SES

This presentation explores the practical considerations for designing pharmaceutical facilities in line with Pharma 4.0 principles. Early input in the design stages is crucial for the successful implementation of smart manufacturing solutions, as demonstrated by AstraZeneca's new API Clinical and Commercial Manufacturing Facility in Dublin. This state-of-the-art, paperless, digital smart factory integrates enterprise IT systems with operational technologies to enhance efficiency, quality, and innovation.

As the demand for cell therapies expands globally, scalable and robust manufacturing solutions are needed. This session will explore how modular automation, closed systems, and digital integration are reshaping the future of cell therapy production. Drawing on real-world implementation at Legend Biotech, we will detail how automated platforms can improve consistency, reduce human error, and streamline regulatory compliance in commercial and pipeline manufacturing.

With the increasing demand for autologous and allogeneic cell therapies in the market, the ever-present bottleneck of scaling to meet the expected demand of the future is a common challenge in our industry. When faced with the complex interconnected processes and unit operation timing associated with cell therapies, the use of simulation can assist with answering what-if scenarios, outputting data estimates, and providing visualizations.

ATMPs are based on genes, cells, or tissues delivered to patients to provide a therapeutic benefit based on a specific target of interest.  These therapies are often referred to as ‘Personalised Medicines’.  A sector of healthcare that is rapidly evolving and expanding with some unique challenges such as aseptic processing requirements and product variability.  Traditional manufacturing processes are for synthetically derived compounds (small molecule), or proteins or peptides expressed by cellular systems (large molecule).  It is not surprising

This presentation discusses problems, risks, and opportunities encountered throughout the commercialization lifecycle of Advanced Therapeutics, such as cell, gene, and tissue products. Applying a Problems, Risks, and Opportunities Management Strategy (PROMS) at key milestones of product development supports the successful development, approval, launch, and sustainable commercialization of advanced therapeutic medicines.

The Future of CGT: Innovations in Decentralized Clinical Trials and Technological Advancements

NOTE: This session is 90 minutes

The regulatory landscape for combination products involving cell and gene therapies (CATMP) is complex and continually evolving in the absence of global harmonization. As these technologies become more prevalent, regulators are developing and enhancing regulatory frameworks and guidelines to manage these products throughout their life cycle.

ATMPs (advanced medicinal products) have undergone an impressive journey over the last years, from successful solutions developed in a scientific environment to a substantial part of the pharma industry, promising cures to patients with a previous lack of treatment possibilities.

Personalized therapies are changing the face of cancer treatment. Autologous CAR-T cell therapy involves genetically modifying the patient’s own cells, to give their immune system the tools to effectively fight off the cancer, potentially with a single treatment. With advancements in process technology, there is now a shift from “speed to market at all costs” toward process optimization and manufacturing efficiency. This is the story of the Bristol Myers Squibb EU Cell Therapy Facility, Project Triton.

Commercial manufacturers of advanced therapy medicinal products (ATMP) encounter significant challenges in ensuring scalability and maintaining product quality while complying with stringent regulatory requirements and managing high production costs. Enhancing process understanding is fundamental to addressing these challenges. Causal AI is emerging as a paradigm shift from traditional machine learning methodologies, as it enables the extraction of understanding from observational data by mapping out the causal relationships within the data-generating process.