Response regression model as a guiding principle for pharmacological mechanistic investigation of host-targeted antivirals

Abstract

The host response to viral infections largely depends on the viral load intensity, and the fluctuating viral kinetics after antiviral treatment should perturb host responses accordingly. Therefore, conventional comparison of individual host factors between virus-infected models with vs without antiviral treatment usually generates biased results that tend to reflect perturbations due to fluctuating viral kinetics. The extent of this bias is predominately determined by the range of viral load fluctuation. Herein, we raised a response regression model to serve as a guiding principle to precisely explore the pharmacological mechanisms of host-targeted antiviral (HTA) agents. The statuses of virus-responsive host factors were estimated to regress toward baseline in accordance with decreased viral load, whereas host factors with regression deviation or regression transcendence statuses were considered potential targets of the pharmacological effects. In a pilot application, we developed a mathematical method for transcriptomic analysis based on the response regression model, to identify the actual differentially expressed genes potentially affected by HTAs. Our study should substantially facilitate future pharmacological mechanistic study of HTAs. Moreover, our adapted strategy could be further expanded to other multi-omics analyses and diverse infectious diseases.

Keywords

Response regression model / Host-targeted antiviral / Pharmacological mechanistic study / Multi-omics analysis

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