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article · Journal of Advances in Microbiology

Optimization of Antigen Concentrations to Improve the Signal-to-Noise Ratio of ELISA Tests for the Detection of Anti-Plasmodium falciparum Antibodies

2026Open accessNazi Boni University

Abstract

Malaria, caused by the parasite Plasmodium falciparum, remains a major public health priority in tropical regions. The Enzyme-Linked Immunosorbent Assay (ELISA) for detecting anti-Plasmodium antibodies is an essential diagnostic tool. However, its sensitivity and specificity are closely dependent on the concentration of antigens used. This study aimed to determine the optimal antigen concentration for seven P. falciparum antigens (AMA1, MSP1, GLURP, CSP, EBA175, MSP3, and EXP1) in order to optimize the signal-to-background noise ratio of indirect ELISA tests. To achieve this, titration microplates were coated with serial dilutions of each antigen (from 2 µg/ml to 0.0625 µg/ml), incubated with patient sera, and then developed using conjugated secondary antibodies. Each antigen concentration was tested in duplicate wells (n=2), using Hyper-Immune Tanzanian (HIT) plasma at antigen-specific dilutions (e.g., 1:200 for CSP/MSP1, 1: 10,000 for EXP1), alongside positive and negative controls. Optimal concentrations were identified by plotting titration curves of optical density (OD450) versus antigen dilution and selecting the point maximizing the signal-to-noise ratio (positive minus negative control OD). Reproducibility was ensured by calculating the coefficient of variation (CV) between duplicates, repeating tests if CV >15%. The optimal antigen concentrations identified for improved diagnostic performance were as follows: AMA1 (1 µg/ml), MSP1 (0.18 µg/ml), GLURP (0.5 µg/ml), CSP (0.25 µg/ml), EXP1 (0.25 µg/ml), EBA175 (2 µg/ml), and MSP3 (0.5 µg/ml). These results may support the standardization of malaria diagnostic protocols and enhance the performance of serological tests.

Research topics

  • Malaria Research and Control
  • Clinical Laboratory Practices and Quality Control
  • Biosensors and Analytical Detection

Sustainable Development Goals

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DOI: 10.9734/jamb/2026/v26i21075

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