Abstract

With the rise of COVID-19, a syndromic approach to diagnostic testing for infectious diseases has become increasingly pertinent and consequential. BioMérieux's BioFire FilmArray, a multiplex Polymerase Chain Reaction (PCR) based system, achieves this aim with a full respiratory panel including SARS-CoV-2 and many other pathogens that cause symptoms common to COVID-19.  From the list of PCR parameters requiring careful optimization to achieve the desired sensitivity and specificity for each pathogen, the fine tuning of sample thermocycling is critical. Given the vulnerability of PCR efficiency to small changes in temperature, thermal modeling can play an invaluable role in this process by providing a full, precise, and reliable thermal profile of the fluid containing the PCR reaction as the fluid is otherwise inaccessible to traditional thermal measurement devices. When combined with known kinetics of the chemistry, this enables fast, in silico optimization of the entire PCR process to ensure ideal efficiency. A 2D FEM-based numerical model has been developed and compared with multiple sets of experiments. The resulting model validation has Sprague and Geers error metrics (Schwer, 2007) of <0.01 for two physical points of comparison. With high confidence in the model accuracy, the resulting temperature profile of the fluid has been evaluated with various sets of thermocycling parameters to demonstrate a strong correlation between the percentage of fluid achieving desired temperatures and the threshold cycle (Ct) with a lower Ct signifying sensitive and robust detection of the pathogen. This thermal model and the observed correlation to PCR efficiency feed into the current effort to develop a comprehensive multiphysics model—coupling this thermal model with a previously developed EDP-based PCR model and a CFD/mass transport component—enabling fully in silico optimization for critical sustaining and future development efforts with the FilmArray diagnostic system.