Optimal Experimental Design for Enhancing Efficiency and Accuracy of Indicator Displacement Assay Sensor Arrays

Chemosensors, specifically, have garnered significant attention and found versatile applications in chemical detection. A crucial category among chemical sensors is based on competitive displacement mechanisms, where the analyte competes for binding with a receptor, usually known as indicator displacement assay (IDA) sensors. The IDA method remains widely employed in sensor applications. In the competitive IDA approach, an indicator initially binds reversibly to a receptor. However, in the presence of a competitive analyte with a higher binding affinity to the receptor, a displacement reaction occurs, leading to the release of the indicator in the system, resulting in a change in the optical signal. It is crucial for the analyte s binding affinity with the receptor to be higher than that of the indicator, based on IDA principles. IDA sensors, despite their lower selectivity, can effectively analyze mixtures and simultaneously determine multiple analytes when designed as sensor arrays. To optimize the design of IDA sensor arrays, we employ the concept of Optimal Experimental Design (OED), a statistical methodology that maximizes the amount of information extracted from data. OED facilitates identifying the most efficient way to collect data, enhancing the accurate and precise fitting of statistical models. In this study, we focus on utilizing OED to design experiments that provide the most efficient sensors for accurate and precise quantitation of mixtures in IDA sensor arrays. The application of OED addresses two fundamental questions in IDA sensor array design and simulations based on hard modeling: 1) How are controllable parameters determined to achieve optimal efficiency? 2) Is the number of sensor elements effective in achieving the first-order advantage? Our findings reveal that OED is highly successful in predicting optimum conditions and answering these critical questions based on simulation data., Also, when only one of the sensor components is active spectroscopically, it s possible to achieve a first-order advantage by designing a suitable sensor array. This advantage can be utilized in quantitative and qualitative analyses based on this active sensor array.