Juliano Ferrari Gianlupi, Indiana University, Coupled PK model of an antiviral and agent-based model reveal the importance of inter-cellular metabolism heterogeneity on treatment outcomes. Coupling a pharmacokinetic model of an antiviral therapy with an agent-based model of viral replication and immune response reveals the importance of inter-cellular metabolic heterogeneity on treatment outcomes. We extend our established CompuCell3D based multicellular agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of Remdesivir. We model Remdesivir treatment for COVID-19; however, our methods generalize to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, time of treatment initiation, antiviral half-life, and variability in cellular uptake and metabolism of Remdesivir and its active metabolite, GS--443902, on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models, which treat all cells of a given class as identical can clarify how treatment dosage and timing influence infection dynamics and treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, moderate cell-to-cell variation in drug uptake and elimination requires higher systemic drug doses (from 50% to 3 times the dose for the homogeneous case) to achieve the same level of control of infection within the tissue patch. Heterogeneity reduces treatment efficacy because the cells with the lowest internal levels of active metabolite can act as super-spreaders within the tissue. YouTube and Slides.