Professor
of Biological Systems Engineering, Department of Chemical Engineering
Model-guided cell and culture engineering
Abstract:
Although commercially successful, cell-based production of therapeutic proteins is a costly, low-yield manufacturing process. CHO cell genome-scale metabolic models (GEM) hold promise for increasing cell line and culture efficiency thanks to their ability to predict whole cell metabolism and protein secretion in silico.
This talk will present a collection of GEM-informed methodologies for CHO cell metabolic engineering at the genetic and process levels. Designed strategies have been comprehensively experimentally validated in-house, demonstrating statistically significant improvements in product titres.
Senior Director
Pharma Tech Ops- Global Digital Delivery and Value Realization
Native CQA Digital Twin Development
Abstract:
The added value of creating predictive digital twins for biologics drug substance processes to control titer and yield has been proven with legacy products thanks to their long production history and availability of data. But for products which are at the early stage of their production lifecycle the creation of digital twins for process prediction is more difficult. The data availability remains a challenge as commercial scale data is scarce during the initial production phase after approval by the health authorities.
In this presentation, we would like to showcase the development of predictive native digital twins for a recently licensed product and focus on the small to large-scale prediction of two critical quality attributes for advanced process control and to reduce the probability for out-of-specification events and related write-off.
Assistant Professor
Department of Chemistry and Applied Biosciences
Digital tools for chemical design, reactivity and property prediction
Abstract:
Digital tools based on simulations and machine learning see an increased interest both in academia and industry to help identify molecular candidates and optimize the processes to make them. A big challenge is often the lack of sufficient data to train machine learning models, and simulations can here help either to generate surrogate training data, provide more informative descriptions, or augment the models themselves (chemistry-informed models).
In this talk, I outline efforts in our group towards prediction of reaction rates of pharmaceutically important reactions using a combination of simulations and machine learning. Furthermore, we develop explainable deep learning models for prediction of molecular and reaction enthalpies. In the second part of the talk, I showcase our work on computer-aided molecular design, primarily in the area of organic electronic materials.
Senior Lecturer
Associate Professor at the Department of Chemical Engineering
Digital tools for accelerated, sustainable process development and scale up
Abstract:
Pharmaceutical process and product development rely primarily on time - and cost - intensive experimentation. In recent years, computer-modelling tools have been gaining increasing interest as means to inform, accelerate, and optimise the industrial workflow. In this talk, we will discuss how such tools can enable adaptive process design, sustainable operation and optimal process performance, harnessing the power and economical sustainability of computer-based experiments.
We will focus on how model-based tools can:
(1) accelerate and inform decisions related to material and process conditions and
(2) support decision-making during process scale-up to ensure continuous, global supply.
Starting from process development, we will present a model-based framework for bioprocess design and optimisation that, beyond the traditional Key Performance Indicators (KPIs), features sustainability metrics. The presented cases studies include biopharmaceutical separation processes, including informed selection of process conditions, as well as a workflow and model-based tools for quantitative comparison of different design options, such as the type of resin. To complement process development, we will demonstrate a model-based framework that can guide decision-making during scale up. We will showcase how computer-modelling tools can be used and embedded in industrial practices to support manufacturers across the product lifecycle, from clinical trials to commercialisation. In that respect, we will discuss how process uncertainties can be identified quantified early on, and we will illustrate case studies where we evaluate different equipment and scale options with respect to productivity, economic feasibility and environmental sustainability.
Global Head
of Group Smart Manufacturing
On Our Journey from MANUfacturing to SMARTfacturing! Miss the shift – miss the future
Abstract:
In today's rapidly evolving technological landscape, the need for intelligent, agile, and sustainable manufacturing solutions is more pressing than ever. Merck KGaA's SMARTfacturing program is at the forefront of this revolution, serving as a beacon of innovation in the Life Science, Healthcare, and Electronics sectors.
SMARTfacturing aims to seamlessly integrate scalable Smart Manufacturing, Supply Chain Analytics capabilities across Merck's diverse business sectors while building the foundations on IT/OT, Data Management, Data Quality and Workforce Readiness. This groundbreaking initiative is designed to foster cross-sector collaboration, enabling the development and implementation of unified strategies and roadmaps.
At its core, the program leverages cutting-edge technologies such as process analytical technologies, robotics, automation, advanced data analytics, and artificial intelligence. These technologies serve as the backbone for creating agile, efficient, and highly adaptable manufacturing ecosystems.
The program not only focuses on technological advancements but also emphasizes the human element, nurturing a culture of curiosity and continuous learning.
Senior Scientist
Development Biologicals
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