2024 Speaker List

Abstract:

Revolutionize the way processes are executed with intelligent manufacturing combining the principles of modularity, AI, machine learning and automation to create adaptive and efficient production systems.

This presentation will explore how Rockwell Automation enables life sciences companies to navigate rapidly changing markets with intelligent solutions digitally enabling process control from bench scale to commercial scale.

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.

Abstract:

The biotech industry is poised for unprecedented advancements, with digital technologies driving significant improvements in the efficiency, productivity, and quality of biotherapeutics. Moreover, many game-changing modalities have emerged. However, these lifesaving therapies are only accessible to a small percentage of patients due to their complex and often expensive manufacturing processes. The emergence of more diverse modalities and their corresponding production processes pose unique challenges. These include personalization, smaller batch sizes, and diverse equipment requirements.

This talk will explore how digital technologies can address these challenges and opportunities to tap in, focusing on new modalities, including cell and gene therapies. By examining industry case studies, we will highlight the pivotal role digital technologies play in managing the complexity and variability of biological materials and the critical need for improved data integration.

The discussion aims to underscore the transformative potential of digital innovations in enhancing the development, manufacturing, and commercial viability of lifesaving therapies, and propelling their journey from the lab to the clinic.

Abstract:

Contributing to BioPhorum and ISPE and Leveraging the work of NIIMBL and Pistoia Alliance.  The facility of the future will have Lights-Out Manufacturing, the removal of people in the operation of a facility.

To achieve this requires full E2E Knowledge Management with Plug and Produce everything in a Sustainable Adaptive Multimodal Facility.  Full Knowledge Centric Tech Transfer, Autonomous Bioreactor Control implementing Hybrid models, AI driven closed loop CPV and Autonomous material transfer leveraging Robotics and AMRs.

Leveraging the latest Biotech Ontology collection to underpin the advances in AI and link it all together.

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.

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.

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.

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.

Abstract:

A significant challenge in bioprocess development and manufacturing is the integration of diverse process equipment and analytical instruments with non-standardized interfaces into a single software suite, enabling complete digitalization of process workflows.

This talk will introduce Lucullus®, Securecell’s agnostic, overarching, and scalable bioprocessing automation and data management software. Lucullus® assists operators in streamlining bioprocess workflows from planning and preparation to execution and evaluation. All bioprocess data is securely stored in an annotated and harmonized manner, creating a robust foundation for advanced data evaluation and machine learning-based approaches.

Various use cases will demonstrate how information on critical process parameters (CPPs) and critical material attributes (CMAs) is used in complex feedback control loops to maintain product critical quality attributes (CQAs) within a defined range and how this aligns with the quality by design (QbD) principle.

Abstract:

Hybrid models coupling mechanistic and data-driven elements are in principle able to represent all time-dependent and static process inputs and outputs in one combined modelling framework. Thereby, these models can describe both the dynamics of key performance indicators (such as e.g. viable cell density) and the final quality of critical product attributes obtained for diverse cultivation conditions.

Such models offer the potential for a great application spectrum including upstream process design, optimization, characterization and robustness analysis. Use of advanced modelling approaches is crucial to increasingly replace costly wet lab experiments by their in silico counterparts, and hence to overall reduce development cost and timelines. This presentation will walk through the iterative steps of hybrid modelling applied for a selected use case in mammalian upstream process development.

Abstract:

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Abstract:

Creating data harmony around bioreactors is essential for enabling a robust bioprocess ecosystem. Bioprocess engineering demands seamless convergence between research and development, as well as scalable production. In this context, the proliferation of information and comparison opportunities has led to increasingly intricate requirements for digital services.

Eppendorf offers an integrated portfolio that includes software, instruments, consumables, and services. This comprehensive suite caters to the entire bioprocess lifecycle, from development to production. Leveraging new digital technologies, we facilitate remote access and information sharing to create insights for all stakeholders involved in the development process. Contextualized data then can be capitalized on through the use of AI technologies.

We will discuss a comprehensive overview of new business models that enable us to deliver integrated solution offerings of combined products and services in order to address our customers’ needs in their entirety. In addition, we will share our vision for the future value creation in a bioprocess ecosystem, which inspires us on our mission: To contribute to improve human living conditions.

Abstract:

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Abstract:

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