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Development of Suprasensors and Assays for Molecular Diagnostics

Periodic Reporting for period 1 - SupraSense (Development of Suprasensors and Assays for Molecular Diagnostics)

Período documentado: 2023-07-01 hasta 2025-12-31

Modern healthcare faces a fundamental challenge: while small molecules such as neurotransmitters,amino acids, and their metabolites provide powerful early warning signals for disease, they are very difficult to measure outside of specialised laboratories. Current clinical practice relies heavily on large instruments such as chromatography or mass spectrometry, which are expensive, time-consuming, and require trained operators. This makes routine monitoring of such biomarkers impossible, even though their levels are known to change in conditions ranging from endocrine tumours to cardiovascular disease and neurological disorders.
The SupraSense project was launched to tackle this gap. Its vision is to design a new class of synthetic chemosensors that are inexpensive, robust, and capable of detecting key biomarkers directly in biofluids such as urine or saliva. These sensors are built by combining nanoporous zeolite frameworks with fluorescent reporter dyes, creating artificial receptor pockets that can selectively bind biologically important metabolites and emit an optical signal.
The overall objectives of the project are:
• to expand the scope of detectable biomarkers from a single proof-of-concept to a panel of aromatic metabolites (e.g. serotonin, dopamine),
• to achieve clinically relevant sensitivity and selectivity, enabling direct application in medical diagnostics,
• to integrate automation and data science, including liquid-handling robots and advanced fitting algorithms, ensuring reproducibility and scalability, and
• to open up new opportunities for personalised medicine and related fields, by providing affordable and accessible metabolite monitoring tools.
The expected impact is substantial: SupraSense aims to transform metabolite detection from an expert-only task into a fast, low-cost, and widely available diagnostic platform, with benefits not only for patients and doctors but also for agriculture, biotechnology, and environmental monitoring.
Over the course of the project, we have advanced both the experimental and computational foundations of chemosensor analytics:
• Expansion of biomarker coverage. We progressed from proof-of-concept demonstrations to a series of metabolites, achieving an order-of-magnitude improvement in binding affinity. These systems currently represent some of the strongest artificial binders known for serotonin, dopamine and related compounds
• Differential selectivity. By varying either the zeolite framework or the attached reporter dye, we created sensors that can differentiate between closely related analytes. This paves the way for array-based sensing.
• Automated workflow. In the laboratory we implemented two liquid-handling robots for reproducible preparation of 96-well plates. Optimised protocols reduced signal variability below manufacturer defaults. These robots are coupled with fluorescence plate readers, and data are processed by our custom fitting toolkit, ensuring reliable extraction of binding constants.
• Computational modelling. In collaboration with simulation experts, we demonstrated that static DFT calculations are insufficient to describe binding inside zeolite pores. Instead, ab initio molecular dynamics (AIMD) revealed the true binding modes of dyes and analytes, including critical roles for water molecules. These insights were validated by experiment and published in Chemical Communications, highlighted on the cover.
• Molecular design innovations. We identified red-emitting reporter dyes with large Stokes shifts, which reduce background interference and improve sensor performance in biofluids.
SupraSense has pushed chemosensor analytics well beyond the state of the art in several dimensions. First, we achieved record-high affinities and selectivity for challenging biomarkers, opening realistic possibilities for their direct measurement in biofluids. Second, by combining automation with advanced data modelling, we established a level of reproducibility and robustness that is essential for translation beyond the academic laboratory. Third, the adoption of ab initio molecular dynamics (AIMD) corrected misconceptions from static simulations and now provides a basis for future sensor design. These advances pave the way for new applications in healthcare and personalised medicine, where SupraSense sensors can be developed into affordable diagnostic kits for monitoring neurotransmitters and metabolites. Such tools would enable earlier disease detection and improved treatment follow-up.
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