ELRIG-Forum 2026: Abstracts
Michael Lukesch, VALANX Biotech GmbH
Small differences in where a payload is attached to an antibody can create big differences in the resulting antibody-drug-conjugate. This affects stability, aggregation, efficacy and tolerability. Yet conjugation site selection is still frequently constrained by legacy formats or evaluated late, after costly development steps. In this presentation we introduce GoldenSite, a technology that enables systematic exploration of conjugation sites and couples it to high-throughput biophysical assay services to rapidly find sites that produce better drug-like molecules.
Dr. Maximilian Kramer-Drauberg, cbmed, Graz
Automation is a key factor for translating functional precision oncology into clinically actionable workflows. We present a fully automated ex vivo compound screening platform designed for high‑throughput profiling of patient‑derived cell (PDC) 3D tumor spheroids. The workflow integrates rapid tissue processing, standardized liquid‑handling-based compound dispensing in 384‑well format, automated ATP‑based viability readouts, and seamless data capture within an integrated TQM/LIMS environment. This high level of automation minimizes manual variability, increases throughput, and supports delivery of patient‑specific drug sensitivity profiles within a maximum of 21 days.
A QC‑guided analytics pipeline performs automated dose-response modeling and computes quantitative metrics including Drug Sensitivity Scores (DSS) and area under the curve metrics (AUC), enabling robust and reproducible assessment of drug efficacy. The platform serves as the technological backbone for ATTRACT, the first randomized Phase II clinical trial evaluating whether PDC‑guided treatment decisions can improve outcomes in newly diagnosed glioblastoma patients (IDH‑wildtype, MGMT‑unmethylated).
To date, several hundred patient samples across diverse tumor types have been processed and biobanked using this automated workflow. This work demonstrates how scalable automation can bridge laboratory innovation and real‑world clinical decision‑making, while also creating a powerful translational resource for drug mechanism studies and combination‑therapy discovery.
Dr. Nico Fleck, Associate Director, Head of High-Throughput Experimentation & Catalysis, Merck Electronics KGaA
The complexity of scientific questions certainly skyrocketed in the past decades and this trend is certainly fueled by rising capabilities to handle large amounts of data. Thus, experimentation moves away from empirical designs to data-driven approaches in multidimensional parameter spaces. Sampling those spaces requires plentiful, yet efficient, experimentation strategies. Here, automation enters the stage to relieve scientists from tedious tasks and boost their productivity. Concomitantly, automated execution provides digital tools for experimental planning traction in the lab and also allows to capture metadata that escaped the notice of the executor beforehand. Another, oftentimes underrated, benefit of automation is the intrinsic reproducibility, since coworker-dependent actions do not exist anymore and the experiment has to be described in every detail.
The talk will start from a short general perspective on lab automation in the context of HTE and then proof the aforementioned points with actual examples from our labs
M.Sc. Laura Herbst, Fraunhofer-Institute for Production Technology IPT, Automation in Life Science
Integrating automation and data-driven modeling is crucial for optimizing cell and gene therapies, particularly in CAR-T cell manufacturing and LNP-encapsulated mRNA production. This study presents two use cases of automation and data-driven approaches: Firstly, a modular, service-oriented AI Agent framework designed to enhance the operational integration of artificial intelligence (AI) in CAR-T cell production. Developed within the EU H2020 AIDPATH project, this framework addresses key requirements such as scalability, regulatory compliance, and process transparency. It facilitates the deployment of AI applications through two applications: a Digital Cell Twin for optimizing cell expansion and a Reactive Online Process Control for monitoring production deviations. Each AI Agent operates autonomously and interfaces with production environments via standardized APIs, ensuring maintainability and adaptability to evolving regulatory standards and technological advancements.
Secondly, a model was developed enabling real-time data exchange and predictive maintenance for mRNA therapeutic production. The integration of a digital twin for managing device states and optimizing production processes highlights the need for standardization in data exchange protocols. Validation in real-world settings underscores the framework’s capability to improve production efficiency and support diverse AI applications. This research underscores the pivotal role of digital twins in ensuring high-quality production and regulatory compliance, positioning them as essential components in the evolution of pharmaceutical manufacturing. Future developments will aim to further automate additional process steps, thereby enhancing the robustness and efficiency of mRNA therapeutic production.
Gleb Konotop, Head of Automation (Development Sciences, Global R&D) at Abbvie Deutschland GmbH & Co.KG and Abbvie Inc.
The centralized laboratory automation team at AbbVie supports a broad spectrum of R&D laboratories across the global product development landscape. One focus area is the regulated bioanalysis high-throughput laboratory, where step-by-step automation of complex processes has led to substantial productivity gains over the past decade. Segmenting the process into dedicated automation islands or workcells has been pivotal for productivity increase. Furthermore, this approach offered higher programming flexibility, implementation of sophisticated error handling, and enabled modular laboratory setups, while ensuring regulatory compliance. In preparation for a move to a new laboratory facility in 2027 and to challenge the status quo toward full end-to-end automation, we developed a novel order-based strategy for orchestrating of multiple stand-alone islands, aiming to build a future-proof modular laboratory that can quickly adapt to evolving requirements and rapid technological advances. To test this strategy in a real-world scenario, we established the Sandbox Lab. This laboratory acts as a proof-of-concept playground, mimicking the regulatory bioanalysis laboratory, where we integrate multiple vendor systems with custom solutions and test both data transfer and different plate logistics options—such as mobile robots and rovers. This approach enabled seamless connectivity between different systems, independent of traditional orchestration software or scheduler, and remains fully agnostic to system type or vendor.
