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Drug combinations constitute an important therapeutic strategy, but the huge combinatorial space makes screening infeasible using conventional methods. The purpose of this project is to develop a novel phenotypic screening platform (ADONIS) that can autonomously explore and identify efficient drug combinations.We will develop iterative screening for drug combinations that shift cells towards a desired state. We use multiplexed microscopy imaging (Cell Painting) as profiling technology, which constitutes an excellent tradeoff between cost, speed, and information content. In our previous VR-funded project we developed a robotized lab and implemented fully automated Cell Painting, and in this project we will expand it to perform: i) autonomous screening for drug combinations towards soft-tissue sarcoma and colorectal cancer

ii) genetic knock-out experiments to illuminate drug resistance

and iii) generative AI to design novel drug leads.Our approach stands out by using image-based cell profiling data that allows for using the latest deep learning methods, and an AI model that selects the next batch of experiments to be carried out in an automated lab. This makes it possible to convert brute-force approaches, such as drug screening, into search problems that can be approached systematically with machine intelligence. With our project we will contribute to realizing this transition to more efficient scientific discoveries where AI iteratively interacts with the physical world.

The rapid development of technologies for molecular profiling paves the way for precision medicine approaches to match patients with effective treatment. Through the development of a coordinating support unit, we aim to provide access to SciLifeLab’s technology platforms to support academic and industry-initiated clinical trials. We will establish customized diagnostic packages jointly with clinical trials units across the country to strengthen our attractiveness as a clinical trial site and to ensure early access of patients to both innovative technologies and emerging treatments.Our goal is to identify mature technologies to be evaluated in observational studies and adopted in interventional biomarker-driven clinical trials. To build this capacity, connecting SciLifeLab national infrastructure to clinical practice, key developments supporting cross-platform data, sample and analysis flows with timelines and quality complying with the needs of clinical trials are needed. Our unit will unite services from current and emerging sequencing technologies, to proteome, metabolome, spatial biology techniques, and drug efficacy testing. Our competitive edge is the repertoire of technologies at SciLifeLab, which once combined provide a unique opportunity for clinical trials. With the ‘service packages’ we aim to make Sweden competitive as a trial site, catalyze academy-industry ecosystems and provide opportunities for early access to the latest diagnostic technologies and treatments.