On the challenges in biosensing for clinical diagnosis

Referencia Apresentador Autores
Osvaldo Novais Oliveira Jr Oliveira Jr, O.N.(Universidade de São Paulo); The use of nanomaterials in combination with biomolecules, especially in nanostructured films, has brought enormous progress to biosensing that can be performed with a variety of principles of detection [1]. Highly sensitive and selective biosensors can now be fabricated for clinical diagnostics, but important challenges remain for fully exploiting the molecular control of film architectures containing biomaterials achievable today. In this lecture, I shall concentrate on four major challenges. The first and foremost arises from the need of device engineering to translate research from the lab bench into the market, by creating robust, low-cost devices that can be used routinely in clinical analysis. Issues like stability of biomolecules activity, and device repeatability and mass production must be addressed. The second challenge is related to the difficulties in reaching biocompatibility and tailored durability of implantable biosensors, on which depends the possible continuous monitoring of health conditions envisaged in personalized medicine. Since there is a wealth of nanomaterials, biomaterials and possible applications, the design of biosensors becomes imperative in order to save time and efforts demanded by experiments. This third challenge can be done with theoretical modeling, including molecular dynamics simulations, which may explain the molecular mechanisms behind biosensing. Examples will be given of steered molecular dynamics to estimate the adhesion force between an enzyme and an herbicide in atomic force spectroscopy experiments [2], and the use of a Langmuir-Freundlich model to account for the adsorption process in the detection of cancer biomarkers [3]. The fourth challenge is associated with the statistical and computational methods required to treat the vast amounts of data generated with biosensors, particularly with continuous monitoring of patients. Especially relevant is the use of Big Data concepts toward the development of computer-aided diagnosis [4]. References [1] OLIVEIRA JR., O.N.; IOST, R.M.; SIQUEIRA JR., J.R.; CRESPILHO, F.N.; CASELI, L.; Nanomaterials for Diagnosis: Challenges and Applications in Smart Devices Based on Molecular Recognition, ACS Appl. Mater. Interfaces, 6, 14745?14766 (2014). [2] AMARANTE, A.M.; OLIVEIRA, G.S.; BUENO, C.C.; CUNHA, R.A.; IERICH, J.C.M.; FREITAS, L.C.G.; FRANCA, E.F.; OLIVEIRA JR., O.N.; LEITE, F.L.; Modeling the coverage of an AFM tip by enzymes and its application in nanobiosensors, J. Molecular Graphics and Modelling 53, 100–104 (2014). [3] SOARES, J.C.; SOARES, A.C.; PEREIRA, P.A.R.; RODRIGUES, V.C.; SHIMIZU, F.M.; MELENDEZ, M.E.; SCAPULATEMPO-NETO, C.; CARVALHO, A.L.; LEITE, F.L.; MACHADO, S.A.S.; OLIVEIRA JR., O.N.; Adsorption according to the Langmuir–Freundlich model is the detection mechanism of the antigen p53 for early diagnosis of cancer; Phys. Chem. Chem. Phys., 18, 8412-8418 (2016). [4] RODRIGUES JR., J.F.; PAULOVICH, F.V.; DE OLIVEIRA, M.C.F.; OLIVEIRA JR., O.N.; On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis, Nanomedicine, 11, 959–982 (2016).
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