AOAC INTERNATIONAL is breaking new ground in method validation by implementing in silico analysis in its recently-launched project to evaluate and certify the performance of test kits that detect the SARS-CoV-2 coronavirus, the causative agent of COVID-19 illness, on environmental surfaces. The project will accelerate availability of validated test kits needed in manufacturing and other situations where maintaining surfaces free of coronavirus is a public health concern.
The term in silico – or “in silicon” — refers to harnessing the power of modern databases and computational power in biological experiments. In this case, test kit manufacturers will compare the SARS-CoV-2 genetic sequence targeted by their test kit against a database of whole genome sequences of target and non-target organisms.
Traditionally, the selectivity of an assay is experimentally determined in a laboratory using a set of target (inclusivity) strains, near-neighbor (exclusivity) strains, and matrix-relevant (background) organisms. Laboratory determination of inclusivity and exclusivity is time-consuming, expensive, and usually limited to at most 150 species/strains. Moreover, obtaining and shipping a large number of SARS-CoV-2 strains and variants to serve as reference materials (samples for comparison) in traditional “wet lab” analysis could be difficult and potentially dangerous. Using In silico analysis can reduce the need for wet lab polymerase chain reaction (PCR) assays to a narrower focus on inclusivity strains that may not be detected and exclusivity strains that may be detected.
In addition, in silico analysis has a significant advantage over wet-lab testing alone in that genetic sequences from tens of thousands of strains of SARS-CoV-2 and near neighbors can be analyzed for inclusivity and exclusivity. The result is a more accurate estimate of the false-positive (a positive result when the virus is not present) and false-negative (a negative result when the virus is present) rates of an assay. This is an important part of the accuracy of a COVID-19 detection method. There have been reports of false-negative rates as high as 30% with clinical-based SARS-CoV-2 methods. False-negative results can be attributed to an assay not detecting certain variants of SARS-CoV-2, a lack of sensitivity at low concentrations, and/or errors in sample collection.
In addition to speeding the availability of test kits, this project will deepen scientific understanding of in silico emerging technology by drawing on comparative data from a broad range of analytical approaches, components, and parameters.
This initiative to implement in silico analysis is supported by AOAC Official Methods of AnalysisSM Appendix Q: Recommendations for Developing Molecular Assays for Microbial Pathogen Detection Using Modern In Silico Approaches. This guideline was developed by the Stakeholder Panel on Agent Detection Assays (SPADA) and published in March 2020. It is expected that the use of in silico analysis will be applied to evaluation of other food microbiology methods based on PCR, and could revolutionize the validation of molecular microbiology methods.
