Development and evaluation of three automated media pooling and molecular diagnostic systems for the detection of SARS-CoV-2

Abstract

Pooled testing combined with molecular diagnostics for the detection of SARS-CoV-2 is a promising method that can increase testing capacities and save costs. However, pooled testing is also associated with the risks of decreased test sensitivity and specificity. To perform reliable pooled testing, we developed and validated three automated media pooling and molecular diagnostic systems. These pooling systems (geneLEAD-PS, Panther-PS, and Biomek-PS) comprised existing automated molecular detection platforms, corresponding automated media pooling devices, and laboratory information management systems. Analytical sensitivity analysis and mock sample evaluation were performed, and the obtained data were used to determine the sizes of the pool for the validation study. In the validation study, a total of 2,448, 3,228, and 6,420 upper respiratory samples were used for geneLEAD-PS, Panther-PS, and Biomek-PS, respectively, and the diagnostic performances were compared with the reference RT‒PCR assay. A pool size of 6 for geneLEAD-PS and a pool size of 4 for Panther-PS and Biomek-PS were selected for the validation studies. All three systems showed high positive percent agreement values of ≥90.5% and negative percent agreement values of ≥99.8% for any specimen type. Pooled testing resulted in a 65%–71% reduction in cost per sample. The testing capacities of geneLEAD-PS, Panther-PS, and Biomek-PS were 144 samples in 3 hours, 384 samples in 5.5 hours, and 376 samples in 4 hours, respectively. The developed pooling systems showed robust diagnostic performances and will increase the testing capacities of molecular diagnostic tests while saving costs and may contribute to infection control of COVID-19. IMPORTANCE During the COVID-19 pandemic, there have been surges in demand for accurate molecular diagnostic testing and laboratory supply shortages. Pooled testing combined with highly sensitive molecular testing, which entails mixing multiple samples as a single sample, is a promising approach to increase testing capacities while reducing the use of consumables. However, pooled testing is associated with risks that compromise diagnostic performance, such as false negatives due to dilution of positive samples or false positives due to cross-contamination. To perform reliable pooled testing, three different pooling systems (an automated pooling device, an automated molecular detection platform, and a laboratory information management system) were developed to accurately interpret pooled testing results. These three systems were validated using multiple clinical samples and showed high concordance with individual testing. The developed pooling systems will contribute to increasing reliable molecular testing capacities while using fewer consumables and saving costs.

Development of a simple and highly sensitive virion concentration method to detect SARS-CoV-2 in saliva

“…Previously reports for detecting SARS-CoV-2 have succeeded in saving labor by pooling a large number of samples, but the analytical sensitivity is reduced because the positive samples are diluted ([ 8 ]; Praharaj et al, 2020[ 10 ]). Increasing the sample volume to 2 ml has succeeded in improving analytical sensitivity, but it lacks versatility because it relies on expensive specialized equipment [ 9 ]. In this study, we conducted a preliminary study to solve these problems by using a large amount of saliva (12 ml) to achieve both higher analytical sensitivity and labor savings.…”

“…In addition, in the early stages of an epidemic, when there are few positive patients, it is crucial to test a large number of samples from a large number of people for negative confirmation to prevent the spread of infection [ [8] , [9] , [10] ]. In this case, to save cost and labor, pooling of samples, such as saliva [ [8] , [9] , [10] ], and nasal, nasopharyngeal and oropharyngeal swabs [ 9 , [11] , [12] , [13] ] from several people and testing them together is sometimes used for screening.…”