Assessment of eight nucleic acid amplification technologies for potential use to detect infectious agents in low-resource settings

Abstract

Nucleic acid amplification technologies (NAATs) are high-performance tools for rapidly and accurately detecting infectious agents. They are widely used in high-income countries to diagnose disease and improve patient care. The complexities associated with test methods, reagents, equipment, quality control and assurance require dedicated laboratories with trained staff, which can exclude their use in low-resource and decentralized healthcare settings. For certain diseases, fully integrated NAAT devices and assays are available for use in environmentally-controlled clinics or emergency rooms where relatively untrained staff can perform testing. However, decentralized settings in many low- and middle-income countries with large burdens of infectious disease are challenged by extreme environments, poor infrastructure, few trained staff and limited financial resources. Therefore, there is an urgent need for low-cost, integrated NAAT tools specifically designed for use in low-resource settings (LRS). Two essential components of integrated NAAT tools are: 1) efficient nucleic acid extraction technologies for diverse and complex sample types; and 2) robust and sensitive nucleic acid amplification and detection technologies. In prior work we reported the performance and workflow capacity for the nucleic acid extraction component. In the current study we evaluated performance of eight novel nucleic acid amplification and detection technologies from seven developers using blinded panels of RNA and/or DNA from three pathogens to assess both diagnostic accuracy and suitability as an essential component for low-cost NAAT in LRS. In this exercise, we noted significant differences in performance among these technologies and identified those most promising for potential further development.

Fig 1

Comparison of the sensitivity and specificity observed for each developer’s (A–G) assays for the three microbial targets. Where developers’ data points are overlapping, the icons are slightly dispersed for greater clarity. INF, influenza A; MTB, M. tuberculosis; SAL, S. Typhimurium.

Fig 2. A comparison of the number of steps and the turnaround time for each product.

Operational data were provided by each developer for amplification and detection of both RNA- and DNA-based targets. Where developers’ data points are overlapping, icons are slightly dispersed for greater clarity. A–G represent the seven developers; developer E did not perform RNA testing; F1, developer F used RT PCR; F2, developer F used LAMP; INF, influenza A; MTB, M. tuberculosis; SAL, S. Typhimurium.