Our simple compartmental model is capable of reproducing the viral dynamics observed during COVID-19 infections, as seen in Figure 3. First, treatment is modeled as prophylaxis: the drug concentrations are at clinical levels when infection begins. Both viral RNA (A) and newly infected cells (B) follow an upward-peak-downward trend. Panel C shows the cumulative number of infected cells.  Figure 3 A-C shows that, under full drug penetration (100%), drug inhibition of viral replication mainly leads to a delay in the viral dynamics with almost no impact on the infected cells. For example, moderate efficacy 60% leads to similar viral RNA and infected cells with a ~ 1 day delay, while high efficacy (90%) leads to a longer delay of ~ 5 days since infection compared to infection under no treatment; nevertheless, higher efficacy also determines a flatter curve (i.e., widening the distribution of infected cell over time). Also, as seen in panels D-F, dynamics are driven by both penetration and efficacy: very similar patterns are observed under high penetration and moderate efficacy vs. moderate efficacy and high penetration, which is expected given the assumption of homogeneous mixing in the modeled target cell population. Including immune response (panels G-I) shows that a synergistic effect results from an amplified reduction of infected cells due to both treatment and immune control. Interestingly, under innate immunity the cumulative number of infected cells is substantially reduced under prophylaxis and correlated with the efficacy.