Cell-based assays are utilised at all stages of drug discovery to explore disease mechanisms and to identify novel molecular therapeutics. To achieve this, the field is continuously looking for improvements in cell reagents, assay formats, and readouts to ensure disease-relevant contexts, aiming at improving translation to human physiology. Implementation of cell assays with more predictive translatability is much needed for improving success rates in drug discovery, and we are now entering an era where Artificial Intelligence and Machine Learning (AI/ML) can become the game changer. This meeting will explore how we continue to adapt to these challenges while working with unprecedented targets, novel biology, emerging drug modalities, the influence of international legislation and a drive for more sustainable drug discovery.

Track 1 – The power of the single cell – how microfluidics & OMICS enable a step change in data generation from individual cells.
Advancement in the ability to study single cells is intrinsically linked to parallel developments in microfluidic and omics technologies. Microfluidic devices enable precision control over cellular environments, while transcriptional, proteomic and metabolomic data provide comprehensive views across cellular components. The power of these technologies combined reveals intricate details about cellular processes, uncovers rare cell types and aims to understand the dynamics of biological systems at a single-cell resolution, impacting multiple fields of research where unmasking heterogeneity among cell populations plays a crucial role.

Track 2 – The promise of advanced cell models – the path to BAU, and the paradigm shift to NAMs (new approach methodologies/non-animal models) .
Understanding the landscape of emerging drug targets, candidate medicines and new modalities within a complex cellular environment is critical to modern research. Academics and industry scientists are increasingly turning to advanced preclinical models in order to identify and validate new biological hypotheses, as well as mitigate the risks of clinical efficacy- and safety-based attrition for new medicines. This session will highlight some of the most exciting preclinical model systems at the forefront of this wave of innovation.

Track 3 – Maximising outputs – how next-generation endpoints, and the use of biosensor are feeding a new wave of multiparametric data generation
A major challenge in drug discovery value chain is our current ability to predict in vivo efficacy and safety of candidate therapeutics. The use of advanced model systems, such as organoids, primary cell cultures, and multi-cellular spheroids, brings the promise of improved disease relevance and ability to recapitulate in vivo response to treatment. However, these come with a need to measure treatment response in multiple cell types and contexts. Next, generation endpoints address this need, providing new tools to quantify treatment response, monitor changes over time, and enable multiple endpoints from complex models. Advances in these techniques and methods are highlighted in this track including the use of multiparametric data and biosensors to capture complex cellular mechanisms.

Track 4 – New technologies – a look under the hood of new leading automation platforms and how they are revolutionising drug discovery

There is a constant need for developing drugs faster for treating human diseases and thus technology developers have for years invested in developing “The next big thing” that can support the need for efficiency in drug discovery. Automation in drug discovery, not only allows faster data generation, but contributes to better decision making based on consistent data and reducing the resources needed. Artificial Intelligence, machine learning, automation and robotics are only a few examples of technologies that can accelerate and transform drug discovery processes. This session will introduce a few examples of novel technologies and automation.

CONFERENCE DIRECTORS

Fredrik Edfeldt – AstraZeneca
Sapna Desai – GSK
Sam Barichievy – AstraZeneca
James Robinson – AstraZeneca
Brinton Seashore-Ludlow – Karolinska Institutet