The challenges of developing biosensors for clinical assessment: a review

Abstract

Emerging research in biosensors has attracted much attention worldwide, particularly in re-sponse to the recent pandemic outbreak of coronavirus disease 2019 (COVID-19). Neverthe-less, initiating research in biosensing applied to the diagnostic of diseases is still challenging for researchers, either from the preferences of biosensor platforms, selection of biomarkers, detection strategies, and other aspects (e.g., cutoff values) to fulfill the clinical purpose. There are two sides to the development of a diagnostic tool: the biosensor development side and the clinical side. From the development side, the research engineers seek the typical characteristics of a biosensor: sensitivity, selectivity, linearity, stability, and reproducibility. On the other side are the physicians that expect a diagnostic tool that provides fast acquisition of patient in-formation to obtain an early diagnostic or an efficient patient stratification, which conse-quently allows making assertive and efficient clinical decisions. The development of diagnostic devices always involves assay developer researchers, working as pivots to bridge both sides, which role is to find detection strategies suitable to the clinical needs. First, by understanding the intended use of the technology and its basic principle; second, the preferable type of test: qualitative or quantitative, sample matrix challenges, biomarker(s) threshold (cutoff value), and if the system requires a mono or multiplex assay format. This review highlights the challenges for the development of biosensors for clinical assessment and its broad application in multidisciplinary fields. This review paper highlights the following biosensor technologies: magnetoresistive (MR)-based, transistor-based, quartz crystal microbalance (QCM), and op-tical-based biosensors. Its working mechanisms are discussed with their pros and cons. The article also gives an overview of the most critical parameters that are optimized by developing a diagnostic tool.

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