction capacity of the software and some optimization features (based on predictive modeling) will be discussed.
The data shows that by updating the models on a daily basis and using the newly acquired data, the prediction accuracy could be improved. The application was also able to use these models to change set-points according to optimization functions, for example to maintain a higher viability during the cell culture.
(Rentschler Biopharma SE) - Predicting the process – A CDMO's journey to computational bioprocessing
Speaker: Walter Denk, Dipl. Math. - Group Lead Bioprocess Data Science Unit
Abstract:
For a CDMO, the efficient utilization of its manufacturing capacities can have the most crucial business impact. In the past, many labor-intensive manual process steps were executed, to produce the client’s product with payment for every hour invested. Nowadays, CDMO’s who can offer the same or even better product quality while reducing manual steps and wet lab activities will outperform those CDMOs lacking behind in digitalization and automation.
Additionally, more clients, especially bigger pharma companies, expect CDMOs to provide transparent access to the data captured during the development and production process, in order to compare these with their in-house processes. Last but not least, regulators put new regulations in place, the pharmaceutical community is expected to respect, as well as, to incorporate into their bioprocesses. That can also open up new development pathways for faster biopharmaceutical approval; provided you have the data to back up any claims to omit certain process steps, for faster drug approvals.
Driven by these factors, Rentschler Biopharma SE is initiating a holistic approach for capturing, managing, and analyzing data that we produce every day during biopharmaceutical production. We will reveal the vision of the Bioprocess Data Science Unit at our company, that aims to address these three key challenges. We will also discuss on how we are planning to leverage this data infrastructure, not just to comply with external drivers, but also to improve and automate many internal processes for the benefit of our clients and their patients.
(Sanofi) -How Digital tools and AI/ML can accelerate R&D for novel modalities?
Speaker: Fernando Ulloa Montoya, PhD - Head of Data and Computational Sciences, mRNA Center of Excellence
Abstract:
I will discuss applications of AI/ML and Digital tools for mRNA platform Research and Development with examples on mRNA Sequence and lipid nano-particles design and optimization.
(Securecell AG) - Overarching device integration and data harmonization to enable process knowledge-driven digital solutions
Speaker: Andreas Koch, PhD - Head of Key Account Management
Abstract:
Lots of valuable data generated during bioprocesses remain unused due to outdated, manual and paper-based workflows. The efficient handling of process data is critical to ensure complete process understanding from the initial raw material to the final product. With the ongoing digitalization and information-driven automation of the biomanufacturing industry, more and more process data are generated and needs to be collected, integrated, and evaluated.
In this talk, we will present how flexible and overarching device integration for bioprocess monitoring and control can be easily implemented in diverse process development and manufacturing environments, and how holistic data management can become the industry standard.
In light of the recently announced of the Securecell and DataHow collaboration, we will further present how to combine harmonized data collection and organization with advanced machine learning and (hybrid)modeling capabilities that will pave the way for the bioprocesses of the future.
(Wheeler Bio) - Adopting digital transformation and machine learning in a CDMO setup
Speaker: Yuk Chiu, MSc – Chief Manufacturing Officer
Abstract:
Wheeler Bio is a biomanufacturing pioneer embracing Pharma 4.0 model to create and deliver speed and efficiency in drug development and clinical manufacturing. The talk will cover Wheeler Bio’s strategy for creating digital infrastructure to automate bioprocesses, acquire and integrate data for advanced data analytics, and using data science tools for efficient process design and development, to ultimately achieve the goal of building a mature digital twin for biomanufacturing. We would like to share digital transformation strategies that are being implemented at Wheeler Bio and highlight how we are building a next generation biomanufacturing facility by utilizing local expertise and resources here in Oklahoma, predominantly a home to oil and gas industry, while maintaining a connectivity with the global pharma industry and leveraging innovative technologies.