Soley Therapeutics has published a peer-reviewed study describing a method to analyse how living cells respond to drugs over time, aiming to provide insight into mechanisms that may be missed by traditional target-based assays.

The research, published in Scientific Reports, outlines a “Live Cell Dynamics” approach that tracks cellular stress responses using label-free imaging and machine learning. The company said the method captures temporal and dose-dependent biological signals that reflect how cells adapt, survive or die when exposed to compounds.

The work is positioned as a foundation for Soley’s proprietary platform, which the company is developing for drug discovery across oncology and other disease areas. As the findings are driven by a single company and linked to its internal platform, the study should be considered early-stage and partially promotional.

Yerem Yeghiazarians, co-founder and ceo of Soley Therapeutics, said: “This work reflects more than a decade of foundational research into how cells sense and respond to drugs and other external signals. By focusing on the dynamic flow of information inside the cell, rather than static endpoints, we can delineate biology that conventional discovery methods often miss.

“Live Cell Dynamics is one piece of a much larger platform that Soley has created to translate whole-cell behavior into medicines. Our growing pipeline demonstrates the real-world impact of this pathbreaking science in moving drug hits to clinical candidates faster and at lower cost than traditional approaches.”

The study uses live, unstained imaging to monitor cells continuously, rather than relying on endpoint assays or reporter-based systems. According to the paper, subtle changes in cellular stress responses can encode mechanistic information, including toxicity and selectivity, even when compounds share similar targets or outcomes such as cell death.

The researchers also report that integrating time- and dose-dependent data improved detection of biological activity and mechanism-of-action, suggesting limitations in morphology-only profiling or binary readouts. This approach could be relevant for complex disease models where traditional assays provide limited insight.

Kurosh Ameri, co-founder and chief scientific officer of Soley Therapeutics, said: “Through this research, our goal was to extract dynamic information from live cells for drug discovery and development purposes. We have now shown that live cells sense drugs dynamically and convey different mechanistic information, which is dose- and time-dependent.

“Our proprietary machine learning methods can extract information about drugs directly from live, unstained images. This ability to measure and interpret detailed information directly from live cells in a scalable and reproducible way demonstrates a significant advance in drug discovery. This comprehensive approach provides a more accurate understanding of complex cellular responses and a drug’s true mechanism-of-action, moving beyond single endpoint and time point assays.”

The company said the experimental principles underpin its broader platform, which combines automation and AI to support internal discovery programmes. Soley added that it is advancing multiple internally discovered candidates toward clinical development, though no clinical-stage data were disclosed in the publication.

Independent validation, comparative benchmarking and clinical relevance will be needed to determine whether the approach can consistently improve hit identification, lead optimisation or translational success compared with existing methods. The study nevertheless adds to the growing interest in time-resolved phenotypic profiling and AI-driven analysis in drug discovery.