In Vitro Analysis and Dynamic Modeling of SARS-CoV-2 Infection Inhibition by Sigma-1 Receptor Antagonist PB28

Abstract Understanding how antiviral compounds modulate infection dynamics is essential for the assessment and optimization of therapeutic candidates. Here, we combined mechanistic modeling with in vitro experimental data to quantify the antiviral efficacy of PB28, a high-affinity sigma receptor ligand, against SARS-CoV-2 infection in A549-ACE2 cells. Viral load measurements from both time-course and end-point assays across multiple PB28 concentrations were used to calibrate a viral dynamics model incorporating a Hill-type inhibitory function. This approach enabled the estimation of key viral life-cycle parameters – including infection rate, latent and infectious phase durations, and virus production rate – alongside parameters characterizing the action of PB28. Despite relying on minimal experimental input, characteristic of early-stage drug repurposing screens, the model provided accurate fits and robust parameter estimates. Our analysis shows that PB28 reduces viral production in a concentration-dependent manner with near-linear cooperativity and demonstrates how time-resolved modeling facilitates post hoc inference of an apparent, time-dependent $$\text {IC}_{50}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>IC</mml:mtext> <mml:mn>50</mml:mn> </mml:msub> </mml:math> . Notably, estimates of $$\text {IC}_{50}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>IC</mml:mtext> <mml:mn>50</mml:mn> </mml:msub> </mml:math> from time-resolved infections were approximately twofold higher than those obtained from end-point assays. Overall, these results demonstrate that dynamic modeling facilitates more targeted characterization of pharmacodynamic parameters, including $$\text {IC}_{50}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>IC</mml:mtext> <mml:mn>50</mml:mn> </mml:msub> </mml:math> .