The enzyme-linked immunosorbent assay (ELISA) has been a transformative tool (Clark et al., 1986) in biomedical and clinical research, enabling the detection and quantification of biomolecules such as proteins, hormones, cytokines, and antibodies (Zhang et al., 2022; Raschi et al., 2008; Hall, 2024). Since its inception, ELISA has been a cornerstone technique in applications ranging from disease diagnosis to drug development (Jani et al., 2016). Its ability to measure biomolecular concentrations with high sensitivity and specificity has made it indispensable in pharmacology, immunology, oncology, and environment science.

ELISA's reliance on optical density (OD) measurements, combined with standardized calibration curves (Feng et al., 2019; Di Veroli et al., 2015), provides a straightforward approach to quantifying unknown samples. However, despite its widespread use and versatility, ELISA-based quantification faces significant limitations when analyzing low-concentration proteins or samples near detection thresholds. These limitations have prompted the need for more robust and accessible analytical solutions.

One of the critical challenges in ELISA data analysis is the reliability of calibration curve fitting, particularly for low OD values corresponding to proteins in the sub-nanogram/mL range. Built-in software with ELISA plate readers often employs simple curve-fitting models, such as linear regression, logistic functions, or sigmoidal models (Findlay and Dillard, 2007; Meesters and Voswinkel, 2018; Helleckes et al., 2022). While these models are adequate for mid-range OD values, they frequently fail to fit nonlinear datasets at the lower boundaries of detection accurately (Duan et al., 2022). This results in substantial errors in quantifying low-concentration proteins, a limitation that can compromise the interpretation of experimental results. In particular, inaccuracies in curve fitting at detection limits are problematic for clinical diagnostics, where detecting minute changes in biomarker levels can influence critical decisions, such as early disease diagnosis or therapeutic monitoring.

Beyond accuracy issues, existing ELISA software also lacks flexibility and customization, leaving researchers unable to optimize curve-fitting approaches to their specific experimental conditions. For example, built-in programs do not typically allow users to test or compare advanced optimization methods that might improve curve-fitting accuracy. While some commercial third-party software offers more sophisticated fitting algorithms, these tools are often prohibitively expensive, require licensing fees (Wilson et al., 2022), or lack the adaptability to meet the unique needs of specific experimental setups (Feng et al., 2019). Moreover, these tools are not always open-source, which restricts their accessibility and limits opportunities for collaborative improvement across laboratories.

To address these challenges, researchers have explored advanced computational approaches such as Bayesian calibration and machine learning-based methods for curve fitting (Helleckes et al., 2022). While these approaches improve quantification accuracy, they often require advanced programming skills and high computational resources, creating barriers for wet lab scientists with limited computational expertise (Feng et al., 2019; Wilson et al., 2022). Consequently, there remains an unmet need for a cost-effective, customizable, and accessible solution to address the limitations of existing ELISA software in low-concentration protein quantification.

The ELISA Protein Detector (EPD) was developed as an open-source, Python-based software solution to address these challenges. EPD integrates eight optimization algorithms (Table 1) to address non-linearity in ELISA data. These algorithms provide users with multiple options for calibration curve fitting, enabling precise quantification of proteins even at sub-nanogram/mL concentrations. Unlike conventional ELISA software, EPD is designed with an intuitive graphical user interface (GUI) that minimizes the need for computational expertise, allowing researchers to perform accurate data analysis with minimal technical training. Additionally, EPD's adherence to FAIR (Findable, Accessible, Interoperable, Reusable) principles ensures transparency and reproducibility, fostering collaboration within the scientific community. EPD represents a significant innovation in ELISA data analysis by combining flexibility, precision, and ease of use.

To validate the performance of EPD, we conducted rigorous testing using experimental ELISA datasets, including rat basal insulin data. These datasets spanned a wide range of protein concentrations and OD values, providing a comprehensive framework for evaluating the software's accuracy, sensitivity, and robustness. Comparative analyses were performed against standard ELISA plate reader software to assess EPD's ability to generate accurate calibration curves and quantify low-concentration proteins. The results demonstrated that EPD consistently outperformed conventional software, particularly for OD values near the lower detection limits, where built-in plate reader programs often fail. Additionally, EPD exhibited robustness in handling diverse experimental setups, including varying initial guesses for OD values and differences in calibration curve parameters.

A key feature of EPD is its flexibility, allowing researchers to select from multiple optimization algorithms based on their specific dataset characteristics. This capability ensures that users can tailor the software to meet the unique requirements of their experimental conditions. EPD also offers real-time visualization of calibration curves, providing immediate feedback on curve-fitting performance and enabling researchers to refine their analysis iteratively. These features make EPD a powerful tool for routine ELISA workflows and specialized applications requiring high sensitivity and accuracy.

In this paper, we describe the development and validation of EPD, highlighting its ability to address the limitations of conventional ELISA software. We detail the methods to implement the software's advanced optimization algorithms and evaluate its performance using experimental datasets. The results demonstrate EPD's effectiveness in improving the accuracy, reproducibility, and flexibility of ELISA-based quantification. By providing a cost-effective and user-friendly solution, EPD has the potential to enhance ELISA workflows across diverse scientific and clinical disciplines, from basic research to translational medicine.