Drug Washout and Viral Rebound: Modeling HIV Reactivation Under ART Discontinuation

Due to the persistence of latently infected CD4 + T cells, achieving a functional cure for HIV-1 remains a significant challenge since the viruses are able to evade immune clearance, which in turn enables post-treatment viral rebound. Because traditional deterministic models assume a constant reactivation rate, they fail to capture the stochastic nature of latency reversal influenced by immune perturbations and ART pharmacokinetics. Thus, in this study, by using a Poisson-driven stochastic framework that incorporates fluctuations in activation rates, we study viral rebound dynamics. Via an exponentially decreasing drug washout model, we accurately quantifies the nonlinear interplay between ART decay and stochastic reactivation, improving the theoretical estimates of post-treatment control. Beyond the introduction of stochasticity, our model establishes a time-dependent viral reactivation framework that integrates periodic and random perturbations in the activation rates. Unlike conventional models that assume uniform (temporally independent reactivation), we show that latency reversal follows structured oscillatory patterns modulated by immune cycles, circadian rhythms, and transient inflammatory episodes. This finding suggests that viral rebound risk is dynamically shaped by immune fluctuations, contrary to the assumption of a constant reactivation probability. We also study the model system by incorporating Gamma-distributed waiting times to account for heterogeneity in reactivation kinetics, which in turn provides a more flexible characterization of reservoir dynamics. We believe that these insights have critical implications for HIV cure strategies. For instance, the shock-and-kill approach relies on latency-reversing agents (LRAs) to reactivate the latent reservoir for immune-mediated clearance. Our findings suggest that periodic immune stimulation could enhance viral clearance, which indicates that synchronizing LRA administration with immune activation cycles may improve therapeutic efficacy. Furthermore, by coupling stochastic reactivation dynamics with ART pharmacokinetics, we identify optimized treatment interruption protocols that potentially delays viral rebound and extending ART-free remission. Moreover, this framework offers a generalizable model for chronic viral infections beyond HIV, including hepatitis B virus (HBV) and cytomegalovirus (CMV) since immune fluctuations and stochastic reactivation play a central role in viral persistence. By expanding theoretical models to incorporate dynamic reactivation rates, immune perturbations, and pharmacokinetic decay, our study refines the predictive modeling of post-treatment control and provides a mathematical foundation for optimizing cure strategies in persistent viral infections. Additionally, we show that the efficacy of latency-reversing interventions, such as the Shock-and-Kill strategy, can be enhanced by synchronizing latency reversal with peak immune activity, improving post-treatment control. Beyond HIV, our framework provides a generalizable model for other persistent viral infections, including hepatitis B virus (HBV) and cytomegalovirus (CMV), offering valuable insights into the interplay between immune dynamics, drug decay, and viral reactivation. PACS numbers Valid PACS appear here