Rationally optimized, nanostructure-based biosensors for multi-biomarker cancer diagnostics

Información del proyecto

TopNanoBiosen

Identificador del acuerdo de subvención: 101030683

DOI

10.3030/101030683

Proyecto cerrado

Fecha de la firma de la CE 27 Abril 2021

Fecha de inicio 1 Septiembre 2022

Fecha de finalización 30 Junio 2025

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 212 933,77

Aportación de la UE

€ 212 933,76

Coordinado por

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
United Kingdom

Periodic Reporting for period 1 - TopNanoBiosen (Rationally optimized, nanostructure-based biosensors for multi-biomarker cancer diagnostics)

Período documentado: 2022-09-01 hasta 2024-08-31

Resumen del contexto y de los objetivos generales del proyecto

Cancer remains a leading cause of death worldwide, with a significant proportion of cases occurring in low- and middle-income countries. Early and accurate detection is critical for improving patient outcomes, but many current diagnostic methods are either costly, slow, or insufficiently sensitive. This project, TopNanoBiosen, addressed the challenge of detecting multiple cancer biomarkers simultaneously—a crucial capability for reliable early diagnosis—using electrochemical biosensors based on nanostructured materials (NSMs). Conventional biosensors typically detect only a single biomarker, limiting their effectiveness in complex biological fluids.
The overall objectives were to:
(1) Synthesize and characterize NSMs with tailored properties for biosensing;
(2) Develop a simulation platform to model and optimize multi-NSM biosensor designs;
(3) Fabricate a multi-biomarker electrochemical biosensor;
(4) Realize a fully printed, low-cost biosensor platform for real-time cancer detection.
In the final period, the project concluded that while multi-material integration posed significant challenges, a strategic shift to a graphene-based platform successfully achieved high-performance multi-biomarker detection using machine learning-assisted electrical feature analysis.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

From the beginning to the end of the project, the following work was carried out:
(1) WP1: Selected and synthesized NSMs (with a focus on graphene), functionalized them with biomarkerspecific receptors, and characterized their electrochemical properties. Key electrical features sensitive to biomarker binding were identified.
(2) WP2: Developed a complex-network-based modelling platform to simulate charge transport and interfacial effects in NSM assemblies. This revealed limitations in hybrid material systems and guided the strategic pivot to graphene.
(3) WP3: Designed and fabricated a 12-channel graphene field-effect transistor (GFET) array functionalized for exosome detection. Integrated machine learning (SVM) to analyze multiple electrical features, achieving 97% accuracy in discriminating cancer vs. healthy samples in clinical plasma.
(4) WP4: Explored all-printed biosensor fabrication but encountered challenges in material integration. Efforts were redirected to optimize the GFET platform.
Main Achievements:
(1) Design of a high-accuracy, machine learning-integrated GFET biosensor for cancer detection.
(2) A simulation tool for modelling NSM-based biosensors.
(3) A manuscript prepared for publication.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

Beyond State of the Art:
This project moved beyond single-parameter, single-biomarker biosensing by integrating multi-feature electrical analysis with machine learning. The use of a complex-network model to understand and predict nanomaterial interactions in biosensors is also a significant methodological advance.
Expected Results:
By the end of the project, the expected results include a manuscript describing the machine learning-enhanced GFET platform and its validation.
Potential Impact:
The development of a low-cost, rapid, and accurate biosensor platform has strong potential to improve early cancer detection, particularly in resource-limited settings. The technology could reduce healthcare costs and increase accessibility to advanced diagnostics. The project has also contributed to the researcher's career development, who has initiated new collaborative research directions within the field of nanobiosensors.

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