Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Microsystems Technology Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Biosens Bioelectron. 2023 Feb 15;222:114977. doi: 10.1016/j.bios.2022.114977. Epub 2022 Dec 7.
Rapid diagnostic tests (RDTs) have shown to be instrumental in healthcare and disease control. However, they have been plagued by many inefficiencies in the laborious empirical development and optimization process for the attainment of clinically relevant sensitivity. While various studies have sought to model paper-based RDTs, most have relied on continuum-based models that are not necessarily applicable to all operation regimes, and have solely focused on predicting the specific interactions between the antigen and binders. It is also unclear how the model predictions may be utilized for optimizing assay performance. Here, we propose a streamlined and simplified model-based framework, only relying on calibration with a minimal experimental dataset, for the acceleration of assay optimization. We show that our models are capable of recapitulating experimental data across different formats and antigen-binder-matrix combinations. By predicting signals due to both specific and background interactions, our facile approach enables the estimation of several pertinent assay performance metrics such as limit-of-detection, sensitivity, signal-to-noise ratio and difference. We believe that our proposed workflow would be a valuable addition to the toolset of any assay developer, regardless of the amount of resources they have in their arsenal, and aid assay optimization at any stage in their assay development process.
快速诊断测试（RDTs）已被证明在医疗保健和疾病控制方面具有重要作用。然而，它们在费力的经验性开发和优化过程中存在许多效率低下的问题，难以达到临床相关的灵敏度。虽然有各种研究试图对基于纸张的 RDT 进行建模，但大多数研究都依赖于基于连续体的模型，这些模型不一定适用于所有操作模式，并且仅专注于预测抗原和结合物之间的特定相互作用。模型预测如何用于优化检测性能也不清楚。在这里，我们提出了一个简化和简化的基于模型的框架，仅依赖于最小的实验数据集进行校准，以加速检测优化。我们表明，我们的模型能够再现不同格式和抗原-结合物-基质组合的实验数据。通过预测由于特异性和背景相互作用引起的信号，我们的简单方法能够估计几个相关的检测性能指标，如检测限、灵敏度、信噪比和差异。我们相信，无论他们的武器库中有多少资源，我们提出的工作流程都将是任何检测开发人员工具集的有价值补充，并在他们的检测开发过程的任何阶段帮助优化检测。
